prepLogitData: Create data for logit model.

In novasmedley/gbmSpm: Find predictive sequential patterns for clinical glioblastoma patients

Description

Create data for logit model.

Usage

prepLogitData(data, formula, labelName, predictors = NULL,
  needToRemove = NULL, createModelMatrix = FALSE)

Arguments

data	dataframe, rows are samples, cols are features plus some metadata not meant for modeling and will be removed
formula	char or formula object
labelName	char, column name of binary label
predictors	char, names of columns in data that should be in logit fit data
needToRemove	char, names of columns in data that should not be in logit fit data
createModelMatrix	logical, call model.matrix

Details

Removes non-features and non-labels. Creates dataframe ready to serve as input to logit fit, see model.matrix. Removes rows with any 'NA' values.

Value

dataframe ready for logit fitting

Examples

# use training partition to create folds for CV
data("features_ratechange_sup0.4g60l2z2") # features and labels for each clinical visit
t <- 'rate'
maxgap <- 60
maxlen <- 2

# format
names <- colnames(feats)
feats <- data.frame(id=row.names(feats),feats)
colnames(feats) <- c('id',names)
feats <- prepLaterality(feats)
feats <- prepLocation(feats)
feats <- removeVisits(feats,
                     maxgap=maxgap,
                     maxlength=maxlen,
                     tType=t,
                     save=F,
                     outDir=NA)
labels <- getClassLabels()
needToRemove <- c('id','iois','eventID', # remove ids
                  labels, # remove labels
                  'IDH1') # not interested

# data partitions
train.ids <- sample(feats$id, size=floor(0.80*nrow(feats)), replace = F) # random
feats <- feats[feats$id %in% train.ids,] #training data
ind <- getTrainingFolds(trainEvents=feats,
                        folds=3,
                        seed=1,
                        verbose=T)
feats <- prepLogitData(data = feats,
                      formula = 'survivalIn60 ~ .',
                      labelName = 'survivalIn60',
                      needToRemove=needToRemove)
                      

novasmedley/gbmSpm documentation built on May 17, 2019, 10:39 a.m.