pmml (version 1.5.5)

pmml.cv.glmnet: Generate PMML for glmnet objects

Description

Generate the PMML representation for a glmnet (elasticnet general linear regression) object. In particular, this gives the PMML representation for an object created by the cv.glmnet function.

Usage

# S3 method for cv.glmnet
pmml(model, model.name="Elasticnet_Model", 
    app.name="Rattle/PMML", 
    description="Generalized Linear Regression Model", 
    copyright=NULL, transforms=NULL, unknownValue=NULL, 
    dataset=NULL, s=NULL, …)

Arguments

model

a cv.glmnet object contained in an object of class glmnet, as contained in the object returned by the function cv.glmnet.

model.name

a name to be given to the model in the PMML code.

app.name

the name of the application that generated the PMML code.

description

a descriptive text for the Header element of the PMML code.

copyright

the copyright notice for the model.

transforms

data transformations represented in PMML via pmmlTransformations.

unknownValue

value to be used as the 'missingValueReplacement' attribute for all MiningFields.

dataset

the dataset using which the model was built.

s

'lambda' parameter at which to output the model. If not given, the lambda.1se parameter from the model is used instead.

…

further arguments passed to or from other methods.

Details

The glmnet package expects the input and predicted values in a matrix format; not as arrays or data frames. As of now, it will also accept numerical values only. As such, any string variables must be converted to numerical ones. One possible way to do so is to use data transformation functions, such as from the pmmlTransformations package. However the result is a data frame. In all cases, lists, arrays and data frames can be converted to a matrix format using the data.matrix function from the base package. Given a data frame df, a matrix m can thus be created by using m <- data.matrix(df).

The PMML language requires variable names which will be read in as the column names of the input matrix. If the matrix does not have variable names, they will be given the default values of "X1", "X2", ...

Use of PMML and pmml.cv.glmnet requires the XML package. Be aware that XML is a very verbose data format.

Currently, only gaussian and poisson family types are supported.

References

R project CRAN package: glmnet: Lasso and elastic-net regularized generalized linear models https://CRAN.R-project.org/package=glmnet

Examples

# NOT RUN {
library(glmnet)

# create a simple predictor (x) and response(y) matrices
x=matrix(rnorm(100*20),100,20)
y=rnorm(100)

# Build a simple gaussian model
model1 = cv.glmnet(x,y)
# Output the model in PMML format
pmml(model1)

# shift y between 0 and 1 to create a poisson response
y = y - min(y)
# give the predictor variables names (default values are V1,V2,...)
name <- NULL
for(i in 1:20){
  name <- c(name,paste("variable",i,sep=""))
}
colnames(x) <- name
# create a simple poisson model
model2 <- cv.glmnet(x,y,family="poisson")
# output in PMML format the regression model at the lambda 
# parameter = 0.006
pmml(model2,s=0.006)

# }

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