pamrML: Wrapper function around the pamr.* functions

In nlcv: Nested Loop Cross Validation

Description

The pamrML functions are wrappers around pamr.train and pamr.predict that provide a more classical R modelling interface than the original versions.

Usage

pamrML(formula, data, ...)
## S3 method for class 'pamrML'
print(x, ...)
## S3 method for class 'pamrML'
predict(object, newdata, ...)

Arguments

formula	model formula
data	data frame
x	object of class pamrML
object	object of class pamrML
newdata	data frame containing new observations for which predicted values will be estimated based on the model contained in object
...	further arguments to the print method

Details

The name of the response variable is kept as an attribute in the pamrML object to allow for predict methods that can be easily used for writing converter functions for use in the MLInterfaces framework.

Value

For pamrML an object of class pamrML which adds an attribute to the original object returned by pamr.train (or pamrTrain).

The print method lists the names of the different components of the pamrML object.

The predict method returns a vector of predicted values

Author(s)

Tobias Verbeke

See Also

pamr.train, pamr.predict

Examples

  set.seed(120)
  x <- matrix(rnorm(1000*20), ncol=20)
  y <- sample(c(1:4), size=20, replace=TRUE)
  # for original pam
  mydata <- list(x=x, y=y)
  mytraindata <- list(x=x[,1:15],y=factor(y[1:15]))
  mytestdata <-  list(x = x[,16:20], y = factor(y[16:20]))

  # for formula-based methods including pamrML
  alldf <- cbind.data.frame(t(mydata$x), y)
  traindf <- cbind.data.frame(t(mytraindata$x), y = mytraindata$y)
  testdf <- cbind.data.frame(t(mytestdata$x), y = mytestdata$y)

  ### create pamrML object
  pamrMLObj <- pamrML(y ~ ., traindf)
  pamrMLObj

  ### test predict method
  predict(object = pamrMLObj, newdata = testdf, 
      threshold = 1) # threshold compulsory

nlcv documentation built on May 2, 2019, 4:56 p.m.