Models comparison trough Widely applicable information criterion (WAIC), using two data sets with the visit length at the feeder in pig production
print(m1.waic)
##
## Computed from 30000 by 50652 log-likelihood matrix
##
## Estimate SE
## elpd_waic -167085.7 222.6
## p_waic 141.3 1.8
## waic 334171.5 445.1
## Warning: 2 (0.0%) p_waic estimates greater than 0.4. We recommend trying
## loo instead.
print(m2.waic)
##
## Computed from 30000 by 50652 log-likelihood matrix
##
## Estimate SE
## elpd_waic -166504.3 225.8
## p_waic 246.1 3.0
## waic 333008.6 451.6
## Warning: 2 (0.0%) p_waic estimates greater than 0.4. We recommend trying
## loo instead.
print(m3.waic)
##
## Computed from 30000 by 50652 log-likelihood matrix
##
## Estimate SE
## elpd_waic -166504.4 225.8
## p_waic 246.4 3.0
## waic 333008.8 451.6
## Warning: 2 (0.0%) p_waic estimates greater than 0.4. We recommend trying
## loo instead.
loo_compare(m1.waic,m2.waic, m3.waic)
## elpd_diff se_diff
## model2 0.0 0.0
## model3 -0.1 0.3
## model1 -581.5 36.4
print(compare(m1.waic,m2.waic, m3.waic))
## elpd_diff se_diff elpd_waic p_waic waic
## m2.waic 0.0 0.0 -166504.3 246.1 333008.6
## m3.waic -0.1 0.3 -166504.4 246.4 333008.8
## m1.waic -581.5 36.4 -167085.7 141.3 334171.5
print(m1.waic)
##
## Computed from 30000 by 6340 log-likelihood matrix
##
## Estimate SE
## elpd_waic -21127.6 92.5
## p_waic 127.5 4.6
## waic 42255.1 185.1
## Warning: 26 (0.4%) p_waic estimates greater than 0.4. We recommend trying
## loo instead.
print(m2.waic)
##
## Computed from 30000 by 6340 log-likelihood matrix
##
## Estimate SE
## elpd_waic -21128.5 92.5
## p_waic 134.9 4.7
## waic 42256.9 184.9
## Warning: 26 (0.4%) p_waic estimates greater than 0.4. We recommend trying
## loo instead.
print(m3.waic)
##
## Computed from 30000 by 6340 log-likelihood matrix
##
## Estimate SE
## elpd_waic -21128.2 92.5
## p_waic 134.6 4.7
## waic 42256.5 184.9
## Warning: 26 (0.4%) p_waic estimates greater than 0.4. We recommend trying
## loo instead.
loo_compare(m1.waic,m2.waic, m3.waic)
## elpd_diff se_diff
## model1 0.0 0.0
## model3 -0.7 1.2
## model2 -0.9 0.8
print(compare(m1.waic,m2.waic, m3.waic))
## elpd_diff se_diff elpd_waic p_waic waic
## m1.waic 0.0 0.0 -21127.6 127.5 42255.1
## m3.waic -0.7 1.2 -21128.2 134.6 42256.5
## m2.waic -0.9 0.8 -21128.5 134.9 42256.9