plot.fitRMU: Plot the synthesis of RMU fit.

In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

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 class '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
)

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.

Details

plot.fitRMU plots the results of a fit RMU.

Value

Return A list with result of CI.RMU()

Author(s)

Marc Girondot

See Also

Other Fill gaps in RMU: CI.RMU(), fitRMU_MHmcmc_p(), fitRMU_MHmcmc(), fitRMU(), logLik.fitRMU()

Examples

## Not run: 
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"))

## End(Not run)

phenology documentation built on April 3, 2023, 5:11 p.m.