Binomial Logistic Regression
Binomial Logistic Regression
Example usage
data('birthwt', package='MASS')
dat <- data.frame(
low = factor(birthwt$low),
age = birthwt$age,
bwt = birthwt$bwt)
logRegBin(data = dat, dep = low,
covs = vars(age, bwt),
blocks = list(list("age", "bwt")),
refLevels = list(list(var="low", ref="0")))
#
# BINOMIAL LOGISTIC REGRESSION
#
# Model Fit Measures
# ───────────────────────────────────────
# Model Deviance AIC R²-McF
# ───────────────────────────────────────
# 1 4.97e-7 6.00 1.000
# ───────────────────────────────────────
#
#
# MODEL SPECIFIC RESULTS
#
# MODEL 1
#
# Model Coefficients
# ────────────────────────────────────────────────────────────
# Predictor Estimate SE Z p
# ────────────────────────────────────────────────────────────
# Intercept 2974.73225 218237.2 0.0136 0.989
# age -0.00653 482.7 -1.35e-5 1.000
# bwt -1.18532 87.0 -0.0136 0.989
# ────────────────────────────────────────────────────────────
# Note. Estimates represent the log odds of "low = 1"
# vs. "low = 0"
#
#
Arguments
| data | the data as a data frame |
| dep | a string naming the dependent variable from data, variable must be a factor |
| covs | a vector of strings naming the covariates from data |
| factors | a vector of strings naming the fixed factors from data |
| blocks | a list containing vectors of strings that name the predictors that are added to the model. The elements are added to the model according to their order in the list |
| refLevels | a list of lists specifying reference levels of the dependent variable and all the factors |
| modelTest | TRUE or FALSE (default), provide the model comparison between the models and the NULL model |
| dev | TRUE (default) or FALSE, provide the deviance (or -2LogLikelihood) for the models |
| aic | TRUE (default) or FALSE, provide Aikaike's Information Criterion (AIC) for the models |
| bic | TRUE or FALSE (default), provide Bayesian Information Criterion (BIC) for the models |
| pseudoR2 | one or more of 'r2mf', 'r2cs', or 'r2n'; use McFadden's, Cox & Snell, and Nagelkerke pseudo-R², respectively |
| omni | TRUE or FALSE (default), provide the omnibus likelihood ratio tests for the predictors |
| ci | TRUE or FALSE (default), provide a confidence interval for the model coefficient estimates |
| ciWidth | a number between 50 and 99.9 (default: 95) specifying the confidence interval width |
| OR | TRUE or FALSE (default), provide the exponential of the log-odds ratio estimate, or the odds ratio estimate |
| ciOR | TRUE or FALSE (default), provide a confidence interval for the model coefficient odds ratio estimates |
| ciWidthOR | a number between 50 and 99.9 (default: 95) specifying the confidence interval width |
| emMeans | a list of lists specifying the variables for which the estimated marginal means need to be calculate. Supports up to three variables per term. |
| ciEmm | TRUE (default) or FALSE, provide a confidence interval for the estimated marginal means |
| ciWidthEmm | a number between 50 and 99.9 (default: 95) specifying the confidence interval width for the estimated marginal means |
| emmPlots | TRUE (default) or FALSE, provide estimated marginal means plots |
| emmTables | TRUE or FALSE (default), provide estimated marginal means tables |
| emmWeights | TRUE (default) or FALSE, weigh each cell equally or weigh them according to the cell frequency |
| class | TRUE or FALSE (default), provide a predicted classification table (or confusion matrix) |
| acc | TRUE or FALSE (default), provide the predicted accuracy of outcomes grouped by the cut-off value |
| spec | TRUE or FALSE (default), provide the predicted specificity of outcomes grouped by the cut-off value |
| sens | TRUE or FALSE (default), provide the predicted sensitivity of outcomes grouped by the cut-off value |
| auc | TRUE or FALSE (default), provide the rea under the ROC curve (AUC) |
| rocPlot | TRUE or FALSE (default), provide a ROC curve plot |
| cutOff | TRUE or FALSE (default), set a cut-off used for the predictions |
| cutOffPlot | TRUE or FALSE (default), provide a cut-off plot |
| collin | TRUE or FALSE (default), provide VIF and tolerence collinearity statistics |
| boxTidwell | TRUE or FALSE (default), provide Box-Tidwell test for linearity of the logit |
| cooks | TRUE or FALSE (default), provide summary statistics for the Cook's distance |
Returns
A results object containing:
| results$modelFit | a table |
| results$modelComp | a table |
| results$models | an array of groups |
Tables can be converted to data frames with asDF or as.data.frame(). For example:
results$modelFit$asDF
as.data.frame(results$modelFit)
Elements in arrays can be accessed with [[n]]. For example:
results$models[[1]] # accesses the first element