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predictions_one_model.R
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predictions_one_model.R
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#goal - make predictions for all params with the same model
require(here)
require(lmtest)
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_w_loom.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)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.17 #this works
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
rsq(model_lm) #0.1731
rsq.partial(model_lm)
# testing for intereaction between temp and gs with lrt
model_lm_int <- lm(sqrt(speed) ~ 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))
lrtest(model_lm,model_lm_int)
# (Intercept) 2.3527820 0.2279874 10.320 < 2e-16 ***
# temp 0.0518650 0.0252381 2.055 0.0401 *
# I(temp^2) -0.0021388 0.0006589 -3.246 0.0012 **
# log(gs, 2) 0.2284672 0.0192687 11.857 < 2e-16 ***
# loom -0.0783119 0.0189434 -4.134 3.83e-05 ***
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$speed99_975 <- results[,3]
write.csv(newData1,here("..","..","..","Documents","data","temp_collective","roi","loom_speed_99_predictions_one_model.csv"))
#acceleration during loom
my_data<-data.frame("temp" = data$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 <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1763 #this works!
# (Intercept) 4.7577198 0.1671428 28.465 < 2e-16 ***
# temp 0.0731255 0.0185027 3.952 8.23e-05 ***
# I(temp^2) -0.0025823 0.0004831 -5.346 1.09e-07 ***
# log(gs, 2) 0.1498198 0.0141263 10.606 < 2e-16 ***
# loom -0.0568259 0.0138878 -4.092 4.58e-05 ***
#
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
rsq(model_lm)
rsq.partial(model_lm)
#
# variable
# [1] "temp" "I(temp^2)" "log(gs, 2)" "loom"
#
# $partial.rsq
# [1] 0.01363420 0.02466482 0.09052954 0.01460022
#
# testing for intereaction between temp and gs with lrt
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))
lrtest(model_lm,model_lm_int) # significant
qqnorm(residuals(model_lm_int), main= "")
qqline(residuals(model_lm_int))
rsq(model_lm_int) #0.1823
rsq.partial(model_lm_int)
# $variable
# [1] "temp" "I(temp^2)" "log(gs, 2)" "loom" "temp:log(gs, 2)"
#
# $partial.rsq
# [1] 0.016541258 0.025582519 0.027135200 0.014654451 0.003755625
#
# > rsq(model_lm_int)
# [1] 0.1823035
#predictions for model without interaction
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$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_predictions_one_model.csv"))
#predictions for model with interaction
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$acc99_975 <- results[,3]
write.csv(newData1,here("..","..","..","Documents","data","temp_collective","roi","loom_acc_99_int_predictions_one_model.csv"))
#latency
my_data<-data.frame("temp" = data$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)]
)
my_new_data2 <- my_data[-c(750,752,327,695),]
model_pois6 <- glm(latency ~ temp + loom + I(temp^2) + log(gs,2), family = quasipoisson, my_new_data2)
summary(model_pois6)
plot(fitted(model_pois6),residuals(model_pois6))
rsq(model_pois6)
rsq.partial(model_pois6)
# > rsq(model_pois6)
# [1] 0.06258895
# $variable
# [1] "temp" "loom" "I(temp^2)" "log(gs, 2)"
#
# $partial.rsq
# [1] 0.017143434 0.004740903 0.016090973 0.041742987
model_pois_int <- glm(latency ~ temp + loom + I(temp^2) + log(gs,2) + temp*log(gs,2), family = quasipoisson, my_new_data2)
summary(model_pois_int)
plot(fitted(model_pois_int),residuals(model_pois_int))
rsq(model_pois_int)
rsq.partial(model_pois_int)
lrtest(model_pois6,model_pois_int)
teststat <- -2*(deviance(model_pois6)-deviance(model_pois_int))
(p.val <- pchisq(teststat, df = 1, lower.tail = FALSE)) # not significant
#predictions
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1),
gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
require(ciTools)
df_ints <- add_ci(newData1, model_pois6, names = c("lcb", "ucb"), alpha = 0.05)
write.csv(df_ints,here("..","..","..","Documents","data","temp_collective","roi","latency_predictions_one_model.csv"))
#prop ind startling
my_data<-data.frame("temp" = data$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$prop_startles)], format = "%H:%M"))
)
model_glm<- glm(prop_startles ~ temp + I(temp^2) + loom + log(gs,2), family = binomial,my_data)
summary(model_glm)
library(arm)
binnedplot(fitted(model_glm),residuals(model_glm))
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_glm))
ymod <- update(model_glm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_glm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$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.csv"))
#trying with interaction
rsq(model_glm) #0.0391
rsq.partial(model_glm)
model_glm_int<- glm(prop_startles ~ temp + I(temp^2) + loom + log(gs,2) + temp*log(gs,2), family = binomial,my_data)
summary(model_glm_int)
library(arm)
binnedplot(fitted(model_glm_int),residuals(model_glm_int))
lrtest(model_glm,model_glm_int) # not significant
#speed before loom
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_wo_loom.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)
rsq.partial(model_lm)
# [1] 0.1274204
# > rsq.partial(model_lm)
# $adjustment
# [1] FALSE
#
# $variable
# [1] "temp" "I(temp^2)" "log(gs, 2)"
#
# $partial.rsq
# [1] 2.659542e-02 1.219798e-02 1.209435e-05
model_lm_int <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2) + temp*log(gs,2),my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
lrtest(model_lm,model_lm_int) # not significant
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$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.csv"))
#pre loom acceleration
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)
#128
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),]
#128
my_new_data <- my_data[-which(abs(studentized)>t_crit),]
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)
rsq.partial(model_lm)
# > rsq(model_lm)
# [1] 0.0984856
# > rsq.partial(model_lm)
# $adjustment
# [1] FALSE
#
# $variable
# [1] "temp" "I(temp^2)" "log(gs, 2)"
#
# $partial.rsq
# [1] 0.01912525 0.01266149 0.05780389
model_lm_int <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + temp*log(gs,2),my_new_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
#r sq = 0.08
lrtest(model_lm,model_lm_int) # not significant
#predictions
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$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.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 <- lm(log(annd) ~ temp +log(gs,2) + loom + I(temp^2), my_data)
summary(model_lm_trans)
plot(fitted(model_lm_trans),residuals(model_lm_trans))
#this works
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))
#this works
lrtest(model_lm_trans,model_lm_trans_int) # significant
rsq(model_lm_trans_int)
rsq.partial(model_lm_trans_int)
# > rsq(model_lm_trans_int)
# [1] 0.4167516
# > rsq.partial(model_lm_trans_int)
# $adjustment
# [1] FALSE
#
# $variable
# [1] "temp" "log(gs, 2)" "loom" "I(temp^2)" "temp:log(gs, 2)"
#
# $partial.rsq
# [1] 1.104543e-02 1.134305e-01 2.800796e-06 3.398251e-03 6.344730e-03
#predictions
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm_trans_int))
ymod <- update(model_lm_trans_int,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm_trans_int,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$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.csv"))
# convex hull area
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))
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
rsq(model_lm)
rsq.partial(model_lm)
model_lm_int <- lm(hull^0.5 ~ temp + I(temp^2) +loom + log(gs,2) +log(gs,2)*temp, my_data)
summary(model_lm_int)
plot(fitted(model_lm_int),residuals(model_lm_int))
lrtest(model_lm,model_lm_int) # not significant
#predictions
# > rsq(model_lm)
# [1] 0.4178037
# > rsq.partial(model_lm)
# $adjustment
# [1] FALSE
#
# $variable
# [1] "temp" "I(temp^2)" "loom" "log(gs, 2)"
#
# $partial.rsq
# [1] 0.0060876909 0.0011404016 0.0004368472 0.3924370723
#predictions
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$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.csv"))
#polarization after loom
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_w_loom_pol.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$pol_975 <- results[,3]
write.csv(newData1,here("..","..","..","Documents","data","temp_collective","roi","pol_postloom_predictions_one_model.csv"))
#latency in seconds
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_w_loom.csv"),header=TRUE,na.strings=c("[nan]"))
my_data<-data.frame("temp" = data$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$latency)]/60 -10
)
my_new_data2 <- my_data[-c(750,752,327,695),]
model_lm <- lm(latency ~ temp + I(temp^2) + log(gs,2) + loom,my_new_data2)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
rsq(model_lm)
#0.0628
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$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.csv"))
#avg acc before loom
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_wo_loom.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))
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$avg_acc_975 <- results[,3]
write.csv(newData1,here("..","..","..","Documents","data","temp_collective","roi","avg_acc_preloom_predictions_one_model.csv"))
#median acc before loom
my_data<-data.frame("temp" = data$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.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
model_lm_int <- lm(log(speed+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))
#temp terms are not significant
lrtest(model_lm,model_lm_int) #not significant
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$avg_speed_975 <- results[,3]
write.csv(newData1,here("..","..","..","Documents","data","temp_collective","roi","avg_speed_preloom_predictions_one_model.csv"))
#median speed before loom
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$speed_percentile50)],
"gs" = data$Groupsize[complete.cases(data$speed_percentile50)],
"speed" = data$speed_percentile50[complete.cases(data$speed_percentile50)]
)
model_lm <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp terms are not significant
model_lm_int <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2) + temp*log(gs,2),my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
#temp terms are not significant
lrtest(model_lm,model_lm_int) #not significant
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$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.csv"))
#mean speed during predation threat
require(rsq)
require(lmtest)
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$Temperature[complete.cases(data$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) #0.0535
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm_int <- lm(log(speed+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))
#temp terms are not significant
lrtest(model_lm,model_lm_int) #not 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))
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$avg_speed_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","avg_speed_loom_predictions_one_model.csv"))
#median speed during predation threat
my_data<-data.frame("temp" = data$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$speed_percentile50)]
)
model_lm <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp terms are not significant
rsq(model_lm) #0.0344
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm_int <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2) + loom + temp*log(gs,2),my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
#temp terms are not significant
lrtest(model_lm,model_lm_int) #not significant
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), gs = c(1,2,4,8,16),
loom = c(1,2,3,4,5)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$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.csv"))
#mean acceleration during loom
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$avg_acc_975 <- results[,3]
write.csv(newData1,here("Documents","data","temp_collective","roi","average_acc_loom_predictions_one_model.csv"))
#median acceleration during loom
my_data<-data.frame("temp" = data$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
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))