jmv (version 1.2.5)

logRegBin: Binomial Logistic Regression

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-R<U+00B2>, 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 rea 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

Value

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

# NOT RUN {
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     R<U+00B2>-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"
#
#

# }