logRegBin: Binomial Logistic Regression

In jmv: The 'jamovi' Analyses

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-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 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$models					an array of model specific results
results$predictOV					an output
results$residsOV					an output
results$cooksOV					an output

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"
#
#

jmv documentation built on June 22, 2024, 10:40 a.m.