mrbin (version 1.9.1)

predictWrapper: A function returning predicted values for use with the fia function.

Description

This function predicts group membership and returns a numeric vector with results.

Usage

predictWrapper(
  model,
  dataSet,
  functionNamePredict = "predict",
  firstLevel = 1,
  parameterNameObject = "object",
  parameterNameData = "x",
  dataFrameFlag = FALSE,
  ...
)

Value

A numeric (integer) vector of predicted group memberships.

Arguments

model

A predictive model. Make sure to have loaded all required packages before starting this function

dataSet

A matrix or dataframe containing data, depending on what your predict function requires. Columns=features, rows=samples

functionNamePredict

The name of the prediction function. This only needs to be changed if the prediction function is not called predict

firstLevel

Numeric value of first level or group. Usually 1 but for glm such as in the example this needs to be 0.

parameterNameObject

The name of the parameter for passing the model to the prediction function

parameterNameData

The name of the parameter for passing the data to the prediction function

dataFrameFlag

Logical value indicating whether the data object is a data frame rather than a matrix.

...

Optional, additional parameters that will be passed to the prediction function.

Examples

 #First, define group membership and create the example feature data
 group<-factor(c(rep("Group1",4),rep("Group2",5)))
 names(group)<-paste("Sample",1:9,sep="")
 dataset<-data.frame(
   Feature1=c(5.1,5.0,6.0,2.9,4.8,4.6,4.9,3.8,5.1),
   Feature2=c(2.6,4.0,3.2,1.2,3.1,2.1,4.5,6.1,1.3),
   Feature3=c(3.1,6.1,5.8,5.1,3.8,6.1,3.4,4.0,4.4),
   Feature4=c(5.3,5.2,3.1,2.7,3.2,2.8,5.9,5.8,3.1),
   Feature5=c(3.2,4.4,4.8,4.9,6.0,3.6,6.1,3.9,3.5)
   )
 rownames(dataset)<-names(group)
 #train a model - here we use a logit model instead of ANN as a demonstration
 mod<-glm(group~Feature1+Feature2+Feature3+Feature4+Feature5,
   data=data.frame(group=group,dataset),family="binomial")
 predictWrapper(model=mod,dataSet=dataset,firstLevel=0,type="response")