jmv: logRegOrd – R documentation

Pricing

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

Get Started for Free

Documentation

jmv

logRegOrd

Ordinal Logistic Regression

Description

Ordinal Logistic Regression

Usage

logRegOrd(
  data,
  dep,
  covs = NULL,
  factors = NULL,
  blocks = list(list()),
  refLevels = NULL,
  modelTest = FALSE,
  dev = TRUE,
  aic = TRUE,
  bic = FALSE,
  pseudoR2 = list("r2mf"),
  omni = FALSE,
  thres = FALSE,
  ci = FALSE,
  ciWidth = 95,
  OR = FALSE,
  ciOR = FALSE,
  ciWidthOR = 95
)

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
`thres`	`TRUE` or `FALSE` (default), provide the thresholds that are used as cut-off scores for the levels of the dependent variable
`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

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

set.seed(1337)

y <- factor(sample(1:3, 100, replace = TRUE))
x1 <- rnorm(100)
x2 <- rnorm(100)

df <- data.frame(y=y, x1=x1, x2=x2)

logRegOrd(data = df, dep = y,
          covs = vars(x1, x2),
          blocks = list(list("x1", "x2")))

#
#  ORDINAL LOGISTIC REGRESSION
#
#  Model Fit Measures
#  ---------------------------------------
#    Model    Deviance    AIC    R²-McF
#  ---------------------------------------
#        1         218    226    5.68e-4
#  ---------------------------------------
#
#
#  MODEL SPECIFIC RESULTS
#
#  MODEL 1
#
#  Model Coefficients
#  ----------------------------------------------------
#    Predictor    Estimate    SE       Z        p
#  ----------------------------------------------------
#    x1             0.0579    0.193    0.300    0.764
#    x2             0.0330    0.172    0.192    0.848
#  ----------------------------------------------------
#
#

jmv

The 'jamovi' Analyses

v1.2.23

GPL (>= 2)

Authors

Ravi Selker, Jonathon Love, Damian Dropmann

Initial release

2020-06-26