Exploring the distribution of response variables

Exploring collinearity among predictors:

##                diet.gini.std litters.yr.std bodymass.std   range.std
## diet.gini.std     1.00000000    -0.11475751    0.2509206 -0.05533852
## litters.yr.std   -0.11475751     1.00000000   -0.2236722 -0.02857648
## bodymass.std      0.25092064    -0.22367218    1.0000000  0.03046890
## range.std        -0.05533852    -0.02857648    0.0304689  1.00000000

Exploring associations between response variables and predictors (both are centered and standardized):

## 
##  Pearson's product-moment correlation
## 
## data:  d$diet.gini.std and d$score_diff_sum.std
## t = -3.9959, df = 159, p-value = 9.837e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.4363722 -0.1546375
## sample estimates:
##        cor 
## -0.3020869

## 
##  Pearson's product-moment correlation
## 
## data:  d$litters.yr.std and d$score_diff_sum.std
## t = 3.2542, df = 148, p-value = 0.001409
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1023900 0.4020236
## sample estimates:
##       cor 
## 0.2584112

## 
##  Pearson's product-moment correlation
## 
## data:  d$bodymass.std and d$score_diff_sum.std
## t = -2.7468, df = 161, p-value = 0.006704
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.35379059 -0.05980112
## sample estimates:
##        cor 
## -0.2115767