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all_params_one_model_corrected.R
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all_params_one_model_corrected.R
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# goal - try one model for all params.
# one model should have temp + temp^2 + groupsize + loom .
#speed during loom
require(here)
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_w_loom_corrected.csv"),header=TRUE,na.strings=c("[nan]"))
#speed during loom
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$speed_percentile99)],
"gs" = data$Groupsize[complete.cases(data$speed_percentile99)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$speed_percentile99)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$speed_percentile99)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$speed_percentile99)],
"subtrial" = data$Subtrial[complete.cases(data$speed_percentile99)],
"loom" = data$Loom[complete.cases(data$speed_percentile99)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$speed_percentile99)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$speed_percentile99)], format = "%H:%M")),
"speed" = data$speed_percentile99[complete.cases(data$speed_percentile99)]
)
model_lm <- lm(sqrt(speed) ~ temp + I(temp^2) + log(gs,2) + loom ,my_data)
summary(model_lm)
rsq(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.17 #this works
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
require(rsq)
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$speed99 <- results[,1]
newData1$speed99_025 <- results[,2]
newData1$speed99_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","loom_speed_99_predictions_one_model_corrected.csv"))
#acc during loom
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$acc_percentile99)],
"gs" = data$Groupsize[complete.cases(data$acc_percentile99)],
"loom" = data$Loom[complete.cases(data$acc_percentile99)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$acc_percentile99)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$acc_percentile99)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$acc_percentile99)],
"subtrial" = data$Subtrial[complete.cases(data$acc_percentile99)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$acc_percentile99)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$acc_percentile99)], format = "%H:%M")),
"acc" = data$acc_percentile99[complete.cases(data$acc_percentile99)],
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$acc_percentile99)], format = "%H:%M")) - as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$acc_percentile99)], format = "%H:%M"))
)
model_lm_int <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + loom + temp*log(gs,2),my_data)
summary(model_lm_int)
rsq(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm_int))
ymod <- update(model_lm_int,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm_int,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$acc99 <- results[,1]
newData1$acc99_025 <- results[,2]
newData1$acc99_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","loom_acc_99_int_predictions_one_model_corrected.csv"))
#latency
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$latency)],
"gs" = data$Groupsize[complete.cases(data$latency)],
"loom" = data$Loom[complete.cases(data$latency)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$latency)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$latency)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$latency)],
"subtrial" = data$Subtrial[complete.cases(data$latency)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$latency)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$latency)], format = "%H:%M")),
"latency" = data$latency[complete.cases(data$latency)]/60 -10
)
#my_new_data2 <-my_data
my_new_data2 <- my_data[-c(743,741,323,686),]
model_lm <- lm(latency ~ temp + I(temp^2) + log(gs,2) + loom,my_new_data2)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
rsq(model_lm)
#0.0628
model_lm_int <- lm(latency ~ temp + I(temp^2) + log(gs,2) + loom + temp*log(gs,2),my_new_data2)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
rsq(model_lm_int)
#0.0628
lrtest(model_lm,model_lm_int) # not significant
library(faraway)
h <- hatvalues(model_lm)
halfnorm(h)
two.p.n <- 2*sum(h)/length(h)
abline(a=two.p.n,b=0)
studentized <- rstudent(model_lm)
halfnorm(studentized)
nn <- length(my_data$latency)
halfnorm(cooks <- cooks.distance(model_lm),5)
t_crit <- qt(0.025/nn,nn-5-1,lower.tail = FALSE)
my_data[which(abs(studentized)>t_crit),]
# #looking at non corrected data
# data <- read.csv(here("Documents","data","temp_collective","roi","all_params_w_loom.csv"),header=TRUE,na.strings=c("[nan]"))
#
# my_data<-data.frame("temp" = data$Temperature[complete.cases(data$latency)],
# "gs" = data$Groupsize[complete.cases(data$latency)],
# "loom" = data$Loom[complete.cases(data$latency)],
# "kt"=1/(0.00008617*(data$Temperature[complete.cases(data$latency)]+273.1)),
# "date"=as.numeric(as.Date(data$Date[complete.cases(data$latency)], format = "%d/%m/%Y")),
# "trial" = data$Trial[complete.cases(data$latency)],
# "subtrial" = data$Subtrial[complete.cases(data$latency)],
# "t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$latency)], format = "%H:%M")),
# "t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$latency)], format = "%H:%M")),
# "latency" = data$latency[complete.cases(data$latency)]/60 -10
# )
# my_new_data2 <-my_data
#
# model_lm <- lm(latency ~ temp + I(temp^2) + log(gs,2) + loom,my_new_data2)
# summary(model_lm)
# plot(fitted(model_lm), residuals(model_lm))
# rsq(model_lm)
# #0.0628
#
# library(faraway)
#
# h <- hatvalues(model_lm)
#
# halfnorm(h)
#
# two.p.n <- 2*sum(h)/length(h)
#
# abline(a=two.p.n,b=0)
#
# studentized <- rstudent(model_lm)
# halfnorm(studentized)
#
# nn <- length(my_data$latency)
# halfnorm(cooks <- cooks.distance(model_lm),5)
# t_crit <- qt(0.025/nn,nn-5-1,lower.tail = FALSE)
# my_data[which(abs(studentized)>t_crit),]
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$latency <- results[,1]
newData1$latency_025 <- results[,2]
newData1$latency_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","latency_seconds_predictions_one_model_corrected.csv"))
#prop ind startling
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$prop_startles)],
"gs" = data$Groupsize[complete.cases(data$prop_startles)],
"loom" = data$Loom[complete.cases(data$prop_startles)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$prop_startles)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$prop_startles)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$prop_startles)],
"subtrial" = data$Subtrial[complete.cases(data$prop_startles)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$prop_startles)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$prop_startles)], format = "%H:%M")),
"prop_startles" = data$prop_startles[complete.cases(data$prop_startles)],
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$prop_startles)], format = "%H:%M")) - as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$prop_startles)], format = "%H:%M"))
)
model_glm<- glm(prop_startles ~ temp + I(temp^2) + loom + log(gs,2), family = binomial,my_data)
summary(model_glm)
rsq(model_glm)
library(arm)
binnedplot(fitted(model_glm),residuals(model_glm))
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_glm))
ymod <- update(model_glm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_glm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$prop_startles <- results[,1]
newData1$prop_startles025 <- results[,2]
newData1$prop_startles975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","prop_startles_predictions_one_model_corrected.csv"))
#annd after loom
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_w_loom.csv"),header=TRUE,na.strings=c("[nan]"))
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$annd)],
"gs" = data$Groupsize[complete.cases(data$annd)],
"loom" = data$Loom[complete.cases(data$annd)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$annd)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$annd)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$annd)],
"subtrial" = data$Subtrial[complete.cases(data$annd)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$annd)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$annd)], format = "%H:%M")),
"annd" = data$annd[complete.cases(data$annd)]
)
model_lm_trans_int <- lm(log(annd) ~ temp +log(gs,2) + loom + I(temp^2) + log(gs,2)*temp, my_data)
summary(model_lm_trans_int)
plot(fitted(model_lm_trans_int),residuals(model_lm_trans_int))
rsq(model_lm_trans_int)
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm_trans_int))
ymod <- update(model_lm_trans_int,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm_trans_int,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$annd <- results[,1]
newData1$annd_025 <- results[,2]
newData1$annd_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","annd_predictions_one_model_corrected.csv"))
#convex hull area after loom
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$convex_hull_area)],
"gs" = data$Groupsize[complete.cases(data$convex_hull_area)],
"loom" = data$Loom[complete.cases(data$convex_hull_area)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$convex_hull_area)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$convex_hull_area)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$convex_hull_area)],
"subtrial" = data$Subtrial[complete.cases(data$convex_hull_area)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$convex_hull_area)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$convex_hull_area)], format = "%H:%M")),
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$convex_hull_area)], format = "%H:%M")) -
as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$convex_hull_area)], format = "%H:%M")),
"hull" = data$convex_hull_area[complete.cases(data$convex_hull_area)]
)
model_lm <- lm(hull^0.5 ~ temp + I(temp^2) +loom + log(gs,2) , my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
rsq(model_lm)
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$hull <- results[,1]
newData1$hull_025 <- results[,2]
newData1$hull_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","hull_predictions_one_model_corrected.csv"))
#polarization after loom
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_w_loom_pol_corrected.csv"),header=TRUE,na.strings=c("[nan]"))
# make new dataframe
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$polarization_1_postloom)],
"gs" = data$Groupsize[complete.cases(data$polarization_1_postloom)],
"loom" = data$Loom[complete.cases(data$polarization_1_postloom)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$polarization_1_postloom)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$polarization_1_postloom)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$polarization_1_postloom)],
"subtrial" = data$Subtrial[complete.cases(data$polarization_1_postloom)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$polarization_1_postloom)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$polarization_1_postloom)], format = "%H:%M")),
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$polarization_1_postloom)], format = "%H:%M")) -
as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$polarization_1_postloom)], format = "%H:%M")),
"pol" = data$polarization_1_postloom[complete.cases(data$polarization_1_postloom)]
)
model_lm <- lm(sqrt(abs(pol)) ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#rsq 0.038 #temp and temp^2 is significant
rsq(model_lm)
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$pol <- results[,1]
newData1$pol_025 <- results[,2]
newData1$pol_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","pol_postloom_predictions_one_model_corrected.csv"))
#speed before loom
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_wo_loom_corrected.csv"),header=TRUE,na.strings=c("[nan]"))
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$speed_percentile99)],
"gs" = data$Groupsize[complete.cases(data$speed_percentile99)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$speed_percentile99)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$speed_percentile99)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$speed_percentile99)],
"subtrial" = data$Subtrial[complete.cases(data$speed_percentile99)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$speed_percentile99)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$speed_percentile99)], format = "%H:%M")),
"speed" = data$speed_percentile99[complete.cases(data$speed_percentile99)]
)
model_lm <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#0.1157
rsq(model_lm)
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$speed99 <- results[,1]
newData1$speed99_025 <- results[,2]
newData1$speed99_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","preloom_speed_99_predictions_one_model_corrected.csv"))
#preloom acc
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$acc_percentile99)],
"gs" = data$Groupsize[complete.cases(data$acc_percentile99)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$acc_percentile99)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$acc_percentile99)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$acc_percentile99)],
"subtrial" = data$Subtrial[complete.cases(data$acc_percentile99)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$acc_percentile99)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$acc_percentile99)], format = "%H:%M")),
"acc" = data$acc_percentile99[complete.cases(data$acc_percentile99)],
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$acc_percentile99)], format = "%H:%M")) - as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$acc_percentile99)], format = "%H:%M"))
)
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.09
studentized <- rstudent(model_lm)
halfnorm(studentized)
#126
halfnorm(cooks <- cooks.distance(model_lm),5)
nn <- length(my_data$acc)
t_crit <- qt(0.025/nn,nn-4-1,lower.tail = FALSE)
my_data[which(abs(studentized)>t_crit),]
#126
my_new_data <- my_data[-126,]
model_lm<- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2),my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.09
rsq(model_lm)
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$acc99 <- results[,1]
newData1$acc99_025 <- results[,2]
newData1$acc99_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","preloom_acc_99_predictions_one_model_corrected.csv"))
#annd during unperturbed swimming
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_wo_loom_corrected.csv"),header=TRUE,na.strings=c("[nan]"))
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$annd)],
"gs" = data$Groupsize[complete.cases(data$annd)],
"annd" = data$annd[complete.cases(data$annd)]
)
model_lm <- lm(log(annd) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp and temp^2 not significant
rsq(model_lm)
#preloom hull
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_wo_loom_corrected.csv"),header=TRUE,na.strings=c("[nan]"))
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$convex_hull_area)],
"gs" = data$Groupsize[complete.cases(data$convex_hull_area)],
"hull" = data$convex_hull_area[complete.cases(data$convex_hull_area)]
)
model_lm <- lm(sqrt(hull) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp and temp^2 not significant
rsq(model_lm)
#pol before loom
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_wo_loom_pol_corrected.csv"),header=TRUE,na.strings=c("[nan]"))
# make new dataframe
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$polarization_1)],
"gs" = data$Groupsize[complete.cases(data$polarization_1)],
"pol" = data$polarization_1[complete.cases(data$polarization_1)]
)
model_lm <- lm(sqrt(abs(pol)) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp and temp^2 not significant
#appendix figures
#avg acc
require(here)
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_wo_loom_corrected.csv"),header=TRUE,na.strings=c("[nan]"))
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$avg_acc)],
"gs" = data$Groupsize[complete.cases(data$avg_acc)],
"acc" = data$avg_acc[complete.cases(data$avg_acc)]
)
model_lm_int <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + temp*log(gs,2),my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
my_new_data <- my_data[-126,]
model_lm_int <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + temp*log(gs,2),my_new_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm_int))
ymod <- update(model_lm_int,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm_int,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$avg_acc <- results[,1]
newData1$avg_acc_025 <- results[,2]
newData1$avg_acc_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","avg_acc_preloom_predictions_one_model_corrected.csv"))
#median acc
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$acc_percentile50)],
"gs" = data$Groupsize[complete.cases(data$acc_percentile50)],
"acc" = data$acc_percentile50[complete.cases(data$acc_percentile50)]
)
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$acc50<- results[,1]
newData1$acc50_025 <- results[,2]
newData1$acc50_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","median_acc_preloom_predictions_one_model_corrected.csv"))
# mean speed before loom
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$avg_speed)],
"gs" = data$Groupsize[complete.cases(data$avg_speed)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$avg_speed)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$avg_speed)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$avg_speed)],
"subtrial" = data$Subtrial[complete.cases(data$avg_speed)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$avg_speed)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$avg_speed)], format = "%H:%M")),
"speed" = data$avg_speed[complete.cases(data$avg_speed)]
)
model_lm <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp terms are not significant
rsq(model_lm)
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$avg_speed<- results[,1]
newData1$avg_speed_025 <- results[,2]
newData1$avg_speed_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","avg_speed_preloom_predictions_one_model_corrected.csv"))
#median speed before loom
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$speed_percentile50)],
"gs" = data$Groupsize[complete.cases(data$speed_percentile50)],
"speed" = data$speed_percentile50[complete.cases(data$speed_percentile50)]
)
model_lm <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp terms are not significant
rsq(model_lm)
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$speed50<- results[,1]
newData1$speed50_025 <- results[,2]
newData1$speed50_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","median_speed_preloom_predictions_one_model_corrected.csv"))
#mean speed during predation threat
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_w_loom_corrected.csv"),header=TRUE,na.strings=c("[nan]"))
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$avg_speed)],
"gs" = data$Groupsize[complete.cases(data$avg_speed)],
"loom" = data$Loom[complete.cases(data$avg_speed)],
"speed" = data$avg_speed[complete.cases(data$avg_speed)]
)
model_lm <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp terms are not significant
rsq(model_lm) #0.053
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$avg_speed<- results[,1]
newData1$avg_speed_025 <- results[,2]
newData1$avg_speed_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","avg_speed_loom_predictions_one_model_corrected.csv"))
#median speed during predation threat
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$speed_percentile50)],
"gs" = data$Groupsize[complete.cases(data$speed_percentile50)],
"loom" = data$Loom[complete.cases(data$speed_percentile50)],
"speed" = data$speed_percentile50[complete.cases(data$speed_percentile50)]
)
model_lm <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp terms are not significant
rsq(model_lm)
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$speed50<- results[,1]
newData1$speed50_025 <- results[,2]
newData1$speed50_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","median_speed_loom_predictions_one_model_corrected.csv"))
#avg acc
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$avg_acc)],
"gs" = data$Groupsize[complete.cases(data$avg_acc)],
"loom" = data$Loom[complete.cases(data$avg_acc)],
"acc" = data$avg_acc[complete.cases(data$avg_acc)]
)
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
rsq(model_lm) #0.0797
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm_int <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + loom + temp*log(gs,2),my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
rsq(model_lm_int) #0.0871
lrtest(model_lm,model_lm_int) # significant
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm_int))
ymod <- update(model_lm_int,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm_int,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$avg_acc<- results[,1]
newData1$avg_acc_025 <- results[,2]
newData1$avg_acc_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","average_acc_loom_predictions_one_model_corrected.csv"))
#median acceleration during loom
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$acc_percentile50)],
"gs" = data$Groupsize[complete.cases(data$acc_percentile50)],
"loom" = data$Loom[complete.cases(data$acc_percentile50)],
"acc" = data$acc_percentile50[complete.cases(data$acc_percentile50)]
)
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
rsq(model_lm) #0.0826
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$acc50<- results[,1]
newData1$acc50_025 <- results[,2]
newData1$acc50_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","median_acc_loom_predictions_one_model_corrected.csv"))