predict.bbl {bbl}

Predict Response Group Using bbl Model

Description

Make prediction of response group identity based on trained model

Usage

## S3 method for class 'bbl'
predict(object, newdata, type = "link", verbose = 1, progress.bar = FALSE, ...)

Arguments

object

Object of class bbl containing trained model

newdata

Data frame of new data for which prediction is to be made. Columns must contain all of those in model@data. If column names are present, the columns will be matched based on them. Extra columns will be ignored. If column names are not provided, the columns should exactly match model@data predictor parts. If NULL, replaced by model@data (self-prediction).

type

Return value type. If 'link', the logit scale probabilities. If 'prob' the probability itself.

verbose

Verbosity level

progress.bar

Display progress of response group probability. Useful for large samples.

...

Other arguments to methods

Details

This method uses a new data set for predictors and trained bbl model parameters to compute posterior probabilities of response group identity.

Value

Data frame of predicted posterior probabilities with samples in rows and response groups in columns. The last column is the predicted response group with maximum probability.

Examples

set.seed(154)

m <- 5
L <- 3
n <- 1000

predictors <- list()
for(i in 1:m) predictors[[i]] <- seq(0,L-1)
names(predictors) <- paste0('v',1:m)
par <- list(randompar(predictors=predictors, dJ=0.5),
            randompar(predictors=predictors, h0=0.1, J0=0.1, dJ=0.5))
dat <- randomsamp(predictors=predictors, response=c('ctrl','case'), par=par, 
                 nsample=n)
dat <- dat[sample(n),]
dtrain <- dat[seq(n/2),]
dtest <- dat[seq(n/2+1,n),]

model <- bbl(y ~ .^2, data=dtrain)
pred <- predict(model, newdata=dtest)
score <- mean(dtest$y==pred$yhat)
score

auc <- pROC::roc(response=dtest$y, predictor=pred$case, direction='<')$auc
auc