plot.fitRMU: Plot the synthesis of RMU fit.

Description

The function plot.fitRMU plots the results of fitRMU().
In most of the cases, replicate.CI can be set to 0 for what="proportions" or "numbers".
The parameter CI.RMU can be used when this function is used several times with the same data.

Usage

# S3 method for fitRMU
plot(
  x,
  ...,
  resultMCMC = NULL,
  chain = 1,
  replicate.CI = 10000,
  CI.RMU = NULL,
  what = "proportions",
  criteria = "50%",
  aggregate = "both",
  order = NULL,
  control.legend = list(),
  show.legend = TRUE,
  col = rainbow,
  border = NA,
  names.legend = NULL
)

Value

Return A list with result of CI.RMU()

Arguments

x: A result file generated by fitRMU
...: Parameters used by plot
resultMCMC: MCMC result for fitRUM
chain: Chain to be plotted for MCMC
replicate.CI: Number of replicates to estimate CI
CI.RMU: A result of CI.RMU()
what: Can be proportions, numbers or total
criteria: What criteria will be used for proportions or numbers: mean or 50%
aggregate: Can be model or both
order: Give the order of series in plot, from bottom to top. Can be used to not show series.
control.legend: Parameters send to legend
show.legend: If FALSE, does not show legend
col: The function used to generate colors.
border: The border of polygons used to represent the proportions.
names.legend: Names to show in legend.

Author

Marc Girondot

Details

plot.fitRMU plots the results of a fit RMU.

Examples

Run this code

if (FALSE) {
library("phenology")
RMU.names.AtlanticW <- data.frame(mean=c("Yalimapo.French.Guiana", 
                                         "Galibi.Suriname", 
                                         "Irakumpapy.French.Guiana"), 
                                 se=c("se_Yalimapo.French.Guiana", 
                                      "se_Galibi.Suriname", 
                                      "se_Irakumpapy.French.Guiana"), stringsAsFactors = FALSE)
data.AtlanticW <- data.frame(Year=c(1990:2000), 
      Yalimapo.French.Guiana=c(2076, 2765, 2890, 2678, NA, 
                               6542, 5678, 1243, NA, 1566, 1566),
      se_Yalimapo.French.Guiana=c(123.2, 27.7, 62.5, 126, NA, 
                                 230, 129, 167, NA, 145, 20),
      Galibi.Suriname=c(276, 275, 290, NA, 267, 
                       542, 678, NA, 243, 156, 123),
      se_Galibi.Suriname=c(22.3, 34.2, 23.2, NA, 23.2, 
                           4.3, 2.3, NA, 10.3, 10.1, 8.9),
      Irakumpapy.French.Guiana=c(1076, 1765, 1390, 1678, NA, 
                               3542, 2678, 243, NA, 566, 566),
      se_Irakumpapy.French.Guiana=c(23.2, 29.7, 22.5, 226, NA, 
                                 130, 29, 67, NA, 15, 20), stringsAsFactors = FALSE)
                           
cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
               colname.year="Year", model.trend="Constant", 
               model.SD="Zero")
expo <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
               colname.year="Year", model.trend="Exponential", 
               model.SD="Zero")
YS <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
             colname.year="Year", model.trend="Year-specific", 
             model.SD="Zero")
YS1 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
             colname.year="Year", model.trend="Year-specific", 
             model.SD="Zero", model.rookeries="First-order")
YS1_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
             colname.year="Year", model.trend="Year-specific", 
             model.SD="Constant", model.rookeries="First-order", 
             parameters=YS1$par)
YS2 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
             colname.year="Year", model.trend="Year-specific",
             model.SD="Zero", model.rookeries="Second-order", 
             parameters=YS1$par)
YS2_cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
             colname.year="Year", model.trend="Year-specific",
             model.SD="Constant", model.rookeries="Second-order", 
             parameters=YS1_cst$par)
               
compare_AIC(Constant=cst, Exponential=expo, 
YearSpecific=YS)

compare_AIC(YearSpecific_ProportionsFirstOrder_Zero=YS1,
YearSpecific_ProportionsFirstOrder_Constant=YS1_cst)

compare_AIC(YearSpecific_ProportionsConstant=YS,
           YearSpecific_ProportionsFirstOrder=YS1,
           YearSpecific_ProportionsSecondOrder=YS2)
           
compare_AIC(YearSpecific_ProportionsFirstOrder=YS1_cst,
           YearSpecific_ProportionsSecondOrder=YS2_cst)

barplot_errbar(YS1_cst$proportions[1, ], y.plus = YS1_cst$proportions.CI.0.95[1, ], 
               y.minus = YS1_cst$proportions.CI.0.05[1, ], las=1, ylim=c(0, 0.7), 
               main="Proportion of the different rookeries in the region")

plot(cst, main="Use of different beaches along the time", what="total")
plot(expo, main="Use of different beaches along the time", what="total")
plot(YS2_cst, main="Use of different beaches along the time", what="total")

plot(YS1, main="Use of different beaches along the time")
plot(YS1_cst, main="Use of different beaches along the time")
plot(YS1_cst, main="Use of different beaches along the time", what="numbers")

parpre <- par(mar=c(4, 4, 2, 5)+0.4)
par(xpd=TRUE)
plot(YS, main="Use of different beaches along the time", 
     control.legend=list(x=2000, y=0.4, legend=c("Yalimapo", "Galibi", "Irakumpapy")))
par(mar=parpre)

# Example to modify order of series
plot(cst, order=c("Galibi.Suriname", "Irakumpapy.French.Guiana"))
plot(cst, order=c("Galibi.Suriname", "Irakumpapy.French.Guiana", "Yalimapo.French.Guiana"))

# Example to change the color
plot(cst, order=c("Galibi.Suriname", "Irakumpapy.French.Guiana", "Yalimapo.French.Guiana"), 
     col=function(n) rep(c("gray", "lightgrey"), floor(n/2)), border="black", 
     names.legend=c("Yalimapo", "Galibi", "Irakumpapy"))
}

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