ORmultivariate {PredictABEL} | R Documentation |
The function estimates multivariate (adjusted) odds ratios (ORs) with 95% confidence intervals (CIs) for all the genetic and non-genetic variables in the risk model.
ORmultivariate(riskModel, filename)
riskModel |
Name of logistic regression model that can be fitted using
the function |
filename |
Name of the output file in which the multivariate
ORs will be saved. If no directory is specified, the file is
saved in the working directory as a txt file.
When |
The function requires that first a logistic regression
model is fitted either by using GLM
function or the function
fitLogRegModel
. In addition to the multivariate ORs,
the function returns summary statistics of model performance, namely the Brier
score and the Nagelkerke's R^2 value.
The Brier score quantifies the accuracy of risk predictions by comparing
predicted risks with observed outcomes at individual level (where outcome
values are either 0 or 1). The Nagelkerke's R^2 value indicates the percentage of variation
of the outcome explained by the predictors in the model.
The function returns:
Predictors Summary |
OR with 95% CI and corresponding p-values for each predictor in the model |
Brier Score |
Brier score |
Nagelkerke Index |
Nagelkerke's R^2 value |
Brier GW. Verification of forecasts expressed in terms of probability. Monthly weather review 1950;78:1-3.
Nagelkerke NJ. A note on a general definition of the coefficient of determination. Biometrika 1991;78:691-692.
# specify dataset with outcome and predictor variables data(ExampleData) # specify column number of outcome variable cOutcome <- 2 # specify column numbers of non-genetic predictors cNonGenPred <- c(3:10) # specify column numbers of non-genetic predictors that are categorical cNonGenPredCat <- c(6:8) # specify column numbers of genetic predictors cGenPred <- c(11,13:16) # specify column numbers of genetic predictors that are categorical cGenPredCat <- c(0) # fit logistic regression model riskmodel <- fitLogRegModel(data=ExampleData, cOutcome=cOutcome, cNonGenPreds=cNonGenPred, cNonGenPredsCat=cNonGenPredCat, cGenPreds=cGenPred, cGenPredsCat=cGenPredCat) # obtain multivariate OR(95% CI) for all predictors of the fitted model ORmultivariate(riskModel=riskmodel, filename="multiOR.txt")