Section author: Ravi Selker, Jonathon Love, Damian Dropmann

Binomial Logistic Regression (`logRegBin`)

Description

Binomial Logistic Regression

Usage

logRegBin(
  data,
  dep,
  covs = NULL,
  factors = NULL,
  blocks = list(list()),
  refLevels = NULL,
  modelTest = FALSE,
  dev = TRUE,
  aic = TRUE,
  bic = FALSE,
  pseudoR2 = list("r2mf"),
  omni = FALSE,
  ci = FALSE,
  ciWidth = 95,
  OR = FALSE,
  ciOR = FALSE,
  ciWidthOR = 95,
  emMeans = list(list()),
  ciEmm = TRUE,
  ciWidthEmm = 95,
  emmPlots = TRUE,
  emmTables = FALSE,
  emmWeights = TRUE,
  class = FALSE,
  acc = FALSE,
  spec = FALSE,
  sens = FALSE,
  auc = FALSE,
  rocPlot = FALSE,
  cutOff = 0.5,
  cutOffPlot = FALSE,
  collin = FALSE,
  boxTidwell = FALSE,
  cooks = FALSE
)

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 area 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

Output

A results object containing:

`results$modelFit`	a table
`results$modelComp`	a table
`results$models`	an array of model specific results

Tables can be converted to data frames with asDF or as.data.frame(). For example:

results$modelFit$asDF

as.data.frame(results$modelFit)

Examples

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"
#
#

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