R/proptest2.h.R
In jamovi/Rjamovi: The 'jamovi' Analyses
Defines functions
propTest2
# This file is automatically generated, you probably don't want to edit this
propTest2Options <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"propTest2Options",
inherit = jmvcore::Options,
public = list(
initialize = function(
vars = NULL,
areCounts = FALSE,
testValue = 0.5,
hypothesis = "notequal",
ci = FALSE,
ciWidth = 95,
bf = FALSE,
priorA = 1,
priorB = 1,
ciBayes = FALSE,
ciBayesWidth = 95,
postPlots = FALSE, ...) {
super$initialize(
package="jmv",
name="propTest2",
requiresData=TRUE,
...)
private$..vars <- jmvcore::OptionVariables$new(
"vars",
vars,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"))
private$..areCounts <- jmvcore::OptionBool$new(
"areCounts",
areCounts,
default=FALSE)
private$..testValue <- jmvcore::OptionNumber$new(
"testValue",
testValue,
default=0.5)
private$..hypothesis <- jmvcore::OptionList$new(
"hypothesis",
hypothesis,
options=list(
"notequal",
"greater",
"less"),
default="notequal")
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=FALSE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..bf <- jmvcore::OptionBool$new(
"bf",
bf,
default=FALSE)
private$..priorA <- jmvcore::OptionNumber$new(
"priorA",
priorA,
min=0.01,
default=1)
private$..priorB <- jmvcore::OptionNumber$new(
"priorB",
priorB,
min=0.01,
default=1)
private$..ciBayes <- jmvcore::OptionBool$new(
"ciBayes",
ciBayes,
default=FALSE)
private$..ciBayesWidth <- jmvcore::OptionNumber$new(
"ciBayesWidth",
ciBayesWidth,
min=50,
max=99.9,
default=95)
private$..postPlots <- jmvcore::OptionBool$new(
"postPlots",
postPlots,
default=FALSE)
self$.addOption(private$..vars)
self$.addOption(private$..areCounts)
self$.addOption(private$..testValue)
self$.addOption(private$..hypothesis)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..bf)
self$.addOption(private$..priorA)
self$.addOption(private$..priorB)
self$.addOption(private$..ciBayes)
self$.addOption(private$..ciBayesWidth)
self$.addOption(private$..postPlots)
}),
active = list(
vars = function() private$..vars$value,
areCounts = function() private$..areCounts$value,
testValue = function() private$..testValue$value,
hypothesis = function() private$..hypothesis$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
bf = function() private$..bf$value,
priorA = function() private$..priorA$value,
priorB = function() private$..priorB$value,
ciBayes = function() private$..ciBayes$value,
ciBayesWidth = function() private$..ciBayesWidth$value,
postPlots = function() private$..postPlots$value),
private = list(
..vars = NA,
..areCounts = NA,
..testValue = NA,
..hypothesis = NA,
..ci = NA,
..ciWidth = NA,
..bf = NA,
..priorA = NA,
..priorB = NA,
..ciBayes = NA,
..ciBayesWidth = NA,
..postPlots = NA)
)
propTest2Results <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"propTest2Results",
inherit = jmvcore::Group,
active = list(
table = function() private$.items[["table"]],
postPlots = function() private$.items[["postPlots"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Proportion Test (2 Outcomes)")
self$add(jmvcore::Table$new(
options=options,
name="table",
title="Binomial Test",
clearWith=list(
"vars",
"areCounts",
"testValue",
"hypothesis",
"ciWidth",
"bf",
"ciBayes",
"ciBayesWidth",
"priorA",
"priorB",
"ci"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="level",
`title`="Level",
`type`="text"),
list(
`name`="count",
`title`="Count",
`type`="integer"),
list(
`name`="total",
`title`="Total",
`type`="integer"),
list(
`name`="prop",
`title`="Proportion",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="cil",
`title`="Lower",
`superTitle`="Confidence Interval",
`type`="number",
`visible`="(ci)"),
list(
`name`="ciu",
`title`="Upper",
`superTitle`="Confidence Interval",
`type`="number",
`visible`="(ci)"),
list(
`name`="bf",
`title`="Bayes factor\u2081\u2080",
`type`="number",
`visible`="(bf)"),
list(
`name`="cilBayes",
`title`="Lower",
`superTitle`="Credible Interval",
`type`="number",
`visible`="(ciBayes)"),
list(
`name`="ciuBayes",
`title`="Upper",
`superTitle`="Credible Interval",
`type`="number",
`visible`="(ciBayes)"))))
self$add(jmvcore::Array$new(
options=options,
name="postPlots",
title="Posterior Plots",
visible="(postPlots)",
items="(vars)",
template=jmvcore::Array$new(
options=options,
title="($key)",
template=jmvcore::Image$new(
options=options,
title="($key)",
renderFun=".postPlot",
width=400,
height=350,
clearWith=list(
"priorA",
"priorB",
"testValue",
"hypothesis")))))}))
propTest2Base <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"propTest2Base",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "propTest2",
version = c(1,0,0),
options = options,
results = propTest2Results$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Proportion Test (2 Outcomes)
#'
#' The Binomial test is used to test the Null hypothesis that the proportion
#' of observations match some expected value. If the p-value is low, this
#' suggests that the Null hypothesis is false, and that the true proportion
#' must be some other value.
#'
#'
#' @examples
#' \donttest{
#' dat <- data.frame(x=c(8, 15))
#'
#' propTest2(dat, vars = x, areCounts = TRUE)
#'
#' #
#' # PROPORTION TEST (2 OUTCOMES)
#' #
#' # Binomial Test
#' # -------------------------------------------------------
#' # Level Count Total Proportion p
#' # -------------------------------------------------------
#' # x 1 8 23 0.348 0.210
#' # 2 15 23 0.652 0.210
#' # -------------------------------------------------------
#' # Note. Ha is proportion != 0.5
#' #
#'}
#' @param data the data as a data frame
#' @param vars a vector of strings naming the variables of interest in
#' \code{data}
#' @param areCounts \code{TRUE} or \code{FALSE} (default), the variables are
#' counts
#' @param testValue a number (default: 0.5), the value for the null hypothesis
#' @param hypothesis \code{'notequal'} (default), \code{'greater'} or
#' \code{'less'}, the alternative hypothesis
#' @param ci \code{TRUE} or \code{FALSE} (default), provide confidence
#' intervals
#' @param ciWidth a number between 50 and 99.9 (default: 95), the confidence
#' interval width
#' @param bf \code{TRUE} or \code{FALSE} (default), provide Bayes factors
#' @param priorA a number (default: 1), the beta prior 'a' parameter
#' @param priorB a number (default: 1), the beta prior 'b' parameter
#' @param ciBayes \code{TRUE} or \code{FALSE} (default), provide Bayesian
#' credible intervals
#' @param ciBayesWidth a number between 50 and 99.9 (default: 95), the
#' credible interval width
#' @param postPlots \code{TRUE} or \code{FALSE} (default), provide posterior
#' plots
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$table} \tab \tab \tab \tab \tab a table of the proportions and test results \cr
#' \code{results$postPlots} \tab \tab \tab \tab \tab an array of the posterior plots \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$table$asDF}
#'
#' \code{as.data.frame(results$table)}
#'
#' @export
propTest2 <- function(
data,
vars,
areCounts = FALSE,
testValue = 0.5,
hypothesis = "notequal",
ci = FALSE,
ciWidth = 95,
bf = FALSE,
priorA = 1,
priorB = 1,
ciBayes = FALSE,
ciBayesWidth = 95,
postPlots = FALSE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("propTest2 requires jmvcore to be installed (restart may be required)")
if ( ! missing(vars)) vars <- jmvcore::resolveQuo(jmvcore::enquo(vars))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(vars), vars, NULL))
for (v in vars) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- propTest2Options$new(
vars = vars,
areCounts = areCounts,
testValue = testValue,
hypothesis = hypothesis,
ci = ci,
ciWidth = ciWidth,
bf = bf,
priorA = priorA,
priorB = priorB,
ciBayes = ciBayes,
ciBayesWidth = ciBayesWidth,
postPlots = postPlots)
analysis <- propTest2Class$new(
options = options,
data = data)
analysis$run()
analysis$results
}
jamovi/Rjamovi documentation built on Jan. 17, 2025, 10:29 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions propTest2
# This file is automatically generated, you probably don't want to edit this
propTest2Options <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"propTest2Options",
inherit = jmvcore::Options,
public = list(
initialize = function(
vars = NULL,
areCounts = FALSE,
testValue = 0.5,
hypothesis = "notequal",
ci = FALSE,
ciWidth = 95,
bf = FALSE,
priorA = 1,
priorB = 1,
ciBayes = FALSE,
ciBayesWidth = 95,
postPlots = FALSE, ...) {
super$initialize(
package="jmv",
name="propTest2",
requiresData=TRUE,
...)
private$..vars <- jmvcore::OptionVariables$new(
"vars",
vars,
suggested=list(
"nominal",
"ordinal"),
permitted=list(
"factor"))
private$..areCounts <- jmvcore::OptionBool$new(
"areCounts",
areCounts,
default=FALSE)
private$..testValue <- jmvcore::OptionNumber$new(
"testValue",
testValue,
default=0.5)
private$..hypothesis <- jmvcore::OptionList$new(
"hypothesis",
hypothesis,
options=list(
"notequal",
"greater",
"less"),
default="notequal")
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=FALSE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..bf <- jmvcore::OptionBool$new(
"bf",
bf,
default=FALSE)
private$..priorA <- jmvcore::OptionNumber$new(
"priorA",
priorA,
min=0.01,
default=1)
private$..priorB <- jmvcore::OptionNumber$new(
"priorB",
priorB,
min=0.01,
default=1)
private$..ciBayes <- jmvcore::OptionBool$new(
"ciBayes",
ciBayes,
default=FALSE)
private$..ciBayesWidth <- jmvcore::OptionNumber$new(
"ciBayesWidth",
ciBayesWidth,
min=50,
max=99.9,
default=95)
private$..postPlots <- jmvcore::OptionBool$new(
"postPlots",
postPlots,
default=FALSE)
self$.addOption(private$..vars)
self$.addOption(private$..areCounts)
self$.addOption(private$..testValue)
self$.addOption(private$..hypothesis)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..bf)
self$.addOption(private$..priorA)
self$.addOption(private$..priorB)
self$.addOption(private$..ciBayes)
self$.addOption(private$..ciBayesWidth)
self$.addOption(private$..postPlots)
}),
active = list(
vars = function() private$..vars$value,
areCounts = function() private$..areCounts$value,
testValue = function() private$..testValue$value,
hypothesis = function() private$..hypothesis$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
bf = function() private$..bf$value,
priorA = function() private$..priorA$value,
priorB = function() private$..priorB$value,
ciBayes = function() private$..ciBayes$value,
ciBayesWidth = function() private$..ciBayesWidth$value,
postPlots = function() private$..postPlots$value),
private = list(
..vars = NA,
..areCounts = NA,
..testValue = NA,
..hypothesis = NA,
..ci = NA,
..ciWidth = NA,
..bf = NA,
..priorA = NA,
..priorB = NA,
..ciBayes = NA,
..ciBayesWidth = NA,
..postPlots = NA)
)
propTest2Results <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"propTest2Results",
inherit = jmvcore::Group,
active = list(
table = function() private$.items[["table"]],
postPlots = function() private$.items[["postPlots"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Proportion Test (2 Outcomes)")
self$add(jmvcore::Table$new(
options=options,
name="table",
title="Binomial Test",
clearWith=list(
"vars",
"areCounts",
"testValue",
"hypothesis",
"ciWidth",
"bf",
"ciBayes",
"ciBayesWidth",
"priorA",
"priorB",
"ci"),
columns=list(
list(
`name`="var",
`title`="",
`type`="text",
`combineBelow`=TRUE),
list(
`name`="level",
`title`="Level",
`type`="text"),
list(
`name`="count",
`title`="Count",
`type`="integer"),
list(
`name`="total",
`title`="Total",
`type`="integer"),
list(
`name`="prop",
`title`="Proportion",
`type`="number",
`format`="zto"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"),
list(
`name`="cil",
`title`="Lower",
`superTitle`="Confidence Interval",
`type`="number",
`visible`="(ci)"),
list(
`name`="ciu",
`title`="Upper",
`superTitle`="Confidence Interval",
`type`="number",
`visible`="(ci)"),
list(
`name`="bf",
`title`="Bayes factor\u2081\u2080",
`type`="number",
`visible`="(bf)"),
list(
`name`="cilBayes",
`title`="Lower",
`superTitle`="Credible Interval",
`type`="number",
`visible`="(ciBayes)"),
list(
`name`="ciuBayes",
`title`="Upper",
`superTitle`="Credible Interval",
`type`="number",
`visible`="(ciBayes)"))))
self$add(jmvcore::Array$new(
options=options,
name="postPlots",
title="Posterior Plots",
visible="(postPlots)",
items="(vars)",
template=jmvcore::Array$new(
options=options,
title="($key)",
template=jmvcore::Image$new(
options=options,
title="($key)",
renderFun=".postPlot",
width=400,
height=350,
clearWith=list(
"priorA",
"priorB",
"testValue",
"hypothesis")))))}))
propTest2Base <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"propTest2Base",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "propTest2",
version = c(1,0,0),
options = options,
results = propTest2Results$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Proportion Test (2 Outcomes)
#'
#' The Binomial test is used to test the Null hypothesis that the proportion
#' of observations match some expected value. If the p-value is low, this
#' suggests that the Null hypothesis is false, and that the true proportion
#' must be some other value.
#'
#'
#' @examples
#' \donttest{
#' dat <- data.frame(x=c(8, 15))
#'
#' propTest2(dat, vars = x, areCounts = TRUE)
#'
#' #
#' # PROPORTION TEST (2 OUTCOMES)
#' #
#' # Binomial Test
#' # -------------------------------------------------------
#' # Level Count Total Proportion p
#' # -------------------------------------------------------
#' # x 1 8 23 0.348 0.210
#' # 2 15 23 0.652 0.210
#' # -------------------------------------------------------
#' # Note. Ha is proportion != 0.5
#' #
#'}
#' @param data the data as a data frame
#' @param vars a vector of strings naming the variables of interest in
#' \code{data}
#' @param areCounts \code{TRUE} or \code{FALSE} (default), the variables are
#' counts
#' @param testValue a number (default: 0.5), the value for the null hypothesis
#' @param hypothesis \code{'notequal'} (default), \code{'greater'} or
#' \code{'less'}, the alternative hypothesis
#' @param ci \code{TRUE} or \code{FALSE} (default), provide confidence
#' intervals
#' @param ciWidth a number between 50 and 99.9 (default: 95), the confidence
#' interval width
#' @param bf \code{TRUE} or \code{FALSE} (default), provide Bayes factors
#' @param priorA a number (default: 1), the beta prior 'a' parameter
#' @param priorB a number (default: 1), the beta prior 'b' parameter
#' @param ciBayes \code{TRUE} or \code{FALSE} (default), provide Bayesian
#' credible intervals
#' @param ciBayesWidth a number between 50 and 99.9 (default: 95), the
#' credible interval width
#' @param postPlots \code{TRUE} or \code{FALSE} (default), provide posterior
#' plots
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$table} \tab \tab \tab \tab \tab a table of the proportions and test results \cr
#' \code{results$postPlots} \tab \tab \tab \tab \tab an array of the posterior plots \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$table$asDF}
#'
#' \code{as.data.frame(results$table)}
#'
#' @export
propTest2 <- function(
data,
vars,
areCounts = FALSE,
testValue = 0.5,
hypothesis = "notequal",
ci = FALSE,
ciWidth = 95,
bf = FALSE,
priorA = 1,
priorB = 1,
ciBayes = FALSE,
ciBayesWidth = 95,
postPlots = FALSE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("propTest2 requires jmvcore to be installed (restart may be required)")
if ( ! missing(vars)) vars <- jmvcore::resolveQuo(jmvcore::enquo(vars))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(vars), vars, NULL))
for (v in vars) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- propTest2Options$new(
vars = vars,
areCounts = areCounts,
testValue = testValue,
hypothesis = hypothesis,
ci = ci,
ciWidth = ciWidth,
bf = bf,
priorA = priorA,
priorB = priorB,
ciBayes = ciBayes,
ciBayesWidth = ciBayesWidth,
postPlots = postPlots)
analysis <- propTest2Class$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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