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