# Plot the synthesis of RMU fit. 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. ```r ## 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. ## Returns Return A list with result of CI.RMU() ## Details plot.fitRMU plots the results of a fit RMU. ## Examples ```r ## 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) ``` ## See Also Other Fill gaps in RMU: `CI.RMU()`, `fitRMU()`, `fitRMU_MHmcmc()`, `fitRMU_MHmcmc_p()`, `logLik.fitRMU()` ## Author(s) Marc Girondot

plot.fitRMU function