R/corrmatrix.h.R
In silkyproject/silkyR: The 'jamovi' Analyses
Defines functions
corrMatrix
# This file is automatically generated, you probably don't want to edit this
corrMatrixOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"corrMatrixOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
vars = NULL,
pearson = TRUE,
spearman = FALSE,
kendall = FALSE,
sig = TRUE,
flag = FALSE,
n = FALSE,
ci = FALSE,
ciWidth = 95,
plots = FALSE,
plotDens = FALSE,
plotStats = FALSE,
hypothesis = "corr", ...) {
super$initialize(
package="jmv",
name="corrMatrix",
requiresData=TRUE,
...)
private$..vars <- jmvcore::OptionVariables$new(
"vars",
vars,
takeFromDataIfMissing=TRUE,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric",
"factor"))
private$..pearson <- jmvcore::OptionBool$new(
"pearson",
pearson,
default=TRUE)
private$..spearman <- jmvcore::OptionBool$new(
"spearman",
spearman,
default=FALSE)
private$..kendall <- jmvcore::OptionBool$new(
"kendall",
kendall,
default=FALSE)
private$..sig <- jmvcore::OptionBool$new(
"sig",
sig,
default=TRUE)
private$..flag <- jmvcore::OptionBool$new(
"flag",
flag,
default=FALSE)
private$..n <- jmvcore::OptionBool$new(
"n",
n,
default=FALSE)
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=FALSE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..plots <- jmvcore::OptionBool$new(
"plots",
plots,
default=FALSE)
private$..plotDens <- jmvcore::OptionBool$new(
"plotDens",
plotDens,
default=FALSE)
private$..plotStats <- jmvcore::OptionBool$new(
"plotStats",
plotStats,
default=FALSE)
private$..hypothesis <- jmvcore::OptionList$new(
"hypothesis",
hypothesis,
options=list(
"corr",
"pos",
"neg"),
default="corr")
self$.addOption(private$..vars)
self$.addOption(private$..pearson)
self$.addOption(private$..spearman)
self$.addOption(private$..kendall)
self$.addOption(private$..sig)
self$.addOption(private$..flag)
self$.addOption(private$..n)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..plots)
self$.addOption(private$..plotDens)
self$.addOption(private$..plotStats)
self$.addOption(private$..hypothesis)
}),
active = list(
vars = function() private$..vars$value,
pearson = function() private$..pearson$value,
spearman = function() private$..spearman$value,
kendall = function() private$..kendall$value,
sig = function() private$..sig$value,
flag = function() private$..flag$value,
n = function() private$..n$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
plots = function() private$..plots$value,
plotDens = function() private$..plotDens$value,
plotStats = function() private$..plotStats$value,
hypothesis = function() private$..hypothesis$value),
private = list(
..vars = NA,
..pearson = NA,
..spearman = NA,
..kendall = NA,
..sig = NA,
..flag = NA,
..n = NA,
..ci = NA,
..ciWidth = NA,
..plots = NA,
..plotDens = NA,
..plotStats = NA,
..hypothesis = NA)
)
corrMatrixResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"corrMatrixResults",
inherit = jmvcore::Group,
active = list(
matrix = function() private$.items[["matrix"]],
plot = function() private$.items[["plot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Correlation Matrix")
self$add(jmvcore::Table$new(
options=options,
name="matrix",
title="Correlation Matrix",
rows="(vars)",
clearWith=list(
"ciWidth",
"hypothesis",
"flag"),
columns=list(
list(
`name`=".name[r]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson)"),
list(
`name`=".stat[r]",
`title`="",
`type`="text",
`content`="Pearson's r",
`visible`="(pearson && (sig || spearman || kendall || ci || n))"),
list(
`name`=".name[rdf]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson && sig)"),
list(
`name`=".stat[rdf]",
`title`="",
`type`="text",
`content`="df",
`visible`="(pearson && sig)"),
list(
`name`=".name[rp]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson && sig)"),
list(
`name`=".stat[rp]",
`title`="",
`type`="text",
`content`="p-value",
`visible`="(pearson && sig)"),
list(
`name`=".name[rciu]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson && ci)"),
list(
`name`=".stat[rciu]",
`title`="",
`type`="text",
`content`="CI Upper",
`visible`="(pearson && ci)"),
list(
`name`=".name[rcil]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson && ci)"),
list(
`name`=".stat[rcil]",
`title`="",
`type`="text",
`content`="CI Lower",
`visible`="(pearson && ci)"),
list(
`name`=".name[rho]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(spearman)"),
list(
`name`=".stat[rho]",
`title`="",
`type`="text",
`content`="Spearman's rho",
`visible`="(spearman && (sig || pearson || kendall || n))"),
list(
`name`=".name[rhodf]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(spearman && sig)"),
list(
`name`=".stat[rhodf]",
`title`="",
`type`="text",
`content`="df",
`visible`="(spearman && sig)"),
list(
`name`=".name[rhop]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(spearman && sig)"),
list(
`name`=".stat[rhop]",
`title`="",
`type`="text",
`content`="p-value",
`visible`="(spearman && sig)"),
list(
`name`=".name[tau]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(kendall)"),
list(
`name`=".stat[tau]",
`title`="",
`type`="text",
`content`="Kendall's Tau B",
`visible`="(kendall && (sig || pearson || spearman || n))"),
list(
`name`=".name[taup]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(kendall && sig)"),
list(
`name`=".stat[taup]",
`title`="",
`type`="text",
`content`="p-value",
`visible`="(kendall && sig)"),
list(
`name`=".name[n]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(n)"),
list(
`name`=".stat[n]",
`title`="",
`type`="text",
`content`="N",
`visible`="(n)"))))
self$add(jmvcore::Image$new(
options=options,
name="plot",
title="Plot",
visible="(plots)",
width=500,
height=500,
renderFun=".plot",
requiresData=TRUE,
clearWith=list(
"vars",
"plotDens",
"plotStats",
"pearson",
"spearman",
"kendall")))}))
corrMatrixBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"corrMatrixBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "corrMatrix",
version = c(1,0,0),
options = options,
results = corrMatrixResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Correlation Matrix
#'
#' Correlation matrices are a way to examine linear relationships between
#' two or more continuous variables.
#'
#' For each pair of variables, a Pearson's r value indicates the strength
#' and direction of the relationship between those two variables. A
#' positive value indicates a positive relationship (higher values of one
#' variable predict higher values of the other variable). A negative
#' Pearson's r indicates a negative relationship (higher values of one
#' variable predict lower values of the other variable, and vice-versa).
#' A value of zero indicates no relationship (whether a variable is high
#' or low, does not tell us anything about the value of the other
#' variable).
#'
#' More formally, it is possible to test the null hypothesis that the
#' correlation is zero and calculate a p-value. If the p-value is low, it
#' suggests the correlation co-efficient is not zero, and there is a linear
#' (or more complex) relationship between the two variables.
#'
#'
#' @examples
#' \donttest{
#' data('mtcars')
#'
#' corrMatrix(mtcars, vars = vars(mpg, cyl, disp, hp))
#'
#' #
#' # CORRELATION MATRIX
#' #
#' # Correlation Matrix
#' # --------------------------------------------------------------
#' # mpg cyl disp hp
#' # --------------------------------------------------------------
#' # mpg Pearson's r — -0.852 -0.848 -0.776
#' # p-value — < .001 < .001 < .001
#' #
#' # cyl Pearson's r — 0.902 0.832
#' # p-value — < .001 < .001
#' #
#' # disp Pearson's r — 0.791
#' # p-value — < .001
#' #
#' # hp Pearson's r —
#' # p-value —
#' # --------------------------------------------------------------
#' #
#'}
#' @param data the data as a data frame
#' @param vars a vector of strings naming the variables to correlate in
#' \code{data}
#' @param pearson \code{TRUE} (default) or \code{FALSE}, provide Pearson's R
#' @param spearman \code{TRUE} or \code{FALSE} (default), provide Spearman's
#' rho
#' @param kendall \code{TRUE} or \code{FALSE} (default), provide Kendall's
#' tau-b
#' @param sig \code{TRUE} (default) or \code{FALSE}, provide significance
#' levels
#' @param flag \code{TRUE} or \code{FALSE} (default), flag significant
#' correlations
#' @param n \code{TRUE} or \code{FALSE} (default), provide the number of cases
#' @param ci \code{TRUE} or \code{FALSE} (default), provide confidence
#' intervals
#' @param ciWidth a number between 50 and 99.9 (default: 95), the width of
#' confidence intervals to provide
#' @param plots \code{TRUE} or \code{FALSE} (default), provide a correlation
#' matrix plot
#' @param plotDens \code{TRUE} or \code{FALSE} (default), provide densities in
#' the correlation matrix plot
#' @param plotStats \code{TRUE} or \code{FALSE} (default), provide statistics
#' in the correlation matrix plot
#' @param hypothesis one of \code{'corr'} (default), \code{'pos'},
#' \code{'neg'} specifying the alernative hypothesis; correlated, correlated
#' positively, correlated negatively respectively.
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$matrix} \tab \tab \tab \tab \tab a correlation matrix table \cr
#' \code{results$plot} \tab \tab \tab \tab \tab a correlation matrix plot \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$matrix$asDF}
#'
#' \code{as.data.frame(results$matrix)}
#'
#' @export
corrMatrix <- function(
data,
vars,
pearson = TRUE,
spearman = FALSE,
kendall = FALSE,
sig = TRUE,
flag = FALSE,
n = FALSE,
ci = FALSE,
ciWidth = 95,
plots = FALSE,
plotDens = FALSE,
plotStats = FALSE,
hypothesis = "corr") {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("corrMatrix 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))
vars <- `if`( ! missing(vars), vars, colnames(data))
options <- corrMatrixOptions$new(
vars = vars,
pearson = pearson,
spearman = spearman,
kendall = kendall,
sig = sig,
flag = flag,
n = n,
ci = ci,
ciWidth = ciWidth,
plots = plots,
plotDens = plotDens,
plotStats = plotStats,
hypothesis = hypothesis)
analysis <- corrMatrixClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
silkyproject/silkyR documentation built on April 15, 2024, 3:08 p.m.
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Defines functions corrMatrix
# This file is automatically generated, you probably don't want to edit this
corrMatrixOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"corrMatrixOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
vars = NULL,
pearson = TRUE,
spearman = FALSE,
kendall = FALSE,
sig = TRUE,
flag = FALSE,
n = FALSE,
ci = FALSE,
ciWidth = 95,
plots = FALSE,
plotDens = FALSE,
plotStats = FALSE,
hypothesis = "corr", ...) {
super$initialize(
package="jmv",
name="corrMatrix",
requiresData=TRUE,
...)
private$..vars <- jmvcore::OptionVariables$new(
"vars",
vars,
takeFromDataIfMissing=TRUE,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric",
"factor"))
private$..pearson <- jmvcore::OptionBool$new(
"pearson",
pearson,
default=TRUE)
private$..spearman <- jmvcore::OptionBool$new(
"spearman",
spearman,
default=FALSE)
private$..kendall <- jmvcore::OptionBool$new(
"kendall",
kendall,
default=FALSE)
private$..sig <- jmvcore::OptionBool$new(
"sig",
sig,
default=TRUE)
private$..flag <- jmvcore::OptionBool$new(
"flag",
flag,
default=FALSE)
private$..n <- jmvcore::OptionBool$new(
"n",
n,
default=FALSE)
private$..ci <- jmvcore::OptionBool$new(
"ci",
ci,
default=FALSE)
private$..ciWidth <- jmvcore::OptionNumber$new(
"ciWidth",
ciWidth,
min=50,
max=99.9,
default=95)
private$..plots <- jmvcore::OptionBool$new(
"plots",
plots,
default=FALSE)
private$..plotDens <- jmvcore::OptionBool$new(
"plotDens",
plotDens,
default=FALSE)
private$..plotStats <- jmvcore::OptionBool$new(
"plotStats",
plotStats,
default=FALSE)
private$..hypothesis <- jmvcore::OptionList$new(
"hypothesis",
hypothesis,
options=list(
"corr",
"pos",
"neg"),
default="corr")
self$.addOption(private$..vars)
self$.addOption(private$..pearson)
self$.addOption(private$..spearman)
self$.addOption(private$..kendall)
self$.addOption(private$..sig)
self$.addOption(private$..flag)
self$.addOption(private$..n)
self$.addOption(private$..ci)
self$.addOption(private$..ciWidth)
self$.addOption(private$..plots)
self$.addOption(private$..plotDens)
self$.addOption(private$..plotStats)
self$.addOption(private$..hypothesis)
}),
active = list(
vars = function() private$..vars$value,
pearson = function() private$..pearson$value,
spearman = function() private$..spearman$value,
kendall = function() private$..kendall$value,
sig = function() private$..sig$value,
flag = function() private$..flag$value,
n = function() private$..n$value,
ci = function() private$..ci$value,
ciWidth = function() private$..ciWidth$value,
plots = function() private$..plots$value,
plotDens = function() private$..plotDens$value,
plotStats = function() private$..plotStats$value,
hypothesis = function() private$..hypothesis$value),
private = list(
..vars = NA,
..pearson = NA,
..spearman = NA,
..kendall = NA,
..sig = NA,
..flag = NA,
..n = NA,
..ci = NA,
..ciWidth = NA,
..plots = NA,
..plotDens = NA,
..plotStats = NA,
..hypothesis = NA)
)
corrMatrixResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"corrMatrixResults",
inherit = jmvcore::Group,
active = list(
matrix = function() private$.items[["matrix"]],
plot = function() private$.items[["plot"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Correlation Matrix")
self$add(jmvcore::Table$new(
options=options,
name="matrix",
title="Correlation Matrix",
rows="(vars)",
clearWith=list(
"ciWidth",
"hypothesis",
"flag"),
columns=list(
list(
`name`=".name[r]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson)"),
list(
`name`=".stat[r]",
`title`="",
`type`="text",
`content`="Pearson's r",
`visible`="(pearson && (sig || spearman || kendall || ci || n))"),
list(
`name`=".name[rdf]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson && sig)"),
list(
`name`=".stat[rdf]",
`title`="",
`type`="text",
`content`="df",
`visible`="(pearson && sig)"),
list(
`name`=".name[rp]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson && sig)"),
list(
`name`=".stat[rp]",
`title`="",
`type`="text",
`content`="p-value",
`visible`="(pearson && sig)"),
list(
`name`=".name[rciu]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson && ci)"),
list(
`name`=".stat[rciu]",
`title`="",
`type`="text",
`content`="CI Upper",
`visible`="(pearson && ci)"),
list(
`name`=".name[rcil]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(pearson && ci)"),
list(
`name`=".stat[rcil]",
`title`="",
`type`="text",
`content`="CI Lower",
`visible`="(pearson && ci)"),
list(
`name`=".name[rho]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(spearman)"),
list(
`name`=".stat[rho]",
`title`="",
`type`="text",
`content`="Spearman's rho",
`visible`="(spearman && (sig || pearson || kendall || n))"),
list(
`name`=".name[rhodf]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(spearman && sig)"),
list(
`name`=".stat[rhodf]",
`title`="",
`type`="text",
`content`="df",
`visible`="(spearman && sig)"),
list(
`name`=".name[rhop]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(spearman && sig)"),
list(
`name`=".stat[rhop]",
`title`="",
`type`="text",
`content`="p-value",
`visible`="(spearman && sig)"),
list(
`name`=".name[tau]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(kendall)"),
list(
`name`=".stat[tau]",
`title`="",
`type`="text",
`content`="Kendall's Tau B",
`visible`="(kendall && (sig || pearson || spearman || n))"),
list(
`name`=".name[taup]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(kendall && sig)"),
list(
`name`=".stat[taup]",
`title`="",
`type`="text",
`content`="p-value",
`visible`="(kendall && sig)"),
list(
`name`=".name[n]",
`title`="",
`type`="text",
`content`="($key)",
`combineBelow`=TRUE,
`visible`="(n)"),
list(
`name`=".stat[n]",
`title`="",
`type`="text",
`content`="N",
`visible`="(n)"))))
self$add(jmvcore::Image$new(
options=options,
name="plot",
title="Plot",
visible="(plots)",
width=500,
height=500,
renderFun=".plot",
requiresData=TRUE,
clearWith=list(
"vars",
"plotDens",
"plotStats",
"pearson",
"spearman",
"kendall")))}))
corrMatrixBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"corrMatrixBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "jmv",
name = "corrMatrix",
version = c(1,0,0),
options = options,
results = corrMatrixResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Correlation Matrix
#'
#' Correlation matrices are a way to examine linear relationships between
#' two or more continuous variables.
#'
#' For each pair of variables, a Pearson's r value indicates the strength
#' and direction of the relationship between those two variables. A
#' positive value indicates a positive relationship (higher values of one
#' variable predict higher values of the other variable). A negative
#' Pearson's r indicates a negative relationship (higher values of one
#' variable predict lower values of the other variable, and vice-versa).
#' A value of zero indicates no relationship (whether a variable is high
#' or low, does not tell us anything about the value of the other
#' variable).
#'
#' More formally, it is possible to test the null hypothesis that the
#' correlation is zero and calculate a p-value. If the p-value is low, it
#' suggests the correlation co-efficient is not zero, and there is a linear
#' (or more complex) relationship between the two variables.
#'
#'
#' @examples
#' \donttest{
#' data('mtcars')
#'
#' corrMatrix(mtcars, vars = vars(mpg, cyl, disp, hp))
#'
#' #
#' # CORRELATION MATRIX
#' #
#' # Correlation Matrix
#' # --------------------------------------------------------------
#' # mpg cyl disp hp
#' # --------------------------------------------------------------
#' # mpg Pearson's r — -0.852 -0.848 -0.776
#' # p-value — < .001 < .001 < .001
#' #
#' # cyl Pearson's r — 0.902 0.832
#' # p-value — < .001 < .001
#' #
#' # disp Pearson's r — 0.791
#' # p-value — < .001
#' #
#' # hp Pearson's r —
#' # p-value —
#' # --------------------------------------------------------------
#' #
#'}
#' @param data the data as a data frame
#' @param vars a vector of strings naming the variables to correlate in
#' \code{data}
#' @param pearson \code{TRUE} (default) or \code{FALSE}, provide Pearson's R
#' @param spearman \code{TRUE} or \code{FALSE} (default), provide Spearman's
#' rho
#' @param kendall \code{TRUE} or \code{FALSE} (default), provide Kendall's
#' tau-b
#' @param sig \code{TRUE} (default) or \code{FALSE}, provide significance
#' levels
#' @param flag \code{TRUE} or \code{FALSE} (default), flag significant
#' correlations
#' @param n \code{TRUE} or \code{FALSE} (default), provide the number of cases
#' @param ci \code{TRUE} or \code{FALSE} (default), provide confidence
#' intervals
#' @param ciWidth a number between 50 and 99.9 (default: 95), the width of
#' confidence intervals to provide
#' @param plots \code{TRUE} or \code{FALSE} (default), provide a correlation
#' matrix plot
#' @param plotDens \code{TRUE} or \code{FALSE} (default), provide densities in
#' the correlation matrix plot
#' @param plotStats \code{TRUE} or \code{FALSE} (default), provide statistics
#' in the correlation matrix plot
#' @param hypothesis one of \code{'corr'} (default), \code{'pos'},
#' \code{'neg'} specifying the alernative hypothesis; correlated, correlated
#' positively, correlated negatively respectively.
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$matrix} \tab \tab \tab \tab \tab a correlation matrix table \cr
#' \code{results$plot} \tab \tab \tab \tab \tab a correlation matrix plot \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$matrix$asDF}
#'
#' \code{as.data.frame(results$matrix)}
#'
#' @export
corrMatrix <- function(
data,
vars,
pearson = TRUE,
spearman = FALSE,
kendall = FALSE,
sig = TRUE,
flag = FALSE,
n = FALSE,
ci = FALSE,
ciWidth = 95,
plots = FALSE,
plotDens = FALSE,
plotStats = FALSE,
hypothesis = "corr") {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("corrMatrix 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))
vars <- `if`( ! missing(vars), vars, colnames(data))
options <- corrMatrixOptions$new(
vars = vars,
pearson = pearson,
spearman = spearman,
kendall = kendall,
sig = sig,
flag = flag,
n = n,
ci = ci,
ciWidth = ciWidth,
plots = plots,
plotDens = plotDens,
plotStats = plotStats,
hypothesis = hypothesis)
analysis <- corrMatrixClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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