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processing.R
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processing.R
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library(plotly)
library(pROC)
library(MASS)
library(pscl)
library(dplyr)
library(EnvStats)
library(fitdistrplus)
#inverse gamma function to estimate variance given sample size
invgammapdf <- function(x, n, v){
alpha <- n/2
beta <- n*v/2
return((beta^alpha/factorial(alpha-1)*(x^(-alpha-1))*exp(-1*beta/x)))
}
(beta^alpha)/factorial(alpha-1)*(0.5^(-alpha-1))*exp(-1*beta/0.5)
ndvi_raw <- read.csv("cdl_ndvivalues.csv", header = TRUE, sep = ",")
ndvi_raw$system.index <- as.character(ndvi_raw$system.index)
ndvi_raw$feat <- 0
for(i in 1:nrow(ndvi_raw)){
if(ndvi_raw$Class[i] == 0){
ndvi_raw$feat[i] <- paste(str_split(ndvi_raw$system.index[i], "_")[[1]][1:2], collapse = "")
}else{
ndvi_raw$feat[i] <- paste(str_split(ndvi_raw$system.index[i], "_")[[1]][1:3], collapse = "")
}
}
new2 <- group_by(ndvi_raw, feat)%>%summarise(Class = first(Class),
NDVI_max = median(NDVI_max),
NDVI_var = median(NDVI_variance),
NDVI_mean = median(NDVI_mean),
NDVI_disp = median(disp),
count = median(NDVI_count))
-3.33734 + ( "RATIO"*(1-"CONVEX")*-0.2876616) +(94.4642983*("Shape_Area"/(POWER("Shape_Leng",2)/4*3.14))/0.000247)< -0.4
##Shape and Landcover data from Arc, after filtering for well detection output polygons
## 1 > x < 15 acres, and falling in scrub or grassland.
NWells <- read.csv("NWell_ID_thresh.csv", header = TRUE, sep = ",")
SWells <- read.csv("SWell_ID_thresh.csv", header = TRUE, sep = ",")
nwe <- read.csv("NewWellEval.csv", header = TRUE, sep = ",")
nwe$X <- NULL
nwe$X.1 <- NULL
nwe <- nwe[,c(3,6,7,8,9,10)]
nwe$RATIO <- 1-(nwe$POS)/nwe$NEG
cde <- read.csv("ChDetEval.csv", header = TRUE, sep = ",")
cde$X <- NULL
cde$X.1 <- NULL
cde <- cde[,c(3,6,7,8,9,10)]
cde$RATIO <- 1-(cde$POS)/cde$NEG
plot_ly(data = cde, x = ~1-NEG, y = ~POS, color = ~ndvi,
type = "scatter", mode = "markers",
text = ~ paste('CV:', cv, "RCV:", rcv, "NDVI:", ndvi, "NSDI:", ndsi),
hoverinfo = text)
pcc(cde[,3:6], cde[,1], nboot = 0)
src(cde[,3:6], cde[,2], nboot = 0)
rawcd <- read.csv("SRange_chngData.csv", header = TRUE, sep = ",")
rawcdN <- read.csv("NRange_chngData.csv", header = TRUE, sep = ",")
plot_ly()%>%
add_trace(data = rawcd[rawcd$Index == 1,], x = ~ mode, y = ~ndsi_z, type = "box")%>%
add_trace(data = rawcd[rawcd$Index == 0,], x = ~ mode, y = ~ndsi_z, type = "box")
layout(barmode = "group")
rawcd_stats <- read.table("SRange_ChangeStats.txt", header = TRUE, sep = "\t")
rawcd_statsN <- read.table("NRange_ChangeStats.txt", header = TRUE, sep = "\t")
rawcdN$cv_z <- (rawcdN$cv - rawcd_statsN$Min[rawcd_statsN$Metric == "cv"])/rawcd_statsN$SD[rawcd_statsN$Metric == "cv"]
rawcdN$rcv_z <- (rawcdN$rcvmax - rawcd_statsN$Min[rawcd_statsN$Metric == "rcv"])/rawcd_statsN$SD[rawcd_statsN$Metric == "rcv"]
rawcdN$ndvi_z <- (rawcdN$ndvi - rawcd_statsN$Mean[rawcd_statsN$Metric == "ndvi"])/rawcd_statsN$SD[rawcd_statsN$Metric == "ndvi"]
rawcdN$ndsi_z <- (rawcdN$ndsi - rawcd_statsN$Mean[rawcd_statsN$Metric == "ndsi"])/rawcd_statsN$SD[rawcd_statsN$Metric == "ndsi"]
ct <- ctree(Index~cv_z+rcv_z+ndvi_z+ndsi_z+mode, data = rawcd[rawcd$mode == 82,], controls = ctree_control(mincriterion = 0.95))
auc <- roc(response = goddamn$group, predictor = unlist(predict(ct, newdata = goddamn, type = "prob"))[seq(1,nrow(goddamn)*2,2)])
##LDA DOESNT PRINT INTERCEPT!!
scrub <- lda(Index ~ cv_z + rcv_z + ndvi_z + ndsi_z, data = rawcd)
scrub_pred <- predict(scrub, type = "prob")
ldahist(data = scrub_pred$x[,1], g = scrub_pred$class)
scrub_out <- data.frame("predict" = scrub_pred$class, "LD1" = scrub_pred$x[,1], "p0" = scrub_pred$posterior[,1], "p1" = scrub_pred$posterior[,2], "observed" = rawcd$Index)
scrub_out$predict <- as.numeric(as.character(scrub_out$predict))
scrub_roc <- roc(response = scrub_out$observed, predictor = scrub_out$LD1)
change_plot <- plot_ly(data = as.data.frame(scrub_roc[2:4]),
y = ~sensitivities, x = ~ 1-specificities,
type = "scatter", mode = "lines", name = "Change metrics",
line = list(width = 3),
text = ~paste("LDA score:", round(thresholds, 3)), hoverinfo = text)%>%
add_trace(data = as.data.frame(shape_roc[2:4]),
y = ~sensitivities, x = ~ 1 - specificities,
type = "scatter", mode = "lines", name = "Shape metrics",
line = list(width = 3),
text = ~paste("LDA score:", round(thresholds, 3)), hoverinfo = text)%>%
layout(xaxis = list(title = "False Positive Rate"),
yaxis = list(title = "True Positive Rate"))
N <- lda(Index ~ cv_z + rcv_z + ndvi_z + ndsi_z, data = rawcdN)
N_pred <- predict(N, type = "prob")
ldahist(data = N_pred$x[,1], g = N_pred$class)
N_out <- data.frame("predict" = N_pred$class, "LD1" = N_pred$x[,1], "p0" = N_pred$posterior[,1], "p1" = N_pred$posterior[,2], "observed" = rawcdN$Index)
N_out$predict <- as.numeric(as.character(N_out$predict))
N_roc <- roc(response = N_out$observed, predictor = N_out$LD1)
shape_plot <- plot_ly(data = as.data.frame(shape_roc[2:4]),
y = ~sensitivities, x = ~ 1 - specificities,
type = "scatter", mode = "markers",
text = ~paste("Thresh:", thresholds), hoverinfo = text)%>%
layout(title = "b) Shape Metrics",
xaxis = list(title = "False Positive Rate"),
yaxis = list(title = "True Positive Rate"))
LSag <- read.csv(file = "KS_ag_raw.csv", header = TRUE)
LSag$ID <- as.character(LSag$ID)
LSag$Feat <- substr(LSag$ID, 3, nchar(LSag$ID))
LSag$Feat <- as.factor(LSag$Feat)
LSchange <- LSag[LSag$Season == 2 & duplicated(LSag$Feat), 9:11] - LSag[LSag$Season == 1, 9:11]
LSchange$Feat <- LSag$Feat[LSag$Season == 1]
LSchange$Class <- LSag$Class[LSag$Season == 1]
LSchange$cv <- sum((LSag[LSag$Season == 2 & duplicated(LSag$Feat), 2:7] - LSag[LSag$Season == 1, 2:7])^2)
for (i in LSag$Feat[LSag$Season == 2 & duplicated(LSag$Feat)]){
LSchange$cv[LSchange$Feat == i] <- sum((LSag[LSag$Season == 2 & LSag$Feat == i,2:7] - LSag[LSag$Season == 1 & LSag$Feat == i, 2:7])^2)
b <- LSag[LSag$Season == 2 & LSag$Feat == i, 2:7]
a <- LSag[LSag$Season == 1 & LSag$Feat == i, 2:7]
m <- c(max(b[1],a[1]), max(b[2],a[2]), max(b[3],a[3]), max(b[4],a[4]), max(b[5],a[5]), max(b[6],a[6]))
LSchange$rcvmax[LSchange$Feat == i] <- sum(((b-a)/m)^2)
}
LSchange$cv_z <- (LSchange$cv - LSchange_stats$Min[LSchange_stats$Metric == "cv"])/LSchange_stats$SD[LSchange_stats$Metric == "cv"]
LSchange$rcv_z <- (LSchange$rcvmax - LSchange_stats$Min[LSchange_stats$Metric == "rcv"])/LSchange_stats$SD[LSchange_stats$Metric == "rcv"]
LSchange$ndvi_z <- (LSchange$ndvi - LSchange_stats$Mean[LSchange_stats$Metric == "ndvi"])/LSchange_stats$SD[LSchange_stats$Metric == "ndvi"]
LSchange$ndsi_z <- (LSchange$ndsi - LSchange_stats$Mean[LSchange_stats$Metric == "ndsi"])/LSchange_stats$SD[LSchange_stats$Metric == "ndsi"]
##Intercept is -0.957484
change <- lda(Class ~ cv_z + rcv_z + ndvi_z + ndsi_z, data = LSchange)
change_pred <- predict(change, type = "prob")
ldahist(data = change_pred$x[,1], g = change_pred$class)
change_out <- data.frame("predict" = change_pred$class, "LD1" = change_pred$x[,1], "p0" = change_pred$posterior[,1], "p1" = change_pred$posterior[,2], "observed" = LSchange$Class)
change_out$predict <- as.numeric(as.character(change_out$predict))
change_roc <- roc(response = change_out$observed, predictor = change_out$LD1)
plot_ly(data = as.data.frame(change_roc[2:4]), y = ~sensitivities, x = ~ 1 - specificities, type = "scatter", text = ~paste("Thresh:", thresholds), hoverinfo = text)
AG_on <- lda(Class ~ cv + + ndvi + ndsi, data = LSag[LSag$Season == 2,])
AGon_pred <- predict(AG_on, type = "prob")
ldahist(data = AGon_pred$x[,1], g = AGon_pred$class)
AGon_out <- data.frame("predict" = AGon_pred$class, "LD1" = AGon_pred$x[,1], "p0" = AGon_pred$posterior[,1], "p1" = AGon_pred$posterior[,2], "observed" = LSag[LSag$Season == 2]$Class)
AGon_out$predict <- as.numeric(as.character(AGon_out$predict))
AGon_roc <- roc(response = AGon_out$observed, predictor = AGon_out$LD1)
plot_ly(data = as.data.frame(AGon_roc[2:4]), y = ~sensitivities, x = ~ 1 - specificities, type = "scatter", text = ~paste("Thresh:", thresholds), hoverinfo = text)
AG_off <- lda(Class ~ cv + + ndvi + ndsi, data = LSag[LSag$Season == 1,])
AGon_pred <- predict(AG_off, type = "prob")
ldahist(data = AGoff_pred$x[,1], g = AGoff_pred$class)
AGoff_out <- data.frame("predict" = AGoff_pred$class, "LD1" = AGoff_pred$x[,1], "p0" = AGoff_pred$posterior[,1], "p1" = AGoff_pred$posterior[,2], "observed" = LSag[LSag$Season == 1]$Class)
AGoff_out$predict <- as.numeric(as.character(AGoff_out$predict))
AGoff_roc <- roc(response = AGoff_out$observed, predictor = AGoff_out$LD1)
intercept <- function(input, model, prediction, i){
prediction$LD1[i] -
sum(input$cv_z[i]*model$scaling["cv_z",1], input$rcv_z[i]*model$scaling["rcv_z",1],
input$ndvi_z[i]*model$scaling["ndvi_z",1], input$ndsi_z[i]*model$scaling["ndsi_z",1])
}
distdiff <- function(d1, d2, iters){
v <- rep(NA, iters)
for(i in 1:iters){
v[i] <- d1-d2
}
d <- density(v)
pdf <- cbind(d$x, d$y/sum(d$y))
colnames(pdf) <- c("x", "y")
return(pdf)
}
#difference in best fit parametric distributions *done on variances
pdiff11 <- distdiff(rweibull(1, fit1$estimate[1], fit1$estimate[2]),
rweibull(1, fit1$estimate[1], fit1$estimate[2]),
5000)
pdiff121 <- distdiff(rweibull(1, fit1$estimate[1], fit1$estimate[2]),
rgevd(1, fit21$estimate[1], fit21$estimate[2], fit21$estimate[3]),
5000)
pdiff122 <- distdiff(rweibull(1, fit1$estimate[1], fit1$estimate[2]),
rgevd(1, fit22$estimate[1], fit22$estimate[2], fit22$estimate[3]),
5000)
pdiff123 <- distdiff(rweibull(1, fit1$estimate[1], fit1$estimate[2]),
rgevd(1, fit23$estimate[1], fit23$estimate[2], fit23$estimate[3]),
5000)
pdiff11var <- distdiff(rgamma(1, fit1var$estimate[1], fit1var$estimate[2]),
rgamma(1, fit1var$estimate[1], fit1var$estimate[2]),
5000)
pdiff31var <- distdiff(rgamma(1, fit23var$estimate[1], fit23var$estimate[2]),
rgamma(1, fit21var$estimate[1], fit21var$estimate[2]),
5000)
pdiff21var <- distdiff(rgamma(1, fit22var$estimate[1], fit22var$estimate[2]),
rgamma(1, fit21var$estimate[1], fit21var$estimate[2]),
5000)
pdiff23var <- distdiff(rgamma(1, fit22var$estimate[1], fit22var$estimate[2]),
rgamma(1, fit23var$estimate[1], fit23var$estimate[2]),
5000)
p1 <- plot_ly(ndvi_raw, type = "histogram", alpha = 0.6)%>%
add_trace(x = ~NDVI[cropland == 1 & NDVI_count > 5], name = 'corn')%>%
add_trace(x = ~NDVI[cropland == 4& NDVI_count > 5], name = 'sorghum')%>%
add_trace(x = ~NDVI[cropland == 24 & NDVI_count > 5], name = 'winter wheat')%>%
add_trace(x = ~NDVI[cropland == 61 & NDVI_count > 5], name = 'barren')%>%
add_trace(x = ~NDVI[cropland == 36 & NDVI_count > 5], name = 'alfalfa')%>%
add_trace(x = ~NDVI[cropland == 152|cropland == 176 & NDVI_count > 5], name = 'habitat')%>%
layout(barmode = "overlay")
p2 <- plot_ly(ndvi_raw, type = "histogram", alpha = 0.6)%>%
add_trace(x = ~NDVI_disp[cropland == 1 & NDVI_count > 5], name = 'corn')%>%
add_trace(x = ~NDVI_disp[cropland == 4& NDVI_count > 5], name = 'sorghum')%>%
add_trace(x = ~NDVI_disp[cropland == 24 & NDVI_count > 5], name = 'winter wheat')%>%
add_trace(x = ~NDVI_disp[cropland == 61 & NDVI_count > 5], name = 'barren')%>%
add_trace(x = ~NDVI_disp[cropland == 36 & NDVI_count > 5], name = 'alfalfa')%>%
add_trace(x = ~NDVI_disp[cropland == 152|cropland == 176 & NDVI_count > 5], name = 'habitat')%>%
layout(barmode = "overlay")
subplot(p1, p2, nrows = 2)
#difference in observed distributions of dispersion values
d_hab_hab <- distdiff2(sample(ndvi_raw$NDVI[ndvi_raw$cropland == 152
|ndvi_raw$cropland == 176
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE),
sample(ndvi_raw$NDVI[ndvi_raw$cropland == 152
|ndvi_raw$cropland == 176
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE)
)
dvar_hab_corn <- distdiff2(sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 152
|ndvi_raw$cropland == 176
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE),
sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 1
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE)
)
dvar_hab_wheat <- distdiff2(sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 152
|ndvi_raw$cropland == 176
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE),
sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 24
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE)
)
dvar_hab_gum <- distdiff2(sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 152
|ndvi_raw$cropland == 176
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE),
sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 4
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE)
)
d_hab_bare <- distdiff2(sample(ndvi_raw$NDVI[ndvi_raw$cropland == 152
|ndvi_raw$cropland == 176
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE),
sample(ndvi_raw$NDVI[ndvi_raw$cropland == 61
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE)
)
dvar_hab_alph <- distdiff2(sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 152
|ndvi_raw$cropland == 176
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE),
sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 36
& ndvi_raw$NDVI_count > 5],
5000, replace = TRUE)
)
dvar_crp_crp <- distdiff2(sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 1
|ndvi_raw$cropland == 24
|ndvi_raw$cropland == 36
|ndvi_raw$cropland == 4
& ndvi_raw$NDVI_count > 5],
20000, replace = TRUE),
sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 1
|ndvi_raw$cropland == 24
|ndvi_raw$cropland == 36
|ndvi_raw$cropland == 4
& ndvi_raw$NDVI_count > 5],
20000, replace = TRUE)
)
dvar_ag_crp <- distdiff(sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 152
|ndvi_raw$cropland == 176
& ndvi_raw$NDVI_count > 5],
1, replace = TRUE),
sample(ndvi_raw$NDVI_disp[ndvi_raw$cropland == 1
|ndvi_raw$cropland == 24
|ndvi_raw$cropland == 36
|ndvi_raw$cropland == 4
& ndvi_raw$NDVI_count > 5],
1, replace = TRUE),
20000)
p3<-plot_ly(type = "scatter", mode = "lines")%>%
#add_trace(x = ~d_crp_crp[,1], y = ~cumsum(d_crp_crp[,2]), line = list(color = "black"),
#name = "crop", showlegend = F)%>%
add_trace(x = ~d_hab_hab[,1], y = ~d_hab_hab[,2]/max(d_hab_hab[,2])*0.5,
line = list(color = "black", dash = "dash"),
name = "hab", showlegend = F)%>%
add_trace(x = ~d_hab_corn[,1], y = ~1-cumsum(d_hab_corn[,2]), line = list(color = "yellow"),
name = 'corn', showlegend = F)%>%
add_trace(x = ~d_hab_wheat[,1], y = ~1-cumsum(d_hab_wheat[,2]), line = list(color = "brown"),
name = 'wheat', showlegend = F)%>%
add_trace(x = ~d_hab_bare[,1], y = ~cumsum(d_hab_bare[,2]), line = list(color = "grey"),
name = 'bare', showlegend = F)%>%
add_trace(x = ~d_hab_alph[,1], y = ~1-cumsum(d_hab_alph[,2]), line = list(color = "green"),
name = 'alphalpha', showlegend = F)%>%
add_trace(x = ~d_hab_gum[,1], y = ~1-cumsum(d_hab_gum[,2]), line = list(color = "purple"),
name = 'sorghum', showlegend = F)
p4<-plot_ly(type = "scatter", mode = "lines")%>%
#add_trace(x = ~dvar_crp_crp[,1], y = ~cumsum(dvar_crp_crp[,2]), line = list(color = "black"),
#name = "crop")%>%
add_trace(x = ~dvar_hab_hab[,1], y = ~dvar_hab_hab[,2]/max(dvar_hab_hab[,2])*0.5,
line = list(color = "black", dash = "dash"),
name = "Habitat")%>%
add_trace(x = ~dvar_hab_corn[,1], y = ~1-cumsum(dvar_hab_corn[,2]), line = list(color = "yellow"),
name = 'Corn')%>%
add_trace(x = ~dvar_hab_wheat[,1], y = ~1-cumsum(dvar_hab_wheat[,2]), line = list(color = "brown"),
name = 'Wheat')%>%
add_trace(x = ~dvar_hab_bare[,1], y = ~cumsum(dvar_hab_bare[,2]), line = list(color = "grey"),
name = 'Bare')%>%
add_trace(x = ~dvar_hab_alph[,1], y = ~1-cumsum(dvar_hab_alph[,2]), line = list(color = "green"),
name = 'Alfalfa')%>%
add_trace(x = ~dvar_hab_gum[,1], y = ~1-cumsum(dvar_hab_gum[,2]),line = list(color = "purple"),
name = 'Sorghum')
p5<-plot_ly(type = "scatter", mode = "lines")%>%
#add_trace(x = ~d_crp_crp[,1], y = ~d_crp_crp[,2], line = list(color = "black"),
#name = "crop", showlegend = F)%>%
add_trace(x = ~d_hab_hab[,1], y = ~d_hab_hab[,2], line = list(color = "black", dash = "dash"),
name = "hab", showlegend = F)%>%
add_trace(x = ~d_hab_corn[,1], y = ~d_hab_corn[,2], line = list(color = "yellow"),
name = 'corn', showlegend = F)%>%
add_trace(x = ~d_hab_wheat[,1], y = ~d_hab_wheat[,2], line = list(color = "brown"),
name = 'wheat', showlegend = F)%>%
add_trace(x = ~d_hab_bare[,1], y = ~d_hab_bare[,2], line = list(color = "grey"),
name = 'bare', showlegend = F)%>%
add_trace(x = ~d_hab_alph[,1], y = ~d_hab_alph[,2], line = list(color = "green"),
name = 'alphalpha', showlegend = F)%>%
add_trace(x = ~d_hab_gum[,1], y = ~d_hab_gum[,2], line = list(color = "purple"),
name = 'sorghum', showlegend = F)
p6<-plot_ly(type = "scatter", mode = "lines")%>%
#add_trace(x = ~dvar_crp_crp[,1], y = ~dvar_crp_crp[,2], line = list(color = "black"),
#name = "crop", showlegend = F)%>%
add_trace(x = ~dvar_hab_hab[,1], y = ~dvar_hab_hab[,2], line = list(color = "black", dash = "dash"),
name = "hab", showlegend = F)%>%
add_trace(x = ~dvar_hab_corn[,1], y = ~dvar_hab_corn[,2], line = list(color = "yellow"),
name = 'corn', showlegend = F)%>%
add_trace(x = ~dvar_hab_wheat[,1], y = ~dvar_hab_wheat[,2], line = list(color = "brown"),
name = 'wheat', showlegend = F)%>%
add_trace(x = ~dvar_hab_bare[,1], y = ~dvar_hab_bare[,2], line = list(color = "grey"),
name = 'bare', showlegend = F)%>%
add_trace(x = ~dvar_hab_alph[,1], y = ~dvar_hab_alph[,2], line = list(color = "green"),
name = 'alphalpha', showlegend = F)%>%
add_trace(x = ~dvar_hab_gum[,1], y = ~dvar_hab_gum[,2],line = list(color = "purple"),
name = 'sorghum', showlegend = F)
subplot(p5, p6, nrows = 2)
distdiff2 <-function(d1, d2){
v <- d1-d2
d <- density(v)
pdf <- cbind(d$x, d$y/sum(d$y))
colnames(pdf) <- c("x", "y")
return(pdf)
}
auc_diff <- function(d1, d2, d3, d4, d5, val){
max(1-sum(d2[d2[,1] < val, 2]),
1-sum(d3[d3[,1] < val, 2]),
1-sum(d4[d4[,1] < val, 2]),
1-sum(d5[d5[,1] < val, 2]))*sum(d1[d1[,1] > val, 2])
}
dgev <- function(x, loc, scl, shp){
t <- if(shp != 0){
(1+(shp*((x-loc)/scl)))^(-1/shp)
}else{
exp(-(x-loc)/scl)
}
d <- (1/scl)*(t^(shp+1))*exp(-t)
return(d)
}
pgev <- function(q, loc, scl, shp){
t <- if(shp != 0){
(1+(shp*((x-loc)/scl)))^(-1/shp)
}else{
exp(-(x-loc)/scl)
}
p <- exp(-t)
}
dinvgamma <- function(x, a, b){
d <- (b^a)/gamma(a)*(x^(-a-1))*exp(-b/x)
return(d)
}
pinvgamma <- function(q, a, b){
p <- pscl::pigamma(q, a, b)
return(p)
}