regressoR (version 1.1.8)

rlr_prediction: rlr_prediction

Description

generates the code to create the prediction of the penalized regression model.

Usage

rlr_prediction(data.a = "datos.aprendizaje", data.p = "datos.prueba",
  variable.pred = NULL, model.var = "modelo.rlr",
  pred.var = "prediccion.rlr", lambda = NULL, cv.var = "cv.glm")

Arguments

data.a

the name of the learning data.

data.p

the name of the test data.

variable.pred

the name of the variable to be predicted.

model.var

the name of the variable that stores the resulting model.

pred.var

the name of the variable that stores the resulting prediction.

lambda

a numerical value in case you don't want to use the optimal lambda.

cv.var

the variable that stores the optimal lambda.

Examples

# NOT RUN {
library(glmnet)
x <- rlr_model('iris', 'Petal.Length')
exe(x)
print(modelo.rlr)

x <- rlr_prediction('iris', 'iris', 'Petal.Length', pred.var = 'my_prediction')
exe(x)
print(my_prediction)

# }

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