ExampleModels: An example code to construct a risk model using logistic...

In PredictABEL: Assessment of Risk Prediction Models

Description

ExampleModels constructs two risk models using logistic regression analysis. Most of the functions in this package require a logistic regression model as an input and estimate predicted risks from this fitted model. To illustrate these functions without repeating the construction of a logistic regression model, this example code has been created. The function returns two different risk models, riskModel1 which is based on non-genetic predictors and riskModel2 which includes genetic and non-genetic predictors.

Examples

# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2
# specify column numbers of non-genetic predictors
cNonGenPred1 <- c(3:10)
cNonGenPred2 <- c(3:10)
# specify column numbers of non-genetic predictors that are categorical
cNonGenPredCat1 <- c(6:8)
cNonGenPredCat2 <- c(6:8)
# specify column numbers of genetic predictors
cGenPred1 <- c(0)
cGenPred2 <- c(11:16)
# specify column numbers of genetic predictors that are categorical
cGenPredsCat1 <- c(0)
cGenPredsCat2 <- c(0)

# fit logistic regression models
riskmodel1 <- fitLogRegModel(data=ExampleData, cOutcome=cOutcome,
cNonGenPreds=cNonGenPred1, cNonGenPredsCat=cNonGenPredCat1,
cGenPreds=cGenPred1, cGenPredsCat=cGenPredsCat1)
riskmodel2 <- fitLogRegModel(data=ExampleData, cOutcome=cOutcome,
cNonGenPreds=cNonGenPred2, cNonGenPredsCat=cNonGenPredCat2,
cGenPreds=cGenPred2, cGenPredsCat=cGenPredsCat2)

# combine output in a list 
ExampleModels <- list(riskModel1=riskmodel1, riskModel2=riskmodel2)

PredictABEL documentation built on March 13, 2020, 3:15 a.m.