dendrometry: Forest Estimations and Dendrometric Computations

Description

Computation of dendrometric and structural parameters from forest inventory data. The objective is to provide a user-friendly R package for researchers, ecologists, foresters, statisticians, loggers and other persons who deal with forest inventory data. The package includes advanced distribution fitting capabilities with multiple estimation methods (Maximum Likelihood, Maximum Product Spacing with ties correction methods following Cheng & Amin (1983), and Method of Moments) for probability distributions commonly used in forestry. Visualization tools with confidence bands using delta method and parametric bootstrap are provided for three-parameter Weibull distribution fitting to diameter data. Useful conversion of angle value from degree to radian, conversion from angle to slope (in percentage) and their reciprocals as well as principal angle determination are also included. Position and dispersion parameters usually found in forest studies are implemented. The package contains Fibonacci series, its extensions and the Golden Number computation. Useful references are Arcadius Y. J. Akossou, Soufianou Arzouma, Eloi Y. Attakpa, Noël H. Fonton and Kouami Kokou (2013) doi:10.3390/d5010099, W. Bonou, R. Glele Kakaï, A.E. Assogbadjo, H.N. Fonton, B. Sinsin (2009) doi:10.1016/j.foreco.2009.05.032, R. C. H. Cheng and N. A. K. Amin (1983) doi:10.1111/j.2517-6161.1983.tb01268.x, and R. C. H. Cheng and M. A. Stephens (1989) doi:10.1093/biomet/76.2.385.

Details

Type RShowDoc("dendrometry", package = "dendrometry") to read a HTML user guide vignette.

Type demo(dendro, package = "dendrometry") for a demo of dendrometric computations. Click on Index bellow to see the index of the package.

Type demo(volume, package = "dendrometry") for a demo of dendrometric computations. Click on Index bellow to see the index of the package.

Author(s)

Maintainer: Narcisse Yehouenou narcisstar211@gmail.com

Other contributors:

• Information and Communication Technology for you ONG (ICT4U-ONG) ict4uong@gmail.com [funder]

See Also

Useful links:

• Report bugs at https://github.com/narcisstar/dendrometry-issues/issues

Extract AIC

Description

S3 method to extract the Akaike Information Criterion from a fitted distribution.

Usage

## S3 method for class 'fit_dist'
AIC(object, ..., k = 2)

Arguments

Value

AIC value.

Extract BIC

Description

S3 method to extract the Bayesian Information Criterion from a fitted distribution.

Usage

## S3 method for class 'fit_dist'
BIC(object, ...)

Arguments

Value

BIC value.

Tree metrics for logging

Description

Data frame of 24 rows and 8 columns containing tree measures.

Usage

data(Logging)

Format

Data frame with twenty five observations and eight variables:

tree

Tree name (scientific gender).

hauteur

Stem length in meter (m).

diametreMedian

Tree median diameter in centimeter (cm).

perimetreMedian

Tree median circumference in centimeter (cm).

diametreSection

Tree diameter at the end in centimeter (cm).

perimetreSection

Tree circumference at the end in centimeter (cm).

diametreBase

Tree diameter at the base in centimeter (cm).

perimetreBase

Tree circumference at the base in centimeter (cm).

Author(s)

Narcisse Yehouenou narcisstar211@gmail.com

Source

Fake data simulated for tutorial purposes.

Examples

# demo(volume)

Dendrometric measures on tree

Description

Data frame of 10 rows and 5 columns containing tree measures.

Usage

data(Tree)

Format

Data frame with ten observations and five variables:

circum

Tree circumference in centimeter (cm).

dist

Horizontal distance between the person measuring angles and the tree (m).

up

Angle measured for the top part of the tree in degree (°). It is used to calculate the total tree height.

down

Angle measured for the bottom part of the tree in degree (°).

fut

Bole angle measure in degree (°); Bole is where the first branch occurs on the trunk. It is used to calculate the merchantable tree height.

Author(s)

Narcisse Yehouenou narcisstar211@gmail.com

Source

Fake data simulated for tutorial purposes.

Examples

# demo(dendro)

Angle - Slope conversion and Principal Measure determination

Description

Conversion of angle to slope values and reciprocally.
angle2slope converts angle to slope values.
slope2angle converts slope to angle values.
principal determines the principal measure of an angle value. Principal measure ranges from -pi to pi for radian unit while it ranges from -180 to 180 for degree unit.

Usage

angle2slope(angle, angleUnit = c("deg", "rad"))

slope2angle(slope, angleUnit = c("deg", "rad"))

principal(angle, angleUnit = c("deg", "rad"))

Arguments

Value

Object of class angle.
angle2slope returns vector of slope values while slope2angle and principal return vector of angle values in unit specified in angle argument.

Note

Use principal in position computations, not distance computations.

See Also

deg and rad.

Examples

angle2slope(10)
angle2slope(angle = 45)
angle2slope(angle = pi / 4, angleUnit = "rad")
angle2slope(1.047198, "rad")
angle2slope(seq(0.2, 1.5, .4), angleUnit = "rad") #'

slope2angle(100)
slope2angle(100, "rad")
round(pi / 4, 2)

slope2angle(17.6327)
slope2angle(angle2slope(30))

principal(303)
principal(23 * pi / 8, "rad")
principal(7 * pi / 4, angleUnit = "rad")
deg(principal(7 * pi / 4, angleUnit = "rad"))
principal(7 * 45)

Bark factor The bark factor (k) is computed for trees in order to assess the importance of the valuable wood in the overall volume of a tree (Husch et al., 1982):

Description

Bark factor The bark factor (k) is computed for trees in order to assess the importance of the valuable wood in the overall volume of a tree (Husch et al., 1982):

Usage

barkFactor(dbh, thickness)

Arguments

References

Husch, B., Miller, C., Beers, T., 1982. Forest mensuration. Ronald Press Company, London, pp. 1 – 410.

The basal area of plots

Description

Computes the basal area of tree stems in a plot. The basal area is the cross sectional area of the bole or stem of a tree at breast height.

Usage

basal(dbh, area, k = 100, circum = NULL)

Arguments

Details

If area is expressed in ha and dbh expressed in cm, the basal area unit is cm\u00b2/ha when k = 1. In order to convert centimeter (cm) to meter (m) for dbh, set k = 100. Because 1m = 100 cm. Then, basal area unit will be m\u00b2/ha.

If dbh is in meter (m), and area in in hectare (ha), setting k = 1 returns basal area in m\u00b2/ha.

If dbh is in feet, and area in acre, setting k = 1 returns basal area in ft\u00b2/ac.

If dbh is in inch, and area in acre, setting k = 12 returns basal area in feet\u00b2/acres (ft\u00b2/ac).

Value

A vector of basal area of stands.

Basal area contribution

Description

The basal area contribution (in per cent) is defined as the part of a given species trees in the overall basal area of all trees in an area.

Usage

basalContribution(basal)

Arguments

Individual Basal Area and DBH (diameter)

Description

basal_i computes the basal area of a tree stem (individual), the area of a circle of diameter dbh.
basal2dbh computes the dbh (diameter) based on the basal area.

Usage

basal_i(dbh, circum = NULL)

basal2dbh(basal)

Arguments

Details

If circum is given, dbh is not used.

Value

basal_i returns individual basal area while basal2dbh returns DBH.

Examples

basal_i(dbh = 10)
basal_i(circum = 31.41)
basal2dbh(78.53982)

Recursive function for creating nested data structures

Description

Internal recursive function that creates nested list structures by sequentially grouping data by factors and applying a function to each subset. This is the core engine behind nestedFunBuilder and related functions.

Usage

baseFunction(currentData, remainingFactors, fun = identity, funArgs = list())

Arguments

Details

This function works recursively:

• Base case: If no factors remain, applies fun to the current data

• Recursive case: Groups data by the first remaining factor, then calls itself on each subset with the remaining factors

Value

A nested list structure where each level corresponds to a factor, or the result of applying fun to the data subset if no factors remain.

Index of Blackman

Description

Index of Blackman

Usage

blackman(density)

Arguments

Value

Index of Blackman.

Compute Confidence Bands Using Parametric Bootstrap

Description

Compute Confidence Bands Using Parametric Bootstrap

Usage

ci_bands_bootstrap(
  x,
  params,
  method,
  ties_method,
  x_range,
  amplitude,
  n_boot = 1000,
  level = 0.95
)

Compute Confidence Bands Using Delta Method

Description

Compute Confidence Bands Using Delta Method

Usage

ci_bands_delta(params, vcov, x_range, amplitude, level = 0.95)

Extract Model Coefficients

Description

S3 method to extract parameter estimates from a fitted distribution.

Usage

## S3 method for class 'fit_dist'
coef(object, ...)

Arguments

Value

Named numeric vector of parameter estimates.

Confidence Intervals for Parameters

Description

S3 method to compute confidence intervals for distribution parameters based on asymptotic normality of maximum likelihood estimates.

Usage

## S3 method for class 'fit_dist'
confint(object, parm, level = 0.95, ...)

Arguments

Value

A matrix with columns giving lower and upper confidence limits for each parameter.

Create Objective Function for MPS or MLE

Description

Creates the objective function for optimization-based estimation methods. For MLE, returns negative log-likelihood. For MPS, returns negative log-product-spacing with optional ties correction.

Usage

create_objective(method, x, dist, tol_spacing, ties_method, custom_functions)

Arguments

Value

A function that computes the objective value given parameters.

Diameter (DBH) and Circumference

Description

DBH computes diameter (at breast height) based on circumference (at breast height).
circum computes circumference (at breast height) based on diameter (at breast height).
They are based on circle diameter and perimeter formulas.

Usage

dbh(circum)

circum(dbh)

Arguments

Value

dbh, returns diameter and circum, returns circumference.

See Also

See also height for tree height.

Examples

perimeter <- seq(30, 60, 1.4)
diameter <- dbh(perimeter)
circum(diameter)

The decrease coefficient

Description

This coefficient expresses the ratio between the diameter (or circumference) at mid-height of the bole and the diameter (or circumference) measured at breast height.

Usage

decrease(middle, breast)

Arguments

Details

Both middle and breast arguments should be of the same type (either diameter or circumference). Not mixture.

Value

A vector of decrease coefficients.

Examples

decrease(30, 120)
decrease(middle = 40, breast = 90)

Metric scrolling or decay

Description

The average metric decay expresses the difference, in centimeters per meter, between the diameter (or circumference) at breast height and its diameter at mid-height of a stem related to the difference between the height at mid-height and that at breast height.

Usage

decreaseMetric(dmh, dbh, mh, bh = 1.3)

Arguments

Value

Metric decay

See Also

reducecoef

Examples

decreaseMetric(dmh = 40, dbh = 90, mh = 7)
decreaseMetric(45, 85, 9)

Degree and Radian

Description

deg converts angle values from radians to degrees.
rad converts angle values from degrees to radians.

Usage

deg(radian)

rad(degree)

Arguments

Value

deg returns vector of degree values while rad returns vector of radian values.

See Also

principal.

Examples

deg(pi / 2)
rad(180)

Density of regeneration (efficient version)

Description

Computes the density per plot of tree regeneration based on counts in subplots. Can be grouped by additional factors for nested analysis.

Usage

densityRegen(data = NULL, plot = NULL, count, nbSubPlot, area, ...)

Arguments

Tree density

Description

Density of trees per plot.

Usage

densityTree(number, area, overall = TRUE)

Arguments

Details

If every plot have same area, area is a numeric value, otherwise area is a vector of each plot area.

Value

Vector of density.

See Also

densityRegen for regeneration density.

Examples

count <- setNames(
  c(87, 104, 83, 132, 107, 84, 110, 115, 112, 94),
  LETTERS[1:10]
)
densityTree(count, 10)
densityTree(count, area = 10, overall = FALSE)
densityTree(count, area = 10:19, overall = FALSE)

Mean diameter

Description

Mean diameter of a forestry stand.

Usage

diameterMean(dbh)

Arguments

Value

Mean diameter.

See Also

dbh, basal_i

Examples

set.seed(1)
diameter <- rnorm(10, 100, 20)
diameterMean(dbh = diameter)

Horizontal distance

Description

Horizontal distance calculation for sloping area.

Usage

distanceH(
  distance,
  angle,
  type = c("slope", "angle"),
  angleUnit = c("deg", "rad")
)

Arguments

Value

A vector of horizontal distance.

Examples

distanceH(20, 30)
distanceH(20, angle = 30, type = "slope")
distanceH(20, angle = 25, type = "angle")

The three-parameter Weibull Distribution

Description

Density, distribution function, quantile function and random generation for the three-parameter Weibull.

Usage

dweibull3(x, shape, scale = 1, loc = 0, log = FALSE)
pweibull3(q, shape, scale, loc = 0, lower.tail = TRUE, log.p = FALSE)
qweibull3(p, shape, scale, loc = 0, lower.tail = TRUE, log.p = FALSE)
rweibull3(n, shape, scale = 1, loc = 0)

Arguments

See Also

dweibull for the Weibull distribution.

Making factor vectors

Description

Changes character vectors of a data set to factor vectors.

Usage

factorize(data, binary = FALSE)

Arguments

Details

When binary = TRUE, variables stored as numeric and which have exactly two levels are changed to factor.

Value

Data frame with all character vectors changed to factor vectors.

Fibonacci series ratio

Description

Computes rates from Fibonacci series.

Usage

fiboRate(n, PrintSer = FALSE, Uo = 0, U1 = 1)

Arguments

Details

The series equation is Un = U_(n-2) /U_(n-1). The function returns golden number when Uo = 0, and U1 = 1. Larger n is, more precise the number (result) is.

Value

Either a numeric, result of the rate of nth and (n-1)th numbers in Fibonacci series or all (n-1)th those rates.

Author(s)

Narcisse Yehouenou narcisstar211@gmail.com

See Also

fibonacci

Examples

## Golden number (Le Nombre d'Or)
fiboRate(n = 18, PrintSer = FALSE, Uo = 0, U1 = 1)
## (1+sqrt(5))/2
fiboRate(n = 10, PrintSer = TRUE, Uo = 0, U1 = 1)

Fibonacci series

Description

Generates numbers from Fibonacci series.

Usage

fibonacci(n, PrintFib = FALSE, Uo = 0, U1 = 1)

Arguments

Details

The series equation is Un = U_(n-2) /U_(n-1).

Value

Either an integer, result of the function or a vector of n first numbers of the series.

Author(s)

Narcisse Yehouenou narcisstar211@gmail.com

See Also

fiboRate

Examples

fibonacci(n = 10, PrintFib = TRUE)
fibonacci(n = 10, Uo = 1, U1 = 3, PrintFib = FALSE)

Unified Parameter Estimation for Probability Distributions

Description

Estimates parameters of probability distributions using various methods: Maximum Likelihood (MLE), Maximum Product Spacing (MPS), or Method of Moments (MOM).

Usage

fit_dist(
  data,
  dist = "normal",
  method = "mle",
  start = NULL,
  lower = NULL,
  upper = NULL,
  optim_method = "Nelder-Mead",
  custom_functions = NULL,
  tol_spacing = 1e-16,
  tol_param = 1e-06,
  ties_method = "cheng_amin",
  ...
)

Arguments

Value

A list with class "fit_dist" containing:

References

Cheng, R. C. H., & Amin, N. A. K. (1983). Estimating parameters in continuous univariate distributions with a shifted origin. Journal of the Royal Statistical Society: Series B, 45(3), 394-403.

Cheng, R. C. H., & Stephens, M. A. (1989). A goodness-of-fit test using Moran's statistic with estimated parameters. Biometrika, 76(2), 385-392.

Examples

# MLE estimation (default)
set.seed(123)
x <- rweibull(100, shape = 2.5, scale = 1.5)
fit1 <- fit_dist(x, dist = "weibull")
print(fit1)

# MPS estimation with Cheng-Amin ties correction
fit2 <- fit_dist(x, dist = "weibull", method = "mps")

# L-Moments estimation (under development)
# fit3 <- fit_dist(x, dist = "weibull", method = "lm")

# Method of Moments
fit4 <- fit_dist(x, dist = "weibull", method = "mom")

# Compare fits
cat("AIC - MLE:", fit1$aic, "MPS:", fit2$aic, "\n")

L-Moments Estimation

Description

Estimates distribution parameters using the method of L-moments.

Usage

fit_lmoments(x, dist)

Arguments

Value

A list containing parameter estimates and placeholder values for other components.

Method of Moments Estimation

Description

Estimates distribution parameters using the classical method of moments, matching sample moments to theoretical moments.

Usage

fit_moments(x, dist, tol_param)

Arguments

Value

A list containing parameter estimates and placeholder values for other components.

Unified Optimization-based Estimation (MPS and MLE)

Description

Internal function that performs parameter estimation using optimization-based methods (Maximum Likelihood or Maximum Product Spacing).

Usage

fit_optimization(
  x,
  dist,
  method,
  start,
  lower,
  upper,
  optim_method,
  custom_functions,
  tol_spacing,
  tol_param,
  ties_method,
  ...
)

Arguments

Value

A list containing parameter estimates, standard errors, objective value, log-likelihood, AIC, BIC, KS test results, and convergence information.

Fit and Plot Three-Parameter Weibull Distribution with Confidence Bands

Description

Fits a three-parameter Weibull distribution to diameter data and optionally visualizes the fit with a histogram, fitted density curve, and confidence bands.

Usage

fit_weibull_plot(
  x,
  amplitude = 10,
  shape = 2,
  plot = TRUE,
  show_ci = TRUE,
  ci_level = 0.95,
  ci_method = "delta",
  n_boot = 1000,
  main = NULL,
  title.col = "black",
  mid = TRUE,
  line.col = "blue",
  ci.col = "lightblue",
  ci.alpha = 0.3,
  legendPos = "topright",
  lowLim = NULL,
  ymax = NULL,
  bg = "aliceblue",
  method = "mle",
  ties_method = "cheng_amin",
  cex.axis = 0.6,
  cex.lab = 0.8,
  las = 1,
  xlab = "Diameter class (cm)",
  ylab = "Relative frequency (%)",
  cex.legend = 1,
  ...
)

adjWeibull(
  x,
  amplitude = 10,
  shape = 2,
  plot = TRUE,
  show_ci = TRUE,
  ci_level = 0.95,
  ci_method = "delta",
  n_boot = 1000,
  main = NULL,
  title.col = "black",
  mid = TRUE,
  line.col = "blue",
  ci.col = "lightblue",
  ci.alpha = 0.3,
  legendPos = "topright",
  lowLim = NULL,
  ymax = NULL,
  bg = "aliceblue",
  method = "mle",
  ties_method = "cheng_amin",
  cex.axis = 0.6,
  cex.lab = 0.8,
  las = 1,
  xlab = "Diameter class (cm)",
  ylab = "Relative frequency (%)",
  cex.legend = 1,
  ...
)

Arguments

Value

A list (invisibly) containing:

See Also

fit_dist

Examples

# Simulate tree diameter data
set.seed(123)
diameters <- rweibull3(100, shape = 2.5, scale = 25, loc = 10)

# Fit with confidence bands
fit1 <- fit_weibull_plot(diameters, amplitude = 5, show_ci = TRUE)

# Fit without confidence bands
fit2 <- fit_weibull_plot(diameters, amplitude = 5, show_ci = FALSE)

# Use bootstrap confidence bands
fit3 <- fit_weibull_plot(diameters,
  amplitude = 5,
  ci_method = "bootstrap", n_boot = 500
)

Format Confidence Interval for Display

Description

Format Confidence Interval for Display

Usage

format_ci(ci_vals, digits = 2)

Format Confidence Interval for Inline Display

Description

Format Confidence Interval for Inline Display

Usage

format_ci_inline(ci_vals, digits = 2)

Get CDF for Standard and Custom Distributions

Description

Evaluates the cumulative distribution function for standard distributions or uses custom CDF function.

Usage

get_cdf(x, params, dist, custom_functions = NULL)

Arguments

Value

Numeric vector of probabilities.

Get Default Parameter Bounds

Description

Returns default lower or upper bounds for distribution parameters used in bounded optimization methods.

Usage

get_default_bounds(dist, bound_type, x, tol_param)

Arguments

Value

Numeric vector of parameter bounds.

Get Parameter Names for Standard Distributions

Description

Returns the parameter names for standard distributions.

Usage

get_param_names(dist)

Arguments

Value

Character vector of parameter names.

Get PDF for Standard and Custom Distributions

Description

Evaluates the probability density function for standard distributions or uses custom PDF function.

Usage

get_pdf(x, params, dist, custom_functions = NULL)

Arguments

Value

Numeric vector of density values.

Get Starting Values for Parameter Estimation

Description

Computes reasonable starting values for optimization based on sample moments and distribution-specific heuristics.

Usage

get_start_values(x, dist, tol_param)

Arguments

Value

Numeric vector of starting parameter values.

Girard Form Class Girard Form Class is a form quotient used to estimate taper.

Description

Girard Form Class Girard Form Class is a form quotient used to estimate taper.

Usage

girard(dbh, dbhIn)

Arguments

References

Strimbu, B. (2021). Dendrometry Field Manual.

Index of Green

Description

Index of Green

Usage

green(density)

Arguments

Value

Index of Green.

Height of Tree or any vertical Object

Description

Computes the height of tree, pillar, girder, mast or any vertical object. It allows either slope (in percent) or angle (in degrees or radians). No matter the relative position of the persons who measures the angle or the slope.

Usage

height(
  distance,
  top,
  bottom,
  type = c("angle", "slope"),
  angleUnit = c("deg", "rad")
)

Arguments

Value

A vector of heights.

Examples

height(10, 80, 17)
height(17, top = -18, bottom = -113)
height(distance = 18, top = 42, bottom = -12, type = "angle", angleUnit = "deg")
height(
  distance = 18:21, top = 42:45, bottom = -12:-15, type = "angle",
  angleUnit = "deg"
)
## Below shows warning messages
height(
  distance = 18:21, top = -42:-45, bottom = -12:-15, type = "angle",
  angleUnit = "deg"
)

Extract Log-Likelihood

Description

S3 method to extract the log-likelihood value from a fitted distribution.

Usage

## S3 method for class 'fit_dist'
logLik(object, ...)

Arguments

Value

Log-likelihood value.

Lorey's mean height

Description

The average height of the trees in a plot, weighted by their basal area.

Usage

loreyHeight(basal, height)

Arguments

Value

Average Lorey height of a stand.

See Also

height, basal_i

Examples

set.seed(1)
donnee <- data.frame(
  hauteur = rnorm(10, 12, 3),
  area = basal_i(rnorm(10, 100, 20))
)
loreyHeight(basal = donnee$area, height = donnee$hauteur)

Create nested data subsets

Description

A convenient wrapper around nestedFunBuilder that creates nested data subsets without applying any function. This is useful for exploring data structure or preparing data for further analysis.

Usage

makedata(data, ...)

Arguments

Details

This function is equivalent to calling nestedFunBuilder with .fun = identity. It provides a simpler interface when you only need to create nested data structures without applying functions.

Value

A nested list structure where each level corresponds to a factor level, with the deepest level containing the actual data subsets. If no valid factors are provided, returns the original data frame with a warning.

See Also

nestedFunBuilder for applying functions to subsets

Examples

## Not run: 
# require(BiodiversityR)
# data(ifri, package = "BiodiversityR")
# a1 <- makedata(ifri, "forest", "plotID", "species")
# a2 <- makedata(ifri, "species")
# a3 <- makedata(ifri, "forest", "plotID", "species", "size_class")
# identical(makedata(ifri), ifri)

## End(Not run)

Create nested data structures with optional function application

Description

Creates nested list structures by grouping data according to specified factors and optionally applying a function to each final subset. This is the main function for creating hierarchical data structures for analysis.

Usage

nestedFunBuilder(data, ..., .fun = identity, .funArgs = list())

Arguments

Details

The function performs the following steps:

• Validates that all specified factors exist in the data

• Warns about missing factors and removes them from processing

• Creates nested subsets using the specified factors

• Applies the specified function to each final subset

Value

A nested list structure where each level corresponds to a factor level. If .fun = identity, returns data subsets. Otherwise, returns the result of applying .fun to each subset.

Examples

## Not run: 
# Create nested data structure
nested_data <- nestedFunBuilder(iris, "Species", "Sepal.Length > 5")

# Apply function to each subset
means <- nestedFunBuilder(iris, "Species",
  .fun = function(data) mean(data$Sepal.Length)
)

# Multiple factors with function
results <- nestedFunBuilder(mydata, "site", "species", "treatment",
  .fun = myAnalysisFunction,
  .funArgs = list(method = "robust")
)

## End(Not run)

Optimization Wrapper

Description

Wrapper function for optim that handles bounded and unbounded optimization methods with appropriate default bounds.

Usage

optimize_params(
  obj_func,
  start,
  lower,
  upper,
  optim_method,
  dist,
  x,
  tol_param,
  ...
)

Arguments

Value

Result from optim.

Structural parameters for stands

Description

Computes various forest stand parameters (basal area, mean diameter, height, etc.) for forest inventory data, with support for grouping by multiple factors and plot-level analysis.

Usage

param(
  data,
  ...,
  plot = "",
  DBH = "",
  height = "",
  crown = "",
  area = NULL,
  k = 100,
  kCrown = 1
)

Arguments

Details

The function supports hierarchical grouping by multiple factors. For example, grouping by species and site will create a nested structure where parameters are calculated for each species within each site.

If plot-level analysis is requested (plot != ""), the function will further subdivide each group by plot and calculate parameters for each plot within each group. Else, it treats the entire dataset as a single plot; area should then be specified accordingly.

Blackman and Green indices are returned if combinations of specified factors contain more than one plot. Otherwise, the right (correct) ones are returned as attributes.

Value

A nested list structure containing calculated parameters for each group. Parameters include:

• MeanDBH: Mean diameter at breast height

• Basal: Basal area per unit area

• MeanCrown: Mean crown diameter

• BasalCrown: Crown basal area per unit area

• Height: Mean height

• LoreyHeight: Lorey's height (basal area weighted mean height)

• Density: Number of trees per unit area

Examples

param(
  data = Logging, plot = "tree", DBH = "diametreMedian",
  height = "hauteur", crown = "perimetreBase", area = 0.03, kCrown = 100
)

set.seed(123)
Logging$surperficie <- abs(rnorm(24, mean = 0.03, sd = 0.01))
head(Logging)

param(
  data = Logging, plot = "tree", DBH = "diametreMedian",
  height = "hauteur", crown = "perimetreBase", area = "surperficie", kCrown = 100
)

## Not run: 
# Basic usage - single plot
params <- param(forest_data, DBH = "dbh", height = "height", area = 1000)

# Multiple plots
params <- param(forest_data,
  plot = "plot_id", DBH = "dbh",
  height = "height", area = "plot_area"
)

# Grouped analysis
params <- param(forest_data,
  plot = "plot_id", DBH = "dbh",
  height = "height", area = 1000,
  "species", "site", "treatment"
)

# Access specific results
oak_site1 <- params$oak$site1

## End(Not run)

Print Angle

Description

Method to print angle and returns it invisibly.

Usage

## S3 method for class 'angle'
print(x, ...)

Arguments

Print Method for Fitted Distributions

Description

S3 method to print a summary of the fitted distribution.

Usage

## S3 method for class 'fit_dist'
print(x, ...)

Arguments

Value

The object invisibly.

Print Slope

Description

Method to print slope and returns it invisibly.

Usage

## S3 method for class 'slope'
print(x, ...)

Arguments

The reduction coefficient

Description

The reduction coefficient is the ratio between the difference in size at breast height and mid-height on the one hand, and the size at breast height on the other. It is thus the complement to 1 of the coefficient of decrease.

Usage

reducecoef(middle, breast)

Arguments

Details

Both middle and breast arguments should be of the same type (either diameter or circumference). Not mixture.

Value

The reduction coefficient.

See Also

decrease

Examples

reducecoef(30, 120)
reducecoef(middle = 40, breast = 90)

Relative Frequency

Description

Relative Frequency in percentage.

Usage

rfreq(x)

Arguments

Sample size

Description

Sample size

Usage

sampleSize(
  confLev = 0.95,
  popPro = 0.5,
  errorMargin = 0.05,
  size = NULL,
  method = "",
  cv = NULL
)

Arguments

Value

The sample size.

Note

Population size to be considered as large or infinite heavily depends on error margin. Lower error margin increases population size to be conidered as large or infinite. For errorMargin = .05, size = 152 231 and cauchran 151 760 when confLev = .05

Examples

sampleSize(confLev = .95, popPro = 0.4, errorMargin = .05)
sampleSize(confLev = .95, popPro = 0.5, errorMargin = .05, size = 150)
sampleSize(
  confLev = .95, popPro = 0.5, errorMargin = .05, size = 150,
  method = "cauchran"
)
sampleSize()

The shape coefficient

Description

The shape coefficient of the tree is the ratio of the actual volume of the tree to the volume of a cylinder having as base the surface of the section at 1.3 m (or a given breast height) and as length, the height (at bole level) of the tree.

Usage

shape(volume, height, dbh, basal = NULL)

Arguments

Value

The shape coefficient.

See Also

volume, for tree real volume.

Examples

shape(volume = 10000, 11, dbh = 40)
shape(volume = 10000, 11, 40)
shape(volume = 10000, 11, basal = 2256.637)
## Bellow gives warning
shape(volume = 10000, height = 11, dbh = 40, basal = 2256.637)

Skewness coefficient

Description

Skewness coefficient

Usage

skewness(x)

Arguments

Value

The skewness coefficient.

Examples

data("Logging")
skewness(Logging$hauteur)
hist(Logging$hauteur, 3)

Abbreviates a Botanical or Zoological Latin Name into an Eight-character from 'Gender epithet' to 'G. epithet'

Description

To abbreviate species name from 'Gender epithet' to 'G. epithet'. Useful in plots with species names.

Usage

spNmReduce(name, sep = " ")

Arguments

Details

Returned reduced names are made unique.

Value

A factor vector of species reduced names in forms 'G. epithet'.

See Also

make.cepnames in vegan package.

Stack all vectors of a data frame or list

Description

Stacking all columns of a data frame or vectors of a list into a single vector.

Usage

stacking(data)

Arguments

Value

A vector of all element of the argument data.

Summary Method for Fitted Distributions

Description

S3 method to provide a detailed summary of the fitted distribution.

Usage

## S3 method for class 'fit_dist'
summary(object, ...)

Arguments

Value

The object invisibly.

Extract Variance-Covariance Matrix

Description

S3 method to extract the variance-covariance matrix from a fitted distribution.

Usage

## S3 method for class 'fit_dist'
vcov(object, ...)

Arguments

Value

Variance-covariance matrix of parameter estimates.

Tree stem and log Volume

Description

Determining the volume of the log or of the tree.

Usage

volume(height, dm, do, ds, circum, circumo, circums,
       method = "huber", successive = FALSE, log)

Arguments

Details

Using method = cone refers to truncated cone method.

Value

A numeric vector of logs or trees volume.

See Also

shape, for shape coefficient.

Examples

## huber method
volume(height = 10, dm = 35)
volume(height = 10, circum = 100)

## smalian method
volume(height = 10, do = 45, ds = 15, method = "smalian")
volume(height = 10, circumo = 200, circums = 110, method = "smalian")

## cone method
volume(height = 10, do = 45, ds = 15, method = "cone")
volume(height = 10, circumo = 200, circums = 110, method = "cone")

## newton method
volume(height = 10, dm = 35, do = 45, ds = 15, method = "newton")
volume(
  height = 10, circum = 100, circumo = 200, circums = 110,
  method = "newton"
)