Generation

generate functionTue, 18 Apr 2023

> table(indvar_FODs$categorical_FOD_FODs) ascending descending identity 1683 1432 206 > table(indvar_FODs$Condition_FODs) A B C D 880 880 880 880 > table(indvar_FODs$Language_used_FODs) English German Hungarian Italian Turkish 880 160 160 160 2160 categorical_FOD_FODs is the dependent variable. please apply multinomial logistic regression with interaction effect Treat each level of the Condition_FODs variable (4 levels) and Language_used_FODs variable (5 levels) equally. include the interaction effect of variables in the analysis.

library(nnet) set.seed(1234) # split dataset into train and test train <- sample(1:nrow(indvar_FODs), nrow(indvar_FODs)*.7) test <- -train # this function will return a predictions where the sum of probabilities of all # possible values of the categorical_FOD_FODs is equal to 1. mn_logit_model <- multinom(categorical_FOD_FODs ~ Condition_FODs*Language_used_FODs, data = indvar_FODs[train,], maxit = 50) # prediction mn_logit_model_pred <- predict(mn_logit_model, indvar_FODs[test,]) # accuracy mean(max.col(mn_logit_model_pred)==max.col(indvar_FODs[test,]))

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