1. Distribution of visit length at the feeder

kable(d)%>%kable_styling(bootstrap_options = c("striped", "hover","condensed"))%>%
  add_header_above(c("Set "=1,"Distribution visit length at the feeder" = 6 ))%>%
  column_spec(1,bold = T)
Set
Distribution visit length at the feeder
Min. 1st Qu. Median Mean 3rd Qu. Max.
All records (74413) 0.183 3.100 7.217 9.010 12.850 70.883
Immediate Replacement (58255) 0.183 3.017 7.117 8.953 12.833 70.883
No Immediate Replacement (6256) 0.217 3.850 8.100 9.653 13.538 60.383

2. Q-Q plot of visit lenght at the feeder

3. Analysis of residuals

The model fitted to analize the visit length variable in Immediate and No immediate replacement data sets was

\[y=X\beta + Zu + Z_fa_f + e\] Where, \(y\) is a \(n x 1\) vector of visit length at the feeder (minutes), \(X\beta\) are the fixed effects, \(location-trial \ (12)\), \(hour \ entry \ at \ the \ feeder \ (23)\) and \(animal \ median \ weigth\) as covariate; \(Z\) is a \(n\) x \(q\) desing matrix (\(q\) is the number of pigs) relates records in \(y\) to the random vector of additive genetic effects \(u\) (\(q\) x \(1\)); \(Z_f\) is the design matrix of the next individual that visited the feeder, named \(followers\), relating to \(y\) with the random vector effects \(a_f\) (\(q\) x \(1\)) and \(e\) (\(n\) x \(1\)) is the random residuals vector.

kable(b)%>%kable_styling(bootstrap_options = c("striped", "hover","condensed"))%>%
  add_header_above(c("Set "=1,"Distribution of Residuals" = 6 ))%>%
  column_spec(1,bold = T)
Set
Distribution of Residuals
Min. 1st Qu. Median Mean 3rd Qu. Max.
Immediate Replacement (58255) -24.335 -4.319 -0.930 0 3.538 58.337
No Immediate Replacement (6256) -19.928 -4.375 -0.745 0 3.528 47.416