rlr_prediction

0th

Percentile

rlr_prediction

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.

Aliases
  • rlr_prediction
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)

# }
Documentation reproduced from package regressoR, version 1.1.7, License: GPL (>= 2)

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