Ordinal Logistic Regression
Ordinal Logistic Regression
Example usage
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
# ────────────────────────────────────────────────────
#
#
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 |
Returns
A results object containing:
| results$modelFit | a table |
| results$modelComp | a table |
| results$models | an array of groups |
Tables can be converted to data frames with asDF or as.data.frame(). For example:
results$modelFit$asDF
as.data.frame(results$modelFit)
Elements in arrays can be accessed with [[n]]. For example:
results$models[[1]] # accesses the first element