Predict method for the nn function
# S3 method for nn predict( object, pred_data = NULL, pred_cmd = "", dec = 3, envir = parent.frame(), ... )
| object | Return value from |
|---|---|
| pred_data | Provide the dataframe to generate predictions (e.g., diamonds). 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)') |
| 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/nn.html for an example in Radiant
nn to generate the result
summary.nn to summarize results
result <- nn(titanic, "survived", c("pclass", "sex"), lev = "Yes") predict(result, pred_cmd = "pclass = levels(pclass)")#> Neural Network #> Data : titanic #> Response variable : survived #> Level(s) : Yes in survived #> Explanatory variables: pclass, sex #> Prediction command : pclass = levels(pclass) #> #> sex pclass Prediction #> male 1st 0.336 #> male 2nd 0.220 #> male 3rd 0.159result <- nn(diamonds, "price", "carat:color", type = "regression") predict(result, pred_cmd = "carat = 1:3")#> Neural Network #> Data : diamonds #> Response variable : price #> Explanatory variables: carat, clarity, cut, color #> Prediction command : carat = 1:3 #> #> clarity cut color carat Prediction #> SI1 Ideal G 1 4987.183 #> SI1 Ideal G 2 15496.837 #> SI1 Ideal G 3 18541.164#> Neural Network #> Data : diamonds #> Response variable : price #> Explanatory variables: carat, clarity, cut, color #> Prediction dataset : diamonds #> #> carat clarity cut color Prediction #> 0.320 VS1 Ideal H 570.442 #> 0.340 SI1 Very Good G 354.859 #> 0.300 VS2 Very Good G 493.759 #> 0.350 VVS2 Ideal H 999.885 #> 0.400 VS2 Premium F 1080.257 #> 0.600 VVS1 Ideal E 3524.251