plot.fitRMU: Plot the synthesis of RMU fit.

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

Description

The function plot.fitRMU plots the results of fitRMU().

Usage

## S3 method for class 'fitRMU'
plot(
  x,
  ...,
  resultMCMC = NULL,
  chain = 1,
  replicate.CI = 10000,
  CI.RMU = NULL,
  what = "proportions",
  aggregate = "both",
  order = NULL,
  control.legend = list(),
  show.legend = TRUE
)

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
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

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"))

## End(Not run)

phenology documentation built on Oct. 23, 2020, 7:22 p.m.