plot.Tagloss: Plot the daily rate of tag loss.
In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

plot.Tagloss

R Documentation

Plot the daily rate of tag loss.

Description

Plot the daily rate of tag loss.
To use this function without a result of Tagloss_fit(), see the hack in examples.

Usage

## S3 method for class 'Tagloss'
plot(
  x,
  t = NULL,
  fitted.parameters = NULL,
  fixed.parameters = NULL,
  scale = 1,
  model_before = NULL,
  model_after = NULL,
  model = c("1", "2", "R1", "R2", "L1", "L2", "cumul", "cumul1", "N2", "N1", "N0", "NLR",
    "N0R", "NL0", "N00"),
  col = rev(grey.colors(4, start = 0.9, end = 0.3)),
  text.col = grey.colors(4, start = 0.9, end = 0.3),
  label.col = "black",
  add = FALSE,
  mcmc = FALSE,
  Hessian = NULL,
  replicates = NULL,
  method = NULL,
  probs = c(0.025, 0.975),
  progressbar = FALSE,
  decoration = FALSE,
  ...
)

Arguments

`x`	Object obteined from Tagloss_fit()
`t`	Time for which values of model must be ploted
`fitted.parameters`	Set of parameters
`fixed.parameters`	Another set of parameters without standard error associated
`scale`	Scale value. When Cumul is used, scale is always 1.
`model_before`	Transformation of parameters before to use Tagloss_model()
`model_after`	Transformation of parameters after to use Tagloss_model()
`model`	Can be 1, 2, R1, R2, L1, L2 or Cumul (2 tags) or Cumul1 (1 tag)
`col`	The colors of shading areas of cumul or the color of line
`text.col`	The text color for cumul model
`label.col`	The text color used for labels when decoration is true
`add`	Should the data be added to a previous plot?
`mcmc`	The mcmc result
`Hessian`	Hessian matrix of parameters
`replicates`	Number of replicates for confidence interval
`method`	Which method to use to estimate confidence interval
`probs`	Quantiles to show for confidence interval
`progressbar`	Is shown a progressbar?
`decoration`	Try to add name of parameters on the graph
`...`	Parameters transmitted to plot

Details

plot.tagloss plots the daily rate of tag loss.

Value

An invisible dataframe with values used for plotting.

Author(s)

Marc Girondot

Examples

## Not run: 
library(phenology)
# Example
t <- 1:1000

par <- c(D1_1=200, D2D1_1=100, D3D2_1=200, 
         A_1=-logit(0.02), B_1=-logit(0.05), C_1=-logit(0.07))
phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="1")
phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="1", 
                         scale=1000, decoration = TRUE)

par <- c(D1_2=200, D2D1_2=100, D3D2_2=200, 
         A_2=-logit(0.05), B_2=-logit(0.03), C_2=-logit(0.03))
phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, ylim=c(0, 1), 
                         scale = 10, model="2", decoration = TRUE)

par <- c(D1_L2=200, D2D1_L2=100, D3D2_L2=200, 
         A_L2=-logit(0.05), B_L2=-logit(0.07), C_L2=-logit(0.07))
phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="L2")

par <- c(D1_R2=200, D2D1_R2=0, D3D2_R2=700, 
         A_R2=-logit(0.02), B_R2=-logit(0.05), C_R2=-logit(0.07))
phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="R2", 
                         col="red", add=TRUE)

par <- c(D1_L1=200, D2D1_L1=2000, D3D2_L1=2000, 
        A_L1=-logit(0.05), B_L1=-logit(0.02), C_L1=-logit(0.1))
phenology:::plot.Tagloss(x=list(), t=t, fitted.parameters=par, model="L1")

# To plot the history of individuals
par <- c(D1_1=200, D2D1_1=100, D3D2_1=200, 
         A_1=-logit(5E-4), B_1=-logit(4E-4), C_1=-logit(5E-4), 
         D1_2=200, D2D1_2=100, D3D2_2=200, 
         A_2=-logit(6E-4), B_2=-logit(5E-4), C_2=-logit(6E-4))
phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, 
                         model="Cumul", 
                         decoration = TRUE)
phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, 
                         model="Cumul", 
                         decoration = TRUE, col=c("red", "green", "blue"))
                         
# To plot the history of individuals
par <- c(D1_R1=200, D2D1_R1=300, D3D2_R1=200, 
         A_R1=-logit(5E-4), B_R1=-logit(4E-4), C_R1=-logit(5E-4), 
         D1_R2=200, D2D1_R2=200, D3D2_R2=200, 
         A_R2=-logit(6E-4), B_R2=-logit(5E-4), C_R2=-logit(6E-4), 
         D1_L1=200, D2D1_L1=400, D3D2_L1=200, 
         A_L1=-logit(5E-4), B_L1=-logit(4E-4), C_L1=-logit(5E-4), 
         D1_L2=200, D2D1_L2=100, D3D2_L2=200, 
         A_L2=-logit(6E-4), B_L2=-logit(5E-4), C_L2=-logit(6E-4))
phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="Cumul", 
                         decoration = TRUE)
phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="R1", 
                         decoration = TRUE)                         
phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="R2", 
                         decoration = TRUE)
# Example of fit

data_f_21 <- Tagloss_format(outLR, model="21")
data_f_21_fast <- subset(data_f_21, subset=(is.na(data_f_21$N20)))
par <- c('D1_2' = 49.78891736351531, 
         'D2D1_2' = 1059.3635769732305, 
         'D3D2_2' = 12.434313273804602, 
         'A_2' = 5.2238379144659683, 
         'B_2' = 8.0050044071275543, 
         'C_2' = 8.4317863609499675, 
         'D1_1' = 701.80273287212935, 
         'D2D1_1' = 0.010951749100596819, 
         'D3D2_1' = 3773.6290607434876, 
         'A_1' = 205.42435592344776, 
         'B_1' = 9.9598342503239863, 
         'C_1' = 6.7234868237164722)
phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="1", 
                         decoration = TRUE)
phenology:::plot.Tagloss(x=list(), t=1:1000, fitted.parameters=par, model="2", 
                         decoration = TRUE)
o <- Tagloss_fit(data=data_f_21_fast, fitted.parameters=par, hessian = TRUE)
plot(x=o, model="1", replicates=0, 
     method=NULL, decoration = TRUE)
plot(x=o, model="1", replicates=1000, 
     method="Hessian", decoration = TRUE)

## End(Not run)

phenology documentation built on Oct. 16, 2023, 9:06 a.m.