This function calculates reduction of the surrogate R-squared goodness-of-fit of each variable to measure their relative explanatory power. This function creates a table containing the reductions of surrogate R-squared by removing each one of variables in the model.
Arguments
- object
A object of class
"surr_rsq"that is generated by the function"surr_rsq". It contains the following components:surr_rsq,reduced_model,full_model, anddata.- avg.num
The number of replication for the averaging of surrogate R-square.
- var.set
A list that contains a few sets. Each component of these sets represents the variables that you want to examine for the contribution of goodness of fit. Then, for one component of this list, a model will fit by removing the specified variables.
- ...
Additional optional arguments.
Value
The default return is a list that contains the contribution of Surrogate R-squared for each
variable in the full_model. If the var.set is specified, the return is a list of the
contribution of the groups of variables in the var.set.
Examples
data("WhiteWine")
sele_formula <- as.formula(quality ~ fixed.acidity + volatile.acidity +
residual.sugar + + free.sulfur.dioxide +
pH + sulphates + alcohol)
sele_mod <- polr(formula = sele_formula,
data = WhiteWine,
method = "probit")
sur1 <- surr_rsq(model = sele_mod,
full_model = sele_mod,
avg.num = 100)
# \donttest{
rank_tab_sur1 <- surr_rsq_rank(object = sur1,
avg.num = 30)
print(rank_tab_sur1)
#> Removed Variable SurrogateRsq Reduction Contribution Ranking
#> alcohol 0.07042 0.23733 77.12% 1
#> volatile.acidity 0.24411 0.06364 20.68% 2
#> residual.sugar 0.28971 0.01804 5.86% 3
#> free.sulfur.dioxide 0.30312 0.00463 1.50% 4
#> sulphates 0.30422 0.00354 1.15% 5
#> fixed.acidity 0.30479 0.00296 0.96% 6
#> pH 0.30717 0.00058 0.19% 7
#> ------------------------------------------------------------------------
#> The total surrogate R-squared of the full model is:
#> [1] 0.30775
# }