R/loglinear.h.R
In jamovi/jmv: The 'jamovi' Analyses
Defines functions
logLinear
# This file is automatically generated, you probably don't want to edit this
logLinearOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logLinearOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
factors = NULL,
counts = NULL,
blocks = list(
list()),
refLevels = NULL,
modelTest = FALSE,
dev = TRUE,
aic = TRUE,
bic = FALSE,
pseudoR2 = list(
"r2mf"),
omni = FALSE,
ci = FALSE,
ciWidth = 95,
RR = FALSE,
ciRR = FALSE,
ciWidthRR = 95,
emMeans = list(
list()),
ciEmm = TRUE,
ciWidthEmm = 95,
emmPlots = TRUE,
emmTables = FALSE,
emmWeights = TRUE, ...) {
super$initialize(
package="jmv",
name="logLinear",
requiresData=TRUE,
...)
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal"),
permitted=list(
"factor"),
default=NULL)
private$..counts <- jmvcore::OptionVariable$new(
"counts",
counts,
default=NULL,
permitted=list(
"numeric"))
private$..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"),
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$..RR <- jmvcore::OptionBool$new(
"RR",
RR,
default=FALSE)
private$..ciRR <- jmvcore::OptionBool$new(
"ciRR",
ciRR,
default=FALSE)
private$..ciWidthRR <- jmvcore::OptionNumber$new(
"ciWidthRR",
ciWidthRR,
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$new(
"emmWeights",
emmWeights,
default=TRUE)
self$.addOption(private$..factors)
self$.addOption(private$..counts)
self$.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$..RR)
self$.addOption(private$..ciRR)
self$.addOption(private$..ciWidthRR)
self$.addOption(private$..emMeans)
self$.addOption(private$..ciEmm)
self$.addOption(private$..ciWidthEmm)
self$.addOption(private$..emmPlots)
self$.addOption(private$..emmTables)
self$.addOption(private$..emmWeights)
}),
active = list(
factors = function() private$..factors$value,
counts = function() private$..counts$value,
blocks = function() private$..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$value,
RR = function() private$..RR$value,
ciRR = function() private$..ciRR$value,
ciWidthRR = function() private$..ciWidthRR$value,
emMeans = function() private$..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$value),
private = list(
..factors = NA,
..counts = NA,
..blocks = NA,
..refLevels = NA,
..modelTest = NA,
..dev = NA,
..aic = NA,
..bic = NA,
..pseudoR2 = NA,
..omni = NA,
..ci = NA,
..ciWidth = NA,
..RR = NA,
..ciRR = NA,
..ciWidthRR = NA,
..emMeans = NA,
..ciEmm = NA,
..ciWidthEmm = NA,
..emmPlots = NA,
..emmTables = NA,
..emmWeights = NA)
)
logLinearResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logLinearResults",
inherit = jmvcore::Group,
active = list(
modelFit = function() private$.items[["modelFit"]],
modelComp = function() private$.items[["modelComp"]],
models = function() private$.items[["models"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Log-Linear Regression")
self$add(jmvcore::Table$new(
options=options,
name="modelFit",
title="Model Fit Measures",
clearWith=list(
"counts",
"blocks"),
visible="(dev || aic || bic || pseudoR2:r2mf || pseudoR2:r2cs || pseudoR2:r2n || modelTest)",
columns=list(
list(
`name`="model",
`title`="Model",
`type`="text"),
list(
`name`="dev",
`title`="Deviance",
`type`="number",
`visible`="(dev)"),
list(
`name`="aic",
`title`="AIC",
`type`="number",
`visible`="(aic)"),
list(
`name`="bic",
`title`="BIC",
`type`="number",
`visible`="(bic)"),
list(
`name`="r2mf",
`title`="R\u00B2<sub>McF</sub>",
`type`="number",
`visible`="(pseudoR2:r2mf)"),
list(
`name`="r2cs",
`title`="R\u00B2<sub>CS</sub>",
`type`="number",
`visible`="(pseudoR2:r2cs)"),
list(
`name`="r2n",
`title`="R\u00B2<sub>N</sub>",
`type`="number",
`visible`="(pseudoR2:r2n)"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"),
list(
`name`="df",
`title`="df",
`type`="integer",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"))))
self$add(jmvcore::Table$new(
options=options,
name="modelComp",
title="Model Comparisons",
clearWith=list(
"counts",
"blocks"),
columns=list(
list(
`name`="model1",
`title`="Model",
`content`=".",
`type`="text",
`superTitle`="Comparison"),
list(
`name`="sep",
`title`="",
`content`="-",
`type`="text",
`format`="narrow",
`superTitle`="Comparison"),
list(
`name`="model2",
`title`="Model",
`content`=".",
`type`="text",
`superTitle`="Comparison"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Array$new(
options=options,
name="models",
title="Model Specific Results",
layout="listSelect",
hideHeadingOnlyChild=TRUE,
clearWith=list(
"counts",
"blocks"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
lrt = function() private$.items[["lrt"]],
coef = function() private$.items[["coef"]],
emm = function() private$.items[["emm"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="")
self$add(jmvcore::Table$new(
options=options,
name="lrt",
title="Omnibus Likelihood Ratio Tests",
clearWith=list(
"counts",
"blocks"),
visible="(omni)",
refs="car",
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coef",
title="Model Coefficients",
clearWith=list(
"counts",
"blocks",
"refLevels"),
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="z",
`title`="Z",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="rate",
`title`="Rate ratio",
`type`="number",
`visible`="(RR)"),
list(
`name`="rateLower",
`title`="Lower",
`type`="number",
`visible`="(ciRR && RR)"),
list(
`name`="rateUpper",
`title`="Upper",
`type`="number",
`visible`="(ciRR && RR)"))))
self$add(jmvcore::Array$new(
options=options,
name="emm",
title="Estimated Marginal Means",
refs="emmeans",
clearWith=list(
"counts",
"blocks",
"emMeans"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
emmPlot = function() private$.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(
"counts",
"blocks",
"refLevels",
"ciEmm",
"ciWidthEmm",
"emmWeights")))
self$add(jmvcore::Table$new(
options=options,
name="emmTable",
title="",
visible="(emmTables)",
columns=list(),
clearWith=list(
"counts",
"blocks",
"refLevels",
"ciWidthEmm",
"emmWeights")))}))$new(options=options)))}))$new(options=options)))}))
logLinearBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logLinearBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "logLinear",
version = c(1,0,0),
options = options,
results = logLinearResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Log-Linear Regression
#'
#' Log-Linear Regression
#'
#' @examples
#' data('mtcars')
#'
#' tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl)
#' dat <- as.data.frame(tab)
#'
#' logLinear(data = dat, factors = vars(gear, cyl), counts = Freq,
#' blocks = list(list("gear", "cyl", c("gear", "cyl"))),
#' refLevels = list(
#' list(var="gear", ref="3"),
#' list(var="cyl", ref="4")))
#'
#' #
#' # LOG-LINEAR REGRESSION
#' #
#' # Model Fit Measures
#' # ---------------------------------------
#' # Model Deviance AIC R²-McF
#' # ---------------------------------------
#' # 1 4.12e-10 41.4 1.000
#' # ---------------------------------------
#' #
#' #
#' # MODEL SPECIFIC RESULTS
#' #
#' # MODEL 1
#' #
#' # Model Coefficients
#' # ------------------------------------------------------------------
#' # Predictor Estimate SE Z p
#' # ------------------------------------------------------------------
#' # Intercept -4.71e-16 1.00 -4.71e-16 1.000
#' # gear:
#' # 4 – 3 2.079 1.06 1.961 0.050
#' # 5 – 3 0.693 1.22 0.566 0.571
#' # cyl:
#' # 6 – 4 0.693 1.22 0.566 0.571
#' # 8 – 4 2.485 1.04 2.387 0.017
#' # gear:cyl:
#' # (4 – 3):(6 – 4) -1.386 1.37 -1.012 0.311
#' # (5 – 3):(6 – 4) -1.386 1.73 -0.800 0.423
#' # (4 – 3):(8 – 4) -26.867 42247.17 -6.36e -4 0.999
#' # (5 – 3):(8 – 4) -2.485 1.44 -1.722 0.085
#' # ------------------------------------------------------------------
#' #
#' #
#'
#' @param data the data as a data frame
#' @param factors a vector of strings naming the factors from \code{data}
#' @param counts a string naming a variable in \code{data} containing counts,
#' or NULL if each row represents a single observation
#' @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 RR \code{TRUE} or \code{FALSE} (default), provide the exponential of
#' the log-rate ratio estimate, or the rate ratio estimate
#' @param ciRR \code{TRUE} or \code{FALSE} (default), provide a confidence
#' interval for the model coefficient rate ratio estimates
#' @param ciWidthRR 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
#' @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$models} \tab \tab \tab \tab \tab an array of model specific results \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$modelFit$asDF}
#'
#' \code{as.data.frame(results$modelFit)}
#'
#' @export
logLinear <- function(
data,
factors = NULL,
counts = NULL,
blocks = list(
list()),
refLevels = NULL,
modelTest = FALSE,
dev = TRUE,
aic = TRUE,
bic = FALSE,
pseudoR2 = list(
"r2mf"),
omni = FALSE,
ci = FALSE,
ciWidth = 95,
RR = FALSE,
ciRR = FALSE,
ciWidthRR = 95,
emMeans = list(
list()),
ciEmm = TRUE,
ciWidthEmm = 95,
emmPlots = TRUE,
emmTables = FALSE,
emmWeights = TRUE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("logLinear requires jmvcore to be installed (restart may be required)")
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if ( ! missing(counts)) counts <- jmvcore::resolveQuo(jmvcore::enquo(counts))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(factors), factors, NULL),
`if`( ! missing(counts), counts, NULL))
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
if (inherits(emMeans, "formula")) emMeans <- jmvcore::decomposeFormula(emMeans)
options <- logLinearOptions$new(
factors = factors,
counts = counts,
blocks = blocks,
refLevels = refLevels,
modelTest = modelTest,
dev = dev,
aic = aic,
bic = bic,
pseudoR2 = pseudoR2,
omni = omni,
ci = ci,
ciWidth = ciWidth,
RR = RR,
ciRR = ciRR,
ciWidthRR = ciWidthRR,
emMeans = emMeans,
ciEmm = ciEmm,
ciWidthEmm = ciWidthEmm,
emmPlots = emmPlots,
emmTables = emmTables,
emmWeights = emmWeights)
analysis <- logLinearClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
jamovi/jmv documentation built on Jan. 17, 2025, 10:31 p.m.
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Defines functions logLinear
# This file is automatically generated, you probably don't want to edit this
logLinearOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logLinearOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
factors = NULL,
counts = NULL,
blocks = list(
list()),
refLevels = NULL,
modelTest = FALSE,
dev = TRUE,
aic = TRUE,
bic = FALSE,
pseudoR2 = list(
"r2mf"),
omni = FALSE,
ci = FALSE,
ciWidth = 95,
RR = FALSE,
ciRR = FALSE,
ciWidthRR = 95,
emMeans = list(
list()),
ciEmm = TRUE,
ciWidthEmm = 95,
emmPlots = TRUE,
emmTables = FALSE,
emmWeights = TRUE, ...) {
super$initialize(
package="jmv",
name="logLinear",
requiresData=TRUE,
...)
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
rejectUnusedLevels=TRUE,
suggested=list(
"nominal"),
permitted=list(
"factor"),
default=NULL)
private$..counts <- jmvcore::OptionVariable$new(
"counts",
counts,
default=NULL,
permitted=list(
"numeric"))
private$..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"),
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$..RR <- jmvcore::OptionBool$new(
"RR",
RR,
default=FALSE)
private$..ciRR <- jmvcore::OptionBool$new(
"ciRR",
ciRR,
default=FALSE)
private$..ciWidthRR <- jmvcore::OptionNumber$new(
"ciWidthRR",
ciWidthRR,
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$new(
"emmWeights",
emmWeights,
default=TRUE)
self$.addOption(private$..factors)
self$.addOption(private$..counts)
self$.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$..RR)
self$.addOption(private$..ciRR)
self$.addOption(private$..ciWidthRR)
self$.addOption(private$..emMeans)
self$.addOption(private$..ciEmm)
self$.addOption(private$..ciWidthEmm)
self$.addOption(private$..emmPlots)
self$.addOption(private$..emmTables)
self$.addOption(private$..emmWeights)
}),
active = list(
factors = function() private$..factors$value,
counts = function() private$..counts$value,
blocks = function() private$..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$value,
RR = function() private$..RR$value,
ciRR = function() private$..ciRR$value,
ciWidthRR = function() private$..ciWidthRR$value,
emMeans = function() private$..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$value),
private = list(
..factors = NA,
..counts = NA,
..blocks = NA,
..refLevels = NA,
..modelTest = NA,
..dev = NA,
..aic = NA,
..bic = NA,
..pseudoR2 = NA,
..omni = NA,
..ci = NA,
..ciWidth = NA,
..RR = NA,
..ciRR = NA,
..ciWidthRR = NA,
..emMeans = NA,
..ciEmm = NA,
..ciWidthEmm = NA,
..emmPlots = NA,
..emmTables = NA,
..emmWeights = NA)
)
logLinearResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logLinearResults",
inherit = jmvcore::Group,
active = list(
modelFit = function() private$.items[["modelFit"]],
modelComp = function() private$.items[["modelComp"]],
models = function() private$.items[["models"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Log-Linear Regression")
self$add(jmvcore::Table$new(
options=options,
name="modelFit",
title="Model Fit Measures",
clearWith=list(
"counts",
"blocks"),
visible="(dev || aic || bic || pseudoR2:r2mf || pseudoR2:r2cs || pseudoR2:r2n || modelTest)",
columns=list(
list(
`name`="model",
`title`="Model",
`type`="text"),
list(
`name`="dev",
`title`="Deviance",
`type`="number",
`visible`="(dev)"),
list(
`name`="aic",
`title`="AIC",
`type`="number",
`visible`="(aic)"),
list(
`name`="bic",
`title`="BIC",
`type`="number",
`visible`="(bic)"),
list(
`name`="r2mf",
`title`="R\u00B2<sub>McF</sub>",
`type`="number",
`visible`="(pseudoR2:r2mf)"),
list(
`name`="r2cs",
`title`="R\u00B2<sub>CS</sub>",
`type`="number",
`visible`="(pseudoR2:r2cs)"),
list(
`name`="r2n",
`title`="R\u00B2<sub>N</sub>",
`type`="number",
`visible`="(pseudoR2:r2n)"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"),
list(
`name`="df",
`title`="df",
`type`="integer",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue",
`superTitle`="Overall Model Test",
`visible`="(modelTest)"))))
self$add(jmvcore::Table$new(
options=options,
name="modelComp",
title="Model Comparisons",
clearWith=list(
"counts",
"blocks"),
columns=list(
list(
`name`="model1",
`title`="Model",
`content`=".",
`type`="text",
`superTitle`="Comparison"),
list(
`name`="sep",
`title`="",
`content`="-",
`type`="text",
`format`="narrow",
`superTitle`="Comparison"),
list(
`name`="model2",
`title`="Model",
`content`=".",
`type`="text",
`superTitle`="Comparison"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Array$new(
options=options,
name="models",
title="Model Specific Results",
layout="listSelect",
hideHeadingOnlyChild=TRUE,
clearWith=list(
"counts",
"blocks"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
lrt = function() private$.items[["lrt"]],
coef = function() private$.items[["coef"]],
emm = function() private$.items[["emm"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="undefined",
title="")
self$add(jmvcore::Table$new(
options=options,
name="lrt",
title="Omnibus Likelihood Ratio Tests",
clearWith=list(
"counts",
"blocks"),
visible="(omni)",
refs="car",
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="chi",
`title`="\u03C7\u00B2",
`type`="number"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coef",
title="Model Coefficients",
clearWith=list(
"counts",
"blocks",
"refLevels"),
columns=list(
list(
`name`="term",
`title`="Predictor",
`type`="text"),
list(
`name`="est",
`title`="Estimate",
`type`="number"),
list(
`name`="lower",
`title`="Lower",
`type`="number",
`visible`="(ci)"),
list(
`name`="upper",
`title`="Upper",
`type`="number",
`visible`="(ci)"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="z",
`title`="Z",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="rate",
`title`="Rate ratio",
`type`="number",
`visible`="(RR)"),
list(
`name`="rateLower",
`title`="Lower",
`type`="number",
`visible`="(ciRR && RR)"),
list(
`name`="rateUpper",
`title`="Upper",
`type`="number",
`visible`="(ciRR && RR)"))))
self$add(jmvcore::Array$new(
options=options,
name="emm",
title="Estimated Marginal Means",
refs="emmeans",
clearWith=list(
"counts",
"blocks",
"emMeans"),
template=R6::R6Class(
inherit = jmvcore::Group,
active = list(
emmPlot = function() private$.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(
"counts",
"blocks",
"refLevels",
"ciEmm",
"ciWidthEmm",
"emmWeights")))
self$add(jmvcore::Table$new(
options=options,
name="emmTable",
title="",
visible="(emmTables)",
columns=list(),
clearWith=list(
"counts",
"blocks",
"refLevels",
"ciWidthEmm",
"emmWeights")))}))$new(options=options)))}))$new(options=options)))}))
logLinearBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"logLinearBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "logLinear",
version = c(1,0,0),
options = options,
results = logLinearResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Log-Linear Regression
#'
#' Log-Linear Regression
#'
#' @examples
#' data('mtcars')
#'
#' tab <- table('gear'=mtcars$gear, 'cyl'=mtcars$cyl)
#' dat <- as.data.frame(tab)
#'
#' logLinear(data = dat, factors = vars(gear, cyl), counts = Freq,
#' blocks = list(list("gear", "cyl", c("gear", "cyl"))),
#' refLevels = list(
#' list(var="gear", ref="3"),
#' list(var="cyl", ref="4")))
#'
#' #
#' # LOG-LINEAR REGRESSION
#' #
#' # Model Fit Measures
#' # ---------------------------------------
#' # Model Deviance AIC R²-McF
#' # ---------------------------------------
#' # 1 4.12e-10 41.4 1.000
#' # ---------------------------------------
#' #
#' #
#' # MODEL SPECIFIC RESULTS
#' #
#' # MODEL 1
#' #
#' # Model Coefficients
#' # ------------------------------------------------------------------
#' # Predictor Estimate SE Z p
#' # ------------------------------------------------------------------
#' # Intercept -4.71e-16 1.00 -4.71e-16 1.000
#' # gear:
#' # 4 – 3 2.079 1.06 1.961 0.050
#' # 5 – 3 0.693 1.22 0.566 0.571
#' # cyl:
#' # 6 – 4 0.693 1.22 0.566 0.571
#' # 8 – 4 2.485 1.04 2.387 0.017
#' # gear:cyl:
#' # (4 – 3):(6 – 4) -1.386 1.37 -1.012 0.311
#' # (5 – 3):(6 – 4) -1.386 1.73 -0.800 0.423
#' # (4 – 3):(8 – 4) -26.867 42247.17 -6.36e -4 0.999
#' # (5 – 3):(8 – 4) -2.485 1.44 -1.722 0.085
#' # ------------------------------------------------------------------
#' #
#' #
#'
#' @param data the data as a data frame
#' @param factors a vector of strings naming the factors from \code{data}
#' @param counts a string naming a variable in \code{data} containing counts,
#' or NULL if each row represents a single observation
#' @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 RR \code{TRUE} or \code{FALSE} (default), provide the exponential of
#' the log-rate ratio estimate, or the rate ratio estimate
#' @param ciRR \code{TRUE} or \code{FALSE} (default), provide a confidence
#' interval for the model coefficient rate ratio estimates
#' @param ciWidthRR 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
#' @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$models} \tab \tab \tab \tab \tab an array of model specific results \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$modelFit$asDF}
#'
#' \code{as.data.frame(results$modelFit)}
#'
#' @export
logLinear <- function(
data,
factors = NULL,
counts = NULL,
blocks = list(
list()),
refLevels = NULL,
modelTest = FALSE,
dev = TRUE,
aic = TRUE,
bic = FALSE,
pseudoR2 = list(
"r2mf"),
omni = FALSE,
ci = FALSE,
ciWidth = 95,
RR = FALSE,
ciRR = FALSE,
ciWidthRR = 95,
emMeans = list(
list()),
ciEmm = TRUE,
ciWidthEmm = 95,
emmPlots = TRUE,
emmTables = FALSE,
emmWeights = TRUE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("logLinear requires jmvcore to be installed (restart may be required)")
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if ( ! missing(counts)) counts <- jmvcore::resolveQuo(jmvcore::enquo(counts))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(factors), factors, NULL),
`if`( ! missing(counts), counts, NULL))
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
if (inherits(emMeans, "formula")) emMeans <- jmvcore::decomposeFormula(emMeans)
options <- logLinearOptions$new(
factors = factors,
counts = counts,
blocks = blocks,
refLevels = refLevels,
modelTest = modelTest,
dev = dev,
aic = aic,
bic = bic,
pseudoR2 = pseudoR2,
omni = omni,
ci = ci,
ciWidth = ciWidth,
RR = RR,
ciRR = ciRR,
ciWidthRR = ciWidthRR,
emMeans = emMeans,
ciEmm = ciEmm,
ciWidthEmm = ciWidthEmm,
emmPlots = emmPlots,
emmTables = emmTables,
emmWeights = emmWeights)
analysis <- logLinearClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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