Nothing
R/logregbin.h.R
In jmv: The 'jamovi' Analyses
Defines functions
logRegBin
Documented in
logRegBin
# This file is automatically generated, you probably don't want to edit this
logRegBinOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logRegBinOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
dep = NULL,
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, ...) {
super$initialize(
package="jmv",
name="logRegBin",
requiresData=TRUE,
...)
private$..dep <- jmvcore::OptionVariable$new(
"dep",
dep,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..covs <- jmvcore::OptionVariables$new(
"covs",
covs,
suggested=list(
"continuous"),
permitted=list(
"numeric"),
default=NULL)
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal"),
permitted=list(
"factor"),
default=NULL)
private$..blocks <- jmvcore::OptionArray$new(
"blocks",
blocks,
default=list(
list()),
template=jmvcore::OptionTerms$new(
"blocks",
NULL))
private$..refLevels <- jmvcore::OptionArray$new(
"refLevels",
refLevels,
items="(factors)",
default=NULL,
template=jmvcore::OptionGroup$new(
"refLevels",
NULL,
elements=list(
jmvcore::OptionVariable$new(
"var",
NULL,
content="$key"),
jmvcore::OptionLevel$new(
"ref",
NULL))))
private$..modelTest <- jmvcore::OptionBool$new(
"modelTest",
modelTest,
default=FALSE)
private$..dev <- jmvcore::OptionBool$new(
"dev",
dev,
default=TRUE)
private$..aic <- jmvcore::OptionBool$new(
"aic",
aic,
default=TRUE)
private$..bic <- jmvcore::OptionBool$new(
"bic",
bic,
default=FALSE)
private$..pseudoR2 <- jmvcore::OptionNMXList$new(
"pseudoR2",
pseudoR2,
options=list(
"r2mf",
"r2cs",
"r2n",
"r2t"),
default=list(
"r2mf"))
private$..omni <- jmvcore::OptionBool$new(
"omni",
omni,
default=FALSE)
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=FALSE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..OR <- jmvcore::OptionBool$new(
"OR",
OR,
default=FALSE)
private$..ciOR <- jmvcore::OptionBool$new(
"ciOR",
ciOR,
default=FALSE)
private$..ciWidthOR <- jmvcore::OptionNumber$new(
"ciWidthOR",
ciWidthOR,
min=50,
max=99.9,
default=95)
private$..emMeans <- jmvcore::OptionArray$new(
"emMeans",
emMeans,
default=list(
list()),
template=jmvcore::OptionVariables$new(
"emMeans",
NULL))
private$..ciEmm <- jmvcore::OptionBool$new(
"ciEmm",
ciEmm,
default=TRUE)
private$..ciWidthEmm <- jmvcore::OptionNumber$new(
"ciWidthEmm",
ciWidthEmm,
min=50,
max=99.9,
default=95)
private$..emmPlots <- jmvcore::OptionBool$new(
"emmPlots",
emmPlots,
default=TRUE)
private$..emmTables <- jmvcore::OptionBool$new(
"emmTables",
emmTables,
default=FALSE)
private$..emmWeights <- jmvcore::OptionBool$new(
"emmWeights",
emmWeights,
default=TRUE)
private$..class <- jmvcore::OptionBool$new(
"class",
class,
default=FALSE)
private$..acc <- jmvcore::OptionBool$new(
"acc",
acc,
default=FALSE)
private$..spec <- jmvcore::OptionBool$new(
"spec",
spec,
default=FALSE)
private$..sens <- jmvcore::OptionBool$new(
"sens",
sens,
default=FALSE)
private$..auc <- jmvcore::OptionBool$new(
"auc",
auc,
default=FALSE)
private$..rocPlot <- jmvcore::OptionBool$new(
"rocPlot",
rocPlot,
default=FALSE)
private$..cutOff <- jmvcore::OptionNumber$new(
"cutOff",
cutOff,
min=0,
max=1,
default=0.5)
private$..cutOffPlot <- jmvcore::OptionBool$new(
"cutOffPlot",
cutOffPlot,
default=FALSE)
private$..collin <- jmvcore::OptionBool$new(
"collin",
collin,
default=FALSE)
private$..boxTidwell <- jmvcore::OptionBool$new(
"boxTidwell",
boxTidwell,
default=FALSE,
hidden=TRUE)
private$..cooks <- jmvcore::OptionBool$new(
"cooks",
cooks,
default=FALSE,
hidden=TRUE)
private$..predictOV <- jmvcore::OptionOutput$new(
"predictOV")
private$..residsOV <- jmvcore::OptionOutput$new(
"residsOV")
private$..cooksOV <- jmvcore::OptionOutput$new(
"cooksOV")
self$.addOption(private$..dep)
self$.addOption(private$..covs)
self$.addOption(private$..factors)
self$.addOption(private$..blocks)
self$.addOption(private$..refLevels)
self$.addOption(private$..modelTest)
self$.addOption(private$..dev)
self$.addOption(private$..aic)
self$.addOption(private$..bic)
self$.addOption(private$..pseudoR2)
self$.addOption(private$..omni)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..OR)
self$.addOption(private$..ciOR)
self$.addOption(private$..ciWidthOR)
self$.addOption(private$..emMeans)
self$.addOption(private$..ciEmm)
self$.addOption(private$..ciWidthEmm)
self$.addOption(private$..emmPlots)
self$.addOption(private$..emmTables)
self$.addOption(private$..emmWeights)
self$.addOption(private$..class)
self$.addOption(private$..acc)
self$.addOption(private$..spec)
self$.addOption(private$..sens)
self$.addOption(private$..auc)
self$.addOption(private$..rocPlot)
self$.addOption(private$..cutOff)
self$.addOption(private$..cutOffPlot)
self$.addOption(private$..collin)
self$.addOption(private$..boxTidwell)
self$.addOption(private$..cooks)
self$.addOption(private$..predictOV)
self$.addOption(private$..residsOV)
self$.addOption(private$..cooksOV)
}),
active = list(
dep = function() private$..dep$value,
covs = function() private$..covs$value,
factors = function() private$..factors$value,
blocks = function() private$..blocks$value,
refLevels = function() private$..refLevels$value,
modelTest = function() private$..modelTest$value,
dev = function() private$..dev$value,
aic = function() private$..aic$value,
bic = function() private$..bic$value,
pseudoR2 = function() private$..pseudoR2$value,
omni = function() private$..omni$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
OR = function() private$..OR$value,
ciOR = function() private$..ciOR$value,
ciWidthOR = function() private$..ciWidthOR$value,
emMeans = function() private$..emMeans$value,
ciEmm = function() private$..ciEmm$value,
ciWidthEmm = function() private$..ciWidthEmm$value,
emmPlots = function() private$..emmPlots$value,
emmTables = function() private$..emmTables$value,
emmWeights = function() private$..emmWeights$value,
class = function() private$..class$value,
acc = function() private$..acc$value,
spec = function() private$..spec$value,
sens = function() private$..sens$value,
auc = function() private$..auc$value,
rocPlot = function() private$..rocPlot$value,
cutOff = function() private$..cutOff$value,
cutOffPlot = function() private$..cutOffPlot$value,
collin = function() private$..collin$value,
boxTidwell = function() private$..boxTidwell$value,
cooks = function() private$..cooks$value,
predictOV = function() private$..predictOV$value,
residsOV = function() private$..residsOV$value,
cooksOV = function() private$..cooksOV$value),
private = list(
..dep = NA,
..covs = NA,
..factors = NA,
..blocks = NA,
..refLevels = NA,
..modelTest = NA,
..dev = NA,
..aic = NA,
..bic = NA,
..pseudoR2 = NA,
..omni = NA,
..ci = NA,
..ciWidth = NA,
..OR = NA,
..ciOR = NA,
..ciWidthOR = NA,
..emMeans = NA,
..ciEmm = NA,
..ciWidthEmm = NA,
..emmPlots = NA,
..emmTables = NA,
..emmWeights = NA,
..class = NA,
..acc = NA,
..spec = NA,
..sens = NA,
..auc = NA,
..rocPlot = NA,
..cutOff = NA,
..cutOffPlot = NA,
..collin = NA,
..boxTidwell = NA,
..cooks = NA,
..predictOV = NA,
..residsOV = NA,
..cooksOV = NA)
)
logRegBinResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logRegBinResults",
inherit = jmvcore::Group,
active = list(
modelFit = function() private$.items[["modelFit"]],
modelComp = function() private$.items[["modelComp"]],
models = function() private$.items[["models"]],
predictOV = function() private$.items[["predictOV"]],
residsOV = function() private$.items[["residsOV"]],
cooksOV = function() private$.items[["cooksOV"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Binomial Logistic Regression")
self$add(jmvcore::Table$new(
options=options,
name="modelFit",
title="Model Fit Measures",
clearWith=list(
"dep",
"blocks"),
visible="(dev || aic || bic || pseudoR2:r2mf || pseudoR2:r2cs || pseudoR2:r2n || modelTest)",
columns=list(
list(
`name`="model",
`title`="Model",
`type`="text"),
list(
`name`="dev",
`title`="Deviance",
`type`="number",
`visible`="(dev)"),
list(
`name`="aic",
`title`="AIC",
`type`="number",
`visible`="(aic)"),
list(
`name`="bic",
`title`="BIC",
`type`="number",
`visible`="(bic)"),
list(
`name`="r2mf",
`title`="R\u00B2<sub>McF</sub>",
`type`="number",
`visible`="(pseudoR2:r2mf)"),
list(
`name`="r2cs",
`title`="R\u00B2<sub>CS</sub>",
`type`="number",
`visible`="(pseudoR2:r2cs)"),
list(
`name`="r2n",
`title`="R\u00B2<sub>N</sub>",
`type`="number",
`visible`="(pseudoR2:r2n)"),
list(
`name`="r2t",
`title`="R\u00B2<sub>T</sub>",
`type`="number",
`visible`="(pseudoR2:r2t)"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"),
list(
`name`="df",
`title`="df",
`type`="integer",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"))))
self$add(jmvcore::Table$new(
options=options,
name="modelComp",
title="Model Comparisons",
clearWith=list(
"dep",
"blocks"),
columns=list(
list(
`name`="model1",
`title`="Model",
`content`=".",
`type`="text",
`superTitle`="Comparison"),
list(
`name`="sep",
`title`="",
`content`="-",
`type`="text",
`format`="narrow",
`superTitle`="Comparison"),
list(
`name`="model2",
`title`="Model",
`content`=".",
`type`="text",
`superTitle`="Comparison"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Array$new(
options=options,
name="models",
title="Model Specific Results",
layout="listSelect",
hideHeadingOnlyChild=TRUE,
clearWith=list(
"blocks"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
lrt = function() private$.items[["lrt"]],
coef = function() private$.items[["coef"]],
dataSummary = function() private$.items[["dataSummary"]],
assump = function() private$.items[["assump"]],
emm = function() private$.items[["emm"]],
pred = function() private$.items[["pred"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="")
self$add(jmvcore::Table$new(
options=options,
name="lrt",
title="Omnibus Likelihood Ratio Tests",
clearWith=list(
"dep",
"blocks"),
visible="(omni)",
refs="car",
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coef",
title="`Model Coefficients - ${dep}`",
clearWith=list(
"dep",
"blocks",
"refLevels"),
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="z",
`title`="Z",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="odds",
`title`="Odds ratio",
`type`="number",
`visible`="(OR)"),
list(
`name`="oddsLower",
`title`="Lower",
`type`="number",
`visible`="(ciOR && OR)"),
list(
`name`="oddsUpper",
`title`="Upper",
`type`="number",
`visible`="(ciOR && OR)"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
cooks = function() private$.items[["cooks"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="dataSummary",
title="Data Summary")
self$add(jmvcore::Table$new(
options=options,
name="cooks",
title="Cook's Distance",
rows=1,
visible="(cooks)",
clearWith=list(
"dep",
"blocks"),
columns=list(
list(
`name`="mean",
`title`="Mean",
`type`="number"),
list(
`name`="median",
`title`="Median",
`type`="number"),
list(
`name`="sd",
`title`="SD",
`type`="number"),
list(
`name`="min",
`title`="Min",
`type`="number",
`superTitle`="Range"),
list(
`name`="max",
`title`="Max",
`type`="number",
`superTitle`="Range"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
collin = function() private$.items[["collin"]],
boxTidwell = function() private$.items[["boxTidwell"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="collin",
title="Collinearity Statistics",
visible="(collin)",
refs="car",
clearWith=list(
"dep",
"blocks"),
columns=list(
list(
`name`="term",
`title`="",
`type`="text"),
list(
`name`="vif",
`title`="VIF",
`type`="number"),
list(
`name`="tol",
`title`="Tolerance",
`type`="number"))))
self$add(jmvcore::Table$new(
options=options,
name="boxTidwell",
title="Box-Tidwell Test for Linearity of the Logit",
visible="(boxTidwell)",
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="z",
`title`="Z",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="emm",
title="Estimated Marginal Means",
refs="emmeans",
clearWith=list(
"dep",
"blocks",
"emMeans"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
emmPlot = function() private$.items[["emmPlot"]],
emmTable = function() private$.items[["emmTable"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="")
self$add(jmvcore::Image$new(
options=options,
name="emmPlot",
title="",
width=450,
height=400,
renderFun=".emmPlot",
visible="(emmPlots)",
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels",
"ciEmm",
"ciWidthEmm",
"emmWeights")))
self$add(jmvcore::Table$new(
options=options,
name="emmTable",
title="",
visible="(emmTables)",
columns=list(),
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels",
"ciWidthEmm",
"emmWeights")))}))$new(options=options)))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
cutOffPlot = function() private$.items[["cutOffPlot"]],
class = function() private$.items[["class"]],
measures = function() private$.items[["measures"]],
rocPlot = function() private$.items[["rocPlot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="pred",
title="Prediction")
self$add(jmvcore::Image$new(
options=options,
name="cutOffPlot",
title="Cut-Off Plot",
width=450,
height=430,
renderFun=".cutOffPlot",
visible="(cutOffPlot)",
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels")))
self$add(jmvcore::Table$new(
options=options,
name="class",
title="Classification Table",
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels"),
rows=1,
visible="(class)",
columns=list()))
self$add(jmvcore::Table$new(
options=options,
name="measures",
title="Predictive Measures",
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels"),
rows=1,
visible="(acc || spec || sens || auc)",
columns=list(
list(
`name`="accuracy",
`title`="Accuracy",
`type`="number",
`visible`="(acc)"),
list(
`name`="spec",
`title`="Specificity",
`type`="number",
`visible`="(spec)"),
list(
`name`="sens",
`title`="Sensitivity",
`type`="number",
`visible`="(sens)"),
list(
`name`="auc",
`title`="AUC",
`type`="number",
`visible`="(auc)"))))
self$add(jmvcore::Image$new(
options=options,
name="rocPlot",
title="ROC Curve",
width=450,
height=400,
renderFun=".rocPlot",
visible="(rocPlot)",
refs="ROCR",
clearWith=list(
"dep",
"blocks",
"refLevels")))}))$new(options=options))}))$new(options=options)))
self$add(jmvcore::Output$new(
options=options,
name="predictOV",
title="Predicted values",
measureType="continuous",
clearWith=list(
"dep",
"blocks",
"refLevels")))
self$add(jmvcore::Output$new(
options=options,
name="residsOV",
title="Residuals",
measureType="continuous",
clearWith=list(
"dep",
"blocks",
"refLevels")))
self$add(jmvcore::Output$new(
options=options,
name="cooksOV",
title="Cook's distance",
measureType="continuous",
clearWith=list(
"dep",
"blocks",
"refLevels")))}))
logRegBinBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logRegBinBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "logRegBin",
version = c(1,0,0),
options = options,
results = logRegBinResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'integerOnly')
}))
#' Binomial Logistic Regression
#'
#' Binomial Logistic Regression
#'
#' @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"
#' #
#' #
#'
#' @param data the data as a data frame
#' @param dep a string naming the dependent variable from \code{data},
#' variable must be a factor
#' @param covs a vector of strings naming the covariates from \code{data}
#' @param factors a vector of strings naming the fixed factors from
#' \code{data}
#' @param 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
#' @param refLevels a list of lists specifying reference levels of the
#' dependent variable and all the factors
#' @param modelTest \code{TRUE} or \code{FALSE} (default), provide the model
#' comparison between the models and the NULL model
#' @param dev \code{TRUE} (default) or \code{FALSE}, provide the deviance (or
#' -2LogLikelihood) for the models
#' @param aic \code{TRUE} (default) or \code{FALSE}, provide Aikaike's
#' Information Criterion (AIC) for the models
#' @param bic \code{TRUE} or \code{FALSE} (default), provide Bayesian
#' Information Criterion (BIC) for the models
#' @param pseudoR2 one or more of \code{'r2mf'}, \code{'r2cs'}, or
#' \code{'r2n'}; use McFadden's, Cox & Snell, and Nagelkerke pseudo-R²,
#' respectively
#' @param omni \code{TRUE} or \code{FALSE} (default), provide the omnibus
#' likelihood ratio tests for the predictors
#' @param ci \code{TRUE} or \code{FALSE} (default), provide a confidence
#' interval for the model coefficient estimates
#' @param ciWidth a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width
#' @param OR \code{TRUE} or \code{FALSE} (default), provide the exponential of
#' the log-odds ratio estimate, or the odds ratio estimate
#' @param ciOR \code{TRUE} or \code{FALSE} (default), provide a confidence
#' interval for the model coefficient odds ratio estimates
#' @param ciWidthOR a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width
#' @param 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.
#' @param ciEmm \code{TRUE} (default) or \code{FALSE}, provide a confidence
#' interval for the estimated marginal means
#' @param ciWidthEmm a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width for the estimated marginal means
#' @param emmPlots \code{TRUE} (default) or \code{FALSE}, provide estimated
#' marginal means plots
#' @param emmTables \code{TRUE} or \code{FALSE} (default), provide estimated
#' marginal means tables
#' @param emmWeights \code{TRUE} (default) or \code{FALSE}, weigh each cell
#' equally or weigh them according to the cell frequency
#' @param class \code{TRUE} or \code{FALSE} (default), provide a predicted
#' classification table (or confusion matrix)
#' @param acc \code{TRUE} or \code{FALSE} (default), provide the predicted
#' accuracy of outcomes grouped by the cut-off value
#' @param spec \code{TRUE} or \code{FALSE} (default), provide the predicted
#' specificity of outcomes grouped by the cut-off value
#' @param sens \code{TRUE} or \code{FALSE} (default), provide the predicted
#' sensitivity of outcomes grouped by the cut-off value
#' @param auc \code{TRUE} or \code{FALSE} (default), provide the rea under the
#' ROC curve (AUC)
#' @param rocPlot \code{TRUE} or \code{FALSE} (default), provide a ROC curve
#' plot
#' @param cutOff \code{TRUE} or \code{FALSE} (default), set a cut-off used for
#' the predictions
#' @param cutOffPlot \code{TRUE} or \code{FALSE} (default), provide a cut-off
#' plot
#' @param collin \code{TRUE} or \code{FALSE} (default), provide VIF and
#' tolerence collinearity statistics
#' @param boxTidwell \code{TRUE} or \code{FALSE} (default), provide
#' Box-Tidwell test for linearity of the logit
#' @param cooks \code{TRUE} or \code{FALSE} (default), provide summary
#' statistics for the Cook's distance
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$modelFit} \tab \tab \tab \tab \tab a table \cr
#' \code{results$modelComp} \tab \tab \tab \tab \tab a table \cr
#' \code{results$models} \tab \tab \tab \tab \tab an array of model specific results \cr
#' \code{results$predictOV} \tab \tab \tab \tab \tab an output \cr
#' \code{results$residsOV} \tab \tab \tab \tab \tab an output \cr
#' \code{results$cooksOV} \tab \tab \tab \tab \tab an output \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$modelFit$asDF}
#'
#' \code{as.data.frame(results$modelFit)}
#'
#' @export
logRegBin <- function(
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) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("logRegBin requires jmvcore to be installed (restart may be required)")
if ( ! missing(dep)) dep <- jmvcore::resolveQuo(jmvcore::enquo(dep))
if ( ! missing(covs)) covs <- jmvcore::resolveQuo(jmvcore::enquo(covs))
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(dep), dep, NULL),
`if`( ! missing(covs), covs, NULL),
`if`( ! missing(factors), factors, NULL))
for (v in dep) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
if (inherits(emMeans, "formula")) emMeans <- jmvcore::decomposeFormula(emMeans)
options <- logRegBinOptions$new(
dep = dep,
covs = covs,
factors = factors,
blocks = blocks,
refLevels = refLevels,
modelTest = modelTest,
dev = dev,
aic = aic,
bic = bic,
pseudoR2 = pseudoR2,
omni = omni,
ci = ci,
ciWidth = ciWidth,
OR = OR,
ciOR = ciOR,
ciWidthOR = ciWidthOR,
emMeans = emMeans,
ciEmm = ciEmm,
ciWidthEmm = ciWidthEmm,
emmPlots = emmPlots,
emmTables = emmTables,
emmWeights = emmWeights,
class = class,
acc = acc,
spec = spec,
sens = sens,
auc = auc,
rocPlot = rocPlot,
cutOff = cutOff,
cutOffPlot = cutOffPlot,
collin = collin,
boxTidwell = boxTidwell,
cooks = cooks)
analysis <- logRegBinClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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Defines functions logRegBin
Documented in logRegBin
# This file is automatically generated, you probably don't want to edit this
logRegBinOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logRegBinOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
dep = NULL,
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, ...) {
super$initialize(
package="jmv",
name="logRegBin",
requiresData=TRUE,
...)
private$..dep <- jmvcore::OptionVariable$new(
"dep",
dep,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..covs <- jmvcore::OptionVariables$new(
"covs",
covs,
suggested=list(
"continuous"),
permitted=list(
"numeric"),
default=NULL)
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal"),
permitted=list(
"factor"),
default=NULL)
private$..blocks <- jmvcore::OptionArray$new(
"blocks",
blocks,
default=list(
list()),
template=jmvcore::OptionTerms$new(
"blocks",
NULL))
private$..refLevels <- jmvcore::OptionArray$new(
"refLevels",
refLevels,
items="(factors)",
default=NULL,
template=jmvcore::OptionGroup$new(
"refLevels",
NULL,
elements=list(
jmvcore::OptionVariable$new(
"var",
NULL,
content="$key"),
jmvcore::OptionLevel$new(
"ref",
NULL))))
private$..modelTest <- jmvcore::OptionBool$new(
"modelTest",
modelTest,
default=FALSE)
private$..dev <- jmvcore::OptionBool$new(
"dev",
dev,
default=TRUE)
private$..aic <- jmvcore::OptionBool$new(
"aic",
aic,
default=TRUE)
private$..bic <- jmvcore::OptionBool$new(
"bic",
bic,
default=FALSE)
private$..pseudoR2 <- jmvcore::OptionNMXList$new(
"pseudoR2",
pseudoR2,
options=list(
"r2mf",
"r2cs",
"r2n",
"r2t"),
default=list(
"r2mf"))
private$..omni <- jmvcore::OptionBool$new(
"omni",
omni,
default=FALSE)
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=FALSE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..OR <- jmvcore::OptionBool$new(
"OR",
OR,
default=FALSE)
private$..ciOR <- jmvcore::OptionBool$new(
"ciOR",
ciOR,
default=FALSE)
private$..ciWidthOR <- jmvcore::OptionNumber$new(
"ciWidthOR",
ciWidthOR,
min=50,
max=99.9,
default=95)
private$..emMeans <- jmvcore::OptionArray$new(
"emMeans",
emMeans,
default=list(
list()),
template=jmvcore::OptionVariables$new(
"emMeans",
NULL))
private$..ciEmm <- jmvcore::OptionBool$new(
"ciEmm",
ciEmm,
default=TRUE)
private$..ciWidthEmm <- jmvcore::OptionNumber$new(
"ciWidthEmm",
ciWidthEmm,
min=50,
max=99.9,
default=95)
private$..emmPlots <- jmvcore::OptionBool$new(
"emmPlots",
emmPlots,
default=TRUE)
private$..emmTables <- jmvcore::OptionBool$new(
"emmTables",
emmTables,
default=FALSE)
private$..emmWeights <- jmvcore::OptionBool$new(
"emmWeights",
emmWeights,
default=TRUE)
private$..class <- jmvcore::OptionBool$new(
"class",
class,
default=FALSE)
private$..acc <- jmvcore::OptionBool$new(
"acc",
acc,
default=FALSE)
private$..spec <- jmvcore::OptionBool$new(
"spec",
spec,
default=FALSE)
private$..sens <- jmvcore::OptionBool$new(
"sens",
sens,
default=FALSE)
private$..auc <- jmvcore::OptionBool$new(
"auc",
auc,
default=FALSE)
private$..rocPlot <- jmvcore::OptionBool$new(
"rocPlot",
rocPlot,
default=FALSE)
private$..cutOff <- jmvcore::OptionNumber$new(
"cutOff",
cutOff,
min=0,
max=1,
default=0.5)
private$..cutOffPlot <- jmvcore::OptionBool$new(
"cutOffPlot",
cutOffPlot,
default=FALSE)
private$..collin <- jmvcore::OptionBool$new(
"collin",
collin,
default=FALSE)
private$..boxTidwell <- jmvcore::OptionBool$new(
"boxTidwell",
boxTidwell,
default=FALSE,
hidden=TRUE)
private$..cooks <- jmvcore::OptionBool$new(
"cooks",
cooks,
default=FALSE,
hidden=TRUE)
private$..predictOV <- jmvcore::OptionOutput$new(
"predictOV")
private$..residsOV <- jmvcore::OptionOutput$new(
"residsOV")
private$..cooksOV <- jmvcore::OptionOutput$new(
"cooksOV")
self$.addOption(private$..dep)
self$.addOption(private$..covs)
self$.addOption(private$..factors)
self$.addOption(private$..blocks)
self$.addOption(private$..refLevels)
self$.addOption(private$..modelTest)
self$.addOption(private$..dev)
self$.addOption(private$..aic)
self$.addOption(private$..bic)
self$.addOption(private$..pseudoR2)
self$.addOption(private$..omni)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..OR)
self$.addOption(private$..ciOR)
self$.addOption(private$..ciWidthOR)
self$.addOption(private$..emMeans)
self$.addOption(private$..ciEmm)
self$.addOption(private$..ciWidthEmm)
self$.addOption(private$..emmPlots)
self$.addOption(private$..emmTables)
self$.addOption(private$..emmWeights)
self$.addOption(private$..class)
self$.addOption(private$..acc)
self$.addOption(private$..spec)
self$.addOption(private$..sens)
self$.addOption(private$..auc)
self$.addOption(private$..rocPlot)
self$.addOption(private$..cutOff)
self$.addOption(private$..cutOffPlot)
self$.addOption(private$..collin)
self$.addOption(private$..boxTidwell)
self$.addOption(private$..cooks)
self$.addOption(private$..predictOV)
self$.addOption(private$..residsOV)
self$.addOption(private$..cooksOV)
}),
active = list(
dep = function() private$..dep$value,
covs = function() private$..covs$value,
factors = function() private$..factors$value,
blocks = function() private$..blocks$value,
refLevels = function() private$..refLevels$value,
modelTest = function() private$..modelTest$value,
dev = function() private$..dev$value,
aic = function() private$..aic$value,
bic = function() private$..bic$value,
pseudoR2 = function() private$..pseudoR2$value,
omni = function() private$..omni$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
OR = function() private$..OR$value,
ciOR = function() private$..ciOR$value,
ciWidthOR = function() private$..ciWidthOR$value,
emMeans = function() private$..emMeans$value,
ciEmm = function() private$..ciEmm$value,
ciWidthEmm = function() private$..ciWidthEmm$value,
emmPlots = function() private$..emmPlots$value,
emmTables = function() private$..emmTables$value,
emmWeights = function() private$..emmWeights$value,
class = function() private$..class$value,
acc = function() private$..acc$value,
spec = function() private$..spec$value,
sens = function() private$..sens$value,
auc = function() private$..auc$value,
rocPlot = function() private$..rocPlot$value,
cutOff = function() private$..cutOff$value,
cutOffPlot = function() private$..cutOffPlot$value,
collin = function() private$..collin$value,
boxTidwell = function() private$..boxTidwell$value,
cooks = function() private$..cooks$value,
predictOV = function() private$..predictOV$value,
residsOV = function() private$..residsOV$value,
cooksOV = function() private$..cooksOV$value),
private = list(
..dep = NA,
..covs = NA,
..factors = NA,
..blocks = NA,
..refLevels = NA,
..modelTest = NA,
..dev = NA,
..aic = NA,
..bic = NA,
..pseudoR2 = NA,
..omni = NA,
..ci = NA,
..ciWidth = NA,
..OR = NA,
..ciOR = NA,
..ciWidthOR = NA,
..emMeans = NA,
..ciEmm = NA,
..ciWidthEmm = NA,
..emmPlots = NA,
..emmTables = NA,
..emmWeights = NA,
..class = NA,
..acc = NA,
..spec = NA,
..sens = NA,
..auc = NA,
..rocPlot = NA,
..cutOff = NA,
..cutOffPlot = NA,
..collin = NA,
..boxTidwell = NA,
..cooks = NA,
..predictOV = NA,
..residsOV = NA,
..cooksOV = NA)
)
logRegBinResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logRegBinResults",
inherit = jmvcore::Group,
active = list(
modelFit = function() private$.items[["modelFit"]],
modelComp = function() private$.items[["modelComp"]],
models = function() private$.items[["models"]],
predictOV = function() private$.items[["predictOV"]],
residsOV = function() private$.items[["residsOV"]],
cooksOV = function() private$.items[["cooksOV"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Binomial Logistic Regression")
self$add(jmvcore::Table$new(
options=options,
name="modelFit",
title="Model Fit Measures",
clearWith=list(
"dep",
"blocks"),
visible="(dev || aic || bic || pseudoR2:r2mf || pseudoR2:r2cs || pseudoR2:r2n || modelTest)",
columns=list(
list(
`name`="model",
`title`="Model",
`type`="text"),
list(
`name`="dev",
`title`="Deviance",
`type`="number",
`visible`="(dev)"),
list(
`name`="aic",
`title`="AIC",
`type`="number",
`visible`="(aic)"),
list(
`name`="bic",
`title`="BIC",
`type`="number",
`visible`="(bic)"),
list(
`name`="r2mf",
`title`="R\u00B2<sub>McF</sub>",
`type`="number",
`visible`="(pseudoR2:r2mf)"),
list(
`name`="r2cs",
`title`="R\u00B2<sub>CS</sub>",
`type`="number",
`visible`="(pseudoR2:r2cs)"),
list(
`name`="r2n",
`title`="R\u00B2<sub>N</sub>",
`type`="number",
`visible`="(pseudoR2:r2n)"),
list(
`name`="r2t",
`title`="R\u00B2<sub>T</sub>",
`type`="number",
`visible`="(pseudoR2:r2t)"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"),
list(
`name`="df",
`title`="df",
`type`="integer",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"))))
self$add(jmvcore::Table$new(
options=options,
name="modelComp",
title="Model Comparisons",
clearWith=list(
"dep",
"blocks"),
columns=list(
list(
`name`="model1",
`title`="Model",
`content`=".",
`type`="text",
`superTitle`="Comparison"),
list(
`name`="sep",
`title`="",
`content`="-",
`type`="text",
`format`="narrow",
`superTitle`="Comparison"),
list(
`name`="model2",
`title`="Model",
`content`=".",
`type`="text",
`superTitle`="Comparison"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Array$new(
options=options,
name="models",
title="Model Specific Results",
layout="listSelect",
hideHeadingOnlyChild=TRUE,
clearWith=list(
"blocks"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
lrt = function() private$.items[["lrt"]],
coef = function() private$.items[["coef"]],
dataSummary = function() private$.items[["dataSummary"]],
assump = function() private$.items[["assump"]],
emm = function() private$.items[["emm"]],
pred = function() private$.items[["pred"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="")
self$add(jmvcore::Table$new(
options=options,
name="lrt",
title="Omnibus Likelihood Ratio Tests",
clearWith=list(
"dep",
"blocks"),
visible="(omni)",
refs="car",
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coef",
title="`Model Coefficients - ${dep}`",
clearWith=list(
"dep",
"blocks",
"refLevels"),
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="z",
`title`="Z",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="odds",
`title`="Odds ratio",
`type`="number",
`visible`="(OR)"),
list(
`name`="oddsLower",
`title`="Lower",
`type`="number",
`visible`="(ciOR && OR)"),
list(
`name`="oddsUpper",
`title`="Upper",
`type`="number",
`visible`="(ciOR && OR)"))))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
cooks = function() private$.items[["cooks"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="dataSummary",
title="Data Summary")
self$add(jmvcore::Table$new(
options=options,
name="cooks",
title="Cook's Distance",
rows=1,
visible="(cooks)",
clearWith=list(
"dep",
"blocks"),
columns=list(
list(
`name`="mean",
`title`="Mean",
`type`="number"),
list(
`name`="median",
`title`="Median",
`type`="number"),
list(
`name`="sd",
`title`="SD",
`type`="number"),
list(
`name`="min",
`title`="Min",
`type`="number",
`superTitle`="Range"),
list(
`name`="max",
`title`="Max",
`type`="number",
`superTitle`="Range"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
collin = function() private$.items[["collin"]],
boxTidwell = function() private$.items[["boxTidwell"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="assump",
title="Assumption Checks")
self$add(jmvcore::Table$new(
options=options,
name="collin",
title="Collinearity Statistics",
visible="(collin)",
refs="car",
clearWith=list(
"dep",
"blocks"),
columns=list(
list(
`name`="term",
`title`="",
`type`="text"),
list(
`name`="vif",
`title`="VIF",
`type`="number"),
list(
`name`="tol",
`title`="Tolerance",
`type`="number"))))
self$add(jmvcore::Table$new(
options=options,
name="boxTidwell",
title="Box-Tidwell Test for Linearity of the Logit",
visible="(boxTidwell)",
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="z",
`title`="Z",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))}))$new(options=options))
self$add(jmvcore::Array$new(
options=options,
name="emm",
title="Estimated Marginal Means",
refs="emmeans",
clearWith=list(
"dep",
"blocks",
"emMeans"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
emmPlot = function() private$.items[["emmPlot"]],
emmTable = function() private$.items[["emmTable"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="")
self$add(jmvcore::Image$new(
options=options,
name="emmPlot",
title="",
width=450,
height=400,
renderFun=".emmPlot",
visible="(emmPlots)",
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels",
"ciEmm",
"ciWidthEmm",
"emmWeights")))
self$add(jmvcore::Table$new(
options=options,
name="emmTable",
title="",
visible="(emmTables)",
columns=list(),
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels",
"ciWidthEmm",
"emmWeights")))}))$new(options=options)))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
cutOffPlot = function() private$.items[["cutOffPlot"]],
class = function() private$.items[["class"]],
measures = function() private$.items[["measures"]],
rocPlot = function() private$.items[["rocPlot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="pred",
title="Prediction")
self$add(jmvcore::Image$new(
options=options,
name="cutOffPlot",
title="Cut-Off Plot",
width=450,
height=430,
renderFun=".cutOffPlot",
visible="(cutOffPlot)",
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels")))
self$add(jmvcore::Table$new(
options=options,
name="class",
title="Classification Table",
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels"),
rows=1,
visible="(class)",
columns=list()))
self$add(jmvcore::Table$new(
options=options,
name="measures",
title="Predictive Measures",
clearWith=list(
"dep",
"blocks",
"cutOff",
"refLevels"),
rows=1,
visible="(acc || spec || sens || auc)",
columns=list(
list(
`name`="accuracy",
`title`="Accuracy",
`type`="number",
`visible`="(acc)"),
list(
`name`="spec",
`title`="Specificity",
`type`="number",
`visible`="(spec)"),
list(
`name`="sens",
`title`="Sensitivity",
`type`="number",
`visible`="(sens)"),
list(
`name`="auc",
`title`="AUC",
`type`="number",
`visible`="(auc)"))))
self$add(jmvcore::Image$new(
options=options,
name="rocPlot",
title="ROC Curve",
width=450,
height=400,
renderFun=".rocPlot",
visible="(rocPlot)",
refs="ROCR",
clearWith=list(
"dep",
"blocks",
"refLevels")))}))$new(options=options))}))$new(options=options)))
self$add(jmvcore::Output$new(
options=options,
name="predictOV",
title="Predicted values",
measureType="continuous",
clearWith=list(
"dep",
"blocks",
"refLevels")))
self$add(jmvcore::Output$new(
options=options,
name="residsOV",
title="Residuals",
measureType="continuous",
clearWith=list(
"dep",
"blocks",
"refLevels")))
self$add(jmvcore::Output$new(
options=options,
name="cooksOV",
title="Cook's distance",
measureType="continuous",
clearWith=list(
"dep",
"blocks",
"refLevels")))}))
logRegBinBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logRegBinBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "logRegBin",
version = c(1,0,0),
options = options,
results = logRegBinResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'integerOnly')
}))
#' Binomial Logistic Regression
#'
#' Binomial Logistic Regression
#'
#' @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"
#' #
#' #
#'
#' @param data the data as a data frame
#' @param dep a string naming the dependent variable from \code{data},
#' variable must be a factor
#' @param covs a vector of strings naming the covariates from \code{data}
#' @param factors a vector of strings naming the fixed factors from
#' \code{data}
#' @param 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
#' @param refLevels a list of lists specifying reference levels of the
#' dependent variable and all the factors
#' @param modelTest \code{TRUE} or \code{FALSE} (default), provide the model
#' comparison between the models and the NULL model
#' @param dev \code{TRUE} (default) or \code{FALSE}, provide the deviance (or
#' -2LogLikelihood) for the models
#' @param aic \code{TRUE} (default) or \code{FALSE}, provide Aikaike's
#' Information Criterion (AIC) for the models
#' @param bic \code{TRUE} or \code{FALSE} (default), provide Bayesian
#' Information Criterion (BIC) for the models
#' @param pseudoR2 one or more of \code{'r2mf'}, \code{'r2cs'}, or
#' \code{'r2n'}; use McFadden's, Cox & Snell, and Nagelkerke pseudo-R²,
#' respectively
#' @param omni \code{TRUE} or \code{FALSE} (default), provide the omnibus
#' likelihood ratio tests for the predictors
#' @param ci \code{TRUE} or \code{FALSE} (default), provide a confidence
#' interval for the model coefficient estimates
#' @param ciWidth a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width
#' @param OR \code{TRUE} or \code{FALSE} (default), provide the exponential of
#' the log-odds ratio estimate, or the odds ratio estimate
#' @param ciOR \code{TRUE} or \code{FALSE} (default), provide a confidence
#' interval for the model coefficient odds ratio estimates
#' @param ciWidthOR a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width
#' @param 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.
#' @param ciEmm \code{TRUE} (default) or \code{FALSE}, provide a confidence
#' interval for the estimated marginal means
#' @param ciWidthEmm a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width for the estimated marginal means
#' @param emmPlots \code{TRUE} (default) or \code{FALSE}, provide estimated
#' marginal means plots
#' @param emmTables \code{TRUE} or \code{FALSE} (default), provide estimated
#' marginal means tables
#' @param emmWeights \code{TRUE} (default) or \code{FALSE}, weigh each cell
#' equally or weigh them according to the cell frequency
#' @param class \code{TRUE} or \code{FALSE} (default), provide a predicted
#' classification table (or confusion matrix)
#' @param acc \code{TRUE} or \code{FALSE} (default), provide the predicted
#' accuracy of outcomes grouped by the cut-off value
#' @param spec \code{TRUE} or \code{FALSE} (default), provide the predicted
#' specificity of outcomes grouped by the cut-off value
#' @param sens \code{TRUE} or \code{FALSE} (default), provide the predicted
#' sensitivity of outcomes grouped by the cut-off value
#' @param auc \code{TRUE} or \code{FALSE} (default), provide the rea under the
#' ROC curve (AUC)
#' @param rocPlot \code{TRUE} or \code{FALSE} (default), provide a ROC curve
#' plot
#' @param cutOff \code{TRUE} or \code{FALSE} (default), set a cut-off used for
#' the predictions
#' @param cutOffPlot \code{TRUE} or \code{FALSE} (default), provide a cut-off
#' plot
#' @param collin \code{TRUE} or \code{FALSE} (default), provide VIF and
#' tolerence collinearity statistics
#' @param boxTidwell \code{TRUE} or \code{FALSE} (default), provide
#' Box-Tidwell test for linearity of the logit
#' @param cooks \code{TRUE} or \code{FALSE} (default), provide summary
#' statistics for the Cook's distance
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$modelFit} \tab \tab \tab \tab \tab a table \cr
#' \code{results$modelComp} \tab \tab \tab \tab \tab a table \cr
#' \code{results$models} \tab \tab \tab \tab \tab an array of model specific results \cr
#' \code{results$predictOV} \tab \tab \tab \tab \tab an output \cr
#' \code{results$residsOV} \tab \tab \tab \tab \tab an output \cr
#' \code{results$cooksOV} \tab \tab \tab \tab \tab an output \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$modelFit$asDF}
#'
#' \code{as.data.frame(results$modelFit)}
#'
#' @export
logRegBin <- function(
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) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("logRegBin requires jmvcore to be installed (restart may be required)")
if ( ! missing(dep)) dep <- jmvcore::resolveQuo(jmvcore::enquo(dep))
if ( ! missing(covs)) covs <- jmvcore::resolveQuo(jmvcore::enquo(covs))
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(dep), dep, NULL),
`if`( ! missing(covs), covs, NULL),
`if`( ! missing(factors), factors, NULL))
for (v in dep) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
if (inherits(emMeans, "formula")) emMeans <- jmvcore::decomposeFormula(emMeans)
options <- logRegBinOptions$new(
dep = dep,
covs = covs,
factors = factors,
blocks = blocks,
refLevels = refLevels,
modelTest = modelTest,
dev = dev,
aic = aic,
bic = bic,
pseudoR2 = pseudoR2,
omni = omni,
ci = ci,
ciWidth = ciWidth,
OR = OR,
ciOR = ciOR,
ciWidthOR = ciWidthOR,
emMeans = emMeans,
ciEmm = ciEmm,
ciWidthEmm = ciWidthEmm,
emmPlots = emmPlots,
emmTables = emmTables,
emmWeights = emmWeights,
class = class,
acc = acc,
spec = spec,
sens = sens,
auc = auc,
rocPlot = rocPlot,
cutOff = cutOff,
cutOffPlot = cutOffPlot,
collin = collin,
boxTidwell = boxTidwell,
cooks = cooks)
analysis <- logRegBinClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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