Section author: Ravi Selker, Jonathon Love, Damian Dropmann

Ordinal Logistic Regression (logRegOrd)

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

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

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
#  ----------------------------------------------------
#
#