この節の作者: 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
# ----------------------------------------------------
#
#