节作者:Ravi Selker, Jonathon Love, Damian Dropmann
Binomial Logistic Regression (logRegBin
)¶
Description¶
Binomial Logistic Regression
Usage¶
logRegBin(
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,
class = FALSE,
acc = FALSE,
spec = FALSE,
sens = FALSE,
auc = FALSE,
rocPlot = FALSE,
cutOff = 0.5,
cutOffPlot = FALSE,
collin = FALSE,
boxTidwell = FALSE,
cooks = FALSE
)
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 |
class |
TRUE or FALSE (default), provide a predicted classification table (or confusion matrix) |
acc |
TRUE or FALSE (default), provide the predicted accuracy of outcomes grouped by the cut-off value |
spec |
TRUE or FALSE (default), provide the predicted specificity of outcomes grouped by the cut-off value |
sens |
TRUE or FALSE (default), provide the predicted sensitivity of outcomes grouped by the cut-off value |
auc |
TRUE or FALSE (default), provide the area under the ROC curve (AUC) |
rocPlot |
TRUE or FALSE (default), provide a ROC curve plot |
cutOff |
TRUE or FALSE (default), set a cut-off used for the predictions |
cutOffPlot |
TRUE or FALSE (default), provide a cut-off plot |
collin |
TRUE or FALSE (default), provide VIF and tolerence collinearity statistics |
boxTidwell |
TRUE or FALSE (default), provide Box-Tidwell test for linearity of the logit |
cooks |
TRUE or FALSE (default), provide summary statistics for the Cook’s distance |
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(
low = factor(birthwt$low),
age = birthwt$age,
bwt = birthwt$bwt)
logRegBin(data = dat, dep = low,
covs = vars(age, bwt),
blocks = list(list("age", "bwt")),
refLevels = list(list(var="low", ref="0")))
#
# BINOMIAL LOGISTIC REGRESSION
#
# Model Fit Measures
# ---------------------------------------
# Model Deviance AIC R²-McF
# ---------------------------------------
# 1 4.97e-7 6.00 1.000
# ---------------------------------------
#
#
# MODEL SPECIFIC RESULTS
#
# MODEL 1
#
# Model Coefficients
# ------------------------------------------------------------
# Predictor Estimate SE Z p
# ------------------------------------------------------------
# Intercept 2974.73225 218237.2 0.0136 0.989
# age -0.00653 482.7 -1.35e-5 1.000
# bwt -1.18532 87.0 -0.0136 0.989
# ------------------------------------------------------------
# Note. Estimates represent the log odds of "low = 1"
# vs. "low = 0"
#
#