timbeR (version 2.0.1)

bi_logs_plot: Visualize the simulation of log cutting along the stem using a Bi (2000) variable-form taper equation.

Description

Plot the shape of the tree and visualize the extracted logs based on the tree measurements, assortments data.frame, and the Bi (2000) variable-form taper equation.

Usage

bi_logs_plot(
  dbh,
  h,
  coef,
  assortments,
  stump_height,
  downgrade,
  broken,
  defect_height,
  lang
)

Arguments

dbh

tree diameter at breast height, in centimeters.

h

total tree height, in meters.

coef

numerical vector containing seven coefficients of the Bi variable-form taper equation.

assortments

a data.frame with five columns and n rows, where n is the number of different wood assortments to be obtained from the tree stem. The first column must contain the names of the assortments, the second, numerical, contains the minimum diameters at the small end of the logs, in centimeters. The third column, numerical, contains the minimum lengths of the logs, in meters. The fourth column, numerical, contains the maximum lengths of the logs, in meters. The fifth column, numerical, contains the values in centimeters referring to the loss of wood due to cutting logs. The algorithm prioritizes the extraction of assortments along the stem in the order presented in the data.frame, starting from the first line, to the last.

stump_height

tree cutting height, in meters. Default is 0.

downgrade

if TRUE, the algorithm,from the defect_height onwards, simulates log extraction only for the last assortment in the assortments data.frame. Default is FALSE.

broken

if TRUE, the algorithm will simulate the extraction of logs only up to the defect_height. Default is FALSE.

defect_height

the height, in meters, from which the logs will be downgraded (if downgrade is TRUE) or log extraction simulation will be stopped (if broken is TRUE). Default is h * 0.5.

lang

language in which plot labels will be displayed. Current options are 'en' and 'pt-BR'. Default is 'en'.

Value

a ggplot object.

Details

check the bi_logs function help for more details.

Examples

# NOT RUN {
library(dplyr)
library(minpack.lm)
library(timbeR)

tree_scaling <- tree_scaling %>%
mutate(did = di/dbh,
       hih = hi/h)

bi <-  nlsLM(di ~ taper_bi(dbh, h, hih, b0, b1, b2, b3, b4, b5, b6),
data=tree_scaling,
start=list(b0=1.8,b1=-0.2,b2=-0.04,b3=-0.9,b4=-0.0006,b5=0.07,b6=-.14))

coef_bi <- coef(bi)

dbh <- 25
h <- 20

assortments <- data.frame(
  NAME = c('15-25','4-15'),
  SED = c(15,4),
  MINLENGTH = c(2.65,2),
  MAXLENGTH = c(2.65,4.2),
  LOSS = c(5,5)
)

bi_logs_plot(dbh, h, coef_bi, assortments)

# }