R: Multinomial Logistic Regression

logRegMulti {jmv} | R Documentation

## Multinomial Logistic Regression

### Description

Multinomial Logistic Regression

### Usage

    logRegMulti(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)

### 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

### 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(
                race = factor(birthwt$race),
                age = birthwt$age,
                low = factor(birthwt$low))

    logRegMulti(data = dat, dep = "race",
                covs = "age", factors = "low",
                blocks = list(list("age", "low")),
                refLevels = list(
                    list(var="race", ref="1"),
                    list(var="low", ref="0")))

    #
    #  MULTINOMIAL LOGISTIC REGRESSION
    #
    #  Model Fit Measures
    #  --------------------------------------
    #    Model    Deviance    AIC    R²-McF
    #  --------------------------------------
    #        1         360    372    0.0333
    #  --------------------------------------
    #
    #
    #  MODEL SPECIFIC RESULTS
    #
    #  MODEL 1
    #
    #  Model Coefficients
    #  ---------------------------------------------------------------
    #    race     Predictor    Estimate    SE        Z         p
    #  ---------------------------------------------------------------
    #    2 - 1    Intercept      0.8155    1.1186     0.729    0.466
    #             age           -0.1038    0.0487    -2.131    0.033
    #             low:
    #             1 – 0          0.7527    0.4700     1.601    0.109
    #    3 - 1    Intercept      1.0123    0.7798     1.298    0.194
    #             age           -0.0663    0.0324    -2.047    0.041
    #             low:
    #             1 – 0          0.5677    0.3522     1.612    0.107
    #  ---------------------------------------------------------------
    #
    #

[Package jmv version 0.8.6.2 Index]