phenology (version 10.1)

plot.IP: Plot a result of Internesting Period fit or data.

Description

This function plots the result of IPFit() or IPModel().
If col is defined with a number of colors, only these colors and shown in legend.

Usage

# S3 method for IP
plot(x, ..., N = NULL, clutch = 1, result = "data")

Value

Nothing

Arguments

x

A result for IPFit() or IPModel().

...

Graphic parameters, see par().

N

Number of replicates for IPModel().

clutch

The rank of clutch when DeltameanIP is used.

result

What result will be plotted: data, model, data&model, IP, Abort, ECF, reverseECF

Author

Marc Girondot

Details

plot.IP plots a result of Internesting Period fit or data

See Also

Other Model of Internesting Period: IPFit(), IPModel(), IPPredict(), summary.IP()

Examples

if (FALSE) {
library(phenology)
# Example
data <- c(0, 47, 15, 6, 5, 4, 2, 5, 57, 203, 205, 103, 35, 24, 12, 10, 
  13, 49, 86, 107, 111, 73, 47, 30, 19, 17, 33, 48, 77, 83, 65, 
  37, 27, 23, 24, 22, 41, 42, 44, 33, 39, 24, 18, 18, 22, 22, 19, 
  24, 28, 17, 18, 19, 17, 4, 12, 9, 6, 11, 7, 11, 12, 5, 4, 6, 
  11, 5, 6, 7, 3, 2, 1, 3, 2, 1, 2, 0, 0, 3, 1, 0, 2, 0, 0, 1)
  class(data) <- unique(append("IP", class(data)))
  plot(data)
  
######### Fit parametric ECF using Maximum-Likelihood

par <- c(meanIP = 9.9959691992722917, 
         sdIP = 0.10066664270893474, 
         minIP = 7.5684588178888754, 
         pAbort = 2.2510012544630911, 
         meanAbort = 2.8969185085603386, 
         sdAbort = 0.92688983853803242, 
         pCapture = -1.0393803705929086, 
         meanECF = 3.9551519427394255, 
         sdECF = 0.31657679943365019)

fML <- IPFit(x=par, 
fixed.parameters=c(N=1000000),
data=data, 
verbose=FALSE, 
model="ML")

# Plot the fitted ECF
plot(fML, result="ECF")

# Plot the Internesting Period distribution
plot(fML, result="IP")

# Plot the distribution of days between tentatives
plot(fML, result="Abort")
plot(fML, result="Abort", xlim=c(0, 10))

# Plot the data
plot(fML, result="data")

# Plot the data and the model
plot(fML, result="data&model")

# Plot the cumulative proportion of ECF according to date of observation
plot(fML, result="reverseECF")
plot(fML_NP_Delta, result="reverseECF", col=grey.colors(128))

######### Fit using Metropolis-Hastings
# ECF.1 = 1 is fixed
par <- c(ECF.2 = 0.044151921569961131, 
         ECF.3 = 2.0020778325280748, 
         ECF.4 = 2.6128345101617083, 
         ECF.5 = 2.6450582416622375, 
         ECF.6 = 2.715198206774927, 
         ECF.7 = 2.0288031327239904, 
         ECF.8 = 1.0028041546528881, 
         ECF.9 = 0.70977432157689235, 
         ECF.10 = 0.086052204035003091, 
         ECF.11 = 0.011400419961702518, 
         ECF.12 = 0.001825219438794076, 
         ECF.13 = 0.00029398731859899116, 
         ECF.14 = 0.002784886479846703, 
         meanIP = 9.9887100433529721, 
         sdIP = 0.10580250625108811, 
         minIP = 6.5159124624132048, 
         pAbort = 2.5702251748938956, 
         meanAbort = 2.2721679285648841, 
         sdAbort = 0.52006431730489933, 
         pCapture = 0.079471782729506113)
         
df <- data.frame(Density=rep("dunif", length(par)), 
Prior1=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), 
Prior2=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8), 
SDProp=unname(c(ECF.2 = 6.366805760909012e-05, 
                ECF.3 = 6.366805760909012e-05, 
                ECF.4 = 6.366805760909012e-05, 
                ECF.5 = 6.366805760909012e-05, 
                ECF.6 = 6.366805760909012e-05, 
                ECF.7 = 6.366805760909012e-05, 
                ECF.8 = 6.366805760909012e-05, 
                ECF.9 = 6.366805760909012e-05, 
                ECF.10 = 6.366805760909012e-05, 
                ECF.11 = 6.366805760909012e-05, 
                ECF.12 = 6.366805760909012e-05, 
                ECF.13 = 6.366805760909012e-05, 
                ECF.14 = 6.366805760909012e-05, 
                meanIP = 6.366805760909012e-05, 
                sdIP = 6.366805760909012e-05, 
                minIP = 6.366805760909012e-05, 
                pAbort = 6.366805760909012e-05, 
                meanAbort = 6.366805760909012e-05, 
                sdAbort = 6.366805760909012e-05, 
                pCapture = 6.366805760909012e-05)),               
Min=c(rep(0, 13), 8, 0.001, 0, -8, 0, 0.001, -8), 
Max=c(rep(10, 13), 12, 1, 10, 8, 2, 1, 8),
Init=par, stringsAsFactors = FALSE)
rownames(df)<- names(par)

fMH <- IPFit(parametersMH=df, 
fixed.parameters=c(N=10000),
data=data, 
verbose=FALSE, 
n.iter = 10000,
n.chains = 1, n.adapt = 100, thin = 1, trace = TRUE,
adaptive = TRUE, 
model="MH")

# Plot the fitted ECF
plot(fMH, result="ECF")

# Plot the posteriors and priors
plot(fMH$MH, parameters="meanIP", xlim=c(6, 14))

plot(x=1:length(fMH$MH$resultLnL[[1]]), y=fMH$MH$resultLnL[[1]], 
type="l", xlab="Iterations", ylab="Ln L", bty="n", las=1)
}