Chapter 3 Model testing

We tested five models with varying degrees of complexity to estimate the probabilities of each animal to win a tube-test encounter against any of their cage mates.

cages=c("C03","C04","C05","C06","C10","C11","C12","C13")

model_performance1 <- data.frame()
model_performance2 <- data.frame()
for(cage in cages){
    #Filter table by cage
    cage_data <- tube_test_data %>% filter(cage_id == cage)
    #Model with temporal autocorrelation (NULL if the model does not converge)
    M1 <- tryCatch({glmmTMB(win_loss_binomial~1+(1|mouse)+(1|treatment:mouse), data = cage_data, family = binomial)}, warning = function(w){NULL})
    M2 <- tryCatch({glmmTMB(win_loss_binomial~1+ar1(time+0|mouse)+(1|treatment:mouse), data = cage_data, family = binomial)}, warning = function(w){NULL})
    M3 <- tryCatch({glmmTMB(win_loss_binomial~1+ar1(time+0|mouse), data = cage_data, family = binomial)}, warning = function(w){NULL})
    M4 <- tryCatch({glmmTMB(win_loss_binomial~1+(1|mouse), data = cage_data, family = binomial)}, warning = function(w){NULL})
    M5 <- tryCatch({glmmTMB(win_loss_binomial~1, data = cage_data, family = binomial)}, warning = function(w){NULL})
    
    aic <- c(M1=if(is.null(M1)){NA}else{AIC(M1)},
             M2=if(is.null(M2)){NA}else{AIC(M2)},
             M3=if(is.null(M3)){NA}else{AIC(M3)},
             M4=if(is.null(M4)){NA}else{AIC(M4)},
             M5=if(is.null(M5)){NA}else{AIC(M5)}) %>% sort()
    
    rank <- names(aic)
             
    # Append predictions to table
    model_performance1 <- bind_rows(model_performance1,rank %>% as.data.frame() %>% t() %>% as.data.frame())
    model_performance2 <- bind_rows(model_performance2,aic %>% as.data.frame() %>% t() %>% as.data.frame())
}

3.1 Model performance

colnames(model_performance2) <- c("M1","M2","M3","M4","M5")
rownames(model_performance2) <- cages
model_performance2 %>% 
  rownames_to_column(var="Cage") %>%
  tt() |> 
      style_tt(j = 1, color = "#6e0f0f", background = "#e39d9d") |> 
      style_tt(i = 0, color = "white", background = "#a52929")

Cage	M1	M2	M3	M4	M5
C03	403.2859	404.1349	405.2859	407.3120	478.8853
C04	483.7269	486.7366	485.7269	493.9658	584.2436
C05	410.3836	415.5269	412.3836	414.9560	575.9259
C06	445.0369	446.3006	447.0369	444.5417	575.9259
C10	423.7239	423.9744	425.7239	421.9744	584.2436
C11	471.6309	481.7121	473.6309	500.8893	581.4710
C12	NA	400.2945	NA	398.2945	584.2436
C13	403.1068	406.1184	405.1068	404.1184	584.2436

3.2 Model rank

colnames(model_performance1) <- c("1","2","3","4","5")
rownames(model_performance1) <- cages
model_performance1 %>% 
  rownames_to_column(var="Cage") %>%
  tt() |> 
      style_tt(j = 1, color = "#6e0f0f", background = "#e39d9d") |> 
      style_tt(i = 0, color = "white", background = "#a52929")

Cage	1	2	3	4	5
C03	M3	M1	M2	M4	M5
C04	M3	M2	M1	M4	M5
C05	M3	M2	M4	M1	M5
C06	M4	M3	M1	M2	M5
C10	M4	M3	M1	M2	M5
C11	M3	M2	M1	M4	M5
C12	M4	M1	M5	NA	NA
C13	M3	M4	M2	M1	M5