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plasticity2.Rmd
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---
title: "Plasticity/Switching 2"
author: "Gabe Zuckerman"
date: "6/23/2021"
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
library(data.table)
library(tidyverse)
library(cluster)
library(factoextra)
library(ClusterR)
library(fpc)
library(ggalluvial)
library(knitr)
library(kableExtra)
library(usdm)
library(furrr)
library(caret)
library(lubridate)
library(Hmisc)
library(vegan)
library(DHARMa)
library(MuMIn)
#setwd("C:/Users/MiddletonLab/Desktop/Gabe/Box Sync/Elk/Plasticity1/")
setwd("~/Documents/GitHub/Plasticity1")
#where was this calculated?
distances <- fread("movementData/distanceChanges.csv")
timing <- fread("timingData/finalTiming.csv", na.strings = "")
```
Single year movements
```{r fixing residents, echo=F}
#change figures to only include elk in final dataset
distances$elk <- map_chr(distances$id, ~strsplit(.x, "_")[[1]][1])
getIndYears <- function(row) {
test <- distances[row,]
#extracting info from each row
years <- strsplit(strsplit(test$id[1], "_")[[1]], "-")[[2]]
dists <- c(test$sp1dist[1], test$sp2dist[1])
elevs <- c(test$sp1elev[1], test$sp2elev[1])
elk <- c(test$elk[1], test$elk[1])
return(data.table(elk, years, dists, elevs, herd = test$herd[1]))
}
allIndYears <- map_dfr(1:nrow(distances), getIndYears) %>% distinct(elk, years, .keep_all = T) %>%
mutate(absElev = abs(elevs)) %>% mutate(elkYear = paste(elk, years, sep = "_"))
#if an elk had no timing in a year, then changing is distance to 0
residents <- allIndYears %>% filter(elkYear %in% filter(timing, reclass == "resident")$elkYear) %>%
mutate(dists = 0, elevs = 0, absElev = 0, class = "R")
nonRes <- allIndYears %>% filter(elkYear %nin% residents$elkYear) %>% mutate(class = NA)
allIndYears <- rbind(residents, nonRes)
#residents are now at (0,0)
ggplot(allIndYears) + geom_point(aes(dists, absElev), alpha = .5) + ylab("Abosulte elevation change") +
xlab("Geographic distance") + theme_bw() + ggtitle("Figure 1.")
```
Classifying each year into tactics
```{r classify, echo=FALSE}
#change figures to only include elk in final dataset
forCluster <- allIndYears %>% filter(is.na(class)) %>%
dplyr::select(dists, absElev) %>% mutate(dists = scale(dists, center = T),
absElev = scale(absElev, center = T))
#kmeans with 3
set.seed(1)
k <- kmeans(forCluster, centers = 3, nstart = 25)
nonRes <- allIndYears %>% filter(is.na(class)) %>% mutate(class = k$cluster) %>%
merge(data.table(class = 1:3, strategy = c("LDM", "EM", "SDM"))) %>% dplyr::select(-class) %>%
rename(class = strategy)
allIndYears <- rbind(allIndYears %>% filter(class == "R"), nonRes)
library(extrafont)
font_import()
loadfonts()
p <- ggplot(allIndYears) + geom_point(aes(dists, absElev, color = class), alpha = .3) +
ylab("Abosolute elevation change (m)") + xlab("Geographic distance (m)") +
geom_text(x=25000, y=1100, label="EM (21.9%)", color = "#1b9e77", size = 4, family = "Century Gothic") +
geom_text(x=50000, y=150, label="SDM (53.7%)", color = "#e7298a", size = 4, family = "Century Gothic") +
geom_text(x = 75000, y = 1000, label = "LDM (19.6%)", color = "#d95f02", size = 4, family = "Century Gothic") +
geom_text(x = 18000, y = 0, label = "R (4.8%)", color = "#7570b3", size = 4, family = "Century Gothic") +
theme_bw() + theme(legend.position = "none", text=element_text(size=12, family = "Century Gothic"),
plot.title = element_text(hjust = 0.5)) +
scale_color_manual(values = c("#1b9e77", "#d95f02", "#7570b3", "#e7298a")) + ggtitle("Migratory Portfolio")
ggsave("/Users/gzuckerman/Desktop/test.jpg", p, dpi=600, height=9.7, width = 8.2, units = "cm")
```
animitation to show transition from dichotomy to full tactics, not used in report
```{r first day DNHM, echo = F, eval = F}
library(plotly)
set.seed(1)
resX <- rnorm(200, mean = 3000, sd = 5000)
resY <- rnorm(200, mean = 0, sd = 100)
resF1 <- data.table(dists = resX, absElev = resY, class = "R", f = 1)
sdmX <- runif(100, min = 100, max = 100)
sdmY <- runif(100, min = 0, max = 0)
sdmF1 <- data.table(dists = sdmX, absElev = sdmY, class = "SDM", f = 1)
emX <- runif(100, min = 100000, max = 100000)
emY <- runif(100, min = 1525, max = 1575)
emF1 <- data.table(dists = emX, absElev = emY, class = "EM", f = 1)
ldmX <- rnorm(490, mean = 100000, sd = 5000)
ldmY <- rnorm(490, mean = 1500, sd = 200)
ldmF1 <- data.table(dists = ldmX, absElev = ldmY, class = "LDM", f = 1)
f1 <- rbindlist(list(resF1, sdmF1, emF1, ldmF1)) %>% mutate(point = 1:890)
ani1 <- f1 %>% rbind(allIndYears %>% dplyr::select(dists, absElev, class) %>%
mutate(f = 2, point = 1:890))
ani1$class <- factor(ani1$class, levels = c("SDM", "EM", "R", "LDM"))
a <- ggplot(ani1) + geom_point(aes(dists, absElev, color = class, frame = f)) +
theme_bw() + theme(legend.position = "none") +
ylab("Abosulte elevation change (m)") + xlab("Geographic distance (m)") +
scale_color_brewer(palette = "Dark2")
ggplotly(a) %>% animation_opts(frame = 1500, transition = 1500, redraw = FALSE) %>%
add_text(x = c(c(3000, 100000),c(0,110000, 30000, 35000)),
y = c(c(0, 1500),c(-200, 500, 1800, -200)),
yshift = 100,
text = c(c("<b>Residents","<b>Migrants"),
c("<b>Residents", "<b>Long \n distance \n migrants",
"<b>Elevational migrants", "<b>Short distance \n migrants")),
frame = c(c(1, 1), c(2, 2, 2, 2)),
showlegend = FALSE,
textfont = list(family = "Century Gothic", size = 20,
color = c("black", "black", "#7570b3",
"#e7298a", "#d95f02", "#1b9e77")))
```
Multiyear movements, identifying switches
```{r repair distance, echo=FALSE}
getY2Y <- function(id){
t <- allIndYears %>% arrange(years) %>% filter(elk == id) %>%
dplyr::select(elk, years, class, dists, elevs)
year1 <- c()
year2 <- c()
strat1 <- c()
strat2 <- c()
sp1dist <- c()
sp2dist <- c()
sp1elev <- c()
sp2elev <- c()
for (i in 1:(nrow(t)-1)) {
year1 <- c(year1, t$years[i])
year2 <- c(year2, t$years[i+1])
strat1 <- c(strat1, t$class[i])
strat2 <- c(strat2, t$class[i+1])
sp1dist <- c(sp1dist, t$dists[i])
sp2dist <- c(sp2dist, t$dists[i+1])
sp1elev <- c(sp1elev, t$elevs[i])
sp2elev <- c(sp2elev, t$elevs[i+1])
}
return(data.table(id, year1, year2, strat1, strat2, sp1dist, sp2dist, sp1elev, sp2elev))
}
y2y <- map_dfr(unique(allIndYears$elk), getY2Y)
y2y$switch <- y2y$strat1 != y2y$strat2
y2y$switchID <- paste0(y2y$id, "_", y2y$year1, "-", y2y$year2)
y2y <- y2y %>% mutate(elevDiff = sp2elev - sp1elev, distanceDiff = sp2dist - sp1dist,
vecDiff =sqrt(elevDiff^2 + distanceDiff^2))
#adding herd and year
gps <- fread("Switching3/movementData/multiburstsNC.csv") %>% distinct(elkYear, startDateYear, herd)
y2y$elkYear <- paste(y2y$id, y2y$year1, sep = "_")
y2y <- y2y %>% merge(gps) %>% mutate(years = paste0(startDateYear+1, "-", startDateYear + 2)) %>%
dplyr::select(elk = id, switchID, herd, years, year1, year2, strat1, strat2, switch, sp1dist, sp2dist,
sp1elev, sp2elev, distanceDiff, elevDiff, vecDiff)
fwrite(y2y, "movementData/updatedSwitchTable.csv")
```
```{r covariates, echo = F}
#removed sand creek, no longer in scope
y2y <- fread("movementData/updatedSwitchTable.csv")
y2y <- y2y %>% filter(herd != "Sand Creek")
#winter range 2 crop prop
crop <- fread("Switching3/covariates/crop/cropProp.csv") %>% filter(p == "winter2") %>%
rename(switchID = id) %>% dplyr::select(-p, -year)
y2y <- y2y %>% merge(crop)
#winter range 2 and migratory period dev prop
dev <- fread("Switching3/covariates/development/devProp.csv")
winter2dev <- dev %>% filter(p == "winter2") %>% dplyr::select(-year, -p) %>%
rename(switchID = id)
colnames(winter2dev)[2] <- paste0("winter2", colnames(winter2dev)[2])
springMigDev <- dev %>% filter(p == "springMig") %>% dplyr::select(-year, -p) %>%
rename(switchID = id)
colnames(springMigDev)[2] <- paste0("springMig", colnames(springMigDev)[2])
y2y <- y2y %>% merge(winter2dev) %>% merge(springMigDev)
#feed ground
feed <- fread("Switching3/covariates/feed/feedAccess.csv")
winter2feed <- feed %>% filter(p == "winter2") %>% dplyr::select(-numFeedgrounds, -p) %>%
rename(switchID = id)
colnames(winter2feed)[2] <- paste0("winter2", colnames(winter2feed)[2])
y2y <- y2y %>% merge(winter2feed)
#spring green up
greenUp <- fread("Switching3/covariates/greenUp/irg.csv")
springMigGreenUp <- greenUp %>% filter(period == "springMig") %>%
dplyr::select(switchID, meanDFP, -period)
y2y <- y2y %>% merge(springMigGreenUp)
#max snow depth
snow <- fread("Switching3/covariates/snow/snodasExtraction.csv") %>% group_by(switchID, period) %>%
dplyr::summarise(meanSnow = mean(Snow_Depth_mean, na.rm = T),
varSnow = var(Snow_Depth_mean, na.rm = T),
maxSnow = max(Snow_Depth_mean, na.rm = T)) %>%
filter(period == "winter2") %>% dplyr::select(switchID, maxSnow)
y2y <- y2y %>% merge(snow)
#add herd diversity
shannonHdiversity <- function(h){
herdTable <- allIndYears %>% filter(elk %in% y2y$elk) %>% merge(
y2y %>% dplyr::select(elk, herd)
) %>% filter(herd == h)
props <- as.numeric(table(herdTable$class) / sum(table(herdTable$class)))
hSum <- 0
for(i in 1:length(props)) {
hSum <- hSum + props[i]*log(props[i])
}
return(data.table(herd = h, diversity = -1*hSum))
}
#max value is 1.386294, where each of the 4 strategies has the same number of individuals
hd <- map_dfr(unique(y2y$herd), shannonHdiversity)
y2y <- y2y %>% merge(hd, by = "herd") %>% dplyr::select(switchID, elk, herd, everything())
```
looking into balance between tactics
``` {r basic movement tactic results, echo = F, eval = F}
y2yResults <- y2y %>% mutate(elkYear1 = paste(elk, year1, sep = "_"), elkYear2 = paste(elk, year2, sep = "_"))
usedIndYears <- allIndYears %>% filter(elkYear %in% y2yResults$elkYear1 |
elkYear %in% y2yResults$elkYear2)
yearlyDist <- strategyByYear <- usedIndYears %>% merge(gps) %>%
mutate(actualYear = startDateYear + 1) %>%
group_by(actualYear, class) %>% summarise(n = n()) %>% merge(
usedIndYears %>% merge(gps) %>% mutate(actualYear = startDateYear + 1) %>%
group_by(actualYear) %>% summarise(total = n())
) %>% mutate(prop = n / total)
ggplot(yearlyDist) + geom_point(aes(actualYear, prop, color = class)) +
geom_line(aes(actualYear, prop, color = class)) +
scale_x_continuous(breaks = unique(yearlyDist$actualYear))
annualSwitching <- y2y %>% group_by(strat1, years) %>% summarise(sr = sum(as.numeric(switch))/n())
sd(filter(annualSwitching, strat1 == "SDM")$sr)
yearlyBalance <- y2y %>% filter(switch == T) %>% group_by(years, strat1, strat2) %>% summarise(n = n()) %>%
mutate(type = paste(strat1, strat2, sep = " - ")) %>% ungroup() %>% dplyr::select(-strat2, -strat1)%>%
pivot_wider(names_from = type, values_from = n)
yearlyBalance[is.na(yearlyBalance)] <- 0
yearlyBalance$totalSwitches <-rowSums(yearlyBalance[, 2:12 ])
fwrite(yearlyBalance, "balancing.csv")
```
taking lines from each of the tactics to use in figure
``` {r basic movement tactic lines for map, echo = F, eval = F}
y2yResults <- y2y %>% mutate(elkYear1 = paste(elk, year1, sep = "_"), elkYear2 = paste(elk, year2, sep = "_"))
#only one year per ind on map
usedIndYears <- allIndYears %>% filter(elkYear %in% y2yResults$elkYear1 |
elkYear %in% y2yResults$elkYear2) %>% distinct(elk, .keep_all = T)
residents <- usedIndYears %>% filter(class == "R") %>% dplyr::select(elkYear, class)
em <- usedIndYears %>% filter(class == "EM") %>% sample_n(22) %>% dplyr::select(elkYear, class)
sdm <- usedIndYears %>% filter(class == "SDM") %>% sample_n(22) %>% dplyr::select(elkYear, class)
ldm <- usedIndYears %>% filter(class == "LDM") %>% sample_n(22) %>% dplyr::select(elkYear, class)
indsForMap <- rbindlist(list(residents, em, sdm, ldm))
library(sf)
lines <- fread("../Switching3/movementData/multiburstsNC.csv") %>% filter(elkYear %in% indsForMap$elkYear) %>%
merge(indsForMap) %>% arrange(acquisition_time) %>%
st_as_sf(coords = c("longitude", "latitude"), crs = 4326) %>% group_by(elkYear, class) %>%
summarise(do_union = F) %>% st_cast("LINESTRING")
st_write(lines, "mapFigs/data", "lines", driver = "ESRI Shapefile", append = F)
```
making switching figure
``` {r alluvial, echo = F, eval = F}
forAlluv <- y2y %>% group_by(strat1, strat2) %>% dplyr::summarise(n = n()) %>% filter(strat1 != strat2)
forAlluv$strat1 <- factor(forAlluv$strat1, levels = c("R", "EM","SDM", "LDM"))
forAlluv <- forAlluv[order(forAlluv$strat1),]
#all = 105/478 = 22%
#EM: 41/108=38%
#R: 6/23=26%
#LDM: 11/86=13%
#SDM: 41/256=16%
alluv <- ggplot(forAlluv,
aes(y = n, axis1 = strat1, axis2 = strat2)) +
geom_alluvium(aes(fill = strat1), width = 1/12) +
geom_stratum(width = 1/12, fill = "black", color = "grey") +
geom_label(stat = "stratum",
aes(label = after_stat(paste0(stratum, ": ", c(17, 41, 41, 6, 21, 45, 33, 6), c("\n n = 91", "\n n = 251", "\n n = 108", "\n n = 23", "", "", "", "")))),
family = "Century Gothic") +
theme_bw() + ylab("") + xlab("") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
axis.ticks = element_blank(), axis.text = element_blank(),
panel.border = element_blank(), text=element_text(family = "Century Gothic")) +
labs(fill = "Strategy") +
scale_fill_manual(values = c("#7570b3", "#1b9e77","#e7298a", "#d95f02")) +
geom_text(x = 1.1, y = 5, label = "10", family = "Century Gothic") +
geom_text(x = 1.1, y = 14, label = "6", family = "Century Gothic") +
geom_text(x = 1.1, y = 18, label = "1", family = "Century Gothic") +
geom_text(x = 1.1, y = 22, label = "10", family = "Century Gothic") +
geom_text(x = 1.1, y = 43, label = "27", family = "Century Gothic") +
geom_text(x = 1.1, y = 57, label = "4", family = "Century Gothic") +
geom_text(x = 1.1, y = 62, label = "10", family = "Century Gothic") +
geom_text(x = 1.1, y = 82, label = "30", family = "Century Gothic") +
# geom_text(x = 1.1, y = 78, label = "1", family = "Century Gothic") +
geom_text(x = 1.1, y = 99, label = "1/1", family = "Century Gothic") +
geom_text(x = 1.1, y = 102, label = "5", family = "Century Gothic")
ggsave("/Users/gzuckerman/Desktop/alluvial.jpeg", alluv, dpi = 600)
```
``` {r switch magnitude figure, echo = F, eval = F}
y2y$switchType <- paste0(y2y$strat1, " to ", y2y$strat2)
magnitude <- ggplot(y2y) + geom_segment(aes(x = 0, y = 0, xend = distanceDiff, yend= elevDiff), alpha = .2,
arrow = arrow(length = unit(0.05, "inches"))) +
geom_point(data = y2y %>% filter(strat1 == strat2), aes(x = distanceDiff, y = elevDiff), alpha = .1, color = "black") +
geom_point(data = y2y %>% filter(strat1 != strat2), aes(x = distanceDiff, y = elevDiff, color = strat1), alpha = .5) +
#geom_point(aes(x = 0, y = 0), color = "red", size = 4, alpha = .25) +
xlab("Difference in distance from year 1 to year 2") +
ylab("Difference in elevation change from year 1 to year 2") +
geom_text(x = 40000, y = 2000, label = "Increased distance and \n elevation change \n (31.2%)", size = 3) +
geom_text(x = 40000, y = -2400, label = "Increased distance and \n decreased elevation change \n (19.2%)", size = 3) +
geom_text(x = -40000, y = -2000, label = "Decreased distance and \n elevation change \n (30.8%)", size = 3) +
geom_text(x = -40000, y = 1000, label = "Decreased distance and \n increased elevation change \n (18.8%)", size = 3) +
theme_bw() + scale_color_manual("Switches From", values = c("#1b9e77", "#d95f02", "#7570b3", "#e7298a"))
ggsave("/Users/gzuckerman/Desktop/magnitude.jpeg", magnitude, dpi = 600)
```
``` {r vifTesting, echo = FALSE, eval = FALSE}
#all two-way combinations of variables pass VIF threshold test
vars <- colnames(y2y)[17:23]
#all two way combinations of variables
combos <- combn(vars, 2) %>% t() %>% as.data.table()
#testing to see if the particular var combination does not violate
#VIF threshold of 2, (Zuur, Ieno, & Elphick, 2010)
#for positive==
vifTest <- function(comboNum) {
vifs <- vif(y2y %>% filter(strat1 == "LDM") %>% dplyr::select(combos[comboNum,] %>% as.character()))
if(!is.na(all(vifs$VIF<2)) & all(vifs$VIF<2) == T){
data.table(vars = list(combos[comboNum,] %>% as.character()), passVIF = T) %>% return()
} else {
data.table(vars = list(combos[comboNum,] %>% as.character()), passVIF = F) %>% return()
}
}
map_dfr(1:21, vifTest)
```
```{r finding dist of tactics in each herd, eval = F, echo = F}
###This is where I can address arthurs comments
allYears <- rbind(y2y %>% dplyr::select(elk, year = year1, tactic = strat1, herd),
y2y %>% dplyr::select(elk, year = year2, tactic = strat2, herd))
allYears <- allYears %>% distinct(elk, year, .keep_all= T)
tacticDist <- allYears %>% group_by(herd, tactic) %>% dplyr::summarise(count = n())
##key
herdTactDistSuppTable <- tacticDist %>% spread(tactic, count) %>% merge(
tacticDist %>% group_by(herd) %>% summarise(total = sum(count))
)
write.table(herdTactDistSuppTable, file = "herdTactDistSuppTable.txt", sep = ",", quote = FALSE, row.names = F)
numberTactics <- tacticDist %>% group_by(herd) %>% dplyr::summarise(count = n()) %>% arrange(herd)
herdCounts <- allYears %>% group_by(herd) %>% dplyr::summarise(count = n()) %>% arrange(herd)
ggplot(tacticDist) + geom_bar(aes(fill = tactic, y = count, x = herd), position = "dodge", stat = "identity")
```
not enough data for resident model
```{r model glm AIC just resident, echo = F, eval = F}
#only six switches, so not enough to fit models
# y2y$winter2feedAccess <- as.character(y2y$winter2feedAccess)
# y2y$winter2feedAccess <- as.factor(y2y$winter2feedAccess)
# y2y$winter2feedAccess <- relevel(y2y$winter2feedAccess, "0")
# y2y <- y2y %>% mutate(switch = ifelse(switch == T, 1, 0))
#
# fitControl <- trainControl()
#
# #competitive, yikes!
# null <- glm(switch ~ 1, data = y2y %>% filter(strat1 == "R"), family = "binomial")
#
# y2y$switch <- as.factor(y2y$switch)
#
# #one variable models
# model1 <- train(switch ~ propCrop, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# model2 <- train(switch ~ winter2propDev, data = y2y %>% filter(strat1 == "R"), method = "glm", family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# #warning
# model3 <- train(switch ~ springMigpropDev,data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
#
# #warning
# model4 <- train(switch ~ winter2feedAccess,data = y2y %>% filter(strat1 == "R"),method = "glm",
# family = "binomial",trControl = fitControl,preProcess = c('center', 'scale'))
#
# model5 <- train(switch ~ meanDFP,data = y2y %>% filter(strat1 == "R"), method = "glm",family = "binomial",
# trControl = fitControl,preProcess = c('center', 'scale'))
#
# model6 <- train(switch ~ maxSnow,data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl,preProcess = c('center', 'scale'))
#
# model7 <- train(switch ~ diversity,data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# #warning
# model8 <- train(switch ~ propCrop + springMigpropDev, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
# #warning
# model9 <- train(switch ~ propCrop + winter2feedAccess, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# model10 <- train(switch ~ propCrop + meanDFP, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# model11 <- train(switch ~ propCrop + maxSnow, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# #warning
# model12 <- train(switch ~ propCrop*diversity, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
# #warning
# model13 <- train(switch ~ winter2propDev + springMigpropDev, data = y2y %>% filter(strat1 == "R"),method = "glm",
# family = "binomial", trControl = fitControl, preProcess = c('center', 'scale'))
# #warning
# model14 <- train(switch ~ winter2propDev + winter2feedAccess, data = y2y %>% filter(strat1 == "R"),method = "glm",
# family = "binomial",trControl = fitControl, preProcess = c('center', 'scale'))
#
#
# model15 <- train(switch ~ winter2propDev + meanDFP, data = y2y %>% filter(strat1 == "R"),method = "glm",
# family = "binomial", trControl = fitControl, preProcess = c('center', 'scale'))
#
# model16 <- train(switch ~ winter2propDev + maxSnow, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
# #warning
# model17 <- train(switch ~ winter2propDev*diversity, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
# #warning
# model18 <- train(switch ~ springMigpropDev + winter2feedAccess, data = y2y %>% filter(strat1 == "R"),method = "glm",
# family = "binomial",trControl = fitControl, preProcess = c('center', 'scale'))
#
# #warning
# model19 <- train(switch ~ springMigpropDev + meanDFP, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
# #warning
# model20 <- train(switch ~ springMigpropDev+maxSnow, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# #warning
# model21 <- train(switch ~ springMigpropDev*diversity, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
#
# #warning
# model22 <- train(switch ~ winter2feedAccess+meanDFP, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
# #warning
# model23 <- train(switch ~ winter2feedAccess+maxSnow, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
# #warning
# model24 <- train(switch ~ winter2feedAccess*diversity, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
#
# model25 <- train(switch ~ meanDFP+maxSnow, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# model26 <- train(switch ~ meanDFP*diversity, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# model27 <- train(switch ~ maxSnow*diversity, data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
#
# #models 2, 5, 6, 7, 24, 27: no 95% sig covs; 1 sig cov at 85% (model 5), as well as null model
# AICcmodavg::aictab(list(model1$finalModel, model2$finalModel, model3$finalModel, model4$finalModel,
# model5$finalModel, model6$finalModel, model7$finalModel, model8$finalModel,
# model9$finalModel, model10$finalModel, model11$finalModel, model12$finalModel,
# model13$finalModel, model14$finalModel, model15$finalModel, model16$finalModel,
# model17$finalModel, model18$finalModel, model19$finalModel, model20$finalModel,
# model21$finalModel, model22$finalModel, model23$finalModel, model24$finalModel,
# model25$finalModel, model26$finalModel, model27$finalModel))
```
```{r visualizing informative covs for residents, echo = F, eval = F}
#dont run above code before running this
#
# y2y$winter2feedAccess <- as.character(y2y$winter2feedAccess)
# y2y$winter2feedAccess <- as.factor(y2y$winter2feedAccess)
# y2y$winter2feedAccess <- relevel(y2y$winter2feedAccess, "0")
# y2y <- y2y %>% mutate(switch = ifelse(switch == T, 1, 0))
#
# fitControl <- trainControl()
#
#
# y2y$switch <- as.factor(y2y$switch)
#
# model5 <- train(switch ~ meanDFP,data = y2y %>% filter(strat1 == "R"), method = "glm",family = "binomial",
# trControl = fitControl,preProcess = c('center', 'scale'))
#
# #log-likelihood
# logLik(model5$finalModel)
#
# #examining residuals: non-significant residual pattern , basically normal
# testDispersion(model5$finalModel)
#
# yardstick::mcc_vec(as.factor(model5$trainingData$.outcome), predict(model5, model5$trainingData))
#
#
# coef <- confint(model5$finalModel, level = .85) %>% as.data.frame() %>%
# mutate(sig = ifelse((`7.5 %` < 0 & `92.5 %` < 0) | (`7.5 %` > 0 & `92.5 %` > 0), T, F))
#
# preproc <- preProcess(y2y %>% filter(strat1 == "R") %>% dplyr::select(meanDFP),
# method = c("center", "scale"))
#
# #meanDFP
# newdata1 <- with(data = model5$trainingData,
# data.frame(meanDFP = seq(min(filter(y2y, strat1 == "R")$meanDFP),
# max(filter(y2y, strat1 == "R")$meanDFP), by = 1)))
#
# newdata2 <- predict(preproc, newdata1)
#
# p <- predict(model5$finalModel, newdata = newdata2,
# interval="confidence",se.fit = T, level = 0.85, type = "response")
#
# newdata1$pred <- p$fit
# newdata1$ll <- p$fit - 1.44 * p$se.fit
# newdata1$ul <- p$fit + 1.44 * p$se.fit
#
# newdata1 <- newdata1 %>% mutate(ll = ifelse(ll < 0, 0, ll), ul = ifelse(ul > 1, 1, ul))
#
#
# meanDFP <- ggplot(newdata1) +
# geom_line(aes(x = meanDFP, y = pred), alpha = .8) +
# geom_ribbon(aes(x = meanDFP, y = pred, ymin = ll,ymax = ul), alpha = 0.2) +
# ylab("Probability of switching") +
# xlab("Mean DFP during herd-level migratory period") +
# theme_bw() + theme(legend.title = element_blank()) +
# geom_rug(data = y2y %>% filter(strat1 == "R"), aes(meanDFP)) +
# scale_x_continuous(breaks = seq(0, 80, 10)) +
# scale_y_continuous(breaks = seq(0, 1, .1)) + ylim(0, 1)
#
#
# ##########################
#
# model6 <- train(switch ~ maxSnow,data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl,preProcess = c('center', 'scale'))
#
# #examining residuals: non-significant residual pattern, fairly normal
# testDispersion(model6$finalModel)
#
# mltools::mcc(predict(model6, model6$trainingData), model6$trainingData$.outcome)
#
#
# coef <- confint(model6$finalModel, level = .85) %>% as.data.frame() %>%
# mutate(sig = ifelse((`7.5 %` < 0 & `92.5 %` < 0) | (`7.5 %` > 0 & `92.5 %` > 0), T, F))
#
# preproc <- preProcess(y2y %>% filter(strat1 == "R") %>% dplyr::select(maxSnow),
# method = c("center", "scale"))
#
# #snow
# newdata1 <- with(data = model5$trainingData,
# data.frame(maxSnow = seq(min(filter(y2y, strat1 == "R")$maxSnow),
# max(filter(y2y, strat1 == "R")$maxSnow), by = .01)))
#
# newdata2 <- predict(preproc, newdata1)
#
# p <- predict(model6$finalModel, newdata = newdata2,
# interval="confidence",se.fit = T, level = 0.85, type = "response")
#
# newdata1$pred <- p$fit
# newdata1$ll <- p$fit - 1.44 * p$se.fit
# newdata1$ul <- p$fit + 1.44 * p$se.fit
#
# newdata1 <- newdata1 %>% mutate(ll = ifelse(ll < 0, 0, ll), ul = ifelse(ul > 1, 1, ul))
#
#
# maxSnow <- ggplot(newdata1) +
# geom_line(aes(x = maxSnow, y = pred), alpha = .8) +
# geom_ribbon(aes(x = maxSnow, y = pred, ymin = ll,ymax = ul), alpha = 0.2) +
# ylab("Probability of switching") +
# xlab("Maximum snow depth in winter range (m)") +
# theme_bw() + theme(legend.title = element_blank()) +
# geom_rug(data = y2y %>% filter(strat1 == "R"), aes(maxSnow)) +
# scale_x_continuous(breaks = seq(0, 4, .5)) +
# scale_y_continuous(breaks = seq(0, 1, .2)) + ylim(0, 1)
#
#
# ##########################
#
# model7 <- train(switch ~ diversity,data = y2y %>% filter(strat1 == "R"),method = "glm",family = "binomial",
# trControl = fitControl, preProcess = c('center', 'scale'))
#
# #examining residuals: non-significant residual pattern, basically normal
# testDispersion(model7$finalModel)
#
# mltools::mcc(predict(model7, model7$trainingData), model7$trainingData$.outcome)
#
#
# coef <- confint(model7$finalModel, level = .85) %>% as.data.frame() %>%
# mutate(sig = ifelse((`7.5 %` < 0 & `92.5 %` < 0) | (`7.5 %` > 0 & `92.5 %` > 0), T, F))
#
# preproc <- preProcess(y2y %>% filter(strat1 == "R") %>% dplyr::select(diversity),
# method = c("center", "scale"))
#
# #diversity
# newdata1 <- with(data = model5$trainingData,
# data.frame(diversity = seq(min(filter(y2y, strat1 == "R")$diversity),
# max(filter(y2y, strat1 == "R")$diversity), by = 0.01)))
#
# newdata2 <- predict(preproc, newdata1)
#
# p <- predict(model7$finalModel, newdata = newdata2,
# interval="confidence",se.fit = T, level = 0.85, type = "response")
#
# newdata1$pred <- p$fit
# newdata1$ll <- p$fit - 1.44 * p$se.fit
# newdata1$ul <- p$fit + 1.44 * p$se.fit
#
# newdata1 <- newdata1 %>% mutate(ll = ifelse(ll < 0, 0, ll), ul = ifelse(ul > 1, 1, ul))
#
#
# diversity <- ggplot(newdata1) +
# geom_line(aes(x = diversity, y = pred), alpha = .8) +
# geom_ribbon(aes(x = diversity, y = pred, ymin = ll,ymax = ul), alpha = 0.2) +
# ylab("Probability of switching") +
# xlab("Herd-level diversity") +
# theme_bw() + theme(legend.title = element_blank()) +
# geom_rug(data = y2y %>% filter(strat1 == "R"), aes(diversity)) + ylim(0, 1)
#
#
#
# ggpubr::ggarrange(meanDFP, maxSnow, diversity, labels = c("A.", "B.", "C."))
```
elevational migrant model
```{r model glm AIC just EM, echo = F, eval = F}
y2y$winter2feedAccess <- as.character(y2y$winter2feedAccess)
y2y$winter2feedAccess <- as.factor(y2y$winter2feedAccess)
y2y$winter2feedAccess <- relevel(y2y$winter2feedAccess, "0")
y2y <- y2y %>% mutate(switch = ifelse(switch == T, 1, 0))
fitControl <- trainControl()
#competitive
null <- glm(switch ~ 1, data = y2y %>% filter(strat1 == "EM"), family = "binomial")
y2y$switch <- as.factor(y2y$switch)
#one variable models
model1 <- train(switch ~ propCrop, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
model2 <- train(switch ~ winter2propDev, data = y2y %>% filter(strat1 == "EM"), method = "glm", family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model3 <- train(switch ~ springMigpropDev,data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model4 <- train(switch ~ winter2feedAccess,data = y2y %>% filter(strat1 == "EM"),method = "glm",
family = "binomial",trControl = fitControl,preProcess = c('center', 'scale'))
model5 <- train(switch ~ meanDFP,data = y2y %>% filter(strat1 == "EM"), method = "glm",family = "binomial",
trControl = fitControl,preProcess = c('center', 'scale'))
model6 <- train(switch ~ maxSnow,data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl,preProcess = c('center', 'scale'))
model7 <- train(switch ~ diversity,data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#only models with diversity are interactive the rest are additive
#warning
model8 <- train(switch ~ propCrop+winter2propDev, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model9 <- train(switch ~ propCrop+springMigpropDev, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model10 <- train(switch ~ propCrop+winter2feedAccess, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
model11 <- train(switch ~ propCrop+meanDFP, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
model12 <- train(switch ~ propCrop+maxSnow, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model13 <- train(switch ~ propCrop*diversity, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model14 <- train(switch ~ winter2propDev+springMigpropDev, data = y2y %>% filter(strat1 == "EM"),method = "glm",
family = "binomial", trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model15 <- train(switch ~ winter2propDev+winter2feedAccess, data = y2y %>% filter(strat1 == "EM"),method = "glm",
family = "binomial",trControl = fitControl, preProcess = c('center', 'scale'))
model16 <- train(switch ~ winter2propDev+meanDFP, data = y2y %>% filter(strat1 == "EM"),method = "glm",
family = "binomial", trControl = fitControl, preProcess = c('center', 'scale'))
model17 <- train(switch ~ winter2propDev+maxSnow, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model18 <- train(switch ~ winter2propDev*diversity, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model19 <- train(switch ~ springMigpropDev+winter2feedAccess, data = y2y %>% filter(strat1 == "EM"),method = "glm",
family = "binomial",trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model20 <- train(switch ~ springMigpropDev+meanDFP, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model21 <- train(switch ~ springMigpropDev+maxSnow, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model22 <- train(switch ~ springMigpropDev*diversity, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model23 <- train(switch ~ winter2feedAccess+meanDFP, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model24 <- train(switch ~ winter2feedAccess+maxSnow, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#warning
model25 <- train(switch ~ winter2feedAccess*diversity, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
model26 <- train(switch ~ meanDFP+maxSnow, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
model27 <- train(switch ~ meanDFP*diversity, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
model28 <- train(switch ~ maxSnow*diversity, data = y2y %>% filter(strat1 == "EM"),method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#models 2, 3, 4, 5, 18, as well as null
AICcmodavg::aictab(list(model1$finalModel, model2$finalModel, model3$finalModel, model4$finalModel,
model5$finalModel, model6$finalModel, model7$finalModel, model8$finalModel,
model9$finalModel, model10$finalModel, model11$finalModel, model12$finalModel,
model13$finalModel, model14$finalModel, model15$finalModel, model16$finalModel,
model17$finalModel, model18$finalModel, model19$finalModel, model20$finalModel,
model21$finalModel, model22$finalModel, model23$finalModel, model24$finalModel,
model25$finalModel, model26$finalModel, model27$finalModel, model28$finalModel))
```
```{r model selection table for EM, echo = F, eval = F}
write.table(model.sel(model18$finalModel, model2$finalModel, model3$finalModel, model4$finalModel,
model5$finalModel, null),
"emModSel.txt")
```
```{r visualizing informative covs for EM, echo = F, eval = F}
y2y$winter2feedAccess <- as.character(y2y$winter2feedAccess)
y2y$winter2feedAccess <- as.factor(y2y$winter2feedAccess)
y2y$winter2feedAccess <- relevel(y2y$winter2feedAccess, "0")
y2y <- y2y %>% mutate(switch = ifelse(switch == T, 1, 0))
fitControl <- trainControl()
y2y$switch <- as.factor(y2y$switch)
model18 <- train(switch ~ winter2propDev*diversity, data = y2y %>% filter(strat1 == "EM"),
method = "glm",family = "binomial",
trControl = fitControl, preProcess = c('center', 'scale'))
#log-likelihood
logLik(model18$finalModel)
#examining residuals: non-significant residual pattern, basically normal
testDispersion(model18$finalModel)
yardstick::mcc_vec(as.factor(model18$trainingData$.outcome), predict(model18, model18$trainingData))
coef <- confint(model18$finalModel, level = .85) %>% as.data.frame() %>%
mutate(sig = ifelse((`7.5 %` < 0 & `92.5 %` < 0) | (`7.5 %` > 0 & `92.5 %` > 0), T, F))
preproc <- preProcess(y2y %>% filter(strat1 == "EM") %>% dplyr::select(winter2propDev, diversity),
method = c("center", "scale"))
#winterDev
newdata1 <- with(data = model18$trainingData,
data.frame(winter2propDev = seq(0,0.052, by = 0.001),
diversity = 0))
newdata2 <- predict(preproc, newdata1)
newdata2$diversity <- 0
newdata2$`winter2propDev:diversity` <- 0
p <- predict(model18$finalModel, newdata = newdata2,
interval="confidence",se.fit = T, level = 0.85, type = "response")
newdata1$pred <- p$fit
newdata1$ll <- p$fit - 1.44 * p$se.fit
newdata1$ul <- p$fit + 1.44 * p$se.fit
newdata1$group <- "avg."
#low diversity
minDiv <- with(data = model18$trainingData, data.frame(winter2propDev = seq(0,0.052, by = 0.001),
diversity = 0.5623351))
minDiv2 <- predict(preproc, minDiv)
minDiv2$`winter2propDev:diversity` <- minDiv2$winter2propDev*minDiv2$diversity
p <- predict(model18$finalModel, newdata = minDiv2,
interval="confidence",se.fit = T, level = 0.85, type = "response")
minDiv$pred <- p$fit
minDiv$ll <- p$fit - 1.44 * p$se.fit
minDiv$ul <- p$fit + 1.44 * p$se.fit
minDiv$group <- "low"
#high diveristy
maxDiv <- with(data = model18$trainingData, data.frame(winter2propDev = seq(0,0.052, by = 0.001),
diversity = 1.3220876))
maxDiv2 <- predict(preproc, maxDiv)
maxDiv2$`winter2propDev:diversity` <- maxDiv2$winter2propDev*maxDiv2$diversity
p <- predict(model18$finalModel, newdata = maxDiv2,
interval="confidence",se.fit = T, level = 0.85, type = "response")
maxDiv$pred <- p$fit
maxDiv$ll <- p$fit - 1.44 * p$se.fit
maxDiv$ul <- p$fit + 1.44 * p$se.fit
maxDiv$group <- "high"
#plotting with all three lines
newdata1 <- rbindlist(list(newdata1, minDiv, maxDiv)) %>%
mutate(ll = ifelse(ll<0, 0, ll), ul = ifelse(ul>1, 1, ul))
newdata1$group <- factor(newdata1$group, levels = c("low", "avg.", "high"), labels = c("Low", "Average", "High"))
emplot <- ggplot(newdata1 %>% rename(`Herd-level diversity` = group)) +
geom_line(aes(x = winter2propDev, y = pred, color = `Herd-level diversity`), alpha = .8) +
geom_ribbon(aes(x = winter2propDev, y = pred, ymin = ll,ymax = ul, fill = `Herd-level diversity`), alpha = 0.2) +
ylab("Probability of switching") +
xlab("Proportion of developed land in winter range") +
theme_bw() + ylim(0, 1) +
scale_fill_brewer(palette = "Dark2") +
geom_rug(data = y2y %>% filter(strat1 == "LDM", switch == 1), aes(winter2propDev), sides = "t") +
geom_rug(data = y2y %>% filter(strat1 == "LDM", switch == 0), aes(winter2propDev), sides = "b") +
scale_color_manual(values = c("#1b9e77", "#d95f02", "#7570b3"), guide = F)
ggsave("/Users/gzuckerman/Desktop/emplot.jpeg", emplot, dpi = 600)