--- tags: Rscripts --- # Test for relationship between weight and response variables ### Author: Jocelyn P. Colella ### (e.g., is weight a covariate?) It's not! No significant relationship between weight and ANY response variable. ## Male and Female EE vs. weight - All comparisons insignificant: no relationship between weight and EE ![](https://i.imgur.com/hfVcNtz.png) ## Male and Female RQ vs. weight - All comparisons insignificant: no relationship between weight and RQ ![](https://i.imgur.com/e1k4VUD.png) ## Male and Female H2O vs. weight - All comparisons insignificant: no relationships between weight and H2O - AFTER corrected 29g female weight to 19g - ![](https://i.imgur.com/jDpbKvj.png) ## Male and Female VO2 vs. weight - All comparisons insignificant: no relationship between weight and VO2 ![](https://i.imgur.com/zFYPesz.png) ## Male and Female VCO2 vs. weight - All comparisons insignificant: no relationship between weight and VCO2 ![](https://i.imgur.com/SFF68QD.png) ``` ### TEST FOR A RELATIONSHIP BETWEEN WEIGHT AND ALL RESPONSE VARIABLES ### setwd("~/Documents/Jocie/projects/Pero_respo/Analyses/data/") ######## BASELINE F (day and night) BL_animalweight <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanEE <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(EE = mean(EE)) %>% select(EE) BL_EEwt <- cbind(BL_animalweight, BL_meanEE) summary(lm(BL_meanEE[[1]] ~ BL_animalweight[[1]])) BL_F_eeXwt_plot <- ggscatter(BL_EEwt, x = "weight", y = "EE", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.37) + stat_regline_equation(label.y = 0.40) + theme(plot.title = element_text(size = 14, hjust=0.5)) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="Baseline", x = "", y ="Female: EE") BL_F_eeXwt_plot ######## hot F (day and night) hot_animalweight <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanEE <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(EE = mean(EE)) %>% select(EE) hot_EEwt <- cbind(hot_animalweight, hot_meanEE) summary(lm(hot_meanEE[[1]] ~ hot_animalweight[[1]])) hot_F_eeXwt_plot <- ggscatter(hot_EEwt, x = "weight", y = "EE", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.37) + stat_regline_equation(label.y = 0.40) + theme(plot.title = element_text(size = 14, hjust=0.5)) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="Hot", x = "", y ="") hot_F_eeXwt_plot ######## cold F (day and night) cold_animalweight <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanEE <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(EE = mean(EE)) %>% select(EE) cold_EEwt <- cbind(cold_animalweight, cold_meanEE) summary(lm(cold_meanEE[[1]] ~ cold_animalweight[[1]])) cold_F_eeXwt_plot <- ggscatter(cold_EEwt, x = "weight", y = "EE", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.37) + stat_regline_equation(label.y = 0.39) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Cold", x = "", y ="") cold_F_eeXwt_plot ######## MALES ########### ######## BASELINE M (day and night) BL_animalweight <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanEE <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(EE = mean(EE)) %>% select(EE) BL_EEwt <- cbind(BL_animalweight, BL_meanEE) summary(lm(BL_meanEE[[1]] ~ BL_animalweight[[1]])) BL_M_eeXwt_plot <- ggscatter(BL_EEwt, x = "weight", y = "EE", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.37) + stat_regline_equation(label.y = 0.40) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="", x = "Males: EE", y ="") BL_M_eeXwt_plot ######## hot M (day and night) hot_animalweight <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanEE <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(EE = mean(EE)) %>% select(EE) hot_EEwt <- cbind(hot_animalweight, hot_meanEE) summary(lm(hot_meanEE[[1]] ~ hot_animalweight[[1]])) hot_M_eeXwt_plot <- ggscatter(hot_EEwt, x = "weight", y = "EE", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.365) + stat_regline_equation(label.y = 0.39) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "Weight (g)", y ="") hot_M_eeXwt_plot ######## cold M (day and night) cold_animalweight <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanEE <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(EE = mean(EE)) %>% select(EE) cold_EEwt <- cbind(cold_animalweight, cold_meanEE) summary(lm(cold_meanEE[[1]] ~ cold_animalweight[[1]])) cold_M_eeXwt_plot <- ggscatter(cold_EEwt, x = "weight", y = "EE", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.375) + stat_regline_equation(label.y = 0.39) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "", y ="") cold_M_eeXwt_plot # Panelled plot, (row 1 = females, row 2= males) #grid.arrange(BL_F_eeXwt_plot, hot_F_eeXwt_plot, cold_F_eeXwt_plot, # BL_M_eeXwt_plot, hot_M_eeXwt_plot, cold_M_eeXwt_plot, # top = "EE X weight", # nrow = 2) #TO CREATE A PLOT WITH EVEN SIZED SUBPLOTS ggdraw() + draw_plot(BL_F_eeXwt_plot, x = 0, y = 0.5, width = .33, height = .5) + draw_plot(hot_F_eeXwt_plot, x = 0.33, y = 0.5, width = .33, height = .5) + draw_plot(cold_F_eeXwt_plot, x = 0.66, y = 0.5, width = .33, height = .5) + draw_plot(BL_M_eeXwt_plot, x = 0, y = 0.02, width = .33, height = .5) + draw_plot(hot_M_eeXwt_plot, x = 0.33, y = 0.02, width = .33, height = .5) + draw_plot(cold_M_eeXwt_plot, x = 0.66, y = 0.02, width = .33, height = .5) ########################################################################### ########################################################################### ######################### RQ plots######################### ######## BASELINE F (day and night) BL_animalweight <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanRQ <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(RQ = mean(RQ)) %>% select(RQ) BL_RQwt <- cbind(BL_animalweight, BL_meanRQ) summary(lm(BL_meanRQ[[1]] ~ BL_animalweight[[1]])) BL_F_rqXwt_plot <- ggscatter(BL_RQwt, x = "weight", y = "RQ", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.25) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Baseline", x = "", y ="Female: RQ") BL_F_rqXwt_plot ######## hot F (day and night) hot_animalweight <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanRQ <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(RQ = mean(RQ)) %>% select(RQ) hot_RQwt <- cbind(hot_animalweight, hot_meanRQ) summary(lm(hot_meanRQ[[1]] ~ hot_animalweight[[1]])) hot_F_rqXwt_plot <- ggscatter(hot_RQwt, x = "weight", y = "RQ", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.24) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Hot", x = "", y ="") hot_F_rqXwt_plot ######## cold F (day and night) cold_animalweight <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanRQ <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(RQ = mean(RQ)) %>% select(RQ) cold_RQwt <- cbind(cold_animalweight, cold_meanRQ) summary(lm(cold_meanRQ[[1]] ~ cold_animalweight[[1]])) cold_F_rqXwt_plot <- ggscatter(cold_RQwt, x = "weight", y = "RQ", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.24) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Cold", x = "", y ="") cold_F_rqXwt_plot ######## MALES ########### ######## BASELINE M (day and night) BL_animalweight <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanRQ <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(RQ = mean(RQ)) %>% select(RQ) BL_RQwt <- cbind(BL_animalweight, BL_meanRQ) summary(lm(BL_meanRQ[[1]] ~ BL_animalweight[[1]])) BL_M_rqXwt_plot <- ggscatter(BL_RQwt, x = "weight", y = "RQ", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.24) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "", y ="Male: RQ") BL_M_rqXwt_plot ######## hot M (day and night) hot_animalweight <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanRQ <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(RQ = mean(RQ)) %>% select(RQ) hot_RQwt <- cbind(hot_animalweight, hot_meanRQ) summary(lm(hot_meanRQ[[1]] ~ hot_animalweight[[1]])) hot_M_rqXwt_plot <- ggscatter(hot_RQwt, x = "weight", y = "RQ", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.24) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "Weight (g)", y ="") hot_M_rqXwt_plot ######## cold M (day and night) cold_animalweight <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanRQ <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(RQ = mean(RQ)) %>% select(RQ) cold_RQwt <- cbind(cold_animalweight, cold_meanRQ) summary(lm(cold_meanRQ[[1]] ~ cold_animalweight[[1]])) cold_M_rqXwt_plot <- ggscatter(cold_RQwt, x = "weight", y = "RQ", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.24) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "", y ="") cold_M_rqXwt_plot #TO CREATE A PLOT WITH EVEN SIZED SUBPLOTS ggdraw() + draw_plot(BL_F_rqXwt_plot, x = 0, y = 0.5, width = .33, height = .5) + draw_plot(hot_F_rqXwt_plot, x = 0.33, y = 0.5, width = .33, height = .5) + draw_plot(cold_F_rqXwt_plot, x = 0.66, y = 0.5, width = .33, height = .5) + draw_plot(BL_M_rqXwt_plot, x = 0, y = 0.02, width = .33, height = .5) + draw_plot(hot_M_rqXwt_plot, x = 0.33, y = 0.02, width = .33, height = .5) + draw_plot(cold_M_rqXwt_plot, x = 0.66, y = 0.02, width = .33, height = .5) ########################################################################### ########################################################################### ######################### H2Omg plots######################### ######## BASELINE F (day and night) BL_animalweight <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanH2Omg <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(H2Omg = mean(H2Omg)) %>% select(H2Omg) BL_H2Omgwt <- cbind(BL_animalweight, BL_meanH2Omg) summary(lm(BL_meanH2Omg[[1]] ~ BL_animalweight[[1]])) BL_F_H2OmgXwt_plot <- ggscatter(BL_H2Omgwt, x = "weight", y = "H2Omg", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.3) + stat_regline_equation(label.y = 0.28) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Baseline", x = "", y ="Female: H2Omg") BL_F_H2OmgXwt_plot ######## hot F (day and night) hot_animalweight <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanH2Omg <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(H2Omg = mean(H2Omg)) %>% select(H2Omg) hot_H2Omgwt <- cbind(hot_animalweight, hot_meanH2Omg) summary(lm(hot_meanH2Omg[[1]] ~ hot_animalweight[[1]])) hot_F_H2OmgXwt_plot <- ggscatter(hot_H2Omgwt, x = "weight", y = "H2Omg", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.3) + stat_regline_equation(label.y = 0.28) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Hot", x = "", y ="") hot_F_H2OmgXwt_plot ######## cold F (day and night) cold_animalweight <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanH2Omg <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(H2Omg = mean(H2Omg)) %>% select(H2Omg) cold_H2Omgwt <- cbind(cold_animalweight, cold_meanH2Omg) summary(lm(cold_meanH2Omg[[1]] ~ cold_animalweight[[1]])) cold_F_H2OmgXwt_plot <- ggscatter(cold_H2Omgwt, x = "weight", y = "H2Omg", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.3) + stat_regline_equation(label.y = 0.28) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Cold", x = "", y ="") cold_F_H2OmgXwt_plot ######## MALES ########### ######## BASELINE M (day and night) BL_animalweight <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanH2Omg <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(H2Omg = mean(H2Omg)) %>% select(H2Omg) BL_H2Omgwt <- cbind(BL_animalweight, BL_meanH2Omg) summary(lm(BL_meanH2Omg[[1]] ~ BL_animalweight[[1]])) BL_M_H2OmgXwt_plot <- ggscatter(BL_H2Omgwt, x = "weight", y = "H2Omg", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.3) + stat_regline_equation(label.y = 0.28) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "", y ="Male: H2Omg") BL_M_H2OmgXwt_plot ######## hot M (day and night) hot_animalweight <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanH2Omg <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(H2Omg = mean(H2Omg)) %>% select(H2Omg) hot_H2Omgwt <- cbind(hot_animalweight, hot_meanH2Omg) summary(lm(hot_meanH2Omg[[1]] ~ hot_animalweight[[1]])) hot_M_H2OmgXwt_plot <- ggscatter(hot_H2Omgwt, x = "weight", y = "H2Omg", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.3) + stat_regline_equation(label.y = 0.28) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "Weight (g)", y ="") hot_M_H2OmgXwt_plot ######## cold M (day and night) cold_animalweight <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanH2Omg <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(H2Omg = mean(H2Omg)) %>% select(H2Omg) cold_H2Omgwt <- cbind(cold_animalweight, cold_meanH2Omg) summary(lm(cold_meanH2Omg[[1]] ~ cold_animalweight[[1]])) cold_M_H2OmgXwt_plot <- ggscatter(cold_H2Omgwt, x = "weight", y = "H2Omg", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.3) + stat_regline_equation(label.y = 0.28) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "", y ="") cold_M_H2OmgXwt_plot #TO CREATE A PLOT WITH EVEN SIZED SUBPLOTS ggdraw() + draw_plot(BL_F_H2OmgXwt_plot, x = 0, y = 0.5, width = .33, height = .5) + draw_plot(hot_F_H2OmgXwt_plot, x = 0.33, y = 0.5, width = .33, height = .5) + draw_plot(cold_F_H2OmgXwt_plot, x = 0.66, y = 0.5, width = .33, height = .5) + draw_plot(BL_M_H2OmgXwt_plot, x = 0, y = 0.02, width = .33, height = .5) + draw_plot(hot_M_H2OmgXwt_plot, x = 0.33, y = 0.02, width = .33, height = .5) + draw_plot(cold_M_H2OmgXwt_plot, x = 0.66, y = 0.02, width = .33, height = .5) ########################################################################## ########################################################################### ######################### VO2 plots######################### ######## BASELINE F (day and night) BL_animalweight <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanVO2 <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(VO2 = mean(VO2)) %>% select(VO2) BL_VO2wt <- cbind(BL_animalweight, BL_meanVO2) summary(lm(BL_meanVO2[[1]] ~ BL_animalweight[[1]])) BL_F_VO2Xwt_plot <- ggscatter(BL_VO2wt, x = "weight", y = "VO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.29) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Baseline", x = "", y ="Female: VO2") BL_F_VO2Xwt_plot ######## hot F (day and night) hot_animalweight <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanVO2 <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(VO2 = mean(VO2)) %>% select(VO2) hot_VO2wt <- cbind(hot_animalweight, hot_meanVO2) summary(lm(hot_meanVO2[[1]] ~ hot_animalweight[[1]])) hot_F_VO2Xwt_plot <- ggscatter(hot_VO2wt, x = "weight", y = "VO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.29) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Hot", x = "", y ="") hot_F_VO2Xwt_plot ######## cold F (day and night) cold_animalweight <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanVO2 <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(VO2 = mean(VO2)) %>% select(VO2) cold_VO2wt <- cbind(cold_animalweight, cold_meanVO2) summary(lm(cold_meanVO2[[1]] ~ cold_animalweight[[1]])) cold_F_VO2Xwt_plot <- ggscatter(cold_VO2wt, x = "weight", y = "VO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.29) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Cold", x = "", y ="") cold_F_VO2Xwt_plot ######## MALES ########### ######## BASELINE M (day and night) BL_animalweight <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanVO2 <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(VO2 = mean(VO2)) %>% select(VO2) BL_VO2wt <- cbind(BL_animalweight, BL_meanVO2) summary(lm(BL_meanVO2[[1]] ~ BL_animalweight[[1]])) BL_M_VO2Xwt_plot <- ggscatter(BL_VO2wt, x = "weight", y = "VO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.29) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "", y ="Male: VO2") BL_M_VO2Xwt_plot ######## hot M (day and night) hot_animalweight <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanVO2 <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(VO2 = mean(VO2)) %>% select(VO2) hot_VO2wt <- cbind(hot_animalweight, hot_meanVO2) summary(lm(hot_meanVO2[[1]] ~ hot_animalweight[[1]])) hot_M_VO2Xwt_plot <- ggscatter(hot_VO2wt, x = "weight", y = "VO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.29) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "Weight (g)", y ="") hot_M_VO2Xwt_plot ######## cold M (day and night) cold_animalweight <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanVO2 <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(VO2 = mean(VO2)) %>% select(VO2) cold_VO2wt <- cbind(cold_animalweight, cold_meanVO2) summary(lm(cold_meanVO2[[1]] ~ cold_animalweight[[1]])) cold_M_VO2Xwt_plot <- ggscatter(cold_VO2wt, x = "weight", y = "VO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.2) + stat_regline_equation(label.y = 1.26) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "", y ="") cold_M_VO2Xwt_plot #TO CREATE A PLOT WITH EVEN SIZED SUBPLOTS ggdraw() + draw_plot(BL_F_VO2Xwt_plot, x = 0, y = 0.5, width = .33, height = .5) + draw_plot(hot_F_VO2Xwt_plot, x = 0.33, y = 0.5, width = .33, height = .5) + draw_plot(cold_F_VO2Xwt_plot, x = 0.66, y = 0.5, width = .33, height = .5) + draw_plot(BL_M_VO2Xwt_plot, x = 0, y = 0.02, width = .33, height = .5) + draw_plot(hot_M_VO2Xwt_plot, x = 0.33, y = 0.02, width = .33, height = .5) + draw_plot(cold_M_VO2Xwt_plot, x = 0.66, y = 0.02, width = .33, height = .5) ########################################################################## ########################################################################### ######################### VCO2 plots######################### ######## BASELINE F (day and night) BL_animalweight <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanVCO2 <- BL_noOL_F %>% group_by(Animal_ID) %>% summarise(VCO2 = mean(VCO2)) %>% select(VCO2) BL_VCO2wt <- cbind(BL_animalweight, BL_meanVCO2) summary(lm(BL_meanVCO2[[1]] ~ BL_animalweight[[1]])) BL_F_VCO2Xwt_plot <- ggscatter(BL_VCO2wt, x = "weight", y = "VCO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.1) + stat_regline_equation(label.y = 1.02) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Baseline", x = "", y ="Female: VCO2") BL_F_VCO2Xwt_plot ######## hot F (day and night) hot_animalweight <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanVCO2 <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(VCO2 = mean(VCO2)) %>% select(VCO2) hot_VCO2wt <- cbind(hot_animalweight, hot_meanVCO2) summary(lm(hot_meanVCO2[[1]] ~ hot_animalweight[[1]])) hot_F_VCO2Xwt_plot <- ggscatter(hot_VCO2wt, x = "weight", y = "VCO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.1) + stat_regline_equation(label.y = 1.02) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Hot", x = "", y ="") hot_F_VCO2Xwt_plot ######## cold F (day and night) cold_animalweight <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanVCO2 <- cold_noOL_F %>% group_by(Animal_ID) %>% summarise(VCO2 = mean(VCO2)) %>% select(VCO2) cold_VCO2wt <- cbind(cold_animalweight, cold_meanVCO2) summary(lm(cold_meanVCO2[[1]] ~ cold_animalweight[[1]])) cold_F_VCO2Xwt_plot <- ggscatter(cold_VCO2wt, x = "weight", y = "VCO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.1) + stat_regline_equation(label.y = 1.04) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Cold", x = "", y ="") cold_F_VCO2Xwt_plot ######## MALES ########### ######## BASELINE M (day and night) BL_animalweight <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) BL_meanVCO2 <- BL_noOL_M %>% group_by(Animal_ID) %>% summarise(VCO2 = mean(VCO2)) %>% select(VCO2) BL_VCO2wt <- cbind(BL_animalweight, BL_meanVCO2) summary(lm(BL_meanVCO2[[1]] ~ BL_animalweight[[1]])) BL_M_VCO2Xwt_plot <- ggscatter(BL_VCO2wt, x = "weight", y = "VCO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.1) + stat_regline_equation(label.y = 1.02) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "", y ="Male: VCO2") BL_M_VCO2Xwt_plot ######## hot M (day and night) hot_animalweight <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanVCO2 <- hot_noOL_M %>% group_by(Animal_ID) %>% summarise(VCO2 = mean(VCO2)) %>% select(VCO2) hot_VCO2wt <- cbind(hot_animalweight, hot_meanVCO2) summary(lm(hot_meanVCO2[[1]] ~ hot_animalweight[[1]])) hot_M_VCO2Xwt_plot <- ggscatter(hot_VCO2wt, x = "weight", y = "VCO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.1) + stat_regline_equation(label.y = 1.02) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "Weight (g)", y ="") hot_M_VCO2Xwt_plot ######## cold M (day and night) cold_animalweight <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) cold_meanVCO2 <- cold_noOL_M %>% group_by(Animal_ID) %>% summarise(VCO2 = mean(VCO2)) %>% select(VCO2) cold_VCO2wt <- cbind(cold_animalweight, cold_meanVCO2) summary(lm(cold_meanVCO2[[1]] ~ cold_animalweight[[1]])) cold_M_VCO2Xwt_plot <- ggscatter(cold_VCO2wt, x = "weight", y = "VCO2", palette = "jco", add = "reg.line") + stat_cor(label.y = 1.1) + stat_regline_equation(label.y = 1.05) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + labs(title="", x = "", y ="") cold_M_VCO2Xwt_plot #TO CREATE A PLOT WITH EVEN SIZED SUBPLOTS ggdraw() + draw_plot(BL_F_VCO2Xwt_plot, x = 0, y = 0.5, width = .33, height = .5) + draw_plot(hot_F_VCO2Xwt_plot, x = 0.33, y = 0.5, width = .33, height = .5) + draw_plot(cold_F_VCO2Xwt_plot, x = 0.66, y = 0.5, width = .33, height = .5) + draw_plot(BL_M_VCO2Xwt_plot, x = 0, y = 0.02, width = .33, height = .5) + draw_plot(hot_M_VCO2Xwt_plot, x = 0.33, y = 0.02, width = .33, height = .5) + draw_plot(cold_M_VCO2Xwt_plot, x = 0.66, y = 0.02, width = .33, height = .5) ``` ### Explore H2O and weight relationship for Females ![](https://i.imgur.com/QxuyA4Q.png) NOTE: Significant relationship for Night+Day combined and daytime alone, but not for nighttime alone (p = 0.062) ``` ################################################################# ################################################################# ################################################################# # EXPLORE WHY H2O and WEIGHT share a relationship for FEMALES ######## hot F (day and night) hot_animalweight <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanH2Omg <- hot_noOL_F %>% group_by(Animal_ID) %>% summarise(H2Omg = mean(H2Omg)) %>% select(H2Omg) hot_H2Omgwt <- cbind(hot_animalweight, hot_meanH2Omg) summary(lm(hot_meanH2Omg[[1]] ~ hot_animalweight[[1]])) hot_F_H2OmgXwt_plot <- ggscatter(hot_H2Omgwt, x = "weight", y = "H2Omg", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.35) + stat_regline_equation(label.y = 0.33) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Night+Day", x = "", y ="H2O (mg)") hot_F_H2OmgXwt_plot ######## hot F - DAY only hot_animalweight <- hot_day_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanH2Omg <- hot_day_noOL_F %>% group_by(Animal_ID) %>% summarise(H2Omg = mean(H2Omg)) %>% select(H2Omg) hot_H2Omgwt <- cbind(hot_animalweight, hot_meanH2Omg) summary(lm(hot_meanH2Omg[[1]] ~ hot_animalweight[[1]])) hot_day_F_H2OmgXwt_plot <- ggscatter(hot_H2Omgwt, x = "weight", y = "H2Omg", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.35) + stat_regline_equation(label.y = 0.33) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Day", x = "Weight", y ="") hot_day_F_H2OmgXwt_plot ######## hot F - NIGHT only hot_animalweight <- hot_night_noOL_F %>% group_by(Animal_ID) %>% summarise(weight = mean(weight)) %>% select(weight) hot_meanH2Omg <- hot_night_noOL_F %>% group_by(Animal_ID) %>% summarise(H2Omg = mean(H2Omg)) %>% select(H2Omg) hot_H2Omgwt <- cbind(hot_animalweight, hot_meanH2Omg) summary(lm(hot_meanH2Omg[[1]] ~ hot_animalweight[[1]])) hot_night_F_H2OmgXwt_plot <- ggscatter(hot_H2Omgwt, x = "weight", y = "H2Omg", palette = "jco", add = "reg.line") + stat_cor(label.y = 0.35) + stat_regline_equation(label.y = 0.33) + theme(plot.margin = unit(c(0.1,0.1,0.1,0.1), "lines")) + theme(plot.title = element_text(size = 14, hjust=0.5)) + labs(title="Night", x = "", y ="") hot_night_F_H2OmgXwt_plot ####COMBINED FIGURE ggdraw() + draw_plot(hot_F_H2OmgXwt_plot, x = 0, y = 0, width = .33, height = 1) + draw_plot(hot_day_F_H2OmgXwt_plot, x = 0.33, y = 0, width = .33, height = 1) + draw_plot(hot_night_F_H2OmgXwt_plot, x = 0.66, y = 0, width = .33, height = 1)