| Title: | Forest Mensuration and Management |
|---|---|
| Description: | Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). <doi:10.1002/9781118902028>. |
| Authors: | Sollano Rabelo Braga [aut, cre, cph], Marcio Leles Romarco de Oliveira [aut], Eric Bastos Gorgens [aut] |
| Maintainer: | Sollano Rabelo Braga <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.9.9.0000009 |
| Built: | 2025-01-30 06:40:46 UTC |
| Source: | https://github.com/sollano/forestmangr |
Generate a ggplot curve of a forest's average tree using the Kozak taper model (Kozak, Munro and Smith, 1969).
avg_tree_curve( df, d, dbh, h, th, facet = NA, color = NA, eq = TRUE, mirror = TRUE )avg_tree_curve( df, d, dbh, h, th, facet = NA, color = NA, eq = TRUE, mirror = TRUE )
df |
A data frame. |
d |
Quoted name of the section diameter variable, in cm. |
dbh |
Quoted name of the diameter at breast height variable, in cm. |
h |
Quoted name of the section height variable, in meters. |
th |
Quoted name of the total height variable, in meters. |
facet |
Optional argument. If supplied with the Quoted name of a factor variable(s), this variable is used to divide the plot into facets. Default: |
color |
Quoted name of a variable. If supplied, this variable will be used to classify the data by color. Default: |
eq |
if |
mirror |
if |
A ggplot object.
Sollano Rabelo Braga [email protected]
Kozak, A., Munro, D. D. and Smith, J. H. G. (1969) Taper Functions and their Application in Forest Inventory, The Forestry Chronicle, 45, pp. 278–283.
library(forestmangr) data("exfm7") head(exfm7) # Get the data's average tree curve inserting the section diameter and height, # total height and dbh variables: avg_tree_curve(df=exfm7,d="di_wb",dbh="DBH",h="hi",th="TH",eq=FALSE) # It's possible to get the average tree curve of each strata with the facet arg., # and divide the data by color with the color argument: avg_tree_curve(exfm7,"di_wb","DBH","hi","TH","STRATA","GENCODE",FALSE)library(forestmangr) data("exfm7") head(exfm7) # Get the data's average tree curve inserting the section diameter and height, # total height and dbh variables: avg_tree_curve(df=exfm7,d="di_wb",dbh="DBH",h="hi",th="TH",eq=FALSE) # It's possible to get the average tree curve of each strata with the facet arg., # and divide the data by color with the color argument: avg_tree_curve(exfm7,"di_wb","DBH","hi","TH","STRATA","GENCODE",FALSE)
This function can be used to plan and execute selective cuttings of a native forest, without damaging the forest's natural structure.
bdq_meyer( df, plot, dbh, plot_area, class_interval = 5, dbh_min = 5, licourt_index = 2, output = "table" )bdq_meyer( df, plot, dbh, plot_area, class_interval = 5, dbh_min = 5, licourt_index = 2, output = "table" )
df |
A data frame. |
plot |
Quoted name of the plot variable. used to differentiate the plot's trees, and calculate the number of sampled plots. |
dbh |
Quoted name of the diameter at breast height variable, in cm. |
plot_area |
Quoted name of the plot area variable, or a numeric vector with the plot area value. The plot area value must be in square meters. |
class_interval |
Numeric value for the class interval used to classify the data. Default: |
dbh_min |
Numeric value for minimum diameter value to be considered in the classifications. dbh values smaller than this will be dismissed from the classification. Default: |
licourt_index |
Numeric value for the start licourt index used. Default: |
output |
Character value for the desired output. Can be either |
a data frame, a lm object, a vector or a list, according to the output argument.
Eric Bastos Gorgens [email protected]
Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.
library(forestmangr) data("exfm20") head(exfm20) # To get the table with the regulated forest: bdq_meyer(exfm20, "transect", "dbh", 1000) # Use different class interval values to get different results: bdq_meyer(exfm20, "transect", "dbh", 1000, class_interval = 10) # It's possible to get different outputs: bdq_meyer(exfm20, "transect", "dbh", 1000, output="model") bdq_meyer(exfm20, "transect", "dbh", 1000, output="coefs") bdq_meyer(exfm20, "transect", "dbh", 1000, output="full")library(forestmangr) data("exfm20") head(exfm20) # To get the table with the regulated forest: bdq_meyer(exfm20, "transect", "dbh", 1000) # Use different class interval values to get different results: bdq_meyer(exfm20, "transect", "dbh", 1000, class_interval = 10) # It's possible to get different outputs: bdq_meyer(exfm20, "transect", "dbh", 1000, output="model") bdq_meyer(exfm20, "transect", "dbh", 1000, output="coefs") bdq_meyer(exfm20, "transect", "dbh", 1000, output="full")
Function for calculating the bias of an estimator.
bias_per(df, y, yhat, na.rm = TRUE)bias_per(df, y, yhat, na.rm = TRUE)
df |
a data frame. |
y |
Quoted name of the variable representing the observed values in the data frame. If a data frame is not provided, |
yhat |
Quoted name of the variable representing the estimated values in the data frame. If a data frame is not provided, |
na.rm |
a logical value indicating whether NA values should be stripped before the computation proceeds. default: |
Function for calculating the bias of an estimator, given the observed values, and the estimated values.
Numeric vector with the bias value, in percentage.
Sollano Rabelo Braga [email protected]
other statistics to evaluate estimators:
rmse_per for the Root mean square error of an estimator
library(forestmangr) data("exfm11") head(exfm11) # Bias of an estimator, given the data frame and quoted variable names: bias_per(exfm11, "TH", "TH_EST3") # Bias of an estimator, given the vectors for observed and estimated values: bias_per(y = exfm11$TH, yhat = exfm11$TH_EST3)library(forestmangr) data("exfm11") head(exfm11) # Bias of an estimator, given the data frame and quoted variable names: bias_per(exfm11, "TH", "TH_EST3") # Bias of an estimator, given the vectors for observed and estimated values: bias_per(y = exfm11$TH, yhat = exfm11$TH_EST3)
Function used to check if a string, or a character vector contains variable names of a given data frame.
check_names(df, var_names, boolean = TRUE)check_names(df, var_names, boolean = TRUE)
df |
a data frame. |
var_names |
Character vector to be compared with the data frame names. |
boolean |
Boolean object used to define if the output is going to be a boolean object |
Function used to check if a string, or a character vector contains variable names of a given data frame. This functions is mainly used to error-proof other functions of this package,
Sollano Rabelo Braga [email protected]
library(forestmangr) check_names(iris, "Species") check_names(iris, "Species", boolean = FALSE ) check_names(iris, c("Especies", "Setal.Width") ) check_names(iris, c("Especies", "Setal.Width"), boolean = FALSE)library(forestmangr) check_names(iris, "Species") check_names(iris, "Species", boolean = FALSE ) check_names(iris, c("Especies", "Setal.Width") ) check_names(iris, c("Especies", "Setal.Width"), boolean = FALSE)
This function can be used to divide the data into classes, based on minimum value and class interval of a given variable, and create a column with the center of each class.
class_center(df, y, ci = 3, ymin = 5)class_center(df, y, ci = 3, ymin = 5)
df |
A data frame. |
y |
Quoted name of a variable, or a vector to be classified. |
ci |
Numeric value for the class interval used to classify the data. Default: |
ymin |
Numeric value for minimum value value to be considered in the classifications. dbh values smaller than this will be dismissed from the classification. Default: |
if df is supplied, a data frame containing the supplied data with a new column for the center of classes; if df is missing, a vetor with the center of class.
Sollano Rabelo Braga [email protected]
library(forestmangr) library(dplyr) data("exfm20") head(exfm20) # n # Number of individuals per ha per diameter class class_center(df = exfm20, y = "dbh", ci = 10, ymin = 10) exfm20 %>% mutate(CC = class_center(y = dbh, ci = 10, ymin = 10))library(forestmangr) library(dplyr) data("exfm20") head(exfm20) # n # Number of individuals per ha per diameter class class_center(df = exfm20, y = "dbh", ci = 10, ymin = 10) exfm20 %>% mutate(CC = class_center(y = dbh, ci = 10, ymin = 10))
Use the site variable to classify a forest management data.
classify_site(df, site, nc = 3, plot, .groups = NA)classify_site(df, site, nc = 3, plot, .groups = NA)
df |
A data frame. |
site |
Quoted name for the site variable. |
nc |
number of categories used to classify the data. If |
plot |
Quoted name for the plot variable. |
.groups |
Optional argument. Quoted name(s) of grouping variables used to fit multiple regressions, one for each level of the provided variable(s). Default |
A data frame classified based on the site index.
Sollano Rabelo Braga [email protected]
other sampling functions:
fit_clutter for fitting Clutter's Growth and Yield, and
est_clutter for estimating Clutter's Growth and Yield model variables.
library(forestmangr) data("exfm17") head(exfm17) # Classify data into 3 classes: ex_class <- classify_site(exfm17, "S", 3, "plot") head(ex_class ,15)library(forestmangr) data("exfm17") head(exfm17) # Classify data into 3 classes: ex_class <- classify_site(exfm17, "S", 3, "plot") head(ex_class ,15)
This function can be used to divide data into diameter classes, get the number of observations, number of observations per ha, and check number of species individuals, volume and G in each diameter class. It's also possible to spread the diameter classes as columns.
diameter_class( df, dbh, plot = NA, plot_area, ci = 5, dbhmin = 5, species = NA, volume = NA, NI_label = "NI", cc_to_column = FALSE, G_to_cc = FALSE, cctc_ha = TRUE, keep_unused_classes = FALSE )diameter_class( df, dbh, plot = NA, plot_area, ci = 5, dbhmin = 5, species = NA, volume = NA, NI_label = "NI", cc_to_column = FALSE, G_to_cc = FALSE, cctc_ha = TRUE, keep_unused_classes = FALSE )
df |
A data frame. |
dbh |
Quoted name of the diameter at breast height variable, in cm. |
plot |
Optional parameter.Quoted name of the plot variable. used to differentiate the plots trees, and calculate the number of sampled plots. Default |
plot_area |
Optional parameter. Quoted name of the plot area variable, or a numeric vector with the plot area value. The plot area value must be in square meters. Default |
ci |
Numeric value for the class interval used to classify the data. Default: |
dbhmin |
Numeric value for minimum diameter value to be considered in the classifications. dbh values smaller than this will be dismissed from the classification. Default: |
species |
Optional parameter. Quoted name of the scientific names variable, or any variable used to differentiate the different species found in data. If supplied, will be used to classify the species in the diameter data. Default |
volume |
Optional parameter. Quoted name of the volume variable. If supplied, will be used classify the volume variable in the different diameter classes. Also, if |
NI_label |
Label used for Species not identified. This parameter works along with species. The level supplied here will not be considered in the classification. Default |
cc_to_column |
If |
G_to_cc |
If |
cctc_ha |
If |
keep_unused_classes |
Some diameter classes may end up empty, depending on the maximum value of diameter and the class interval used. If this is |
A data frame containing the supplied data divided into diameter classes.
Sollano Rabelo Braga [email protected]
library(forestmangr) data("exfm20") head(exfm20) # n # Number of individuals per ha per diameter class diameter_class(df = exfm20, dbh = "dbh", ci = 10, dbhmin = 10, volume = "vol") # Number of individuals per ha per diameter class per species diameter_class(exfm20,"dbh", "transect", 10000, 10, 10, "scientific.name") %>% head(10) # Number of individuals per ha per diameter class, with each diameter class as a column diameter_class(exfm20,"dbh", "transect", 10000, 10, 10, "scientific.name", cc_to_column=TRUE) %>% head(10) # G # Basal area per ha per diameter class, with each diameter class as a column diameter_class(exfm20,"dbh", "transect",10000,10,10,"scientific.name", cc_to_column=TRUE,G_to_cc=FALSE) %>% head(10) # Volume # Volume per ha per diameter class diameter_class(exfm20,"dbh", "transect", 10000, 10, 10, "scientific.name",volume = "vol") %>% head(10) # Volume per ha per diameter class, with each diameter class as a column diameter_class(exfm20,"dbh","transect",10000,10,10,"scientific.name","vol",cc_to_column=TRUE) %>% head(10)library(forestmangr) data("exfm20") head(exfm20) # n # Number of individuals per ha per diameter class diameter_class(df = exfm20, dbh = "dbh", ci = 10, dbhmin = 10, volume = "vol") # Number of individuals per ha per diameter class per species diameter_class(exfm20,"dbh", "transect", 10000, 10, 10, "scientific.name") %>% head(10) # Number of individuals per ha per diameter class, with each diameter class as a column diameter_class(exfm20,"dbh", "transect", 10000, 10, 10, "scientific.name", cc_to_column=TRUE) %>% head(10) # G # Basal area per ha per diameter class, with each diameter class as a column diameter_class(exfm20,"dbh", "transect",10000,10,10,"scientific.name", cc_to_column=TRUE,G_to_cc=FALSE) %>% head(10) # Volume # Volume per ha per diameter class diameter_class(exfm20,"dbh", "transect", 10000, 10, 10, "scientific.name",volume = "vol") %>% head(10) # Volume per ha per diameter class, with each diameter class as a column diameter_class(exfm20,"dbh","transect",10000,10,10,"scientific.name","vol",cc_to_column=TRUE) %>% head(10)
This function is used to get a data frame with Dominant height values for each plot in an forest inventory data.
dom_height( df, th, dbh, plot, obs, dom, .groups, merge_data = FALSE, dh_name = "DH" )dom_height( df, th, dbh, plot, obs, dom, .groups, merge_data = FALSE, dh_name = "DH" )
df |
A data frame. |
th |
Quoted name of the total height variable. |
dbh |
Quoted name of the diameter at breast height variable. Used to filter out trees with no diameter measurement. |
plot |
Quoted name of the plot variable. used to differentiate the data's plots. If this argument is missing, the defined groups in the data frame will be used, If there are no groups in the data, the function will fail. |
obs |
Quoted name of the observations variable. This will be used to tell which trees are dominant, i.e. it's the variable that tells the type of tree; if it is normal, dominant, suppressed, etc. If this argument is not supplied, the function will calculate the average value of 2 trees with bigger height values in each plot, and use that as the dominant value. |
dom |
Character value for the dominant tree code used in the observations variable variable supplied in the |
.groups |
Optional argument. Quoted name(s) of grouping variables that can be added to differentiate subdivisions of the data. Default: |
merge_data |
If |
dh_name |
Character value for the name of the dominant height variable created. Default: |
A data frame with the the dominant height values by plot.
Sollano Rabelo Braga [email protected]
library(forestmangr) data("exfm9") head(exfm9) # Let's say we need to get the dominant height (DH) values for a forest inventory data. # If we don't have a variable that tells which trees are dominant, it's ok. We can # still estimate DH using this function. It will average the top 2 trees of each plot: dom_height(df=exfm9,th="TH",dbh="DBH",plot="PLOT") # Of course, if we do have a variable that differentiate the dominant trees, it's # best we use it. For that we use the obs argument, and the dom argument. # In this data, the OBS variable is used to tell the type of tree. # Let's check the levels in our OBS variable, to see which one is associated # with dominant trees. levels(as.factor(exfm9$OBS)) # So, the "D" level must be the one that tells which trees are dominant. Let's use it: dom_height(df=exfm9,th="TH",dbh="DBH",plot="PLOT",obs="OBS",dom="D") # If there are subdivisions of the data, like different strata, they can be included in the # .groups argument: dom_height(df=exfm9,th="TH",dbh="DBH",plot="PLOT",obs="OBS",dom="D",.groups="STRATA") # It's possible to automatically bind the dominant heights table to the original data, # using the merge_data argument: dom_height(df=exfm9,th="TH",dbh="DBH",plot="PLOT",obs="OBS", dom="D",.groups="STRATA", merge_data=TRUE)library(forestmangr) data("exfm9") head(exfm9) # Let's say we need to get the dominant height (DH) values for a forest inventory data. # If we don't have a variable that tells which trees are dominant, it's ok. We can # still estimate DH using this function. It will average the top 2 trees of each plot: dom_height(df=exfm9,th="TH",dbh="DBH",plot="PLOT") # Of course, if we do have a variable that differentiate the dominant trees, it's # best we use it. For that we use the obs argument, and the dom argument. # In this data, the OBS variable is used to tell the type of tree. # Let's check the levels in our OBS variable, to see which one is associated # with dominant trees. levels(as.factor(exfm9$OBS)) # So, the "D" level must be the one that tells which trees are dominant. Let's use it: dom_height(df=exfm9,th="TH",dbh="DBH",plot="PLOT",obs="OBS",dom="D") # If there are subdivisions of the data, like different strata, they can be included in the # .groups argument: dom_height(df=exfm9,th="TH",dbh="DBH",plot="PLOT",obs="OBS",dom="D",.groups="STRATA") # It's possible to automatically bind the dominant heights table to the original data, # using the merge_data argument: dom_height(df=exfm9,th="TH",dbh="DBH",plot="PLOT",obs="OBS", dom="D",.groups="STRATA", merge_data=TRUE)
This function estimates the present the present value of basal area for each class using either the class mean, or a linear quadratic model, and then uses it's value to calculate the basal area from Clutter's growth and yield model.
est_clutter( df, age, basal_area, site, category, coeffs, method = "average", annual_increment = FALSE, gray_scale = TRUE, output = "table" )est_clutter( df, age, basal_area, site, category, coeffs, method = "average", annual_increment = FALSE, gray_scale = TRUE, output = "table" )
df |
A data frame. |
age |
A numeric vector with the desired age range to be used in the estimation, or a Quoted name for the age variable. |
basal_area |
Quoted name for the basal area variable. |
site |
Quoted name for the average site variable. |
category |
Quoted name for the category variable. |
coeffs |
Numeric vector or a data frame with the fitted values of Clutter's growth and yield model. It must be a named vector, with b0,b1,b2,b3,a0 and a1 as names. a1 is not obligatory. |
method |
Method used for estimating the present basal area of each class. It can either be the class' average basal area |
annual_increment |
If |
gray_scale |
If |
output |
Type of output the function should return. This can either be |
A data frame, a ggplot object or a list, according to output.
Sollano Rabelo Braga [email protected]
other sampling functions:
fit_clutter for fitting the clutter growth and Yield model, and
classify_site for classifying data according to site.
library(forestmangr) data("exfm17") head(exfm17) clutter <- fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot") clutter # Classify data into 3 classes: ex_class <- classify_site(exfm17, "S", 3, "plot") head(ex_class ,15) # Estimate basal area using the average basal area as the initial basal area, # volume, Mean Monthly Increment (MMI) and Current Monthly Increment (CMI) # values using Clutter's model: est_clutter(ex_class,20:125, "B","S","category_",clutter,"average") # For a more detailed output, including a plot, use output="full": est_clutter(ex_class,20:125, "B","S","category_",clutter, output="full") # Estimate basal area using an estimated basal area as the initial basal area: est_clutter(ex_class,20:125,"B","S","category_",clutter,"model") # age can be a variable: est_clutter(ex_class,"age","B","S","category_", clutter,"model")library(forestmangr) data("exfm17") head(exfm17) clutter <- fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot") clutter # Classify data into 3 classes: ex_class <- classify_site(exfm17, "S", 3, "plot") head(ex_class ,15) # Estimate basal area using the average basal area as the initial basal area, # volume, Mean Monthly Increment (MMI) and Current Monthly Increment (CMI) # values using Clutter's model: est_clutter(ex_class,20:125, "B","S","category_",clutter,"average") # For a more detailed output, including a plot, use output="full": est_clutter(ex_class,20:125, "B","S","category_",clutter, output="full") # Estimate basal area using an estimated basal area as the initial basal area: est_clutter(ex_class,20:125,"B","S","category_",clutter,"model") # age can be a variable: est_clutter(ex_class,"age","B","S","category_", clutter,"model")
In this data, each observation is a plot.
data(exfm1)data(exfm1)
A data frame with 22 observations and 4 variables:
stratum number
area of each strata, in hectares
area of plots, in square meters
volume with bark, in cubic meters
volume with bark, in cubic meters per hectare
Sollano Rabelo Braga [email protected]
Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.
In this data, each observation is a tree.
data(exfm10)data(exfm10)
A data frame with 900 observations and 14 variables:
map numbers
project number
stratum number
genetic code of plots
area of each strata, in hectares
date of planting
Spacing used in the plots, in meters
plot number
date of measurement
area of plots, in square meters
pit number
diameter at breast height, in centimeters
total height, in meters
quality of trees, N = normal tree, D = dominant tree, F = a failure, or dead tree
dominant height, in meters
Sollano Rabelo Braga [email protected]
In this data, each observation is a tree.
data(exfm11)data(exfm11)
A data frame with 199 observations and 6 variables:
stratum number
plot number
total height, in meters
total estimated height with model 1, in meters
total estimated height with model 2, in meters
total estimated height with model 3, in meters
Sollano Rabelo Braga [email protected]
In this data, each observation is a plot. The site was estimated using the Chapman & Richards model.
data(exfm12)data(exfm12)
A data frame with 139 observations and 8 variables:
stratum number
plot number
average age of plots, in months
dominant height, in meters
number of individuals
volume of trees, in cubic meters
basal area, in square meters
site variable, in meters
Sollano Rabelo Braga [email protected]
In this data, each observation is a tree.
data(exfm13)data(exfm13)
A data frame with 36 observations and 7 variables:
species common name
treatment number
species number
quantity of nitrogen used, in grams
squared quantity of nitrogen used, in squared grams
block number
diameter at breast height, in centimeters
Sollano Rabelo Braga [email protected]
In this data, each observation is a plot.
data(exfm14)data(exfm14)
A data frame with 859 observations and 4 variables:
stratum number
plot number
average age of plots, in months
dominant height, in meters
Sollano Rabelo Braga [email protected]
In this data, each observation is a tree.
data(exfm15)data(exfm15)
A data frame with 900 observations and 7 variables:
stratum number
area of each strata, in hectares
plot number
area of plots, in square meters
diameter at breast height, in centimeters
total height, in meters
quality of trees, N = normal tree, D = dominant tree, F = a failure, or dead tree
Sollano Rabelo Braga [email protected]
In this data, each observation is a plot.
data(exfm16)data(exfm16)
A data frame with 139 observations and 8 variables:
stratum number
plot number
average age of plots, in months
dominant height, in meters
number of individuals
volume of trees, in cubic meters
basal area, in square meters
Sollano Rabelo Braga [email protected]
In this data, each observation is a plot. The site was estimated using the Schumacher model.
data(exfm17)data(exfm17)
A data frame with 139 observations and 8 variables:
stratum number
plot number
average age of plots, in months
dominant height, in meters
number of individuals
volume of trees, in cubic meters
basal area, in square meters
site variable, in meters
Sollano Rabelo Braga [email protected]
In this data, each observation is a tree.
data(exfm18)data(exfm18)
A data frame with 877 observations and 6 variables:
plot number
species scientific name
tree number
trunk number
circumference at breast height, in centimeters
diameter at breast height, in centimeters
Sollano Rabelo Braga [email protected]
In this data, each observation is a section of a tree.
data(exfm19)data(exfm19)
A data frame with 16 observations and 3393 variables:
stratum number
number of trees
diameter at breast height, in centimeters
total height, in meters
cross section area with bark, in square meters
volume with bark, in cubic meters
form factor for each section
average form vactor value
Sollano Rabelo Braga [email protected]
In this data, each observation is a plot.
data(exfm2)data(exfm2)
A data frame with 57 observations and 4 variables:
stratum number
area of each strata, in hectares
area of plots, in square meters
volume with bark, in cubic meters
volume with bark, in cubic meters per hectare
Sollano Rabelo Braga [email protected]
Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.
In this data, each observation is a tree.
data(exfm20)data(exfm20)
A data frame with 12295 observations and 18 variables:
area code
plot number
tree number
species common name
species scientific name
species family name
diameter at breast height, in centimeters
canopy position
level of light received by the tree
tells if the tree is dead or not
commercial height, in meters
total height, in meters
date of measurement
utm east position value
utm north position value
volume of trees, in cubic meters
plot area, in square meters
total area, in hectares
Sollano Rabelo Braga [email protected]
In this data, each observation is a tree.
data(exfm21)data(exfm21)
A data frame with 900 observations and 13 variables:
stratum number
area of each strata, in hectares
plot number
area of plots, in square meters
diameter at breast height, in centimeters
total height, in meters
quality of trees, N = normal tree, D = dominant tree, F = a failure, or dead tree
dominant height, in meters
estimated total height, in meters
cross sectional area, in square meters
average age of plots, in months
volume with bark, in cubic meters
volume without bark, in cubic meters
Sollano Rabelo Braga [email protected]
In this data, each observation is the year's revenue.
data(exfm22)data(exfm22)
A data frame with 8 observations and 3 variables:
Revenue year
cost values for that year, in dollars
revenue values for that year, in dollars
Sollano Rabelo Braga [email protected]
In this data, each observation is a plot.
data(exfm3)data(exfm3)
A data frame with 10 observations and 3 variables:
total area, in hectares
area of plots, in square meters
volume with bark, in cubic meters
volume with bark, in cubic meters per hectare
Sollano Rabelo Braga [email protected]
Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.
In this data, each observation is a plot.
data(exfm4)data(exfm4)
A data frame with 25 observations and 3 variables:
total area, in hectares
area of plots, in square meters
volume with bark, in cubic meters
Sollano Rabelo Braga [email protected]
Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.
In this data, each observation is a plot.
data(exfm5)data(exfm5)
A data frame with 18 observations and 3 variables:
total area, in hectares
area of plots, in square meters
volume with bark, in cubic meters
volume with bark, in cubic meters per hectare
Sollano Rabelo Braga [email protected]
Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.
In this data, each observation is a plot.
data(exfm6)data(exfm6)
A data frame with 10 observations and 14 variables:
genetic code of plots
map numbers
stratum number
plot number
average age of plots, in months
area of each strata, in hectares
area of plots, in square meters
diameter at breast height, in centimeters
quadratic diameter, in centimeters
total height, in meters
dominant height, in meters
basal area, in square meters
volume with bark, in cubic meters
volume with bark, in cubic meters per hectare
volume without bark, in cubic meters
Sollano Rabelo Braga [email protected]
In this data, each observation is a section of a tree.
data(exfm7)data(exfm7)
A data frame with 11 observations and 3393 variables:
map numbers
Project's name
Spacing used in the plots, in meters
stratum number
genetic code of plots
number of trees
diameter at breast height, in centimeters
total height, in meters
height of sections, in meters
diameter of sections with bark, in centimeters
bark of thickness, in millimeters
Sollano Rabelo Braga [email protected]
In this data, each observation is a section of a tree.
data(exfm8)data(exfm8)
A data frame with 10 observations and 596 variables:
Clone number
stratum number
number of trees
log number
diameter at breast height, in centimeters
total height, in meters
height of sections, in meters
diameter of sections with bark, in centimeters
bark of thickness, in millimeters
section length, in meters
Sollano Rabelo Braga [email protected]
In this data, each observation is a tree.
data(exfm9)data(exfm9)
A data frame with 900 observations and 14 variables:
map numbers
project number
stratum number
genetic code of plots
area of each strata, in hectares
date of planting
Spacing used in the plots, in meters
plot number
date of measurement
area of plots, in square meters
pit number
diameter at breast height, in centimeters
total height, in meters
quality of trees, N = normal tree, D = dominant tree, F = a failure, or dead tree
Sollano Rabelo Braga [email protected]
Fit the Clutter model for growth and yield using the two stage least squares method (2SLS).
fit_clutter( df, age, dh, basal_area, volume, site, plot, .groups = NA, model = "full", keep_model = FALSE )fit_clutter( df, age, dh, basal_area, volume, site, plot, .groups = NA, model = "full", keep_model = FALSE )
df |
A data frame. |
age |
Quoted name for the age variable. |
dh |
Quoted name for the dominant height variable. |
basal_area |
Quoted name for the basal area variable. |
volume |
Quoted name for the volume area variable. |
site |
Quoted name for the site variable. |
plot |
Quoted name for the plot variable. |
.groups |
Optional argument. Quoted name(s) of grouping variables used to fit multiple regressions, one for each level of the provided variable(s). Default |
model |
Character variable for the type of the model fitted. If |
keep_model |
If |
A data frame with the regression coefficients.
Sollano Rabelo Braga [email protected]
Clutter, J. L. (1963) Compatible Growth For Loblolly by the Southeastern, Forest Science, 9(3), pp. 354–371. Sullivan, A. D. and Clutter, J. L. (1972) A Simultaneous Growth and Yield for Loblolly Pine, Forest Science, 18(1), pp. 76–86. Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
other sampling functions:
est_clutter for estimating the Clutter growth and yield model variables, and
classify_site for classifying data according to site.
library(forestmangr) data("exfm17") head(exfm17) # To fit the Clutter model we just need to define the data, and age, dominant height, # basal area, volume, site and plot variables: fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot") # To fit the alternate model (without a1) just use model="mod": fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot",model="mod") # To keep the regression model, use keep_model=TRUE: fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot", keep_model=TRUE)library(forestmangr) data("exfm17") head(exfm17) # To fit the Clutter model we just need to define the data, and age, dominant height, # basal area, volume, site and plot variables: fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot") # To fit the alternate model (without a1) just use model="mod": fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot",model="mod") # To keep the regression model, use keep_model=TRUE: fit_clutter(exfm17, "age", "DH", "B", "V", "S", "plot", keep_model=TRUE)
This function calculates the horizontal structure of a given forest inventory data, with information like absolute frequency, relative frequency, absolute density, relative density, absolute dominance, relative dominance, importance value index, and coverage value index. If additional variables are supplied, the vertical and internal structures are also provided.
forest_structure( df, species, dbh, plot, plot_area, vertical_est = NA, internal_est = NA, NI_label = "" )forest_structure( df, species, dbh, plot, plot_area, vertical_est = NA, internal_est = NA, NI_label = "" )
df |
A data frame. |
species |
Quoted name of the scientific names variable, or any variable used to differentiate the different species found in data. |
dbh |
Quoted name of the diameter at breast height variable, in cm. |
plot |
Quoted name of the plot variable. used to differentiate the plot's trees, and calculate the number of sampled plots. |
plot_area |
Quoted name of the plot area variable, or a numeric vector with the plot area value. The plot area value must be in square meters. |
vertical_est |
Optional argument. Quoted name of the vertical strata variable, or the height variable. If this is a factor variable, it's levels will be used to classify the forest vertically. If it's a height variable, the vertical strata will be created based on it's mean and standard deviation values. Default: |
internal_est |
Optional argument. Quoted name of the internal strata variable. Default: |
NI_label |
Label used for Species not identified. This parameter works along with species. The level supplied here will not be considered in the classification. Default |
a data frame with the forest's structure.
Eric Bastos Gorgens [email protected]
Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.
library(forestmangr) data("exfm20") head(exfm20) # Get the forest's horizontal structure: forest_structure(exfm20, "scientific.name", "dbh", "transect", 10000) # area plot as a variable name: forest_structure(exfm20, "scientific.name", "dbh", "transect", "plot.area") # Get the forest's horizontal and vertical structure. # The vertical structure variable can either be the height variable, # or a factor variable with the horizontal strata: forest_structure(exfm20, "scientific.name", "dbh", "transect", 10000, "canopy.pos") # Get the forest's horizontal, vertical and internal structure: forest_structure(exfm20, "scientific.name", "dbh", "transect", 10000, "canopy.pos", "light")library(forestmangr) data("exfm20") head(exfm20) # Get the forest's horizontal structure: forest_structure(exfm20, "scientific.name", "dbh", "transect", 10000) # area plot as a variable name: forest_structure(exfm20, "scientific.name", "dbh", "transect", "plot.area") # Get the forest's horizontal and vertical structure. # The vertical structure variable can either be the height variable, # or a factor variable with the horizontal strata: forest_structure(exfm20, "scientific.name", "dbh", "transect", 10000, "canopy.pos") # Get the forest's horizontal, vertical and internal structure: forest_structure(exfm20, "scientific.name", "dbh", "transect", 10000, "canopy.pos", "light")
Hypothesis test as described by Graybill (1976).
graybill_f(df, Y1, Yj, signif = 0.05, output = "simple")graybill_f(df, Y1, Yj, signif = 0.05, output = "simple")
df |
A data frame. |
Y1 |
Quoted name of the standard variable. |
Yj |
Quoted name of the proposed variable. |
signif |
Numeric value for the significance level used in the test. Default: |
output |
Defines the type of output. If |
This test is used to compare two variables, usually a proposed method, and a standard variable.This test is popular among forestry engineers, specially because, since it considers all data in it's analysis, it's usually more precise than a standard mean t-test. If the data has outliers, the mean may not represent the data correctly, so Graybill F test is specially useful for heterogeneous data.
A simple model regression is applied, and it's significance is evaluated by applying Graybill F test for the parameters estimate, according to the methodology described by Graybill (1976).
A data frame. Its dimensions will vary, according to the output argument.
Sollano Rabelo Braga [email protected]
Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
Graybill, F. A. (1976) Theory and application of the linear model. Massachusets: Ouxburg 239 Press.
Leite, H. G. and Oliveira, F. H. T. (2006) Statistical procedure to test identity between analytical methods, Communications in Soil Science and Plant Analysis, 33(7–8), pp. 1105–1118.
library(forestmangr) data("exfm11") head(exfm11) # The data frame exfm11 contains a height variable called "TH". This will be our # standard value. We'll compare it to height estimated using different hypsometric equations. # These are variables "TH_EST1" and "TH_EST2": graybill_f( exfm11,"TH", "TH_EST1") # TH_EST1 is statistically different from "TH". # It's possible to alter the test's significance level using the signif argument: graybill_f( exfm11,"TH", "TH_EST1", signif = 0.01) # Different output options are available through the output argument: graybill_f( exfm11,"TH", "TH_EST2", output="table") graybill_f( exfm11,"TH", "TH_EST2", output="full")library(forestmangr) data("exfm11") head(exfm11) # The data frame exfm11 contains a height variable called "TH". This will be our # standard value. We'll compare it to height estimated using different hypsometric equations. # These are variables "TH_EST1" and "TH_EST2": graybill_f( exfm11,"TH", "TH_EST1") # TH_EST1 is statistically different from "TH". # It's possible to alter the test's significance level using the signif argument: graybill_f( exfm11,"TH", "TH_EST1", signif = 0.01) # Different output options are available through the output argument: graybill_f( exfm11,"TH", "TH_EST2", output="table") graybill_f( exfm11,"TH", "TH_EST2", output="full")
Get the guide curve for growth and yield analysis of inventory data using the factor method, and different statistical models.
guide_curve( df, dh, age, age_index, n_class = 4, model = "Schumacher", start_chap = c(b0 = 23, b1 = 0.03, b2 = 1.3), start_bailey = c(b0 = 3, b1 = -130, b2 = 1.5), round_classes = FALSE, font = "serif", gray_scale = TRUE, output = "plot" )guide_curve( df, dh, age, age_index, n_class = 4, model = "Schumacher", start_chap = c(b0 = 23, b1 = 0.03, b2 = 1.3), start_bailey = c(b0 = 3, b1 = -130, b2 = 1.5), round_classes = FALSE, font = "serif", gray_scale = TRUE, output = "plot" )
df |
A data frame. |
dh |
Quoted name for the dominant height variable. |
age |
Quoted name for the age variable. |
age_index |
Numeric value for the age index. |
n_class |
Numeric value for the number of classes used to divide the data. Default |
model |
model used to fit dh as a function of age. The models available are |
start_chap |
Numeric vector with the start values for the Chapman-Richards model. This must be a named vector, with b0, b1 and b2 as parameter names. Default: |
start_bailey |
Numeric vector with the start values for the Bailey-Clutter model. This must be a named vector, with b0, b1 and b2 as parameter names. Default: |
round_classes |
If |
font |
Type of font used in the plot. Default: |
gray_scale |
If |
output |
Type of output the function should return. This can either be |
A data frame, a ggplot object, or a list, varying according to the "output" argument.
Sollano Rabelo Braga [email protected]
data("exfm14") head(exfm14) # To get a guide curve plot for this data, we simply need to input # dominant height and age variables, age index, and number of classes to be used: guide_curve(exfm14, "dh", "age", 72, 5) # if we want to get the table used to get the plot, we can choose the output "table": guide_curve(exfm14, "dh", "age", 72, 5, output = "table") # Other models are available for use, such as Curtis, Chapman Richards, and Bailey: # CR and BC models are non linear, and thus need start values. There are default values, # but they may fail, depending on the data used, so it's recommended to try start values that # are ideal for the data used: guide_curve(exfm14, "dh", "age", 72, 5, model = "Chapman-Richards", start_chap = c(b0=23, b1=0.03, b2 = 1.3)) # Or, to get more information on the analysis, such as details on the regression, # bias, rmse, plot for residuals and more (cpu taxing): ## Not run: guide_curve(exfm14, "dh", "age", 72, 5, output = "full") ## End(Not run)data("exfm14") head(exfm14) # To get a guide curve plot for this data, we simply need to input # dominant height and age variables, age index, and number of classes to be used: guide_curve(exfm14, "dh", "age", 72, 5) # if we want to get the table used to get the plot, we can choose the output "table": guide_curve(exfm14, "dh", "age", 72, 5, output = "table") # Other models are available for use, such as Curtis, Chapman Richards, and Bailey: # CR and BC models are non linear, and thus need start values. There are default values, # but they may fail, depending on the data used, so it's recommended to try start values that # are ideal for the data used: guide_curve(exfm14, "dh", "age", 72, 5, model = "Chapman-Richards", start_chap = c(b0=23, b1=0.03, b2 = 1.3)) # Or, to get more information on the analysis, such as details on the regression, # bias, rmse, plot for residuals and more (cpu taxing): ## Not run: guide_curve(exfm14, "dh", "age", 72, 5, output = "full") ## End(Not run)
Function used to calculate the volume with bark of trees using the Huber method.
This function has integration with dplyr, so it can be used inside a pipe, along with the
group_by function.
huberwb(df, di, section_length, tree, .groups = NA, di_mm_to_cm = FALSE)huberwb(df, di, section_length, tree, .groups = NA, di_mm_to_cm = FALSE)
df |
A data frame. |
di |
Quoted name of the section diameter variable, in centimeters. |
section_length |
Quoted name of the section length variable, in meters |
tree |
Quoted name of the tree variable. used to differentiate the trees' sections. If this argument is |
.groups |
Optional argument. Quoted name(s) of additional grouping variables that can be added to differentiate subdivisions of the data.
If this argument is |
di_mm_to_cm |
Boolean argument that, if |
Data frame with volume values by section.
Sollano Rabelo Braga [email protected]
Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
Complementary functions:
huberwob, For calculation of volume without bark using the Huber method,
smalianwb, for calculation of volume with bark using the Smalian method,
smalianwob, for calculation of volume without bark the Smalian method.
library(forestmangr) data("exfm8") head(exfm8) # Calculate the volume with bark using the Huber method: huberwb(exfm8,"di_wb", "sec_length", "TREE") # Using pipes: library(dplyr) exfm8 %>% group_by(TREE) %>% huberwb("di_wb", "sec_length")library(forestmangr) data("exfm8") head(exfm8) # Calculate the volume with bark using the Huber method: huberwb(exfm8,"di_wb", "sec_length", "TREE") # Using pipes: library(dplyr) exfm8 %>% group_by(TREE) %>% huberwb("di_wb", "sec_length")
Function used to calculate the volume without bark of trees using the Huber method.
This function has integration without dplyr, so it can be used inside a pipe, along with the
group_by function.
huberwob( df, di, section_length, bt, tree, .groups = NA, di_mm_to_cm = FALSE, bt_mm_to_cm = FALSE )huberwob( df, di, section_length, bt, tree, .groups = NA, di_mm_to_cm = FALSE, bt_mm_to_cm = FALSE )
df |
A data frame. |
di |
Quoted name of the section diameter variable, in centimeters. |
section_length |
Quoted name of the section length variable, in meters |
bt |
Quoted name of the bark thickness variable, in centimeters. |
tree |
Quoted name of the tree variable. used to differentiate the trees' sections. If this argument is |
.groups |
Optional argument. Quoted name(s) of additional grouping variables that can be added to differentiate subdivisions of the data.
If this argument is |
di_mm_to_cm |
Boolean argument that, if |
bt_mm_to_cm |
Boolean argument that, if |
Data frame with volume values by section.
Sollano Rabelo Braga [email protected]
Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
Complementary functions:
huberwb, For calculation of volume with bark using the Huber method,
smalianwb, for calculation of volume with bark using the Smalian method,
smalianwob, for calculation of volume without bark the Smalian method.
library(forestmangr) data("exfm8") head(exfm8) # Calculate the volume without bark using the Huber method: huberwob(exfm8,"di_wb", "sec_length", "bark_t", "TREE") # Using pipes: library(dplyr) exfm8 %>% group_by(TREE) %>% huberwob("di_wb", "sec_length", "bark_t")library(forestmangr) data("exfm8") head(exfm8) # Calculate the volume without bark using the Huber method: huberwob(exfm8,"di_wb", "sec_length", "bark_t", "TREE") # Using pipes: library(dplyr) exfm8 %>% group_by(TREE) %>% huberwob("di_wb", "sec_length", "bark_t")
Function for using the Identity of a Model test, as described by Regazzi (1999).
ident_model( df, factor, reduced_model, filter = NA, gray_scale = TRUE, signif = 0.05, font = "serif", output = "table", eq = TRUE )ident_model( df, factor, reduced_model, filter = NA, gray_scale = TRUE, signif = 0.05, font = "serif", output = "table", eq = TRUE )
df |
a data frame. |
factor |
Quoted name of the factor variable used to differentiate the data projects in the test. |
reduced_model |
Quoted or unquoted reduced model used in the test. The variables mentioned in the model must exist in the provided data frame. X and Y sides of the model must be separated by "~". |
filter |
Optional argument. If supplied with levels present in |
gray_scale |
If |
signif |
Numeric value for the significance level used in the test. Default: |
font |
font family used in the plots. Can be either |
output |
Defines the type of output. If |
eq |
if |
A data frame, a ggplot object, or a list, varying according to the output argument.
Sollano Rabelo Braga [email protected]
Marcio leles Romarco de Oliveira [email protected]
Regazzi, A. J. (1999) Teste para verificar a identidade de modelos de regressao e a igualdade de parametros no caso de dados de delineamentos experimentais, Ceres, 46(266), pp. 383–409.
library(forestmangr) data("exfm13") head(exfm13) # The objective is to know if the diameter's behavior is similar among 3 species. # For this we'll use a quadratic model. We'll use nitrogen (N) as our X variable. ident_model(exfm13, "species", dbh ~ N + N2) # This test shows that there are differences between the species. # We can get more details on this using a different output, that will also # give us a plot: ident_model(exfm13, "species", dbh ~ N + N2, output = "table_plot",eq=FALSE) # This gives us only the plot: ident_model(exfm13, "species", dbh ~ N + N2, output = "table_plot",eq=FALSE) # And this gives us additional information on the test: ident_model(exfm13, "species", dbh ~ N + N2, output = "full",eq=FALSE) # Looking at the plot, it seems that 2 species are behaving very similar, while # the Pequi species is different from the other 2. We can confirm this by running # the test in a paired fashion, using the filter argument: ident_model(exfm13, "species", dbh ~ N + N2, filter = c("PEQUI", "SUCUPIRA-PRETA"), output = "table_plot",eq=FALSE) ident_model(exfm13, "species", dbh ~ N + N2, filter = c("PEQUI", "VINHATICO"), output = "table_plot",eq=FALSE) ident_model(exfm13, "species", dbh ~ N + N2, filter = c("SUCUPIRA-PRETA", "VINHATICO"), output = "table_plot",eq=FALSE) # As we imagined, a single model can be used to describe the behavior of # the "Sucupira-preta" and "Vinhatico" species, # and a second model is needed to explain the Pequi Variable. # It's possible to apply a color scale to the plots, and also change it's font to arial: ident_model(exfm13, "species", dbh ~ N + N2,output="plot",gray_scale=FALSE,font="sans",eq=FALSE)library(forestmangr) data("exfm13") head(exfm13) # The objective is to know if the diameter's behavior is similar among 3 species. # For this we'll use a quadratic model. We'll use nitrogen (N) as our X variable. ident_model(exfm13, "species", dbh ~ N + N2) # This test shows that there are differences between the species. # We can get more details on this using a different output, that will also # give us a plot: ident_model(exfm13, "species", dbh ~ N + N2, output = "table_plot",eq=FALSE) # This gives us only the plot: ident_model(exfm13, "species", dbh ~ N + N2, output = "table_plot",eq=FALSE) # And this gives us additional information on the test: ident_model(exfm13, "species", dbh ~ N + N2, output = "full",eq=FALSE) # Looking at the plot, it seems that 2 species are behaving very similar, while # the Pequi species is different from the other 2. We can confirm this by running # the test in a paired fashion, using the filter argument: ident_model(exfm13, "species", dbh ~ N + N2, filter = c("PEQUI", "SUCUPIRA-PRETA"), output = "table_plot",eq=FALSE) ident_model(exfm13, "species", dbh ~ N + N2, filter = c("PEQUI", "VINHATICO"), output = "table_plot",eq=FALSE) ident_model(exfm13, "species", dbh ~ N + N2, filter = c("SUCUPIRA-PRETA", "VINHATICO"), output = "table_plot",eq=FALSE) # As we imagined, a single model can be used to describe the behavior of # the "Sucupira-preta" and "Vinhatico" species, # and a second model is needed to explain the Pequi Variable. # It's possible to apply a color scale to the plots, and also change it's font to arial: ident_model(exfm13, "species", dbh ~ N + N2,output="plot",gray_scale=FALSE,font="sans",eq=FALSE)
This function returns the inverse of a numeric vector.
inv(x)inv(x)
x |
A numeric vector |
This function is manly used when fitting statistical models.
If one of the variables in a model is an inverse of a vector, the lm
function does not properly compute the variable, if 1/vector is inserted directly
in the model, leading to the need of creating a separate variable. This function allows the user to get the inverse
of a given numeric vector inside the model, without the need to create a new variable.
a numeric vector containing the inverse of x.
Sollano Rabelo Braga [email protected]
library(forestmangr) data("exfm15") head(exfm15) # Get the inverse of a vector inv(iris$Petal.Length) # Fit a model that contains the inverse of a variable, without the need to # create a new variable for the inverse: lm(log(TH) ~ inv(DBH), exfm15 ) # or lm_table(exfm15, log(TH) ~ inv(DBH) )library(forestmangr) data("exfm15") head(exfm15) # Get the inverse of a vector inv(iris$Petal.Length) # Fit a model that contains the inverse of a variable, without the need to # create a new variable for the inverse: lm(log(TH) ~ inv(DBH), exfm15 ) # or lm_table(exfm15, log(TH) ~ inv(DBH) )
With this function it's possible to fit linear regressions by a grouping variable, and evaluate each equation via an interactive plot of residuals, and get a data frame. with each column as a coefficient and quality of fit variables, and other output options. Works with dplyr grouping functions.
lm_resid( df, model, output_mode = "table", est.name = "est", keep_model = FALSE, rmoutliers = FALSE, fct_to_filter = NA, rmlevels = NA, onlyfiteddata = FALSE, group_print = NA )lm_resid( df, model, output_mode = "table", est.name = "est", keep_model = FALSE, rmoutliers = FALSE, fct_to_filter = NA, rmlevels = NA, onlyfiteddata = FALSE, group_print = NA )
df |
A data frame. |
model |
A linear regression model, with or without quotes. The variables mentioned in the model must exist in the provided data frame. X and Y sides of the model must be separated by "~". |
output_mode |
Selects different output options. Can be either |
est.name |
Name of the estimated y value. Used only if |
keep_model |
If |
rmoutliers |
If |
fct_to_filter |
Name of a factor or character column to be used as a filter to remove levels. Default: |
rmlevels |
Levels of the fct_to_filter variable to be removed from the fit Default: |
onlyfiteddata |
If |
group_print |
This argument is only used internally by another function. Please ignore. |
this function uses lm_table as a basis, but calls a plot of residuals for each fitted model, for the user to evaluate. If one decides to remove any of the points, one can click and drag, and then click on the 'remove points' button. After that, one must simply click 'done' and the coefficients will be printed out.
It's possible to use the output argument to get a merged table if output="merge", that binds
the original data frame and the fitted coefficients.
If output="merge_est" we get a merged table as well, but with y estimated using the coefficients. If the fit is made using groups, this is taken into account, i.e. the estimation is made by group.
If output="nest", a data frame with nested columns is provided. This can be used if the user desires to get a customized output.
A data frame. Different data frame options are available using the output argument.
Sollano Rabelo Braga [email protected]
if (interactive() ){ library(forestmangr) library(dplyr) data("exfm19") # Fit SH model: lm_resid(exfm19, log(VWB) ~ log(DBH) + log(TH)) }if (interactive() ){ library(forestmangr) library(dplyr) data("exfm19") # Fit SH model: lm_resid(exfm19, log(VWB) ~ log(DBH) + log(TH)) }
With this function it's possible to fit linear regressions by a grouping variable, and evaluate each equation via a interactive plot of residuals, and get a data frame. with each column as a coefficient and quality of fit variables, and other output options. Works with dplyr grouping functions.
lm_resid_group( df, model, .groups = NA, output_mode = "table", est.name = "est", keep_model = FALSE, rmoutliers = FALSE, fct_to_filter = NA, rmlevels = NA, onlyfiteddata = FALSE )lm_resid_group( df, model, .groups = NA, output_mode = "table", est.name = "est", keep_model = FALSE, rmoutliers = FALSE, fct_to_filter = NA, rmlevels = NA, onlyfiteddata = FALSE )
df |
A data frame. |
model |
A linear regression model, with or without quotes. The variables mentioned in the model must exist in the provided data frame. X and Y sides of the model must be separated by "~". |
.groups |
Quoted name(s) of grouping variables used to fit multiple regressions, one for each level of the provided variable(s). Default |
output_mode |
Selects different output options. Can be either |
est.name |
Name of the estimated y value. Used only if |
keep_model |
If |
rmoutliers |
If |
fct_to_filter |
Name of a factor or character column to be used as a filter to remove levels. Default: |
rmlevels |
Levels of the fct_to_filter variable to be removed from the fit Default: |
onlyfiteddata |
If |
this function uses lm_table as a basis, but calls a plot of residuals for each fitted model, for the user to evaluate. If one decides to remove any of the points, one can click and drag, and then click on the 'remove points' button. After that, one must simply click 'done' and the coefficients will be printed out.
It's possible to use the output argument to get a merged table if output="merge", that binds
the original data frame and the fitted coefficients.
If output="merge_est" we get a merged table as well, but with y estimated using the coefficients. If the fit is made using groups, this is taken into account, i.e. the estimation is made by group.
If output="nest", a data frame with nested columns is provided. This can be used if the user desires to get a customized output.
A data frame. Different data frame options are available using the output argument.
Sollano Rabelo Braga [email protected]
if (interactive() ){ library(forestmangr) library(dplyr) data("exfm19") # Fit SH model by group: lm_resid_group(exfm19, log(VWB) ~ log(DBH) + log(TH), "STRATA") }if (interactive() ){ library(forestmangr) library(dplyr) data("exfm19") # Fit SH model by group: lm_resid_group(exfm19, log(VWB) ~ log(DBH) + log(TH), "STRATA") }
With this function it's possible to fit linear regressions by a grouping variable, and get a data frame with each column as a coefficient and quality of fit variables, and other output options. Works with dplyr grouping functions.
lm_table( df, model, .groups = NA, output = "table", est.name = "est", keep_model = FALSE, rmoutliers = FALSE, fct_to_filter = NA, rmlevels = NA, boolean_filter = NA, onlyfiteddata = FALSE, del_boolean = FALSE )lm_table( df, model, .groups = NA, output = "table", est.name = "est", keep_model = FALSE, rmoutliers = FALSE, fct_to_filter = NA, rmlevels = NA, boolean_filter = NA, onlyfiteddata = FALSE, del_boolean = FALSE )
df |
A data frame. |
model |
A linear regression model, with or without quotes. The variables mentioned in the model must exist in the provided data frame. X and Y sides of the model must be separated by "~". |
.groups |
Optional argument. Quoted name(s) of grouping variables used to fit multiple regressions, one for each level of the provided variable(s). Default: |
output |
Selects different output options. Can be either |
est.name |
Name of the estimated y value. Used only if |
keep_model |
If |
rmoutliers |
If |
fct_to_filter |
Name of a factor or character column to be used as a filter to remove levels. Default: |
rmlevels |
Levels of the fct_to_filter variable to be removed from the fit Default: |
boolean_filter |
Name of a Boolean column to be used as a filter to remove data. Default: |
onlyfiteddata |
If |
del_boolean |
If |
With this function there's no more need to use the do function when fitting a linear regression in a pipe line.
It's also possible to easily make fit multiple regressions, specifying a grouping variable.
In addition to that, the default output sets each coefficient as a column, making it easy to call coefficients by name or position
when estimating values.
It's possible to use the output argument to get a merged table if output="merge", that binds
the original data frame and the fitted coefficients.
If output="merge_est" we get a merged table as well, but with y estimated using the coefficients. If the fit is made using groups, this is taken into account, i.e. the estimation is made by group.
If output="nest", a data frame with nested columns is provided. This can be used if the user desires to get a customized output.
A data frame. Different data frame options are available using the output argument.
Sollano Rabelo Braga [email protected]
library(forestmangr) library(dplyr) data("exfm19") head(exfm19) # Fit Schumacher and Hall model for volume estimation, and get # coefficient, R2 and error values: lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH)) # Fit SH model by group: lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH), "STRATA") # This can also be done using dplyr::group_by: exfm19 %>% group_by(STRATA) %>% lm_table(log(VWB) ~ log(DBH) + log(TH) ) # It's possible to merge the original data with the table containg the coefficients # using the output parameter: fit <- lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH), "STRATA", output = "merge") head(fit) # It's possible to merge the original data with the table, # and get the estimated values for this model: fit <- lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH),"STRATA", output = "merge_est", est.name = "VWB_EST") head(fit) # It's possible to further customize the output, # unnesting the nested variables provided when output is defined as "nest": lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH),"STRATA", output = "nest")library(forestmangr) library(dplyr) data("exfm19") head(exfm19) # Fit Schumacher and Hall model for volume estimation, and get # coefficient, R2 and error values: lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH)) # Fit SH model by group: lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH), "STRATA") # This can also be done using dplyr::group_by: exfm19 %>% group_by(STRATA) %>% lm_table(log(VWB) ~ log(DBH) + log(TH) ) # It's possible to merge the original data with the table containg the coefficients # using the output parameter: fit <- lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH), "STRATA", output = "merge") head(fit) # It's possible to merge the original data with the table, # and get the estimated values for this model: fit <- lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH),"STRATA", output = "merge_est", est.name = "VWB_EST") head(fit) # It's possible to further customize the output, # unnesting the nested variables provided when output is defined as "nest": lm_table(exfm19, log(VWB) ~ log(DBH) + log(TH),"STRATA", output = "nest")
This function Convert NA to zero on numeric variables outside of mutate. this is used due to changes on dplyr 1.1.0.
na_to_0(df)na_to_0(df)
df |
A data frame |
a data frame
Sollano Rabelo Braga [email protected]
library(forestmangr) data("exfm15") head(exfm15) # Turn NA values to zero only on numeric values exfm15 %>% na_to_0()library(forestmangr) data("exfm15") head(exfm15) # Turn NA values to zero only on numeric values exfm15 %>% na_to_0()
With this function it's possible to fit non-linear regressions using Levenberg-Marquardt or Gauss-Newton algorithms by a grouping variable, and get a data frame with each column as a coefficient and quality of fit variables, and other output options. Works with dplyr grouping functions.
nls_table( df, model, mod_start, .groups = NA, output = "table", est.name = "est", replace = FALSE, keep_model = FALSE, global_start, algorithm = "LM" )nls_table( df, model, mod_start, .groups = NA, output = "table", est.name = "est", replace = FALSE, keep_model = FALSE, global_start, algorithm = "LM" )
df |
A data frame. |
model |
A linear regression model, with or without quotes. The variables mentioned in the model must exist in the provided data frame. X and Y sides of the model must be separated by "~". |
mod_start |
A vector or data frame, with start values for coefficients used in the model. This can be a data frame containing the same group variables used in the .groups argument, and the start values. |
.groups |
Optional argument. Quoted name(s) of grouping variables used to fit multiple regressions, one for each level of the provided variable(s). Default |
output |
Selects different output options. Can be either |
est.name |
Name of the estimated y value. Used only if |
replace |
If |
keep_model |
If |
global_start |
Optional argument. A vector or data frame, with start values for the global fit regression used when |
algorithm |
Algorithm to be used in the non-linear regression. It can be |
This function Levenberg-Marquardt algorithm as default for fitting non-linear regression models.
Also, with this function there no more need to use the do function when fitting a linear regression in a pipe line.
It's also possible to easily make fit multiple regressions, specifying a grouping variable.
In addition to that, the default output sets each coefficient as a column, making it easy to call coefficients by name or position
when estimating values. The Levenberg-Marquardt fit uses nlsLM.
A data frame. Different data frame options are available using the output argument.
Sollano Rabelo Braga [email protected]
library(forestmangr) library(dplyr) data("exfm14") head(exfm14) # Fit Chapman & Richards non-linear model for dominant Height: nls_table(exfm14, dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ) ) # Fit CR model by strata: nls_table(exfm14,dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ), .groups = "strata") %>% as.data.frame # or, using group_by exfm14 %>% group_by(strata) %>% nls_table(dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ) ) # If there are multiple start values, for each strata, they can be supplied like so: tab_coef <- data.frame(strata = c(1:20, 24,25,27,28,30,31,33,35,36,37), rbind( data.frame(b0 = rep(23, 20), b1 = rep(0.03, 20), b2 = rep(1.3, 20) ), data.frame(b0 = rep(23, 10), b1 = rep(0.03, 10), b2 = rep(.5, 10) ))) tab_coef nls_table(exfm14, dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = tab_coef, .groups = "strata" ) # mod_start needs to be a data frame in this case. # It's possible to bind the coefficients to the original data, # to estimate y. We'll also estimate bias and rmse for this estimation. # This can also be done directly using "merge_est" as output: nls_table(exfm14,dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = tab_coef , .groups = "strata", output = "merge_est", est.name = "dh_est" ) %>% mutate( bias = bias_per(y = dh, yhat = dh_est), rmse = rmse_per(y = dh, yhat = dh_est) ) %>% head(15) # It's possible to further customize the output, using nested columns: nls_table(exfm14,dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = tab_coef , .groups = "strata", output = "nest" ) # It's possible to use Gauss-Newton's algorithm. In this case, # some regressions will not converge. exfm14 %>% group_by(strata) %>% nls_table(dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ),algorithm="GN" ) # If some regressions don't converge, it's possible to fill those NAs with # regression coefficients from a general fit, using the entire data: nls_table(exfm14,dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ), .groups = "strata", replace = TRUE, algorithm="GN" )library(forestmangr) library(dplyr) data("exfm14") head(exfm14) # Fit Chapman & Richards non-linear model for dominant Height: nls_table(exfm14, dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ) ) # Fit CR model by strata: nls_table(exfm14,dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ), .groups = "strata") %>% as.data.frame # or, using group_by exfm14 %>% group_by(strata) %>% nls_table(dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ) ) # If there are multiple start values, for each strata, they can be supplied like so: tab_coef <- data.frame(strata = c(1:20, 24,25,27,28,30,31,33,35,36,37), rbind( data.frame(b0 = rep(23, 20), b1 = rep(0.03, 20), b2 = rep(1.3, 20) ), data.frame(b0 = rep(23, 10), b1 = rep(0.03, 10), b2 = rep(.5, 10) ))) tab_coef nls_table(exfm14, dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = tab_coef, .groups = "strata" ) # mod_start needs to be a data frame in this case. # It's possible to bind the coefficients to the original data, # to estimate y. We'll also estimate bias and rmse for this estimation. # This can also be done directly using "merge_est" as output: nls_table(exfm14,dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = tab_coef , .groups = "strata", output = "merge_est", est.name = "dh_est" ) %>% mutate( bias = bias_per(y = dh, yhat = dh_est), rmse = rmse_per(y = dh, yhat = dh_est) ) %>% head(15) # It's possible to further customize the output, using nested columns: nls_table(exfm14,dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = tab_coef , .groups = "strata", output = "nest" ) # It's possible to use Gauss-Newton's algorithm. In this case, # some regressions will not converge. exfm14 %>% group_by(strata) %>% nls_table(dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ),algorithm="GN" ) # If some regressions don't converge, it's possible to fill those NAs with # regression coefficients from a general fit, using the entire data: nls_table(exfm14,dh ~ b0 * (1 - exp( -b1 * age ) )^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3 ), .groups = "strata", replace = TRUE, algorithm="GN" )
Get the net present value, internal rate of return, and other economic variables, given cost and revenue values.
npv_irr( df, year, cost, revenue, rate, output = "full", sens_limits = c(1, 30), big_mark = ",", dec_mark = ".", prefix = "$" )npv_irr( df, year, cost, revenue, rate, output = "full", sens_limits = c(1, 30), big_mark = ",", dec_mark = ".", prefix = "$" )
df |
A data frame. |
year |
Quoted name of the year variable. |
cost |
Quoted name of the costs variable. |
revenue |
Quoted name of the revenue variable. |
rate |
Numeric value of the yearly rate to be used. |
output |
Selects different output options. It can be either |
sens_limits |
Selects the rate rage used in the sensibility plot. This is a numeric vector with two elements, the initial and final rate to be used as range. These can vary between 0 and 100. Default: |
big_mark |
Selects thousands separator. Can be either |
dec_mark |
Selects decimal separator. Can be either |
prefix |
selects the prefix for the y axis in the sensibility plot. Can be either |
A data frame, or a list, according to output.
Sollano Rabelo Braga [email protected]
## Not run: library(forestmangr) data(exfm22) npv_irr(exfm22,"year","cost","revenue",rate=8.75) # To also get a sensibility plot, use npv_irr(exfm22,"year","cost","revenue",rate=8.75, output="full") ## End(Not run)## Not run: library(forestmangr) data(exfm22) npv_irr(exfm22,"year","cost","revenue",rate=8.75) # To also get a sensibility plot, use npv_irr(exfm22,"year","cost","revenue",rate=8.75, output="full") ## End(Not run)
Calculate interquartile range for a given vector
outliersiqr(x)outliersiqr(x)
x |
a vector. |
Sollano Rabelo Braga [email protected]
library(forestmangr) outliersiqr(iris$Sepal.Length)library(forestmangr) outliersiqr(iris$Sepal.Length)
Get informations about forest inventory plots, like number of individuals, mean DBH, q, height, basal area, volume, etc.
plot_summarise( df, plot, plot_area, dbh, th, .groups, total_area, vwb, vwob, dh, age, dec_places = 4 )plot_summarise( df, plot, plot_area, dbh, th, .groups, total_area, vwb, vwob, dh, age, dec_places = 4 )
df |
A data frame. |
plot |
Quoted name of the plot variable. used to differentiate the data's plots. If this argument is missing, the defined groups in the data frame will be used, If there are no groups in the data, the function will fail. |
plot_area |
Quoted name of the plot area variable, or a numeric vector with the plot area value. The plot area value must be in square meters. |
dbh |
Optional parameter. Quoted name of the diameter at breast height variable. If supplied, will be used to calculate the mean diameter per plot, quadratic diameter (q), basal area and basal area per hectare. Default |
th |
Optional parameter. Quoted name of the total height variable. If supplied, will be used to calculate the mean total height, and the dominant height variable, if the |
.groups |
Optional argument. Quoted name(s) of grouping variables that can be added to differentiate subdivisions of the data. Default: |
total_area |
Optional argument. Quoted name of the total area variable, or a numeric vector with the total area value. The total area value must be in hectares. Default: |
vwb |
Optional parameter. Quoted name of the volume with bark variable. If supplied, will be used to calculate the total vwb per plot, and vwb per hectare per plot. Default |
vwob |
Optional parameter. Quoted name of the volume without bark variable. If supplied, will be used to calculate the total vwob per plot, and vwob per hectare per plot. Default |
dh |
Optional parameter. Quoted name of the dominant height variable. If supplied, will be used to calculate the mean dominant height per plot. If not, the |
age |
Optional parameter. Quoted name of the age variable. If supplied, will be used to calculate the average age per plot. Default: |
dec_places |
Numeric value for the number of decimal places to be used in the output tables. Default: |
A data frame with informations per plot.
Sollano Rabelo Braga [email protected]
library(forestmangr) data("exfm21") head(exfm21) # Obligatory arguments. Basic informations about the plot. plot_summarise(exfm21, "PLOT", 810) # Area values can be numeric, or a variable name plot_summarise(exfm21, "PLOT", "PLOT_AREA") # With DBH supplied, we get the mean diameter, quadratic diameter, # basal area and basal area per hectare: plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH") # With TH supplied, we get the mean total height and dominant height plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST") # With strata supplied, we divide the data into 2 strata plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA") # The strata area can also be supplied plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA") # With VWB supplied, we get the total vwb, and vwb per hectare plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA", "VWB") # With VWOB supplied, we get the total vwob, and vwob per hectare plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA", "VWB", "VWOB") # If the data already has a dominant height variable, it can also be supplied here plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA", "VWB", "VWOB", "DH") # With the AGE variable supplied, we get the average age of each plot plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA", "VWB", "VWOB", "DH", "AGE")library(forestmangr) data("exfm21") head(exfm21) # Obligatory arguments. Basic informations about the plot. plot_summarise(exfm21, "PLOT", 810) # Area values can be numeric, or a variable name plot_summarise(exfm21, "PLOT", "PLOT_AREA") # With DBH supplied, we get the mean diameter, quadratic diameter, # basal area and basal area per hectare: plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH") # With TH supplied, we get the mean total height and dominant height plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST") # With strata supplied, we divide the data into 2 strata plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA") # The strata area can also be supplied plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA") # With VWB supplied, we get the total vwb, and vwb per hectare plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA", "VWB") # With VWOB supplied, we get the total vwob, and vwob per hectare plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA", "VWB", "VWOB") # If the data already has a dominant height variable, it can also be supplied here plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA", "VWB", "VWOB", "DH") # With the AGE variable supplied, we get the average age of each plot plot_summarise(exfm21, "PLOT", "PLOT_AREA", "DBH", "TH_EST", "STRATA", "STRATA_AREA", "VWB", "VWOB", "DH", "AGE")
This function returns a numeric vector raised to a given power.
pow(x, y)pow(x, y)
x |
A numeric vector. |
y |
A numeric value for the power |
This function is manly used when fitting statistical models.
If one of the variables in a model is a variable raised to a given power, the lm
function does not properly compute the variable, if vector^power is inserted directly
in the model, leading to the need of creating a separate variable. This function allows the user to get the power
of a given numeric vector to y inside the model, without the need to create a new variable.
a numeric vector containing x to the power of y.
Sollano Rabelo Braga [email protected]
library(forestmangr) data("exfm15") head(exfm15) # Raise a numeric vector to the power of 2: pow(iris$Petal.Length, 2) # Fit a model that contains the dbh squared, without the need to create a new variable: lm(log(TH) ~ DBH + pow(DBH,2), exfm15 ) # or lm_table(exfm15, log(TH) ~ DBH + pow(DBH,2) )library(forestmangr) data("exfm15") head(exfm15) # Raise a numeric vector to the power of 2: pow(iris$Petal.Length, 2) # Fit a model that contains the dbh squared, without the need to create a new variable: lm(log(TH) ~ DBH + pow(DBH,2), exfm15 ) # or lm_table(exfm15, log(TH) ~ DBH + pow(DBH,2) )
Function for creating plots and tables for residual values from observed and estimated values.
resid_plot( df, obs, ..., type = "scatterplot", point_size = 3, color = NA, nrow = NA, ncol = NA, lim_y = NA, xlab = "Observed values", clab = NA, font = "serif", legend_pos = "bottom", gray_scale = TRUE, res_table = FALSE )resid_plot( df, obs, ..., type = "scatterplot", point_size = 3, color = NA, nrow = NA, ncol = NA, lim_y = NA, xlab = "Observed values", clab = NA, font = "serif", legend_pos = "bottom", gray_scale = TRUE, res_table = FALSE )
df |
A data frame. |
obs |
Quoted name of the observed values variable. |
... |
Quoted name(s) for the estimated values variable(s). Multiple variables must be separated by comma. |
type |
Character object for the type of plot created, The available plots are: |
point_size |
Numeric value for the point size in scatter plots. Default: |
color |
Quoted name of a variable. If supplied, this variable will be used to classify the data by color. Default: |
nrow |
Numeric value for number of rows in the plot matrix. If not supplied, the plots will be automatically sorted. Default: |
ncol |
Numeric value for number of columns in the plot matrix. If not supplied, the plots will be automatically sorted. Default: |
lim_y |
Numeric value for the y axis upper and lower limit. If |
xlab |
Character value for the x label used in some plots. Default: |
clab |
Character value for the color label used, if a color variable is supplied. If not supplied, the |
font |
Type of font used in the plot. Default: |
legend_pos |
Position of legend, when a color variable is supplied. This can either be |
gray_scale |
If |
res_table |
If |
A ggplot object, or, if res_table = TRUE, a data frame.
Sollano Rabelo Braga [email protected]
library(forestmangr) data("exfm11") head(exfm11) # Specifying the observed and estimated variables, we get a scatter plot # for the percentage residuals: resid_plot(exfm11, "TH", "TH_EST1") # It's possible to get other types of plots, with the type argument: resid_plot(exfm11, "TH", "TH_EST1", type = "histogram_curve") resid_plot(exfm11, "TH", "TH_EST1", type = "versus") # It's possible to add a factor variable as color in the plots: resid_plot(exfm11, "TH", "TH_EST1", "TH_EST2", color="STRATA", xlab="Total Height (m)", clab="Strata", gray_scale=FALSE) # If there are more estimated values variables, they can also be used # in the comparison: resid_plot(exfm11, "TH", "TH_EST1", "TH_EST2", "TH_EST3") # It's possible to rearrange the plots with ncol and nrow: resid_plot(exfm11, "TH", "TH_EST1", "TH_EST2", "TH_EST3", ncol=1) # It's possible to get the residuals table used to generate these plots, with res_table=TRUE: head( resid_plot(exfm11, "TH", "TH_EST1", "TH_EST2", res_table = TRUE) )library(forestmangr) data("exfm11") head(exfm11) # Specifying the observed and estimated variables, we get a scatter plot # for the percentage residuals: resid_plot(exfm11, "TH", "TH_EST1") # It's possible to get other types of plots, with the type argument: resid_plot(exfm11, "TH", "TH_EST1", type = "histogram_curve") resid_plot(exfm11, "TH", "TH_EST1", type = "versus") # It's possible to add a factor variable as color in the plots: resid_plot(exfm11, "TH", "TH_EST1", "TH_EST2", color="STRATA", xlab="Total Height (m)", clab="Strata", gray_scale=FALSE) # If there are more estimated values variables, they can also be used # in the comparison: resid_plot(exfm11, "TH", "TH_EST1", "TH_EST2", "TH_EST3") # It's possible to rearrange the plots with ncol and nrow: resid_plot(exfm11, "TH", "TH_EST1", "TH_EST2", "TH_EST3", ncol=1) # It's possible to get the residuals table used to generate these plots, with res_table=TRUE: head( resid_plot(exfm11, "TH", "TH_EST1", "TH_EST2", res_table = TRUE) )
This function removes columns filled with NA or 0 from a dataframe.
rm_empty_col(x)rm_empty_col(x)
x |
A dataframe |
This function is mainly used inside other functions, to remove optional variables when they are not opted in.
a dataframe.
Sollano Rabelo Braga [email protected]
library(forestmangr) library(dplyr) data("exfm15") head(exfm15) exfm15 %>% mutate(emptycol=NA) %>% rm_empty_collibrary(forestmangr) library(dplyr) data("exfm15") head(exfm15) exfm15 %>% mutate(emptycol=NA) %>% rm_empty_col
Function for calculating the Root-Mean-Square-Error of an estimator.
rmse_per(df, y, yhat, na.rm = TRUE)rmse_per(df, y, yhat, na.rm = TRUE)
df |
a data frame. |
y |
Quoted name of the variable representing the observed values in the data frame. If a data frame is not provided, |
yhat |
Quoted name of the variable representing the estimated values in the data frame. If a data frame is not provided, |
na.rm |
a logical value indicating whether NA values should be stripped before the computation proceeds. default: |
Function for calculating the Root-Mean-Square-Error of an estimator, given the observed values, and the estimated values.
Numeric vector with the RMSE value, in percentage.
Sollano Rabelo Braga [email protected]
other statistics to evaluate estimators:
bias_per for the bias of an estimator
library(forestmangr) data("exfm11") head(exfm11) # RMSE of an estimator, given the data frame and quoted variable names: rmse_per(exfm11, "TH", "TH_EST3") # RMSE of an estimator, given the vectors for observed and estimated values: rmse_per(y = exfm11$TH, yhat = exfm11$TH_EST3)library(forestmangr) data("exfm11") head(exfm11) # RMSE of an estimator, given the data frame and quoted variable names: rmse_per(exfm11, "TH", "TH_EST3") # RMSE of an estimator, given the vectors for observed and estimated values: rmse_per(y = exfm11$TH, yhat = exfm11$TH_EST3)
This function allows the user to round all numeric values of a data frame, directly, even if the data frame contains non-numeric variables (which would throw an error in the round function).
round_df(df, digits, rf = "round")round_df(df, digits, rf = "round")
df |
A data frame. |
digits |
Numeric vector for the desired number of digits. |
rf |
Type of round to be used. It can either be |
A data frame, with all the numeric variables rounded up to the number given to digits.
Sollano Rabelo Braga [email protected]
library(forestmangr) # Round all numeric variables round_df(iris) # Round all numeric variables using the floor function round_df(iris, rf="floor") # Do not run # trying this with the the base function throws an error: # round(iris)library(forestmangr) # Round all numeric variables round_df(iris) # Round all numeric variables using the floor function round_df(iris, rf="floor") # Do not run # trying this with the the base function throws an error: # round(iris)
Calculates the Jaccard similarity index and Sorensen similarity index.
similarity_matrix( df, species, comparison, NI_label = "", index = "Sorensen", dendrogram = FALSE, n_groups = 3 )similarity_matrix( df, species, comparison, NI_label = "", index = "Sorensen", dendrogram = FALSE, n_groups = 3 )
df |
A data frame. |
species |
Quoted name of the scientific names variable, or any variable used to differentiate the different species found in data. If supplied, will be used to classify the species in the diameter data. |
comparison |
Quoted name of the variable containing levels to be compared with each other. |
NI_label |
Label used for Species not identified. This parameter works along with species. The level supplied here will not be considered in the classification. Default |
index |
Character value for the desired index to be used. Can be either |
dendrogram |
If |
n_groups |
Number of groups in the dendrogram. Default |
a matrix object with a similarity matrix, or a list, according to the "index" and "dendrogram" arguments.
Eric Bastos Gorgens [email protected]
Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.
library(forestmangr) data("exfm20") head(exfm20) # To get the similarity matrix of an area, we simply need to provide # the species variable name, and a subdivision variable name, like # transect. By default we get a a matrix based on the Sorensen index: similarity_matrix(exfm20, "scientific.name", "transect") # To get the similarity matrix of Jaccard, use the index argument: similarity_matrix(exfm20, "scientific.name", "transect", index = "Jaccard") # To get a dendrogram with the matrix, use dendrogram=TRUE: similarity_matrix(exfm20, "scientific.name", "transect", index = "Jaccard", dendrogram = TRUE) # To get a list with both matrices, use index="all": similarity_matrix(exfm20, "scientific.name", "transect", index = "all") # If the data supplied only has 2 levels, a paired comparison is made instead: ex_pair <- exfm20[exfm20$transect %in% c("T01", "T02") , ] ex_pair similarity_matrix(ex_pair, "scientific.name", "transect", index = "all")library(forestmangr) data("exfm20") head(exfm20) # To get the similarity matrix of an area, we simply need to provide # the species variable name, and a subdivision variable name, like # transect. By default we get a a matrix based on the Sorensen index: similarity_matrix(exfm20, "scientific.name", "transect") # To get the similarity matrix of Jaccard, use the index argument: similarity_matrix(exfm20, "scientific.name", "transect", index = "Jaccard") # To get a dendrogram with the matrix, use dendrogram=TRUE: similarity_matrix(exfm20, "scientific.name", "transect", index = "Jaccard", dendrogram = TRUE) # To get a list with both matrices, use index="all": similarity_matrix(exfm20, "scientific.name", "transect", index = "all") # If the data supplied only has 2 levels, a paired comparison is made instead: ex_pair <- exfm20[exfm20$transect %in% c("T01", "T02") , ] ex_pair similarity_matrix(ex_pair, "scientific.name", "transect", index = "all")
Function used to calculate the volume with bark of trees using the Smalian method.
This function has integration with dplyr, so it can be used inside a pipe, along with the
group_by function.
smalianwb( df, di, hi, tree, .groups = NA, di_mm_to_cm = FALSE, hi_cm_to_m = FALSE )smalianwb( df, di, hi, tree, .groups = NA, di_mm_to_cm = FALSE, hi_cm_to_m = FALSE )
df |
A data frame. |
di |
Quoted name of the section diameter variable, in centimeters. |
hi |
Quoted name of the section height variable, in meters |
tree |
Quoted name of the tree variable. used to differentiate the trees' sections. If this argument is |
.groups |
Optional argument. Quoted name(s) of additional grouping variables that can be added to differentiate subdivisions of the data.
If this argument is not supplied, the defined groups in the data frame will be used. Default: |
di_mm_to_cm |
Boolean argument that, if |
hi_cm_to_m |
Boolean argument that, if |
Data frame with volume values by section.
Sollano Rabelo Braga [email protected]
Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
Complementary functions:
smalianwob, For calculation of volume without bark using the Smalian method,
huberwb, for calculation of volume with bark using the Huber method,
huberwob, for calculation of volume without bark the Huber method.
library(forestmangr) data("exfm7") head(exfm7) # Calculate the volume with bark using the Smalian method: smalianwb(exfm7,"di_wb", "hi", "TREE") # Using pipes: library(dplyr) exfm7 %>% group_by(TREE) %>% smalianwb("di_wb", "hi")library(forestmangr) data("exfm7") head(exfm7) # Calculate the volume with bark using the Smalian method: smalianwb(exfm7,"di_wb", "hi", "TREE") # Using pipes: library(dplyr) exfm7 %>% group_by(TREE) %>% smalianwb("di_wb", "hi")
Function used to calculate the volume without bark of trees using the Smalian method.
This function has integration with dplyr, so it can be used inside a pipe, along with the
group_by function.
smalianwob( df, di, hi, bt, tree, .groups = NA, di_mm_to_cm = FALSE, hi_cm_to_m = FALSE, bt_mm_to_cm = FALSE )smalianwob( df, di, hi, bt, tree, .groups = NA, di_mm_to_cm = FALSE, hi_cm_to_m = FALSE, bt_mm_to_cm = FALSE )
df |
A data frame. |
di |
Quoted name of the section diameter variable, in centimeters. |
hi |
Quoted name of the section height variable, in meters |
bt |
Quoted name of the bark thickness variable, in centimeters. |
tree |
Quoted name of the tree variable. used to differentiate the trees' sections. If this argument is |
.groups |
Optional argument. Quoted name(s) of additional grouping variables that can be added to differentiate subdivisions of the data.
If this argument is |
di_mm_to_cm |
Boolean argument that, if |
hi_cm_to_m |
Boolean argument that, if |
bt_mm_to_cm |
Boolean argument that, if |
Data frame with volume values by section.
Sollano Rabelo Braga [email protected]
Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
Complementary functions:
smalianwb, For calculation of volume with bark using the Smalian method,
huberwb, for calculation of volume with bark using the Huber method,
huberwob, for calculation of volume without bark the Huber method.
library(forestmangr) data("exfm7") head(exfm7) # Calculate the volume without bark using Smalian's method: smalianwob(exfm7,"di_wb", "hi", "bark_t", "TREE",bt_mm_to_cm=TRUE) # Using pipes: library(dplyr) exfm7 %>% group_by(TREE) %>% smalianwob("di_wb", "hi", "bark_t")library(forestmangr) data("exfm7") head(exfm7) # Calculate the volume without bark using Smalian's method: smalianwob(exfm7,"di_wb", "hi", "bark_t", "TREE",bt_mm_to_cm=TRUE) # Using pipes: library(dplyr) exfm7 %>% group_by(TREE) %>% smalianwob("di_wb", "hi", "bark_t")
Get the aggregation state of species according to the Payandeh, Hazen and Morista methods.
species_aggreg(df, species, plot, NI_label = "")species_aggreg(df, species, plot, NI_label = "")
df |
A data frame. |
species |
Quoted name of the scientific names variable, or any variable used to differentiate the different species found in data. |
plot |
Quoted name of the plot variable. used to differentiate the plots trees, and calculate the number of sampled plots. |
NI_label |
Label used for Species not identified. This parameter works along with species. The level supplied here will not be considered in the classification. Default |
a data frame with the aggregation classification.
Eric Bastos Gorgens [email protected]
Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.
library(forestmangr) data("exfm20") head(exfm20) # Get the aggregation indexes of species: species_aggreg(exfm20, "scientific.name", "transect")library(forestmangr) data("exfm20") head(exfm20) # Get the aggregation indexes of species: species_aggreg(exfm20, "scientific.name", "transect")
Calculate the diversity of species for the following indexes: Shannon, Simpson, Equitability, Pielou and Jentsch.
species_diversity(df, species, plot = NA, NI_label = "", index = "all")species_diversity(df, species, plot = NA, NI_label = "", index = "all")
df |
A data frame. |
species |
Quoted name of the scientific names variable, or any variable used to differentiate the different species found in data. If supplied, will be used to classify the species in the diameter data. |
plot |
Optional parameter. Quoted name of the plot variable. used to differentiate the plots, and calculate the indexes by plot, or other subdivision variable. |
NI_label |
Label used for Species not identified. This parameter works along with species. The level supplied here will not be considered in the classification. Default |
index |
Character value for the desired index to be used. Can be either |
a data frame with the indexes, or a numeric value of the desired index specified in the index argument.
Eric Bastos Gorgens [email protected]
Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.
library(forestmangr) data("exfm20") head(exfm20) # By default, the function returns all indexes: species_diversity(exfm20, "scientific.name") # It's possible to use a subdivision variable, like plot, to get # the indexes for each subdivision: species_diversity(exfm20, "scientific.name", "transect") # To only get one specific index, use the index argument: species_diversity(exfm20, "scientific.name", index = "H") species_diversity(exfm20, "scientific.name", index = "S") species_diversity(exfm20, "scientific.name", index = "Hmax") species_diversity(exfm20, "scientific.name", index = "J")library(forestmangr) data("exfm20") head(exfm20) # By default, the function returns all indexes: species_diversity(exfm20, "scientific.name") # It's possible to use a subdivision variable, like plot, to get # the indexes for each subdivision: species_diversity(exfm20, "scientific.name", "transect") # To only get one specific index, use the index argument: species_diversity(exfm20, "scientific.name", index = "H") species_diversity(exfm20, "scientific.name", index = "S") species_diversity(exfm20, "scientific.name", index = "Hmax") species_diversity(exfm20, "scientific.name", index = "J")
Function for processing forest inventory data using simple random sampling.
sprs( df, Yi, plot_area, total_area, m3ha = FALSE, age = NA, .groups = NA, alpha = 0.05, error = 10, dec_places = 4, pop = "inf", tidy = TRUE )sprs( df, Yi, plot_area, total_area, m3ha = FALSE, age = NA, .groups = NA, alpha = 0.05, error = 10, dec_places = 4, pop = "inf", tidy = TRUE )
df |
a data frame. |
Yi |
Quoted name of the volume variable, or other variable one desires to evaluate, in quotes. |
plot_area |
Quoted name of the plot area variable, or a numeric vector with the plot area value. The plot area value must be in square meters. |
total_area |
Quoted name of the total area variable, or a numeric vector with the total area value.The total area value must be in hectares. |
m3ha |
Boolean value. If |
age |
Optional parameter. Quoted name of the age variable. Calculates the average age supplied. |
.groups |
Optional argument. Quoted name(s) of additional grouping variable(s) that, if supplied, will be used to run multiple surveys, one for each level.
If this argument is |
alpha |
Numeric value for the significance value used in the t-student estimation. Default: |
error |
Numeric value for the minimum admitted error value in the survey, in percentage. Default: |
dec_places |
Numeric value for the number of decimal places to be used in the output tables. Default: |
pop |
Character value for the type of population considered in the calculations. This can be either infinite ( |
tidy |
Boolean value that defines if the output tables should be tidied up or not. Default: |
This function allows the user to processes inventory data using simple random sampling for finite or infinite populations.
It's possible to run multiple sampling analysis using a factor variable indicated in the .groups() parameter.
A data frame with the sampling results.
Sollano Rabelo Braga [email protected]
Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.
other sampling functions:
strs for stratified random sampling, and
ss_diffs for Systematic Sampling.
library(forestmangr) data("exfm2") data("exfm3") data("exfm4") # The objective is to sample an area, with an error of 20%. # First we run a pilot inventory, considering a 20% error and a finite population: head(exfm3) sprs(exfm3, "VWB", "PLOT_AREA", "TOTAL_AREA", error = 20, pop = "fin") # With these results, in order to obtain the desired error, we'll need to sample new # plots, and run the definitive inventory. Again, we aim for a 20% error, and consider # the population as finite: exfm4 sprs(exfm4, "VWB", "PLOT_AREA", "TOTAL_AREA", error = 20, pop = "fin") # The desired error was met # area values can be numeric sprs(exfm4, "VWB", 3000, 46.8, error = 20, pop = "fin") # Here we run a simple random sampling inventory for each forest subdivision, # using the STRATA variable as a group variable: exfm2 sprs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA",.groups = "STRATA" ,error = 20, pop = "fin") # If the volume variable is in m3ha, you should set m3ha to TRUE: sprs(exfm3, "VWB_m3ha", "PLOT_AREA", "TOTAL_AREA",m3ha = TRUE,error = 20, pop = "fin")library(forestmangr) data("exfm2") data("exfm3") data("exfm4") # The objective is to sample an area, with an error of 20%. # First we run a pilot inventory, considering a 20% error and a finite population: head(exfm3) sprs(exfm3, "VWB", "PLOT_AREA", "TOTAL_AREA", error = 20, pop = "fin") # With these results, in order to obtain the desired error, we'll need to sample new # plots, and run the definitive inventory. Again, we aim for a 20% error, and consider # the population as finite: exfm4 sprs(exfm4, "VWB", "PLOT_AREA", "TOTAL_AREA", error = 20, pop = "fin") # The desired error was met # area values can be numeric sprs(exfm4, "VWB", 3000, 46.8, error = 20, pop = "fin") # Here we run a simple random sampling inventory for each forest subdivision, # using the STRATA variable as a group variable: exfm2 sprs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA",.groups = "STRATA" ,error = 20, pop = "fin") # If the volume variable is in m3ha, you should set m3ha to TRUE: sprs(exfm3, "VWB_m3ha", "PLOT_AREA", "TOTAL_AREA",m3ha = TRUE,error = 20, pop = "fin")
Function for processing forest inventory data using systematic sampling.
ss_diffs( df, Yi, plot_area, total_area, m3ha = FALSE, age = NA, .groups = NA, alpha = 0.05, error = 10, dec_places = 4, tidy = TRUE )ss_diffs( df, Yi, plot_area, total_area, m3ha = FALSE, age = NA, .groups = NA, alpha = 0.05, error = 10, dec_places = 4, tidy = TRUE )
df |
a data frame. |
Yi |
Quoted name of the volume variable, or other variable one desires to evaluate, in quotes. |
plot_area |
Quoted name of the plot area variable, or a numeric vector with the plot area value. The plot area value must be in square meters. |
total_area |
Quoted name of the total area variable, or a numeric vector with the total area value.The total area value must be in hectares. |
m3ha |
Boolean value. If |
age |
Optional parameter. Quoted name of the age variable. Calculates the average age supplied. |
.groups |
Optional argument. Quoted name(s) of additional grouping variable(s) that, if supplied, will be used to run multiple surveys, one for each level.
If this argument is |
alpha |
Numeric value for the significance value used in the t-student estimation. Default: |
error |
Numeric value for the minimum admitted error value in the survey, in percentage. Default: |
dec_places |
Numeric value for the number of decimal places to be used in the output tables. Default: |
tidy |
Boolean value that defines if the output tables should be tidied up or not. Default: |
This function allows the user to processes inventory data using simple random sampling for finite or infinite populations.
It's possible to run multiple sampling analysis using a factor variable indicated in the .groups() parameter.
A data frame with the sampling results.
Sollano Rabelo Braga [email protected]
Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.
other sampling functions:
sprs for Simple Random Sampling, and
strs for stratified random sampling, and
library(forestmangr) data("exfm2") data("exfm5") # We're trying to run a inventory for an area This data was collected systematically, # but we'll try to run the data using simple random sampling, # to show the difference between the two methods: head(exfm5) sprs(exfm5, "VWB", "PLOT_AREA", "TOTAL_AREA") # We get a 22% error value. Now, we run this same data # considering the data as a systematic inventory, using the # successive differences method: exfm5 ss_diffs(exfm5, "VWB", "PLOT_AREA", "TOTAL_AREA") # The error was significantly lowered. # Area Values can be numeric; ss_diffs(exfm5, "VWB", 200, 18) # Here we run a systematic sampling inventory for each forest subdivision, # using the STRATA variable as a group variable: exfm2 ss_diffs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA",.groups = "STRATA")library(forestmangr) data("exfm2") data("exfm5") # We're trying to run a inventory for an area This data was collected systematically, # but we'll try to run the data using simple random sampling, # to show the difference between the two methods: head(exfm5) sprs(exfm5, "VWB", "PLOT_AREA", "TOTAL_AREA") # We get a 22% error value. Now, we run this same data # considering the data as a systematic inventory, using the # successive differences method: exfm5 ss_diffs(exfm5, "VWB", "PLOT_AREA", "TOTAL_AREA") # The error was significantly lowered. # Area Values can be numeric; ss_diffs(exfm5, "VWB", 200, 18) # Here we run a systematic sampling inventory for each forest subdivision, # using the STRATA variable as a group variable: exfm2 ss_diffs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA",.groups = "STRATA")
Function for processing forest inventory data using stratified random sampling.
strs( df, Yi, plot_area, strata_area, strata, m3ha = FALSE, .groups = NA, age = NA, alpha = 0.05, error = 10, dec_places = 4, pop = "inf", tidy = TRUE )strs( df, Yi, plot_area, strata_area, strata, m3ha = FALSE, .groups = NA, age = NA, alpha = 0.05, error = 10, dec_places = 4, pop = "inf", tidy = TRUE )
df |
a data frame. |
Yi |
Quoted name of the volume variable, or other variable one desires to evaluate, in quotes. |
plot_area |
Quoted name of the plot area variable, or a numeric vector with the plot area value. The plot area value must be in square meters. |
strata_area |
Quoted name of the strata area variable, or a numeric vector with the plot strata values. If there are more than 1 area values, it's possible to use a vector with all area values, like so: |
strata |
Quoted name of the subdivision variable(s), also known as strata. If this argument is not supplied, the defined groups in the data frame will be used, if they exist. |
m3ha |
Boolean value. If |
.groups |
Optional argument. Quoted name(s) of additional grouping variable(s) that, if supplied, will be used to run multiple surveys, one for each level.
If this argument is |
age |
Optional parameter. Quoted name of the age variable. Calculates the average age supplied. |
alpha |
Numeric value for the significance value used in the t-student estimation. Default: |
error |
Numeric value for the minimum admitted error value in the survey, in percentage. Default: |
dec_places |
Numeric value for the number of decimal places to be used in the output tables. Default: |
pop |
Character value for the type of population considered in the calculations. This can be either infinite ( |
tidy |
Boolean value that defines if the output tables should be tidied up or not. Default: |
This function allows the user to processes inventory data using stratified random sampling for n forest subdivisions (strata),
for finite or infinite populations. It's possible to run multiple sampling analysis using a factor variable indicated in the
.groups() parameter.
A list containing two data frames, one with information for each strata, and one with the stratified sampling results.
Sollano Rabelo Braga [email protected]
Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.
Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.
other sampling functions:
sprs for Simple Random Sampling, and
ss_diffs for Systematic Sampling.
library(forestmangr) data("exfm1") data("exfm2") data("exfm6") # The objective is to sample an area, with an error of 5%. # First we run a pilot inventory, considering a 5% error and a finite population: head(exfm1) strs(exfm1, "VWB", "PLOT_AREA", "STRATA_AREA", strata = "STRATA", error = 5, pop = "fin") # With these results, in order to meet the desired error of 5%, we'll need to sample 24 more plots, # 4 in stratum 1, 8 in stratum 2, and 12 in stratum 3. # After sampling the necessary plots, we now run a definitive inventory, # considering an 5% error and a finite population: exfm2 strs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA", strata = "STRATA", error = 5, pop = "fin") # The desired error value was met. # Area values can be numeric: strs(exfm2, "VWB", 1000, c(14.4, 16.4,14.2), strata = "STRATA", error = 5, pop = "fin") # Optional variable age, and one stratified sampled inventory for each map: exfm6 strs(exfm6, "VWB", "PLOT_AREA", "STRATA_AREA", strata ="STRATA", .groups = "MAP", age = "AGE")library(forestmangr) data("exfm1") data("exfm2") data("exfm6") # The objective is to sample an area, with an error of 5%. # First we run a pilot inventory, considering a 5% error and a finite population: head(exfm1) strs(exfm1, "VWB", "PLOT_AREA", "STRATA_AREA", strata = "STRATA", error = 5, pop = "fin") # With these results, in order to meet the desired error of 5%, we'll need to sample 24 more plots, # 4 in stratum 1, 8 in stratum 2, and 12 in stratum 3. # After sampling the necessary plots, we now run a definitive inventory, # considering an 5% error and a finite population: exfm2 strs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA", strata = "STRATA", error = 5, pop = "fin") # The desired error value was met. # Area values can be numeric: strs(exfm2, "VWB", 1000, c(14.4, 16.4,14.2), strata = "STRATA", error = 5, pop = "fin") # Optional variable age, and one stratified sampled inventory for each map: exfm6 strs(exfm6, "VWB", "PLOT_AREA", "STRATA_AREA", strata ="STRATA", .groups = "MAP", age = "AGE")
This function uses takes the square root of the diameters squared sum, in order to estimate the equivalent diameter of trees. Other supplied variables are summed up, or averaged, depending on the variable.
tree_summarise(df, dbh, tree, .groups = NA, vwb = NA, vwob = NA)tree_summarise(df, dbh, tree, .groups = NA, vwb = NA, vwob = NA)
df |
A data frame. |
dbh |
Quoted name of the diameter at breast height variable. |
tree |
Quoted name of the tree variable. used to differentiate the trees' sections. If this argument is missing, the defined groups in the data frame will be used. If there are no groups in the data, the function will fail. |
.groups |
Optional argument. Quoted name(s) of grouping variables that can be added to differentiate subdivisions of the data. Default: |
vwb |
Optional argument. Quoted name of the volume with bark variable, in cubic meters. Default: |
vwob |
Optional argument. Quoted name of the volume without bark variable, in cubic meters. Default: |
A data frame with the the equivalent diameter calculated.
Sollano Rabelo Braga [email protected]
Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.
library(forestmangr) data("exfm18") head(exfm18) # Calculate the equivalent diameter of trees with more than one trunk: eq_diam <- tree_summarise(exfm18, "DBH",tree="Tree", .groups=c("Plot", "Species") ) head(eq_diam, 10)library(forestmangr) data("exfm18") head(exfm18) # Calculate the equivalent diameter of trees with more than one trunk: eq_diam <- tree_summarise(exfm18, "DBH",tree="Tree", .groups=c("Plot", "Species") ) head(eq_diam, 10)
Get the vertical strata of data based on the height variable. The data will be divided into inferior, medium and superior strata.
vertical_stratum(df, th)vertical_stratum(df, th)
df |
A data frame. |
th |
Quoted name of the total height variable. |
a data frame.
Eric Bastos Gorgens [email protected]
Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.
library(forestmangr) data("exfm10") head(exfm10) # To classify the data, supply the data frame and the height variable name: vertical_stratum(exfm10, "TH" )library(forestmangr) data("exfm10") head(exfm10) # To classify the data, supply the data frame and the height variable name: vertical_stratum(exfm10, "TH" )
This function can be used to summarize volume with and without bark of trees in a data frame.
vol_summarise(df, dbh, th, vwb, tree, .groups = NA, vwob = NA)vol_summarise(df, dbh, th, vwb, tree, .groups = NA, vwob = NA)
df |
A data frame. |
dbh |
Quoted name of the diameter at breast height variable, in cm. |
th |
Quoted name of the total height variable, in meters. |
vwb |
Quoted name of the volume with bark variable, in cubic meters. |
tree |
Quoted name of the tree variable. used to differentiate the trees' sections. If this argument is |
.groups |
Optional argument. Quoted name(s) of additional grouping variables that can be added to differentiate subdivisions of the data. |
vwob |
Optional argument. Quoted name of the volume without bark variable, in cubic meters. Default: |
A data frame summarized by the .groups variable(s).
Sollano Rabelo Braga [email protected]
Complementary functions:
smalianwb, For calculation of volume with bark using the Smalian method,
smalianwob, For calculation of volume without bark using the Smalian method,
huberwb, for calculation of volume with bark using the Huber method,
huberwob, for calculation of volume without bark the Huber method.
library(forestmangr) data("exfm7") head(exfm7) # In order to calculate the volume of each tree, first we # Calculate the volume by tree section using the Smalian method: sec_data_vol <- exfm7 %>% smalianwb("di_wb", "hi", "TREE") %>% smalianwob("di_wb", "hi", "bark_t", "TREE", bt_mm_to_cm = TRUE) sec_data_vol # Now, we summarize the tree's volume: vol_summarise(sec_data_vol, dbh = "DBH", th = "TH", vwb = "VWB", tree = "TREE", .groups = "STRATA",vwob = "VWOB") # It's possible to do everything using pipes: exfm7 %>% smalianwb("di_wb", "hi", "TREE") %>% smalianwob("di_wb", "hi", "bark_t", "TREE", bt_mm_to_cm = TRUE) %>% vol_summarise("DBH", "TH", "VWB", "TREE", "STRATA", "VWOB")library(forestmangr) data("exfm7") head(exfm7) # In order to calculate the volume of each tree, first we # Calculate the volume by tree section using the Smalian method: sec_data_vol <- exfm7 %>% smalianwb("di_wb", "hi", "TREE") %>% smalianwob("di_wb", "hi", "bark_t", "TREE", bt_mm_to_cm = TRUE) sec_data_vol # Now, we summarize the tree's volume: vol_summarise(sec_data_vol, dbh = "DBH", th = "TH", vwb = "VWB", tree = "TREE", .groups = "STRATA",vwob = "VWOB") # It's possible to do everything using pipes: exfm7 %>% smalianwb("di_wb", "hi", "TREE") %>% smalianwob("di_wb", "hi", "bark_t", "TREE", bt_mm_to_cm = TRUE) %>% vol_summarise("DBH", "TH", "VWB", "TREE", "STRATA", "VWOB")