Section author: Ravi Selker, Jonathon Love, Damian Dropmann

Multinomial Logistic Regression (`logRegMulti`)

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

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(
            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
#  ---------------------------------------------------------------
#
#

Multinomial Logistic Regression (logRegMulti)