--- tags: allometry, Rscript --- # exploring allometry relationships this is the code for subsetting means for each individual for nights and days. Takehome is that there is no relationship ``` night <- with(all_animals ,all_animals[ hour( StartTime ) >= 0 & hour( StartTime ) < 6 , ] ) meanEE <- night %>% group_by(Animal_ID) %>% summarise(EE = mean(EE)) %>% select(EE) ``` next i need a vector of weights from the animals that is in the same order. ``` animalweight <- night %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) ``` now plotting ``` plot(log(meanEE[[1]]) ~ log(animalweight[[1]]), main = "EE 0000-0600", ylab="EE", xlab="weight") abline(lm(log(meanEE[[1]]) ~ log(animalweight[[1]]))) ``` regression ``` summary(lm(log(meanEE[[1]]) ~ log(animalweight[[1]])) ``` #### All together ``` night <- with(all_animals ,all_animals[ hour( StartTime ) >= 0 & hour( StartTime ) < 6 , ] ) day <- with(all_animals ,all_animals[ hour( StartTime ) >= 13 & hour( StartTime ) < 19 , ] ) animalweight <- night %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) # this is the code to change - when changing the thing you want to plot. meanVCO2 <- day %>% group_by(Animal_ID) %>% summarise(VCO2 = mean(VO2)) %>% select(VCO2) plot(meanVCO2[[1]] ~ animalweight[[1]], main = "meanVCO2 1300-1900", ylab="meanVCO2", xlab="weight") abline(lm(meanVCO2[[1]] ~ animalweight[[1]])) summary(lm(meanVCO2[[1]] ~ animalweight[[1]])) ``` night ![](https://i.imgur.com/9e9Xa2k.png) ![](https://i.imgur.com/PF9Sysi.png) ![](https://i.imgur.com/hLJqJcA.png) ![](https://i.imgur.com/XjHS0HA.png) day ![](https://i.imgur.com/P20my8I.png) ![](https://i.imgur.com/8G8iq7t.png) ![](https://i.imgur.com/LLOxXJZ.png) ![](https://i.imgur.com/l5pyhuO.png)