# Adjust incomplete timeseries with various constraints. 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 correspondence between mean, se and density for each rookery are given in the RMU.names data.frame. This data.frame must have a column named mean, another named se and a third named density. If no sd column exists, no sd will be considered for the series and if no density column exists, it will be considered as being "dnorm" (Gaussian distribution). The aggregated number of nests and its confidence interval can be obtained using CI.RMU(). 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. Variance for each value is additive based on both the observed SE (in the RMU.data object) and a value. The value is a global constant when model.SD is "global-constant". The value is proportional to the observed number of nests when model.SD is "global-proportional" with aSD_*observed+SD_ with aSD_ and SD_ being fitted values. This value is fixed to zero when model.SD is "Zero". The value is dependent on the rookery when model.SD is equal to "Rookery-constant" or "Rookery-proportional" with a similar formula as previously described for "global". if method is NULL, it will simply return the names of required parameters. ```r fitRMU( RMU.data = stop("data parameter must be provided"), years.byrow = TRUE, RMU.names = NULL, model.trend = "Constant", model.rookeries = "Constant", model.SD = "Global-constant", parameters = NULL, fixed.parameters = NULL, SE = NULL, method = c("Nelder-Mead", "BFGS"), control = list(trace = 1), itnmax = c(1500, 1500), cptmax.optim = 100, limit.cpt.optim = 1e-05, hessian = TRUE, replicate.CI = 1000, colname.year = "Year", maxL = 1e+09 ) ``` ## Arguments - `RMU.data`: A data.frame with a column Year (the name is defined in colname.year) and one to three columns per rookery defined in RMU.names - `years.byrow`: If TRUE, the RMU.data data.frame is organized with years in rows - `RMU.names`: A dataframe with one to three columns indicating name of columns for mean, standard deviation, and distribution for roockeris - `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, Global-constant, Global-proportional or Rookery-constant. See description. - `parameters`: Parameters to fit - `fixed.parameters`: Parameters that are fixed - `SE`: Parameters SE for example from fitRMU_MHmcmc() - `method`: Methods to be used by optim() - `control`: List of controls for optim() - `itnmax`: A vector with maximum iterations for each method. - `cptmax.optim`: How many times optim can be ran when likelihood is better. - `limit.cpt.optim`: Limit to consider that likelihood is better. - `hessian`: If TRUE, the hessian matrix is calculated and then the standard error of parameters. - `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 - `maxL`: If an error is produced during the estimation of likelihood, replace -Ln L by this value ## Returns 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 ## 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"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_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), density_Yalimapo.French.Guiana=rep("dnorm", 11), 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), density_Galibi.Suriname=rep("dnorm", 11), 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), density_Irakumpapy.French.Guiana=rep("dnorm", 11) ) 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))) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero", 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", 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"), 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"), 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$par, method=c("Nelder-Mead","BFGS")) YS2 <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Year-specific", model.SD="Zero", model.rookeries="Second-order", parameters=YS1$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", 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) # Example of different types of plots plot(cst, main="Use of different beaches along the time", what="total", ylim=c(0, 4000)) plot(cst, main="Use of different beaches along the time", what = "proportions", replicate.CI=0) plot(cst, main="Use of different beaches along the time", what = "numbers", aggregate="model", ylim=c(0, 4000), replicate.CI=0) plot(cst, main="Use of different beaches along the time", what = "numbers", aggregate="both", ylim=c(0, 11000), replicate.CI=0) 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") # Gamma distribution should be used for MCMC outputs 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"), density=c("density_Yalimapo.French.Guiana", "density_Galibi.Suriname", "density_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), density_Yalimapo.French.Guiana=rep("dgamma", 11), 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), density_Galibi.Suriname=rep("dgamma", 11), 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), density_Irakumpapy.French.Guiana=rep("dgamma", 11), stringsAsFactors = FALSE ) cst <- fitRMU(RMU.data=data.AtlanticW, RMU.names=RMU.names.AtlanticW, colname.year="Year", model.trend="Constant", model.SD="Zero") ## End(Not run) ``` ## See Also Other Fill gaps in RMU: `CI.RMU()`, `fitRMU_MHmcmc()`, `fitRMU_MHmcmc_p()`, `logLik.fitRMU()`, `plot.fitRMU()` ## Author(s) Marc Girondot marc.girondot@gmail.com

fitRMU function