## ----setup, include = FALSE--------------------------------------------------- require(rmarkdown) require(knitr) knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) rm(list = ls()) library(GenEst) vers <- packageVersion("GenEst") today <- Sys.Date() set.seed(951) ## ----------------------------------------------------------------------------- library(GenEst) data(solar_PV) names(solar_PV) ## ----pk data------------------------------------------------------------------ data_SE <- solar_PV$SE head(data_SE) ## ----pk one model------------------------------------------------------------- SE_model <- pkm(p ~ 1, k ~ 1, data = data_SE) SE_model ## ----pk two models------------------------------------------------------------ SE_model_set <- pkm(p~Season, k~1, data = data_SE, allCombos = TRUE) class(SE_model_set) length(SE_model_set) names(SE_model_set) class(SE_model_set[[1]]) ## ----pk set AICc-------------------------------------------------------------- aicc(SE_model_set) ## ----pk size set-------------------------------------------------------------- SE_size_model <- pkm(p ~ Season, k ~ 1, sizeCol = "Size", data = data_SE) class(SE_size_model) names(SE_size_model) # A list is created with a model set per size class. class(SE_size_model$small) names(SE_size_model$small) # Each model set contains one model in this case. ## ----------------------------------------------------------------------------- SE_size_model_set <- pkm(p ~ Season, k ~ 1, sizeCol = "Size", data = data_SE, allCombos = TRUE) aicc(SE_size_model_set) SE_models <- list() ## ----------------------------------------------------------------------------- SE_models$small <- SE_size_model_set$small[[2]] ## ----pk size Medium----------------------------------------------------------- SE_models$med <- SE_size_model_set$med[[2]] ## ----pk Size Large------------------------------------------------------------ SE_models$lrg <- SE_size_model_set$lrg[[1]] ## ----cp data------------------------------------------------------------------ data_CP <- solar_PV$CP head(data_CP) ## ----cp----------------------------------------------------------------------- cpm(l ~ Season, s ~ 1, data = data_CP, left = "LastPresent", right = "FirstAbsent", dist = "weibull") ## ----cp set------------------------------------------------------------------- CP_weibull_set <- cpm(l ~ Season, s ~ 1, data = data_CP, left = "LastPresent", right = "FirstAbsent", dist = "weibull", allCombos = TRUE) class(CP_weibull_set) aicc(CP_weibull_set) ## ----cp Size Set-------------------------------------------------------------- CP_size_model_set <- cpm(formula_l = l ~ Season, formula_s = s ~ 1, left = "LastPresent", right = "FirstAbsent", dist = c("exponential", "weibull"), sizeCol = "Size", data = data_CP, allCombos = TRUE) class(CP_size_model_set) names(CP_size_model_set) class(CP_size_model_set$small) length(CP_size_model_set$small) names(CP_size_model_set$small) ## ----------------------------------------------------------------------------- aicc(CP_size_model_set) CP_models <- list() ## ----cp Size Small------------------------------------------------------------ CP_models$small <- CP_size_model_set$small[[4]] ## ----cp size Medium----------------------------------------------------------- CP_models$med <- CP_size_model_set$med[[4]] ## ----Size Large--------------------------------------------------------------- CP_models$lrg <- CP_size_model_set$lrg[[2]] ## ----Load CO SS and DWP------------------------------------------------------- data_CO <- solar_PV$CO head(data_CO) ## ----SS Data------------------------------------------------------------------ data_SS <- solar_PV$SS data_SS[1:5 , 1:10] ## ----DWP data----------------------------------------------------------------- data_DWP <- solar_PV$DWP head(data_DWP) ## ----Arrival Times, options--------------------------------------------------- Mest <- estM( nsim = 100, frac = 1, data_CO = data_CO, data_SS = data_SS, data_DWP = data_DWP, model_SE = SE_models, model_CP = CP_models, unitCol = "Unit", sizeCol = "Size", COdate = "DateFound", SSdate = "DateSearched" ) ## ---- fig.show = "hold", fig.height = 4, fig.width = 6, fig.align = 'center'---- plot(Mest) ## ----Summary - Season--------------------------------------------------------- unique(data_SS[, "Season"]) M_season <- calcSplits(M = Mest, split_SS = "Season", data_SS = data_SS, split_CO = NULL, data_CO = data_CO ) ## ----splitFull plot, fig.height = 4, fig.width = 4, fig.align = 'center'------ plot(M_season) ## ----SplitFull Summary-------------------------------------------------------- summary(M_season, CL = 0.95) ## ----Summary - Weekly--------------------------------------------------------- SSdat <- prepSS(data_SS) # Creates an object of type prepSS. schedule <- seq(from = 0, to = max(SSdat$days), by = 7) tail(schedule) ## ----Summary - Weekly Part 2, fig.height = 4, fig.width = 7, fig.align = 'center'---- M_week <- calcSplits(M = Mest, split_time = schedule, data_SS = SSdat, data_CO = data_CO ) plot(x = M_week, rate = TRUE) ## ----Summary - Unit, fig.height = 4, fig.width = 7, fig.align = 'center'------ M_unit <- calcSplits(M = Mest, split_CO = "Unit", data_CO = data_CO, data_SS = data_SS ) plot(M_unit, rate = FALSE) ## ----individual unit summary-------------------------------------------------- dim(summary(M_unit)) # only 164 arrays had observations. # A list of the arrays without observed carcasses: setdiff(paste0("Unit", 1:300), data_CO$Unit) # Create summaries for arrays Unit12 and Unit100. whichRow <- rownames(summary(M_unit)) %in% c("Unit12", "Unit100") summary(M_unit)[whichRow, ] ## ----Summary - season and species, fig.height = 5, fig.width = 3, fig.align = 'center'---- M_unit_and_species <- calcSplits(M = Mest, split_SS = c("Season"), split_CO = c("Size"), data_CO = data_CO, data_SS = data_SS ) plot(M_unit_and_species, rate = FALSE)