Predict method for the mnl function
# S3 method for mnl predict( object, pred_data = NULL, pred_cmd = "", pred_names = "", dec = 3, envir = parent.frame(), ... )
| object | Return value from |
|---|---|
| pred_data | Provide the dataframe to generate predictions (e.g., ketchup). The dataset must contain all columns used in the estimation |
| pred_cmd | Generate predictions using a command. For example, `pclass = levels(pclass)` would produce predictions for the different levels of factor `pclass`. To add another variable, create a vector of prediction strings, (e.g., c('pclass = levels(pclass)', 'age = seq(0,100,20)') |
| pred_names | Names for the predictions to be stored. If one name is provided, only the first column of predictions is stored. If empty, the levels in the response variable of the mnl model will be used |
| dec | Number of decimals to show |
| envir | Environment to extract data from |
| ... | further arguments passed to or from other methods |
See https://radiant-rstats.github.io/docs/model/mnl.html for an example in Radiant
mnl to generate the result
summary.mnl to summarize results
result <- mnl( ketchup, rvar = "choice", evar = c("price.heinz28", "price.heinz32", "price.heinz41", "price.hunts32"), lev = "heinz28" )#> Warning: NaNs produced#> Multinomial logistic regression (MNL) #> Data : ketchup #> Response variable : choice #> Level(s) : heinz28, heinz32, heinz41, hunts32 in choice #> Explanatory variables: price.heinz28, price.heinz32, price.heinz41, price.hunts32 #> Prediction command : price.heinz28 = seq(3, 5, 0.1) #> Rows shown : 10 of 21 #> #> price.heinz32 price.heinz41 price.hunts32 price.heinz28 heinz28 heinz32 #> 3.143 4.634 3.355 3.000 0.621 0.304 #> 3.143 4.634 3.355 3.100 0.596 0.325 #> 3.143 4.634 3.355 3.200 0.570 0.348 #> 3.143 4.634 3.355 3.300 0.544 0.370 #> 3.143 4.634 3.355 3.400 0.517 0.393 #> 3.143 4.634 3.355 3.500 0.491 0.416 #> 3.143 4.634 3.355 3.600 0.464 0.439 #> 3.143 4.634 3.355 3.700 0.437 0.462 #> 3.143 4.634 3.355 3.800 0.411 0.484 #> 3.143 4.634 3.355 3.900 0.385 0.507 #> heinz41 hunts32 #> 0.051 0.023 #> 0.053 0.025 #> 0.055 0.028 #> 0.056 0.030 #> 0.057 0.032 #> 0.059 0.035 #> 0.060 0.038 #> 0.061 0.040 #> 0.062 0.043 #> 0.062 0.046#> Multinomial logistic regression (MNL) #> Data : ketchup #> Response variable : choice #> Level(s) : heinz28, heinz32, heinz41, hunts32 in choice #> Explanatory variables: price.heinz28, price.heinz32, price.heinz41, price.hunts32 #> Prediction dataset : slice(ketchup, 1:20) #> Rows shown : 10 of 20 #> #> price.heinz28 price.heinz32 price.heinz41 price.hunts32 heinz28 heinz32 #> 5.200 3.700 4.600 3.400 0.272 0.423 #> 5.200 4.300 4.600 4.400 0.591 0.244 #> 4.600 2.500 4.600 4.800 0.067 0.918 #> 5.200 3.700 4.600 3.400 0.272 0.423 #> 4.600 3.000 4.600 4.800 0.182 0.784 #> 4.700 3.000 5.000 3.000 0.175 0.657 #> 4.600 3.100 5.100 4.100 0.258 0.706 #> 4.700 3.400 4.600 3.100 0.262 0.460 #> 4.700 3.400 5.000 3.100 0.294 0.444 #> 5.000 3.400 5.000 2.800 0.177 0.365 #> heinz41 hunts32 #> 0.098 0.206 #> 0.121 0.043 #> 0.015 0.000 #> 0.098 0.206 #> 0.033 0.001 #> 0.035 0.133 #> 0.027 0.010 #> 0.081 0.197 #> 0.049 0.213 #> 0.041 0.418