phenology (version 7.3)

plot.fitRMU: Plot the synthesis of RMU fit.

Description

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

Usage

# S3 method for fitRMU
plot(x, ..., what = "proportions", aggregate = "both",
  order = NULL, control.legend = list(), show.legend = TRUE)

Arguments

x

A result file generated by fitRMU

...

Parameters used by plot

what

Can be proportions, numbers or total

aggregate

Can be model of 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

Value

Return Nothing

Details

plot.fitRMU plots the results of a fit RMU.

See Also

Other Fill gaps in 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"))
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))
                           
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"))
# }