phenology (version 4.2.4)

plot.fitRMU: Plot the synthesis of RMU fit.

Description

The function plot.fitRMU plots the results of fitRMU(). The parameter at.legend can be bottomright, bottomleft, topleft or topright or a vector with c(x= , y= ).

Usage

## S3 method for class 'fitRMU':
plot(x, ..., what = "proportions",
  at.legend = "bottomright", legend = NULL)

Arguments

x

A result file generated by fitRMU

...

Parameters used by plot

what

Can be proportions, numbers or total

at.legend

Position of the legend

legend

Text to show as legend. If FALSE, does not show legend. If NULL, it will use the column names for mean from RMU.names.

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

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",
at.legend=c(x=2000, y=0.4), legend=c("Yalimapo", "Galibi", "Irakumpapy"))
par(mar=parpre)