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aic_all.R
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aic_all.R
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###############################################################################
########################## 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,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
#r sq =0.1127
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
extractAIC(model_lm) #-683
#this is best
model_lm <- lm(log(speed+1) ~ 1,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
#r sq =0.1127
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
extractAIC(model_lm) #-657
model_lm <- lm(log(speed+1) ~ temp + log(gs,2) ,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
extractAIC(model_lm) #-681
model_lm <- lm(log(speed+1) ~ temp + log(gs,2) + temp*log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
extractAIC(model_lm) #-680
model_lm <- lm(log(speed+1) ~ temp + log(gs,2) + temp*log(gs,2) + date,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
extractAIC(model_lm) #-678
model_lm <- lm(log(speed+1) ~ temp + log(gs,2) + temp*log(gs,2) + t,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
extractAIC(model_lm) #-678
model_lm <- lm(log(speed+1) ~ temp + log(gs,2) + temp*log(gs,2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
extractAIC(model_lm) #-678
model_lm <- lm(log(speed+1) ~ I(temp^2) ,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
extractAIC(model_lm) #-680
model_lm <- lm(log(speed+1) ~ I(temp^2) + temp ,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
extractAIC(model_lm) #-684
#2nd predictions
newData1<- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1)))
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$speed99_975 <- results[,3]
write.csv(newData1,"/home/maria/Documents/data/temp_collective/roi/speed99_before_loom_predictions_new.csv")
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$speed_percentile50)],
"gs" = data$Groupsize[complete.cases(data$speed_percentile50)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$speed_percentile50)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$speed_percentile50)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$speed_percentile50)],
"subtrial" = data$Subtrial[complete.cases(data$speed_percentile50)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$speed_percentile50)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$speed_percentile50)], format = "%H:%M")),
"speed" = data$speed_percentile50[complete.cases(data$speed_percentile50)]
)
model_lm <- lm(log(speed+1) ~ temp,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
#r sq =0.02
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
extractAIC(model_lm) #-812
#this is best
my_data<-data.frame("temp" = data$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$avg_speed)]
)
model_lm <- lm(log(speed+1) ~ temp,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
#r sq =0.04
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
extractAIC(model_lm) #-834
#this is best
model_lm <- lm(log(speed+1) ~ temp + I(temp^2) ,my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
#r sq =0.04
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
extractAIC(model_lm) #-834
#this is best
###############################################################################
############################# acc before loom ###############################
###############################################################################
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_wo_loom.csv"),header=TRUE,na.strings=c("[nan]"))
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$acc_percentile99)],
"gs" = data$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$acc_percentile99)], format = "%H:%M"))
)
model_lm <- lm(log(acc+1) ~ temp + log(gs,2) + I(log(gs,2)^2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1936 # temp is significant
extractAIC(model_lm) #-579
studentized <- rstudent(model_lm)
halfnorm(studentized)
#128
halfnorm(cooks <- cooks.distance(model_lm),5)
nn <- length(my_data$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 + log(gs,2) + I(log(gs,2)^2),my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1936 # temp is significant
extractAIC(model_lm) #-657 #best
model_lm <- lm(log(acc+1) ~ log(gs,2) + I(log(gs,2)^2),my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.16
extractAIC(model_lm) #-651
model_lm <- lm(log(acc+1) ~ temp + log(gs,2),my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1936 # temp is significant
extractAIC(model_lm) #-632
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + I(log(gs,2)^2),my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1962 #neither temp terms significant
extractAIC(model_lm) #-657
model_lm <- lm(log(acc+1) ~ I(temp^2) + log(gs,2) + I(log(gs,2)^2),my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1962 #neither temp terms significant
extractAIC(model_lm) #-656
model_lm <- lm(log(acc+1) ~ temp + log(gs,2) + I(log(gs,2)^2) + temp*log(gs,2),my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1936 # interaction is not significant
extractAIC(model_lm) #-657
model_lm <- lm(log(acc+1) ~ temp + log(gs,2) + I(log(gs,2)^2) + date,my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1904 # date is not significant
extractAIC(model_lm) #-655
model_lm <- lm(log(acc+1) ~ temp + log(gs,2) + I(log(gs,2)^2) + t,my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1922 # date is not significant
extractAIC(model_lm) #-656
model_lm <- lm(log(acc+1) ~ temp + log(gs,2) + I(log(gs,2)^2) + t1,my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1901 # date is not significant
extractAIC(model_lm) #-655
model_lm <- lm(log(acc+1) ~ temp + log(gs,2) + I(log(gs,2)^2) + t1 + date,my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.187 # date is not significant
extractAIC(model_lm) #-654
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$avg_acc)],
"gs" = data$Groupsize[complete.cases(data$avg_acc)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$avg_acc)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$avg_acc)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$avg_acc)],
"subtrial" = data$Subtrial[complete.cases(data$avg_acc)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$avg_acc)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$avg_acc)], format = "%H:%M")),
"acc" = data$avg_acc[complete.cases(data$avg_acc)],
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$avg_acc)], format = "%H:%M")) - as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$avg_acc)], format = "%H:%M"))
)
model_lm <- lm(log(acc+1) ~ temp + log(gs,2) + I(log(gs,2)^2) + t1 + date,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.2382 # date is not significant
extractAIC(model_lm) #-705
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + I(log(gs,2)^2) + t1 + date,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.273 # date is not significant
extractAIC(model_lm) #-715
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + I(log(gs,2)^2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.2786 # date is not significant
extractAIC(model_lm) #-719
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
#median
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$acc_percentile50)],
"gs" = data$Groupsize[complete.cases(data$acc_percentile50)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$acc_percentile50)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$acc_percentile50)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$acc_percentile50)],
"subtrial" = data$Subtrial[complete.cases(data$acc_percentile50)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$acc_percentile50)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$acc_percentile50)], format = "%H:%M")),
"acc" = data$acc_percentile50[complete.cases(data$acc_percentile50)],
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$acc_percentile50)], format = "%H:%M")) - as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$acc_percentile50)], format = "%H:%M"))
)
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + I(log(gs,2)^2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.3091 # date is not significant
extractAIC(model_lm) #-741
model_lm <- lm(log(acc+1) ~ temp + log(gs,2) + I(log(gs,2)^2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.2592 # date is not significant
extractAIC(model_lm) #-726
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + I(log(gs,2)^2) + date + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.3091 # date is not significant
extractAIC(model_lm) #-737
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 = 0.302 # date is not significant
extractAIC(model_lm) #-740
###############################################################################
####################### convex hull before loom ###############################
###############################################################################
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_wo_loom.csv"),header=TRUE,na.strings=c("[nan]"))
# make new dataframe
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$convex_hull_area)],
"gs" = data$Groupsize[complete.cases(data$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")),
"hull" = data$convex_hull_area[complete.cases(data$convex_hull_area)],
"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"))
)
hist(my_data$hull,40)
hist(log(my_data$hull),40)
model_lm <- lm(hull ~ temp+ log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.55
extractAIC(model_lm) #395
model_lm <- lm(hull ~ temp+ I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.5584
extractAIC(model_lm) #394
model_lm <- lm(hull ~ temp+ I(temp^2) + log(gs,2) + I(log(gs,2)^2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.5584
extractAIC(model_lm) #396
#all model residuals have trend
model_lm <- lm(hull ~ temp+ I(temp^2) + log(gs,2) + date,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.5584
extractAIC(model_lm) #396
model_lm <- lm(hull ~ temp+ I(temp^2) + log(gs,2) + date + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.5584
extractAIC(model_lm) #398
model_lm <- lm(hull ~ temp+ I(temp^2) + log(gs,2) + date + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.5584
extractAIC(model_lm) #398
model_lm <- lm(hull ~ temp + log(gs,2) + date + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.5584
extractAIC(model_lm) #399
model_lm <- lm(log(hull) ~ temp + log(gs,2) + date + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.5584
extractAIC(model_lm) #-340
model_lm <- lm(log(hull) ~ temp + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.5584
extractAIC(model_lm) #-343
#best?
model_lm <- lm(log(hull) ~ temp + log(gs,2) + temp*log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.56
extractAIC(model_lm) #-341
model_lm <- lm(log(hull) ~ log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.52
extractAIC(model_lm) #-332
###############################################################################
########################## speed during 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$Temperature[complete.cases(data$speed_percentile99)],
"gs" = data$Groupsize[complete.cases(data$speed_percentile99)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$speed_percentile99)]+273.1)),
"loom" = data$Loom[complete.cases(data$speed_percentile99)],
"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")),
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$speed_percentile99)], format = "%H:%M")) -
as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$speed_percentile99)], format = "%H:%M")),
"speed" = data$speed_percentile99[complete.cases(data$speed_percentile99)]
)
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1772
extractAIC(model_lm) #-236
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
#glm?
model_lm <- glm(speed ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2),family = Gamma, my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#residuals are really bad
extractAIC(model_lm) #6796
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + trial,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1799
extractAIC(model_lm) #-239
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + trial + date,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1823
extractAIC(model_lm) #-241
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + t ,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1777
extractAIC(model_lm) #-235
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + t1 ,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1829
extractAIC(model_lm) #-243
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + t1 + date ,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1844
extractAIC(model_lm) #-244
model_lm <- lm(log(speed) ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + t1 + date ,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.2197
extractAIC(model_lm) #-397
#heteroscedatic
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + date,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1852
extractAIC(model_lm) #-240
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
#mixed model?
require(lme4)
model_lmm <- lmer((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + (1|date), my_data)
summary(model_lmm)
plot(fitted(model_lmm), residuals(model_lmm))
extractAIC(model_lmm) #2973
model_lm <- lm((speed)^0.5 ~ temp + log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1872
extractAIC(model_lm) #-232
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm(log(speed) ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1872
extractAIC(model_lm) #-398 but residuals are terrible
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm((speed)^0.5 ~ log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1872
extractAIC(model_lm) #-179
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1872
extractAIC(model_lm) #-243
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
#This is the best model
#prediction
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1), loom = c(1,2,3,4,5), t1 = unique(my_data$t1),
gs = c(1,2,4,8,16)))
boots <- 10000
yest <- matrix(NA,nrow=nrow(newData1),ncol=boots)
for(i in 1:boots){
ynew <- unlist(simulate(model_lm))
ymod <- update(model_lm,ynew ~ .)
yest[,i] <- predict(ymod,newdata = newData1, type="response")
}
results <- matrix(NA,nrow=nrow(newData1),ncol=3)
results[,1] <- predict(model_lm,newData1, type = "response")
for(j in 1:nrow(newData1)){
results[j,2] <- quantile(yest[j,],probs = c(0.025))
results[j,3] <- quantile(yest[j,],probs = c(0.975))
}
newData1$speed99 <- results[,1]
newData1$speed99_025 <- results[,2]
newData1$speed99_975 <- results[,3]
write.csv(newData1,"/home/maria/Documents/data/temp_collective/roi/99th_percentile_speed_during_loom_with_t1_predictions2.csv")
# average speed during 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)),
"loom" = data$Loom[complete.cases(data$avg_speed)],
"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")),
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$avg_speed)], format = "%H:%M")) -
as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$avg_speed)], format = "%H:%M")),
"speed" = data$avg_speed[complete.cases(data$avg_speed)]
)
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.06
extractAIC(model_lm) #-2609
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm((speed)^0.5 ~ temp + log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.058
extractAIC(model_lm) #-2607
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm(log(speed) ~ temp + log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.08
extractAIC(model_lm) #-859
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm(log(speed) ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.0928 #bad residuals with log transformation
extractAIC(model_lm) #-862
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
# avg speed follows same trend
#median
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$speed_percentile50)],
"gs" = data$Groupsize[complete.cases(data$speed_percentile50)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$speed_percentile50)]+273.1)),
"loom" = data$Loom[complete.cases(data$speed_percentile50)],
"date"=as.numeric(as.Date(data$Date[complete.cases(data$speed_percentile50)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$speed_percentile50)],
"subtrial" = data$Subtrial[complete.cases(data$speed_percentile50)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$speed_percentile50)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$speed_percentile50)], format = "%H:%M")),
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$speed_percentile50)], format = "%H:%M")) -
as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$speed_percentile50)], format = "%H:%M")),
"speed" = data$speed_percentile50[complete.cases(data$speed_percentile50)]
)
model_lm <- lm((speed)^0.5 ~ temp + I(temp^2) + log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.0362
extractAIC(model_lm) #-3094
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm((speed)^0.5 ~ temp + log(gs,2) + loom + I(log(gs,2)^2) + t1,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.03
extractAIC(model_lm) #-3094
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
#########################################
###### acceleration during loom #########
#########################################
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_w_loom.csv"),header=TRUE,na.strings=c("[nan]"))
# make new dataframe
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 + date + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1871
extractAIC(model_lm) #-941
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
# this is the best model
model_lm <- lm(log(acc+1) ~ temp*log(gs,2)*loom + date + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.166
extractAIC(model_lm) #-913
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm(log(acc+1) ~ temp + I(temp^2)*log(gs,2)*loom + date ,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1852
extractAIC(model_lm) #-939
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm(log(acc+1) ~ temp + I(temp^2)*log(gs,2)*loom + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.182
extractAIC(model_lm) #-935
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2) + loom + date + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1821
extractAIC(model_lm) #-938
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm(log(acc+1) ~ temp + log(gs,2) + I(temp^2)*loom + date + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.1815
extractAIC(model_lm) #-936
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
#average 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)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$avg_acc)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$avg_acc)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$avg_acc)],
"subtrial" = data$Subtrial[complete.cases(data$avg_acc)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$avg_acc)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$avg_acc)], format = "%H:%M")),
"acc" = data$avg_acc[complete.cases(data$avg_acc)],
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$avg_acc)], format = "%H:%M")) - as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$avg_acc)], format = "%H:%M"))
)
model_lm <- lm(log(acc+1) ~ temp + I(temp^2)*log(gs,2)*loom + date + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.099
extractAIC(model_lm) #-2567
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
# this is the best model
model_lm <- lm(log(acc+1) ~ temp*log(gs,2)*loom + date + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.064
extractAIC(model_lm) #-2523
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
# this is the best model
#median acc
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)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$acc_percentile50)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$acc_percentile50)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$acc_percentile50)],
"subtrial" = data$Subtrial[complete.cases(data$acc_percentile50)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$acc_percentile50)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$acc_percentile50)], format = "%H:%M")),
"acc" = data$acc_percentile50[complete.cases(data$acc_percentile50)],
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$acc_percentile50)], format = "%H:%M")) - as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$acc_percentile50)], format = "%H:%M"))
)
model_lm <- lm(log(acc+1) ~ temp + I(temp^2)*log(gs,2)*loom + date + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.091
extractAIC(model_lm) #-2804
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
# this is the best model
model_lm <- lm(log(acc+1) ~ temp*log(gs,2)*loom + date + t,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.061
extractAIC(model_lm) #-2768
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
# this is the best model
################################
########## LATENCY #############
################################
#data <- read.csv(here("Documents","data","temp_collective","roi","all_params_w_loom.csv"),header=TRUE,na.strings=c("[nan]"))
data <- read.csv(here("data","temp_collective","roi","all_params_w_loom.csv"),header=TRUE,na.strings=c("[nan]"))
# make new dataframe
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")),
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$latency)], format = "%H:%M")) -
as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$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*gs + I(temp^2) + loom, family = quasipoisson, my_new_data2)
summary(model_pois6)
plot(fitted(model_pois6),residuals(model_pois6))
#residual dev = 199 out of 215
(chat <- deviance(model_pois6) / df.residual(model_pois6))
require(MuMIn)
QAIC(model_pois6, chat = chat)
##trying again
dfun <- function(object){with(object,sum((weights * residuals^2)[weights > 0])/df.residual)}
model_pois <- glm(latency ~ temp*gs + I(temp^2) + loom, family = poisson, my_new_data2)
model_qpois <- glm(latency ~ temp*gs + I(temp^2) + loom, family = quasipoisson, my_new_data2)
#extract log likelihoods
(sum(dpois(my_new_data2$latency,lambda=exp(predict(model_pois)),log=TRUE)))
(logLik(model_pois))
library(MuMIn); packageVersion("MuMIn")
x.quasipoisson <- function(...){
res <- quasipoisson(...)
res$aic <- poisson(...)$aic
res
}
model_qpois2 <- update(model_pois,family="x.quasipoisson",na.action=na.fail)
(gg <- dredge(model_qpois2,rank="QAIC", chat=dfun(model_pois)))
###########################################
########## Prop ind startling #############
###########################################
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_w_loom.csv"),header=TRUE,na.strings=c("[nan]"))
# make new dataframe
my_data<-data.frame("temp" = data$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"))
)
library(arm)
model_glm <- glm(prop_startles ~ temp + I(temp^2) + loom + t+date, family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1312
model_glm <- glm(prop_startles ~ temp + I(temp^2) + loom + t, family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1317
model_glm <- glm(prop_startles ~ temp + I(temp^2) + loom + date, family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1311 #residuals not bad #best model?
model_glm <- glm(prop_startles ~ temp + I(temp^2) + loom + date + temp*log(gs,2), family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1318
model_glm <- glm(prop_startles ~ temp + I(temp^2) + loom + date + I(temp^2)*log(gs,2), family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1318
model_glm <- glm(prop_startles ~ temp + I(temp^2) + loom + date + I(temp^2)*loom, family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1313
model_glm <- glm(prop_startles ~ temp + I(temp^2) + loom + date +temp*loom, family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1312
model_glm <- glm(prop_startles ~ temp + I(temp^2) + loom + date, family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1311 #residuals not bad #best model?
model_glm <- glm(prop_startles ~ loom + date, family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1350 #residuals not bad
model_glm <- glm(prop_startles ~ temp + I(temp^2) + loom + date + log(gs,2), family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1316 #residuals not bad
model_glm <- glm(prop_startles ~ temp + loom + date + log(gs,2), family = binomial,my_data)
summary(model_glm)
binnedplot(fitted(model_glm),residuals(model_glm))
#aic = 1330 #residuals not bad
newData1 <- data.frame(expand.grid(temp = seq(from = 9, to = 29, by = 1),
loom = c(1,2,3,4,5), date = unique(my_data$date)))
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$prop_startles975 <- results[,3]
write.csv(newData1,"/home/maria/Documents/data/temp_collective/roi/prop_startles_predictions2.csv")
###########################################
########## annd after loom ################
###########################################
data <- read.csv(here("Documents","data","temp_collective","roi","all_params_w_loom.csv"),header=TRUE,na.strings=c("[nan]"))
# make new dataframe
my_data<-data.frame("temp" = data$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) + trial + date, my_data)
summary(model_lm_trans)
plot(fitted(model_lm_trans),residuals(model_lm_trans))
#r sq = 0.414
extractAIC(model_lm_trans) #-689
qqnorm(residuals(model_lm_trans), main= "")