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testassumptions.r
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testassumptions.r
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yearModels <- vector(mode = 'list', length = 14)
if(!exists('danger_all')) {danger_all <- readRDS("../Rscripts/DangerEstimatesUpdated/Danger_Estimates_all.rds") %>%
rename(danger = propD) %>% mutate(IntArea = Intertial / 1000**2, IntCells = IntArea ) }
test_assumption <- function(distD_, radius_, quantiles_dates, run_name, dat = acss_dat, danger_dat = danger_all)
{ lgonly_ <- TRUE
danger <- danger_all %>% filter(distD == distD_ & radius == radius_)
usearea <- ifelse(run_name == "Area Sorted PMD", TRUE, FALSE)
clippeddat <- prepareDat(dat, danger, quantiles_dates)
if(run_name == "No MAPT or GRAN") {
clippeddat <- filter(clippeddat, proportionDanger != 0 &
Site.ID != "45.72_-64.65" & Site.ID !="45.81_-64.5" )
lgonly_ = F
}
if(run_name %in% c("Date_0_20","Date_80_100","No MAPT or GRAN)")){
outlier_ = NULL
}else{outlier_ = 1995}
results_ <- calculateNewDataAnalysis(dat = clippeddat,
covars = covars, outlier = outlier_,plotOut = F, useArea = usearea, lgonly = lgonly_)
mods_ <- results_$models
yearModels <- cbind(broom::tidy(mods_$`Year linear`), confint(mods_$`Year linear`, type = 'boot')) %>% mutate(
runName = run_name,
rsq = summary(mods_$`Year linear`)$r.squared
)
return(yearModels)
}
assumptions_df <- tibble(distD_ = c(rep(150,6),300,450,50,rep(150,6)),
radius_ =c(rep(2500,9), 1000,5000, rep(2500,4)),
quantiles = list(c(0.1,0.90),c(0.20, 0.80),
c(0.05, 0.95),
c(0.025, 0.975),
c(0.005, 0.995),
c(0.0, 1.0),
c(0.1, 0.9),
c(0.1, 0.9),c(0.1, 0.9),c(0.1, 0.9),c(0.1, 0.9),c(0.0, 0.2),c(0.8, 1.0),c(0.1, 0.9),c(0.1, 0.9) ),
run_name = c("Baseline",
"Dates_20_80",
"Dates_5_95",
"Dates_25_975",
"Dates_1_99",
"All_Dates",
"Danger_300m",
"Danger_450m",
"Danger_50m",
"Radius_1k",
"Radius_5k",
"Date_0_20",
"Date_80_100",
"Area Sorted PMD",
"No MAPT or GRAN") )
# yearModels <- pmap_df(assumptions_df, test_assumption)
# baselineResults <- calculateNewDataAnalysis(dat = ACSS_Danger_150_10_90, covars = covars,plotOut = T)
# baselineModels <- baselineResults$models#$NJ_Falc_per_hr %>% summary
# yearModels[[1]] <- cbind(broom::tidy(baselineModels$`Year linear`), confint(baselineModels$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Baseline",
# rsq = summary(baselineModels$`Year linear`)$r.squared
# )
# ACSS_Danger_150_20_80 <- prepareDat(acss_dat, danger_150, c(0.20, 0.80))
# expandedDatesResults60 <- calculateNewDataAnalysis(dat = ACSS_Danger_150_20_80, covars = covars, outlier = 1995,plotOut = T)
# expandedModels_60 <- expandedDatesResults60$models
# yearModels[[2]] <- cbind(broom::tidy(expandedModels_60$`Year linear`), confint(expandedModels_60$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Dates_20_80",
# rsq = summary(expandedModels_60$`Year linear`)$r.squared
# )
# ACSS_Danger_150_5_95 <- prepareDat(acss_dat, danger_150, c(0.05, 0.95))
# expandedDatesResults90 <- calculateNewDataAnalysis(dat = ACSS_Danger_150_5_95, covars = covars, outlier = 1995,plotOut = T)
# expandedModels_90 <- expandedDatesResults90$models
# yearModels[[3]] <- cbind(broom::tidy(expandedModels_90$`Year linear`), confint(expandedModels_90$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Dates_5_95",
# rsq = summary(expandedModels_90$`Year linear`)$r.squared
# )
# ACSS_Danger_150_2.5_97.5 <- prepareDat(acss_dat, danger_150, c(0.025, 0.975))
# expandedDatesResults <- calculateNewDataAnalysis(dat = ACSS_Danger_150_2.5_97.5, covars = covars, outlier = 1995,plotOut = T)
# expandedModels <- expandedDatesResults$models
# yearModels[[4]] <- cbind(broom::tidy(expandedModels$`Year linear`), confint(expandedModels$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Dates_25_975",
# rsq = summary(expandedModels$`Year linear`)$r.squared
# )
# ACSS_Danger_150_99 <- prepareDat(acss_dat, danger_150, c(0.005, 0.995))
# expandedDatesResults_99 <- calculateNewDataAnalysis(dat = ACSS_Danger_150_99, covars = covars, outlier = 1995,plotOut = T)
# expandedModels_99 <- expandedDatesResults_99$models
# yearModels[[5]] <- cbind(broom::tidy(expandedModels_99$`Year linear`), confint(expandedModels_99$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Dates_1_99",
# rsq = summary(expandedModels_99$`Year linear`)$r.squared
# )
# ACSS_Danger_150_all <- prepareDat(acss_dat, danger_150, c(0.0, 1.0))
# AllDatesResults <- calculateNewDataAnalysis(dat = ACSS_Danger_150_all, covars = covars, outlier = 1995,plotOut = T)
# AllDatesModels <- AllDatesResults$models
# yearModels[[6]] <- cbind(broom::tidy(AllDatesModels$`Year linear`), confint(AllDatesModels$`Year linear`, type = 'boot')) %>% mutate(
# runName = "All_Dates",
# rsq = summary(AllDatesModels$`Year linear`)$r.squared
# )
# danger_300 <- danger_all %>% filter(distD == 300 & radius == 2500)
# #read.csv("./danger.r_Prism300.csv")
# ACSS_Danger_300_10_90 <- prepareDat(acss_dat, danger_300, c(0.1, 0.9))
# baselineResults_300 <- calculateNewDataAnalysis(dat = ACSS_Danger_300_10_90, covars = covars, outlier = 1995,plotOut = T)
# baselineModels_300 <- baselineResults_300$models
# yearModels[[7]] <- cbind(broom::tidy(baselineModels_300$`Year linear`), confint(baselineModels_300$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Danger_300m",
# rsq = summary(baselineModels_300$`Year linear`)$r.squared
# )
# danger_450 <- danger_all %>% filter(distD == 450 & radius == 2500)#read.csv("./danger.r_Prism450.csv")
# ACSS_Danger_450_10_90 <- prepareDat(acss_dat, danger_450, c(0.1, 0.9))
# baselineResults_450 <- calculateNewDataAnalysis(dat = ACSS_Danger_450_10_90, covars = covars, outlier = 1995,plotOut = T)
# baselineModels_450 <- baselineResults_450$models
# yearModels[[8]] <- cbind(broom::tidy(baselineModels_450$`Year linear`), confint(baselineModels_450$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Danger_450m",
# rsq = summary(baselineModels_450$`Year linear`)$r.squared
# )
# danger_50 <- danger_all %>% filter(distD == 50 & radius == 2500)##read.csv("./danger.r_Prism50.csv")
# ACSS_Danger_50_10_90 <- prepareDat(acss_dat, danger_50, c(0.1, 0.9))
# baselineResults_50 <- calculateNewDataAnalysis(dat = ACSS_Danger_50_10_90, covars = covars, outlier = 1995,plotOut = T)
# baselineModels_50 <- baselineResults_50$models
# yearModels[[9]] <- cbind(broom::tidy(baselineModels_50$`Year linear`), confint(baselineModels_50$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Danger_50m",
# rsq = summary(baselineModels_50$`Year linear`)$r.squared
# )
# danger_1000 <- danger_all %>% filter(distD == 150 & radius == 1000)##read.csv("./danger.r_Prism50.csv")
# ACSS_Danger_1k_10_90 <- prepareDat(acss_dat, danger_1000, c(0.1, 0.9))
# baselineResults_1k <- calculateNewDataAnalysis(dat = ACSS_Danger_1k_10_90, covars = covars, outlier = 1995,plotOut = T,rad_ = 1000)
# baselineModels_1k <- baselineResults_1k$models
# yearModels[[10]] <- cbind(broom::tidy(baselineModels_1k$`Year linear`), confint(baselineModels_1k$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Radius_1k",
# rsq = summary(baselineModels_1k$`Year linear`)$r.squared
# )
# danger_1000 <- danger_all %>% filter(distD == 150 & radius == 5000)##read.csv("./danger.r_Prism50.csv")
# ACSS_Danger_5k_10_90 <- prepareDat(acss_dat, danger_1000, c(0.1, 0.9))
# baselineResults_5k <- calculateNewDataAnalysis(dat = ACSS_Danger_5k_10_90, covars = covars, outlier = 1995,plotOut = T, rad_ = 5000)
# baselineModels_5k <- baselineResults_5k$models
# yearModels[[11]] <- cbind(broom::tidy(baselineModels_5k$`Year linear`), confint(baselineModels_5k$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Radius_5k",
# rsq = summary(baselineModels_5k$`Year linear`)$r.squared
# )
# ACSS_Danger_150_early <- prepareDat(acss_dat, danger_150, c(0.0, 0.2))
# baselineResults_early <- calculateNewDataAnalysis(dat = ACSS_Danger_150_early, covars = covars, outlier = NULL,plotOut = T)
# baselineModels_early <- baselineResults_early$models
# yearModels[[12]] <- cbind(broom::tidy(baselineModels_early$`Year linear`), confint(baselineModels_early$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Date_0_20",
# rsq = summary(baselineModels_early$`Year linear`)$r.squared
# )
# ACSS_Danger_150_late <- prepareDat(acss_dat, danger_150, c(0.8, 1.0))
# baselineResults_late <- calculateNewDataAnalysis(dat = ACSS_Danger_150_late, covars = covars, outlier = NULL,plotOut = T)
# baselineModels_late <- baselineResults_late$models
# yearModels[[13]] <- cbind(broom::tidy(baselineModels_late$`Year linear`), confint(baselineModels_late$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Date_80_100",
# rsq = summary(baselineModels_late$`Year linear`)$r.squared
# )
# ACSS_South_Danger_forAnalysis_noGRAN <- ACSS_Danger_150_10_90 %>%
# filter(proportionDanger != 0 &
# Site.ID != "45.72_-64.65" & Site.ID !="45.81_-64.5" )
# baselineResults_Area <- calculateNewDataAnalysis(dat = ACSS_Danger_150_10_90, covars = covars, outlier = 1995,plotOut = T,useArea = T)
# baselineModels_Area <- baselineResults_Area$models
# yearModels[[14]] <- cbind(broom::tidy(baselineModels_Area$`Year linear`), confint(baselineModels_Area$`Year linear`, type = 'boot')) %>% mutate(
# runName = "Area Sorted PMD",
# rsq = summary(baselineModels_Area$`Year linear`)$r.squared
# )
# # This should be the analysis sequence for using the mean counts and then bootstrapping the results.
# AUC.results_noLg <- calculateNewDataAnalysis(ACSS_South_Danger_forAnalysis_noGRAN, covars = covars,
# outlier = NULL, lgonly = F)
# # AUC.musthaveLg <- calculateDstats(data = filter(ACSS_South_Danger, proportionDanger !=0 & Year !=1995 &
# # Year %in% yrs.w.lg), seed = 565465, plotOut = T,
# # transform = "None", average = T,useMice = F,calcTrends = F,modelType = "lm" )
# # ggplot(AUC.results_noLg$AUC, aes(Year, aucRatio)) + geom_point() + geom_smooth(method="lm")
# noLg <- AUC.results_noLg$models$`Year linear` %>% tidy %>%
# cbind(as.data.frame(confint(AUC.results_noLg$models$`Year linear`, method='boot'))) %>%
# mutate(runName = "No MAPT or GRAN")