phenology (version 5.1)

fitRMU: Adjust incomplete timeseries with various constraints.

Description

The data must be a data.frame with the first column being years and two columns for each beach: the average and the se for the estimate. The correspondance between mean and se for each rookery are given in the RMU.names data.frame. In the result list, the mean proportions for each rookeries are in $proportions, $proportions.CI.0.05 and $proportions.CI.0.95. The names of beach columns must not begin by T_, SD_, a0_, a1_ or a2_ and cannot be r. A RMU is the acronyme for Regional Managment Unit. See: Wallace, B.P., DiMatteo, A.D., Hurley, B.J., Finkbeiner, E.M., Bolten, A.B., Chaloupka, M.Y., Hutchinson, B.J., Abreu-Grobois, F.A., Amorocho, D., Bjorndal, K.A., Bourjea, J., Bowen, B.W., Dueñas, R.B., Casale, P., Choudhury, B.C., Costa, A., Dutton, P.H., Fallabrino, A., Girard, A., Girondot, M., Godfrey, M.H., Hamann, M., López-Mendilaharsu, M., Marcovaldi, M.A., Mortimer, J.A., Musick, J.A., Nel, R., Seminoff, J.A., Troëng, S., Witherington, B., Mast, R.B., 2010. Regional management units for marine turtles: a novel framework for prioritizing conservation and research across multiple scales. PLoS One 5, e15465. Standard deviation for each value is supposed to be additive based on both the observed SE (in the RMU.data object) and a constant value dependent on the rookery if model.SD is equal to Constant. This value is fixed to zero for model.SD="Zero". The support for optimx is still experimental. It has been fully tested only with BFGS and Nelder-Mead method. Several methods can be used in a single run for optimx. In this case the best likelihood is used.

Usage

fitRMU(RMU.data = stop("data parameter must be provided"), RMU.names = NULL, model.trend = "Constant", model.rookeries = "Constant", model.SD = "Zero", parameters = NULL, parametersfixed = NULL, SE = NULL, method = "BFGS", replicate.CI = 1000, colname.year = "Year", control = list(trace = 1, REPORT = 100, maxit = 500), optim = "optim", maxL = 1e+09)

Arguments

RMU.data

A data.frame with a column Year (the name is defined in colname.year) and two columns per rookery defined in RMU.names

RMU.names

A dataframe with two columns indicating name of columns for mean and standard error for roockerys

model.trend

Can be Constant, Exponential or Year-specific

model.rookeries

Description temporal change in rookeries proportion. It be Constant, First-order or Second-order

model.SD

Can be Zero or Constant. See description.

parameters

Parameters to fit

parametersfixed

Parameters that are fixed

SE

Parameters SE for example from fitRMU_MHmcmc()

method

Method of optimization, default is BFGS

replicate.CI

Number of replicates to estimate CI of proportion for each rookery

colname.year

Name of the column to be used as time index

control

List for control parameters of optim

optim

Function to be called for optimization, default is optim but it could be optimx

maxL

If an error is produced during the estimation of likelihood, replace -Ln L by this value

Value

Return a list with the results from optim and synthesis for proportions and numbers

Details

fitRMU is used to estimate missing information when several linked values are observed along a timeseries

See Also

Other Fill gaps in RMU: fitRMU_MHmcmc_p, fitRMU_MHmcmc, logLik.fitRMU, plot.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")
# cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
#                colname.year="Year", model.trend="Constant", 
#                model.SD="Zero", 
#                control=list(trace=1, REPORT=100, maxit=500, parscale = c(3000, -0.2, 0.6)))
#                
# # Example with optimx
# require("optimx")
# cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
#                colname.year="Year", model.trend="Constant", 
#                model.SD="Zero", optim="optimx", method=c("Nelder-Mead","BFGS"), 
#                control = list(trace = 0, REPORT = 100, maxit = 500, 
#                parscale = c(3000, -0.2, 0.6)))
# expo <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
#                colname.year="Year", model.trend="Exponential", 
#                model.SD="Zero", optim="optimx", method=c("Nelder-Mead","BFGS"), 
#                control = list(trace = 0, REPORT = 100, maxit = 500, 
#                parscale = c(6000, -0.05, -0.25, 0.6)))
# YS <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
#              colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), 
#              optim="optimx", model.SD="Zero")
# YS1 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
#              colname.year="Year", model.trend="Year-specific", method=c("Nelder-Mead","BFGS"), 
#              optim="optimx", 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", 
#              optim="optimx", parameters=YS1$par, method=c("Nelder-Mead","BFGS"))
# YS2 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, 
#              colname.year="Year", model.trend="Year-specific",
#              model.SD="Zero", model.rookeries="Second-order", 
#              optim="optimx", parameters=YS1$par, method=c("Nelder-Mead","BFGS"))
# 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", 
#              optim="optimx", parameters=YS1_cst$par, method=c("Nelder-Mead","BFGS"))
#                
# compare_AIC(Constant=cst, Exponential=expo, 
# YearSpecific=YS)
# 
# compare_AIC(YearSpecific_ProportionsFirstOrder_Zero=YS1,
# YearSpecific_ProportionsFirstOrder_Constant=YS1_cst)
# 
# compare_AIC(YearSpecific_ProportionsConstant=YS,
#            YearSpecific_ProportionsFirstOrder=YS1,
#            YearSpecific_ProportionsSecondOrder=YS2)
#            
# compare_AIC(YearSpecific_ProportionsFirstOrder=YS1_cst,
#            YearSpecific_ProportionsSecondOrder=YS2_cst)
# 
# barplot_errbar(YS1_cst$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")
# 
# ## End(Not run)