R: Binomial Logistic Regression

logRegBin {jmv} | R Documentation

## Binomial Logistic Regression

### 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 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

### Value

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 = c("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"
    #
    #

[Package jmv version 0.8.6.2 Index]