Pipe

Description

Pipe an object forward into a function or call expression. See magrittr for more details.

Search for quasiquotation for more details.

Tee

Description

Pipe an object forward into a function or call expression. See magrittr for more details.

Generate the curve of a forest's average tree using the Kozak taper model

Description

Generate a ggplot curve of a forest's average tree using the Kozak taper model (Kozak, Munro and Smith, 1969).

Usage

avg_tree_curve(
  df,
  d,
  dbh,
  h,
  th,
  facet = NA,
  color = NA,
  eq = TRUE,
  mirror = TRUE
)

Arguments

Value

A ggplot object.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

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.

Examples

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)

Classify a forest for selective cutting using the Meyer BDq method

Description

This function can be used to plan and execute selective cuttings of a native forest, without damaging the forest's natural structure.

Usage

bdq_meyer(
  df,
  plot,
  dbh,
  plot_area,
  class_interval = 5,
  dbh_min = 5,
  licourt_index = 2,
  output = "table"
)

Arguments

Value

a data frame, a lm object, a vector or a list, according to the output argument.

Author(s)

Eric Bastos Gorgens e.gorgens@gmail.com

References

Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.

Examples

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")

Bias of an estimator in percentage

Description

Function for calculating the bias of an estimator.

Usage

bias_per(df, y, yhat, na.rm = TRUE)

Arguments

Details

Function for calculating the bias of an estimator, given the observed values, and the estimated values.

Value

Numeric vector with the bias value, in percentage.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

See Also

other statistics to evaluate estimators: rmse_per for the Root mean square error of an estimator

Examples

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)

Check if character vector contains variable names

Description

Function used to check if a string, or a character vector contains variable names of a given data frame.

Usage

check_names(df, var_names, boolean = TRUE)

Arguments

Details

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,

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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)

Classify a given variable and get center of class

Description

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.

Usage

class_center(df, y, ci = 3, ymin = 5)

Arguments

Value

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.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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))

Classify inventory data based on site index

Description

Use the site variable to classify a forest management data.

Usage

classify_site(df, site, nc = 3, plot, .groups = NA)

Arguments

Value

A data frame classified based on the site index.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

See Also

other sampling functions: fit_clutter for fitting Clutter's Growth and Yield, and est_clutter for estimating Clutter's Growth and Yield model variables.

Examples

library(forestmangr)
data("exfm17")
head(exfm17)

# Classify data into 3 classes:
ex_class <- classify_site(exfm17, "S", 3, "plot")
head(ex_class ,15)

Divide data into diameter classes, and get number of observations

Description

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.

Usage

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
)

Arguments

Value

A data frame containing the supplied data divided into diameter classes.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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)

Calculate the dominant height of forest inventory data plots

Description

This function is used to get a data frame with Dominant height values for each plot in an forest inventory data.

Usage

dom_height(
  df,
  th,
  dbh,
  plot,
  obs,
  dom,
  .groups,
  merge_data = FALSE,
  dh_name = "DH"
)

Arguments

Value

A data frame with the the dominant height values by plot.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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)

Estimate future and present basal area, volume, TCA, CMI and MMI values of the Clutter Growth and Yield Model

Description

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.

Usage

est_clutter(
  df,
  age,
  basal_area,
  site,
  category,
  coeffs,
  method = "average",
  annual_increment = FALSE,
  gray_scale = TRUE,
  output = "table"
)

Arguments

Value

A data frame, a ggplot object or a list, according to output.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

See Also

other sampling functions: fit_clutter for fitting the clutter growth and Yield model, and classify_site for classifying data according to site.

Examples

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")  
  

Stratified random inventory pilot data

Description

In this data, each observation is a plot.

Usage

data(exfm1)

Format

A data frame with 22 observations and 4 variables:

STRATA

stratum number

STRATA_AREA

area of each strata, in hectares

PLOT_AREA

area of plots, in square meters

VWB

volume with bark, in cubic meters

VWB_m3ha

volume with bark, in cubic meters per hectare

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Source

Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.

Inventory data of an eucalyptus forest Brazil with dominant height variable

Description

In this data, each observation is a tree.

Usage

data(exfm10)

Format

A data frame with 900 observations and 14 variables:

MAP

map numbers

PROJECT

project number

STRATA

stratum number

GENCODE

genetic code of plots

STRATA_AREA

area of each strata, in hectares

PLANTING_DATE

date of planting

SPACING

Spacing used in the plots, in meters

PLOT

plot number

MEASUREMENT_DATE

date of measurement

PLOT_AREA

area of plots, in square meters

PIT

pit number

DBH

diameter at breast height, in centimeters

TH

total height, in meters

OBS

quality of trees, N = normal tree, D = dominant tree, F = a failure, or dead tree

DH

dominant height, in meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Observed hieght values of trees, and estimated values using 3 different models

Description

In this data, each observation is a tree.

Usage

data(exfm11)

Format

A data frame with 199 observations and 6 variables:

STRATA

stratum number

PLOT

plot number

TH

total height, in meters

TH_EST1

total estimated height with model 1, in meters

TH_EST2

total estimated height with model 2, in meters

TH_EST3

total estimated height with model 3, in meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Inventory data of an eucalyptus forest in Brazil, with age and site variables

Description

In this data, each observation is a plot. The site was estimated using the Chapman & Richards model.

Usage

data(exfm12)

Format

A data frame with 139 observations and 8 variables:

strata

stratum number

plot

plot number

age

average age of plots, in months

DH

dominant height, in meters

N

number of individuals

V

volume of trees, in cubic meters

B

basal area, in square meters

S

site variable, in meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Experimental data on nitrogen effect in species fertilizing

Description

In this data, each observation is a tree.

Usage

data(exfm13)

Format

A data frame with 36 observations and 7 variables:

species

species common name

trat

treatment number

esp

species number

N

quantity of nitrogen used, in grams

N2

squared quantity of nitrogen used, in squared grams

block

block number

dbh

diameter at breast height, in centimeters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Inventory data of an eucalyptus forest in Brazil, with age and dominant height variables

Description

In this data, each observation is a plot.

Usage

data(exfm14)

Format

A data frame with 859 observations and 4 variables:

strata

stratum number

plot

plot number

age

average age of plots, in months

dh

dominant height, in meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Simplified Inventory data of an eucalyptus forest in Brazil

Description

In this data, each observation is a tree.

Usage

data(exfm15)

Format

A data frame with 900 observations and 7 variables:

STRATA

stratum number

STRATA_AREA

area of each strata, in hectares

PLOT

plot number

PLOT_AREA

area of plots, in square meters

DBH

diameter at breast height, in centimeters

TH

total height, in meters

OBS

quality of trees, N = normal tree, D = dominant tree, F = a failure, or dead tree

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Inventory data of an eucalyptus forest in Brazil, with age variable

Description

In this data, each observation is a plot.

Usage

data(exfm16)

Format

A data frame with 139 observations and 8 variables:

strata

stratum number

plot

plot number

age

average age of plots, in months

DH

dominant height, in meters

N

number of individuals

V

volume of trees, in cubic meters

B

basal area, in square meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Inventory data of an eucalyptus forest in Brazil, with age and site variables

Description

In this data, each observation is a plot. The site was estimated using the Schumacher model.

Usage

data(exfm17)

Format

A data frame with 139 observations and 8 variables:

strata

stratum number

plot

plot number

age

average age of plots, in months

DH

dominant height, in meters

N

number of individuals

V

volume of trees, in cubic meters

B

basal area, in square meters

S

site variable, in meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Experimental data on nitrogen effect in species fertilizing

Description

In this data, each observation is a tree.

Usage

data(exfm18)

Format

A data frame with 877 observations and 6 variables:

Plot

plot number

Species

species scientific name

Tree

tree number

Trunk

trunk number

CBH

circumference at breast height, in centimeters

DBH

diameter at breast height, in centimeters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Volume of felled trees, measured in the Smalian method

Description

In this data, each observation is a section of a tree.

Usage

data(exfm19)

Format

A data frame with 16 observations and 3393 variables:

STRATA

stratum number

TREE

number of trees

DBH

diameter at breast height, in centimeters

TH

total height, in meters

CSA

cross section area with bark, in square meters

VWB

volume with bark, in cubic meters

FFWB

form factor for each section

FFWB_mean

average form vactor value

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Stratified random inventory definite data

Description

In this data, each observation is a plot.

Usage

data(exfm2)

Format

A data frame with 57 observations and 4 variables:

STRATA

stratum number

STRATA_AREA

area of each strata, in hectares

PLOT_AREA

area of plots, in square meters

VWB

volume with bark, in cubic meters

VWB_m3ha

volume with bark, in cubic meters per hectare

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Source

Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.

Inventory data of a natural forest in Brazil

Description

In this data, each observation is a tree.

Usage

data(exfm20)

Format

A data frame with 12295 observations and 18 variables:

cod

area code

transect

plot number

tree

tree number

common.name

species common name

scientific.name

species scientific name

family

species family name

dbh

diameter at breast height, in centimeters

canopy.pos

canopy position

light

level of light received by the tree

dead

tells if the tree is dead or not

Hcom

commercial height, in meters

Htot

total height, in meters

date

date of measurement

utm.east

utm east position value

utm.north

utm north position value

vol

volume of trees, in cubic meters

plot.area

plot area, in square meters

total.area

total area, in hectares

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Inventory data of an eucalyptus forest in Brazil

Description

In this data, each observation is a tree.

Usage

data(exfm21)

Format

A data frame with 900 observations and 13 variables:

STRATA

stratum number

STRATA_AREA

area of each strata, in hectares

PLOT

plot number

PLOT_AREA

area of plots, in square meters

DBH

diameter at breast height, in centimeters

TH

total height, in meters

OBS

quality of trees, N = normal tree, D = dominant tree, F = a failure, or dead tree

DH

dominant height, in meters

TH_EST

estimated total height, in meters

CSA

cross sectional area, in square meters

AGE

average age of plots, in months

VWB

volume with bark, in cubic meters

VWOB

volume without bark, in cubic meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Revenue data if an eucalyptus forest

Description

In this data, each observation is the year's revenue.

Usage

data(exfm22)

Format

A data frame with 8 observations and 3 variables:

year

Revenue year

cost

cost values for that year, in dollars

revenue

revenue values for that year, in dollars

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Simple random inventory pilot data

Description

In this data, each observation is a plot.

Usage

data(exfm3)

Format

A data frame with 10 observations and 3 variables:

TOTAL_AREA

total area, in hectares

PLOT_AREA

area of plots, in square meters

VWB

volume with bark, in cubic meters

VWB_m3ha

volume with bark, in cubic meters per hectare

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Source

Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.

Simple random inventory definite data

Description

In this data, each observation is a plot.

Usage

data(exfm4)

Format

A data frame with 25 observations and 3 variables:

TOTAL_AREA

total area, in hectares

PLOT_AREA

area of plots, in square meters

VWB

volume with bark, in cubic meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Source

Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.

Systematic inventory data

Description

In this data, each observation is a plot.

Usage

data(exfm5)

Format

A data frame with 18 observations and 3 variables:

TOTAL_AREA

total area, in hectares

PLOT_AREA

area of plots, in square meters

VWB

volume with bark, in cubic meters

VWB_m3ha

volume with bark, in cubic meters per hectare

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Source

Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.

Stratified random inventory definite data 2

Description

In this data, each observation is a plot.

Usage

data(exfm6)

Format

A data frame with 10 observations and 14 variables:

GENCODE

genetic code of plots

MAP

map numbers

STRATA

stratum number

PLOT

plot number

AGE

average age of plots, in months

STRATA_AREA

area of each strata, in hectares

PLOT_AREA

area of plots, in square meters

DBH

diameter at breast height, in centimeters

q

quadratic diameter, in centimeters

TH

total height, in meters

DH

dominant height, in meters

G

basal area, in square meters

VWB

volume with bark, in cubic meters

VWB_m3ha

volume with bark, in cubic meters per hectare

VWOB

volume without bark, in cubic meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Data of felled trees sections, measured in the Smalian method

Description

In this data, each observation is a section of a tree.

Usage

data(exfm7)

Format

A data frame with 11 observations and 3393 variables:

MAP

map numbers

PROJECT

Project's name

SPACING

Spacing used in the plots, in meters

STRATA

stratum number

GENCODE

genetic code of plots

TREE

number of trees

DBH

diameter at breast height, in centimeters

TH

total height, in meters

hi

height of sections, in meters

di_wb

diameter of sections with bark, in centimeters

bark_t

bark of thickness, in millimeters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Data of felled trees sections, measured in the Huber method

Description

In this data, each observation is a section of a tree.

Usage

data(exfm8)

Format

A data frame with 10 observations and 596 variables:

CLONE

Clone number

STRATA

stratum number

TREE

number of trees

LOG

log number

DBH

diameter at breast height, in centimeters

TH

total height, in meters

hi

height of sections, in meters

di_wb

diameter of sections with bark, in centimeters

bark_t

bark of thickness, in millimeters

sec_length

section length, in meters

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Inventory data of an eucalyptus forest in minas gerais, Brazil

Description

In this data, each observation is a tree.

Usage

data(exfm9)

Format

A data frame with 900 observations and 14 variables:

MAP

map numbers

PROJECT

project number

STRATA

stratum number

GENCODE

genetic code of plots

STRATA_AREA

area of each strata, in hectares

PLANTING_DATE

date of planting

SPACING

Spacing used in the plots, in meters

PLOT

plot number

MEASUREMENT_DATE

date of measurement

PLOT_AREA

area of plots, in square meters

PIT

pit number

DBH

diameter at breast height, in centimeters

TH

total height, in meters

OBS

quality of trees, N = normal tree, D = dominant tree, F = a failure, or dead tree

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Fit the Clutter model for growth and yield

Description

Fit the Clutter model for growth and yield using the two stage least squares method (2SLS).

Usage

fit_clutter(
  df,
  age,
  dh,
  basal_area,
  volume,
  site,
  plot,
  .groups = NA,
  model = "full",
  keep_model = FALSE
)

Arguments

Value

A data frame with the regression coefficients.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

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.

See Also

other sampling functions: est_clutter for estimating the Clutter growth and yield model variables, and classify_site for classifying data according to site.

Examples

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) 
             

Get the forest horizontal, vertical and internal structure

Description

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.

Usage

forest_structure(
  df,
  species,
  dbh,
  plot,
  plot_area,
  vertical_est = NA,
  internal_est = NA,
  NI_label = ""
)

Arguments

Value

a data frame with the forest's structure.

Author(s)

Eric Bastos Gorgens e.gorgens@gmail.com

References

Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.

Examples

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") 

Graybill F Test

Description

Hypothesis test as described by Graybill (1976).

Usage

graybill_f(df, Y1, Yj, signif = 0.05, output = "simple")

Arguments

Details

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).

Value

A data frame. Its dimensions will vary, according to the output argument.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

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.

Examples

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 plot for growth and yield analysis of inventory data

Description

Get the guide curve for growth and yield analysis of inventory data using the factor method, and different statistical models.

Usage

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"
)

Arguments

Value

A data frame, a ggplot object, or a list, varying according to the "output" argument.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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)

Calculate the volume with bark of trees using the Huber method

Description

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.

Usage

huberwb(df, di, section_length, tree, .groups = NA, di_mm_to_cm = FALSE)

Arguments

Value

Data frame with volume values by section.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.

See Also

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.

Examples

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")

Calculate the volume without bark of trees using the Huber method

Description

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.

Usage

huberwob(
  df,
  di,
  section_length,
  bt,
  tree,
  .groups = NA,
  di_mm_to_cm = FALSE,
  bt_mm_to_cm = FALSE
)

Arguments

Value

Data frame with volume values by section.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.

See Also

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.

Examples

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")

Identity of a Model Test

Description

Function for using the Identity of a Model test, as described by Regazzi (1999).

Usage

ident_model(
  df,
  factor,
  reduced_model,
  filter = NA,
  gray_scale = TRUE,
  signif = 0.05,
  font = "serif",
  output = "table",
  eq = TRUE
)

Arguments

Value

A data frame, a ggplot object, or a list, varying according to the output argument.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Marcio leles Romarco de Oliveira marcioromarco@gmail.com

References

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.

Examples

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)

Calculate the inverse of a number

Description

This function returns the inverse of a numeric vector.

Usage

inv(x)

Arguments

Details

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.

Value

a numeric vector containing the inverse of x.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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) )

Fit linear regressions, with the option of removing outliers using a interactive plot of residuals.

Description

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.

Usage

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
)

Arguments

Details

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.

Value

A data frame. Different data frame options are available using the output argument.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

if (interactive() ){
  library(forestmangr)
  library(dplyr)

  data("exfm19")

  # Fit SH model:
  lm_resid(exfm19, log(VWB) ~  log(DBH) + log(TH))

}

Fit linear regressions by group, with the option of removing outliers using a interactive plot of residuals.

Description

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.

Usage

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
)

Arguments

Details

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.

Value

A data frame. Different data frame options are available using the output argument.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

if (interactive() ){
  library(forestmangr)
  library(dplyr)

  data("exfm19")

  # Fit SH model by group:
  lm_resid_group(exfm19, log(VWB) ~  log(DBH) + log(TH), "STRATA")

}

Fit linear regressions by group, and get different output options.

Description

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.

Usage

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
)

Arguments

Details

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.

Value

A data frame. Different data frame options are available using the output argument.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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")

Convert NA to zero on numeric variables

Description

This function Convert NA to zero on numeric variables outside of mutate. this is used due to changes on dplyr 1.1.0.

Usage

na_to_0(df)

Arguments

Value

a data frame

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

library(forestmangr)
data("exfm15")
head(exfm15)

# Turn NA values to zero only on numeric values
exfm15 %>% na_to_0()

Fit non-linear regressions by group, using LM algorithm and get different output options.

Description

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.

Usage

nls_table(
  df,
  model,
  mod_start,
  .groups = NA,
  output = "table",
  est.name = "est",
  replace = FALSE,
  keep_model = FALSE,
  global_start,
  algorithm = "LM"
)

Arguments

Details

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.

Value

A data frame. Different data frame options are available using the output argument.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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" )

Calculate Net Present Value and other economic variables

Description

Get the net present value, internal rate of return, and other economic variables, given cost and revenue values.

Usage

npv_irr(
  df,
  year,
  cost,
  revenue,
  rate,
  output = "full",
  sens_limits = c(1, 30),
  big_mark = ",",
  dec_mark = ".",
  prefix = "$"
)

Arguments

Value

A data frame, or a list, according to output.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

## 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

Description

Calculate interquartile range for a given vector

Usage

outliersiqr(x)

Arguments

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

library(forestmangr)
outliersiqr(iris$Sepal.Length)

Summarize forest inventory data

Description

Get informations about forest inventory plots, like number of individuals, mean DBH, q, height, basal area, volume, etc.

Usage

plot_summarise(
  df,
  plot,
  plot_area,
  dbh,
  th,
  .groups,
  total_area,
  vwb,
  vwob,
  dh,
  age,
  dec_places = 4
)

Arguments

Value

A data frame with informations per plot.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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")

Raise a numeric vector to a given power

Description

This function returns a numeric vector raised to a given power.

Usage

pow(x, y)

Arguments

Details

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.

Value

a numeric vector containing x to the power of y.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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) )

Calculate residual values and create plots

Description

Function for creating plots and tables for residual values from observed and estimated values.

Usage

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
)

Arguments

Value

A ggplot object, or, if res_table = TRUE, a data frame.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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) )

Remove empty columns

Description

This function removes columns filled with NA or 0 from a dataframe.

Usage

rm_empty_col(x)

Arguments

Details

This function is mainly used inside other functions, to remove optional variables when they are not opted in.

Value

a dataframe.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

library(forestmangr)
library(dplyr)
data("exfm15")
head(exfm15)

exfm15 %>% 
mutate(emptycol=NA) %>% 
rm_empty_col

RMSE of an estimator in percentage

Description

Function for calculating the Root-Mean-Square-Error of an estimator.

Usage

rmse_per(df, y, yhat, na.rm = TRUE)

Arguments

Details

Function for calculating the Root-Mean-Square-Error of an estimator, given the observed values, and the estimated values.

Value

Numeric vector with the RMSE value, in percentage.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

See Also

other statistics to evaluate estimators: bias_per for the bias of an estimator

Examples

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)

Round all numeric variables of a data frame to a given digit

Description

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).

Usage

round_df(df, digits, rf = "round")

Arguments

Value

A data frame, with all the numeric variables rounded up to the number given to digits.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

Examples

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)

Get the similarity matrix of an area

Description

Calculates the Jaccard similarity index and Sorensen similarity index.

Usage

similarity_matrix(
  df,
  species,
  comparison,
  NI_label = "",
  index = "Sorensen",
  dendrogram = FALSE,
  n_groups = 3
)

Arguments

Value

a matrix object with a similarity matrix, or a list, according to the "index" and "dendrogram" arguments.

Author(s)

Eric Bastos Gorgens e.gorgens@gmail.com

References

Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.

Examples

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")

Calculate the volume with bark of trees using the Smalian method

Description

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.

Usage

smalianwb(
  df,
  di,
  hi,
  tree,
  .groups = NA,
  di_mm_to_cm = FALSE,
  hi_cm_to_m = FALSE
)

Arguments

Value

Data frame with volume values by section.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.

See Also

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.

Examples

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")

Calculate the volume without bark of trees using the Smalian method

Description

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.

Usage

smalianwob(
  df,
  di,
  hi,
  bt,
  tree,
  .groups = NA,
  di_mm_to_cm = FALSE,
  hi_cm_to_m = FALSE,
  bt_mm_to_cm = FALSE
)

Arguments

Value

Data frame with volume values by section.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.

See Also

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.

Examples

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

Description

Get the aggregation state of species according to the Payandeh, Hazen and Morista methods.

Usage

species_aggreg(df, species, plot, NI_label = "")

Arguments

Value

a data frame with the aggregation classification.

Author(s)

Eric Bastos Gorgens e.gorgens@gmail.com

References

Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.

Examples

library(forestmangr)
data("exfm20")
head(exfm20)

# Get the aggregation indexes of species:
species_aggreg(exfm20, "scientific.name", "transect")

Get the species diversity indexes

Description

Calculate the diversity of species for the following indexes: Shannon, Simpson, Equitability, Pielou and Jentsch.

Usage

species_diversity(df, species, plot = NA, NI_label = "", index = "all")

Arguments

Value

a data frame with the indexes, or a numeric value of the desired index specified in the index argument.

Author(s)

Eric Bastos Gorgens e.gorgens@gmail.com

References

Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.

Examples

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")

Simple Random Sampling

Description

Function for processing forest inventory data using simple random sampling.

Usage

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
)

Arguments

Details

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.

Value

A data frame with the sampling results.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

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.

See Also

other sampling functions: strs for stratified random sampling, and ss_diffs for Systematic Sampling.

Examples

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")

Systematic Sampling

Description

Function for processing forest inventory data using systematic sampling.

Usage

ss_diffs(
  df,
  Yi,
  plot_area,
  total_area,
  m3ha = FALSE,
  age = NA,
  .groups = NA,
  alpha = 0.05,
  error = 10,
  dec_places = 4,
  tidy = TRUE
)

Arguments

Details

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.

Value

A data frame with the sampling results.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

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.

See Also

other sampling functions: sprs for Simple Random Sampling, and strs for stratified random sampling, and

Examples

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")

Stratified Random Sampling

Description

Function for processing forest inventory data using stratified random sampling.

Usage

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
)

Arguments

Details

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.

Value

A list containing two data frames, one with information for each strata, and one with the stratified sampling results.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

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.

See Also

other sampling functions: sprs for Simple Random Sampling, and ss_diffs for Systematic Sampling.

Examples

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")

Calculate the equivalent diameter of trees with more than one trunk

Description

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.

Usage

tree_summarise(df, dbh, tree, .groups = NA, vwb = NA, vwob = NA)

Arguments

Value

A data frame with the the equivalent diameter calculated.

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

References

Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.

Examples

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)

Divide data into 3 vertical strata

Description

Get the vertical strata of data based on the height variable. The data will be divided into inferior, medium and superior strata.

Usage

vertical_stratum(df, th)

Arguments

Value

a data frame.

Author(s)

Eric Bastos Gorgens e.gorgens@gmail.com

References

Souza, A. L. and Soares, C. P. B. (2013) Florestas Nativas: estrutura, dinamica e manejo. Vicosa: UFV.

Examples

library(forestmangr)
data("exfm10")
head(exfm10)

# To classify the data, supply the data frame and the height variable name:
vertical_stratum(exfm10, "TH" )

Summarize volume of trees

Description

This function can be used to summarize volume with and without bark of trees in a data frame.

Usage

vol_summarise(df, dbh, th, vwb, tree, .groups = NA, vwob = NA)

Arguments

Value

A data frame summarized by the .groups variable(s).

Author(s)

Sollano Rabelo Braga sollanorb@gmail.com

See Also

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.

Examples

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")