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Wrapper Prediction Function

Source: R/get_yhat.R
get.yhat.Rd

get.yhat() works as a proxy prediction function for many classes of fitted models.

Usage

get.yhat(X.model, newdata, ...)

# Default S3 method
get.yhat(X.model, newdata, target = -1L, ...)

# S3 method for class 'mid'
get.yhat(X.model, newdata, ...)

# S3 method for class 'lm'
get.yhat(X.model, newdata, ...)

# S3 method for class 'glm'
get.yhat(X.model, newdata, ...)

# S3 method for class 'rpart'
get.yhat(X.model, newdata, target = -1L, ...)

# S3 method for class 'randomForest'
get.yhat(X.model, newdata, target = -1L, ...)

# S3 method for class 'ranger'
get.yhat(X.model, newdata, target = -1L, ...)

# S3 method for class 'svm'
get.yhat(X.model, newdata, target = -1L, ...)

# S3 method for class 'ksvm'
get.yhat(X.model, newdata, target = -1L, ...)

# S3 method for class 'AccurateGLM'
get.yhat(X.model, newdata, ...)

# S3 method for class 'glmnet'
get.yhat(X.model, newdata, ...)

# S3 method for class 'model_fit'
get.yhat(X.model, newdata, target = -1L, ...)

# S3 method for class 'rpf'
get.yhat(X.model, newdata, target = -1L, ...)

Arguments

X.model

a fitted model object.

newdata

a data.frame or matrix.

...

optional parameters that are passed to the prediction method for the model.

target

an integer or character vector specifying the target levels for the prediction, used for the models that returns a matrix or data.frame of class probabilities. Default is -1, representing the probability of not being the base level.

Value

get.yhat() returns a numeric vector of model predictions for the newdata.

Details

get.yhat() is a wrapper prediction function for many classes of models. Although many predictive models have their own method of stats::predict(), the structure and the type of the output of these methods are not uniform. get.yhat() is designed to always return a simple numeric vector of model predictions. The design of get.yhat() is strongly influenced by DALEX::yhat().

Examples

data(trees, package = "datasets")
model <- glm(Volume ~ ., trees, family = Gamma(log))
predict(model, trees[1:5, ], type = "response")
#>        1        2        3        4        5 
#> 11.68832 11.23782 11.19185 16.63260 19.87816 
get.yhat(model, trees[1:5, ])
#> [1] 11.68832 11.23782 11.19185 16.63260 19.87816