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all_params_one_model.R
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all_params_one_model.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.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(log(speed+1) ~ temp + I(temp^2) + log(gs,2) + loom + t1 + I(log(gs,2)^2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.20
model_lm <- lm(sqrt(speed) ~ temp + I(temp^2) + log(gs,2) + loom + t1 + I(log(gs,2)^2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.18
model_lm <- lm(log(speed) ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.20
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))
#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!
model_lm <- lm(log(acc) ~ 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!
#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)]
)
model_pois6 <- glm(latency ~ temp + loom + I(temp^2) + log(gs,2), family = quasipoisson, my_data)
summary(model_pois6)
plot(fitted(model_pois6),residuals(model_pois6))
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))
#residual dev = 202 out of 215
#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)
#resid = 693/739, residual plot bad
library(arm)
binnedplot(fitted(model_glm),residuals(model_glm))
#goodness of fit
X2 <- sum((my_data$prop_startles - fitted(model_glm))^2/fitted(model_glm))
nn <- length(my_data$prop_startles)
pchisq(X2, df = nn-length(coef(model_glm)), lower.tail = FALSE)
#p = 1
#annd after loom
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))
model_lm_trans <- lm(log(annd+1) ~ temp*log(gs,2) + trial + date, my_data)
summary(model_lm_trans)
plot(fitted(model_lm_trans),residuals(model_lm_trans))
model_lm_trans <- lm(log(annd+1) ~ temp +log(gs,2) + loom + I(temp^2), my_data)
summary(model_lm_trans)
plot(fitted(model_lm_trans),residuals(model_lm_trans))
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
qqnorm(residuals(model_lm_trans), main= "")
qqline(residuals(model_lm_trans))
#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 ~ temp + I(temp^2) +loom + log(gs,2) , my_data)
summary(model_lm)
plot(fitted(model_lm),residuals(model_lm))
require(MASS)
boxcox(model_lm, lambda = seq(0, 1, 1/10), plotit = TRUE)
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
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))
#unperturbed
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_wo_loom.csv"),header=TRUE,na.strings=c("[nan]"))
#speed beforeloom
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))
#r sq = 0.20
#acceleration before loom
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
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)
#128
halfnorm(cooks <- cooks.distance(model_lm),5)
nn <- length(my_data$acc)
t_crit <- qt(0.025/nn,nn-4-1,lower.tail = FALSE)
my_data[which(abs(studentized)>t_crit),]
#128
my_new_data <- my_data[-which(abs(studentized)>t_crit),]
model_lm <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2),my_new_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#r sq = 0.09
#polarization during loom
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_w_loom_pol.csv"),header=TRUE,na.strings=c("[nan]"))
# make new dataframe
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$polarization_1)],
"gs" = data$Groupsize[complete.cases(data$polarization_1)],
"loom" = data$Loom[complete.cases(data$polarization_1)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$polarization_1)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$polarization_1)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$polarization_1)],
"subtrial" = data$Subtrial[complete.cases(data$polarization_1)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$polarization_1)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$polarization_1)], format = "%H:%M")),
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$polarization_1)], format = "%H:%M")) -
as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$polarization_1)], format = "%H:%M")),
"pol" = data$polarization_1[complete.cases(data$polarization_1)]
)
hist(my_data$pol,20)
hist(abs(my_data$pol),20)
hist(sqrt(abs(my_data$pol)),20)
model_lm <- lm(pol ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#rsq 0.023 #temp not significant
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 0.027 #temp not significant
model_lm_int <- lm(pol ~ 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 0.022 #temp not significant
lrtest(model_lm,model_lm_int) # not significant
#polarization during loom with 2nd closest neighbor
my_data<-data.frame("temp" = data$Temperature[complete.cases(data$polarization_2)],
"gs" = data$Groupsize[complete.cases(data$polarization_2)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$polarization_2)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$polarization_2)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$polarization_2)],
"subtrial" = data$Subtrial[complete.cases(data$polarization_2)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$polarization_2)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$polarization_2)], format = "%H:%M")),
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$polarization_2)], format = "%H:%M")) -
as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$polarization_2)], format = "%H:%M")),
"pol" = data$polarization_2[complete.cases(data$polarization_2)]
)
#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(pol ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#rsq 0.014 #temp and temp^2 is significant
model_lm <- lm(abs(pol) ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#rsq 0.053 #temp and temp^2 is significant
require(MASS)
boxcox(model_lm, lambda = seq(0, 1, 1/10), plotit = TRUE) #0.5
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) #0.04256
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm_int <- lm(sqrt(abs(pol)) ~ temp + I(temp^2) + log(gs,2) + loom + log(gs,2)*temp,my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
#rsq 0.037 #temp and temp^2 is significant
lrtest(model_lm,model_lm_int) #not significant
#polarization of 2nd closest neighbor
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_2_postloom)],
"gs" = data$Groupsize[complete.cases(data$polarization_2_postloom)],
"loom" = data$Loom[complete.cases(data$polarization_2_postloom)],
"pol" = data$polarization_2_postloom[complete.cases(data$polarization_2_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 0.019 #temp and temp^2 is not significant
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
model_lm <- lm(sqrt(abs(pol)) ~ temp + I(temp^2) + log(gs,2) + loom + log(gs,2)*temp,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#rsq 0.017 #temp and temp^2 is not significant
#trying latency with negative values
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
)
model_lm <- lm(latency ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
rsq(model_lm)
#0.0558
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
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)
halfnorm(cooks <- cooks.distance(model_lm),5)
my_data[which(abs(studentized)>t_crit),]
t_crit <- qt(0.025/nn,nn-5-1,lower.tail = FALSE)
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))
model_lm_int <- lm(latency ~ temp + I(temp^2) + log(gs,2) + loom +log(gs,2)*temp,my_new_data2)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
rsq(model_lm_int)
#0.0659
lrtest(model_lm,model_lm_int) #not significant
#polarization before loom
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_wo_loom_pol.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)],
"kt"=1/(0.00008617*(data$Temperature[complete.cases(data$polarization_1)]+273.1)),
"date"=as.numeric(as.Date(data$Date[complete.cases(data$polarization_1)], format = "%d/%m/%Y")),
"trial" = data$Trial[complete.cases(data$polarization_1)],
"subtrial" = data$Subtrial[complete.cases(data$polarization_1)],
"t1" = as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$polarization_1)], format = "%H:%M")),
"t2" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$polarization_1)], format = "%H:%M")),
"t" = as.numeric(as.POSIXct(data$Time_start_record[complete.cases(data$polarization_1)], format = "%H:%M")) -
as.numeric(as.POSIXct(data$Time_fish_in[complete.cases(data$polarization_1)], format = "%H:%M")),
"pol" = data$polarization_1[complete.cases(data$polarization_1)]
)
model_lm <- lm(pol ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#rsq negative #temp and temp^2 is not significant
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
# trying proportion with linear model
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$Temperature[complete.cases(data$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_lm <- lm(prop_startles ~ temp + I(temp^2) + log(gs,2) + loom,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#residuals suck
qqnorm(residuals(model_lm), main= "")
qqline(residuals(model_lm))
#mean 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$Temperature[complete.cases(data$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 <- lm(speed ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
require(MASS)
boxcox(model_lm, lambda = seq(0, 1, 1/10), plotit = TRUE) #0.5
model_lm <- lm(sqrt(speed) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp terms not significant
model_lm <- lm(log(speed+1) ~ temp ,my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp term is significant
model_lm_int <- lm(log(speed+1) ~ temp + I(temp^2) + log(gs,2)+log(gs,2)*temp,my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
#temp terms are not significant
model_lm <- lm(log(speed+1) ~ temp + I(temp^2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
#temp terms are not significant
#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
#avg acc before loom
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 <- lm(log(acc+1) ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
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))
lrtest(model_lm,model_lm_int) # significant
#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))
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))
lrtest(model_lm,model_lm_int) # not significant
#avg 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$Temperature[complete.cases(data$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
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))
#temp terms are not significant
#median speed during loom
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
#avg 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))
#temp terms are significant
#median acc 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))
#collective behavior during unperturbed swimming
#annd
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$Temperature[complete.cases(data$annd)],
"gs" = data$Groupsize[complete.cases(data$annd)],
"annd" = data$annd[complete.cases(data$annd)]
)
hist(my_data$annd)
hist(log(my_data$annd+1))
hist(log(my_data$annd))
hist(sqrt(my_data$annd))
model_lm <- lm(annd ~ temp + I(temp^2) + log(gs,2),my_data)
summary(model_lm)
plot(fitted(model_lm), residuals(model_lm))
require(MASS)
boxcox(model_lm, lambda = seq(0, 1, 1/10), plotit = TRUE) #0
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
model_lm_int <- lm(log(annd) ~ temp + I(temp^2) + log(gs,2)+log(gs,2)*temp,my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
#temp and temp^2 not significant
rsq(model_lm_int)
lrtest(model_lm_int,model_lm)
#convex hull
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$Temperature[complete.cases(data$convex_hull_area)],
"gs" = data$Groupsize[complete.cases(data$convex_hull_area)],
"hull" = data$convex_hull_area[complete.cases(data$convex_hull_area)]
)
hist(my_data$hull)
hist(sqrt(my_data$hull))
model_lm <- lm(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
require(MASS)
boxcox(model_lm, lambda = seq(0, 1, 1/10), plotit = TRUE) #0.5
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)
model_lm_int <- lm(sqrt(hull) ~ temp + I(temp^2) + log(gs,2) +log(gs,2)*temp,my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
#temp and temp^2 not significant
lrtest(model_lm,model_lm_int) #not significant
#pol before loom
data <- read.csv(here("..","..","..","Documents","data","temp_collective","roi","all_params_wo_loom_pol.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
model_lm <- lm(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
model_lm <- lm(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
rsq(model_lm)
model_lm_int <- lm(abs(pol) ~ temp + I(temp^2) + log(gs,2) +log(gs,2)*temp,my_data)
summary(model_lm_int)
plot(fitted(model_lm_int), residuals(model_lm_int))
#temp and temp^2 not significant
lrtest(model_lm,model_lm_int) #not significant