R/kmeans.h.R
In hyunsooseol/snowCluster: Multivariate Analysis
Defines functions
kmeans
Documented in
kmeans
# This file is automatically generated, you probably don't want to edit this
kmeansOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"kmeansOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
vars = NULL,
factors = NULL,
k = 2,
k1 = 2,
kp = FALSE,
oc = FALSE,
algo = "Hartigan-Wong",
nstart = 10,
stand = FALSE,
plot = FALSE,
angle = 0,
plot1 = FALSE,
plot2 = FALSE,
plot3 = FALSE,
width = 500,
height = 500,
width1 = 500,
height1 = 500,
width2 = 500,
height2 = 500,
width3 = 500,
height3 = 500,
plot4 = FALSE,
width4 = 500,
height4 = 500, ...) {
super$initialize(
package="snowCluster",
name="kmeans",
requiresData=TRUE,
...)
private$..vars <- jmvcore::OptionVariables$new(
"vars",
vars,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..k <- jmvcore::OptionInteger$new(
"k",
k,
default=2,
min=2)
private$..k1 <- jmvcore::OptionInteger$new(
"k1",
k1,
default=2,
min=2)
private$..kp <- jmvcore::OptionBool$new(
"kp",
kp,
default=FALSE)
private$..oc <- jmvcore::OptionBool$new(
"oc",
oc,
default=FALSE)
private$..clust1 <- jmvcore::OptionOutput$new(
"clust1")
private$..algo <- jmvcore::OptionList$new(
"algo",
algo,
options=list(
"Hartigan-Wong",
"Lloyd",
"Forgy",
"MacQueen"),
default="Hartigan-Wong")
private$..nstart <- jmvcore::OptionInteger$new(
"nstart",
nstart,
default=10)
private$..stand <- jmvcore::OptionBool$new(
"stand",
stand,
default=FALSE)
private$..plot <- jmvcore::OptionBool$new(
"plot",
plot,
default=FALSE)
private$..angle <- jmvcore::OptionNumber$new(
"angle",
angle,
min=0,
max=90,
default=0)
private$..plot1 <- jmvcore::OptionBool$new(
"plot1",
plot1,
default=FALSE)
private$..plot2 <- jmvcore::OptionBool$new(
"plot2",
plot2,
default=FALSE)
private$..plot3 <- jmvcore::OptionBool$new(
"plot3",
plot3,
default=FALSE)
private$..clust <- jmvcore::OptionOutput$new(
"clust")
private$..width <- jmvcore::OptionInteger$new(
"width",
width,
default=500)
private$..height <- jmvcore::OptionInteger$new(
"height",
height,
default=500)
private$..width1 <- jmvcore::OptionInteger$new(
"width1",
width1,
default=500)
private$..height1 <- jmvcore::OptionInteger$new(
"height1",
height1,
default=500)
private$..width2 <- jmvcore::OptionInteger$new(
"width2",
width2,
default=500)
private$..height2 <- jmvcore::OptionInteger$new(
"height2",
height2,
default=500)
private$..width3 <- jmvcore::OptionInteger$new(
"width3",
width3,
default=500)
private$..height3 <- jmvcore::OptionInteger$new(
"height3",
height3,
default=500)
private$..plot4 <- jmvcore::OptionBool$new(
"plot4",
plot4,
default=FALSE)
private$..width4 <- jmvcore::OptionInteger$new(
"width4",
width4,
default=500)
private$..height4 <- jmvcore::OptionInteger$new(
"height4",
height4,
default=500)
self$.addOption(private$..vars)
self$.addOption(private$..factors)
self$.addOption(private$..k)
self$.addOption(private$..k1)
self$.addOption(private$..kp)
self$.addOption(private$..oc)
self$.addOption(private$..clust1)
self$.addOption(private$..algo)
self$.addOption(private$..nstart)
self$.addOption(private$..stand)
self$.addOption(private$..plot)
self$.addOption(private$..angle)
self$.addOption(private$..plot1)
self$.addOption(private$..plot2)
self$.addOption(private$..plot3)
self$.addOption(private$..clust)
self$.addOption(private$..width)
self$.addOption(private$..height)
self$.addOption(private$..width1)
self$.addOption(private$..height1)
self$.addOption(private$..width2)
self$.addOption(private$..height2)
self$.addOption(private$..width3)
self$.addOption(private$..height3)
self$.addOption(private$..plot4)
self$.addOption(private$..width4)
self$.addOption(private$..height4)
}),
active = list(
vars = function() private$..vars$value,
factors = function() private$..factors$value,
k = function() private$..k$value,
k1 = function() private$..k1$value,
kp = function() private$..kp$value,
oc = function() private$..oc$value,
clust1 = function() private$..clust1$value,
algo = function() private$..algo$value,
nstart = function() private$..nstart$value,
stand = function() private$..stand$value,
plot = function() private$..plot$value,
angle = function() private$..angle$value,
plot1 = function() private$..plot1$value,
plot2 = function() private$..plot2$value,
plot3 = function() private$..plot3$value,
clust = function() private$..clust$value,
width = function() private$..width$value,
height = function() private$..height$value,
width1 = function() private$..width1$value,
height1 = function() private$..height1$value,
width2 = function() private$..width2$value,
height2 = function() private$..height2$value,
width3 = function() private$..width3$value,
height3 = function() private$..height3$value,
plot4 = function() private$..plot4$value,
width4 = function() private$..width4$value,
height4 = function() private$..height4$value),
private = list(
..vars = NA,
..factors = NA,
..k = NA,
..k1 = NA,
..kp = NA,
..oc = NA,
..clust1 = NA,
..algo = NA,
..nstart = NA,
..stand = NA,
..plot = NA,
..angle = NA,
..plot1 = NA,
..plot2 = NA,
..plot3 = NA,
..clust = NA,
..width = NA,
..height = NA,
..width1 = NA,
..height1 = NA,
..width2 = NA,
..height2 = NA,
..width3 = NA,
..height3 = NA,
..plot4 = NA,
..width4 = NA,
..height4 = NA)
)
kmeansResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"kmeansResults",
inherit = jmvcore::Group,
active = list(
instructions = function() private$.items[["instructions"]],
text = function() private$.items[["text"]],
ss = function() private$.items[["ss"]],
clustering = function() private$.items[["clustering"]],
centroids = function() private$.items[["centroids"]],
plot = function() private$.items[["plot"]],
plot1 = function() private$.items[["plot1"]],
plot2 = function() private$.items[["plot2"]],
plot3 = function() private$.items[["plot3"]],
clust = function() private$.items[["clust"]],
clust1 = function() private$.items[["clust1"]],
oc = function() private$.items[["oc"]],
kp = function() private$.items[["kp"]],
plot4 = function() private$.items[["plot4"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="K-means Clustering",
refs="snowCluster")
self$add(jmvcore::Html$new(
options=options,
name="instructions",
title="Instructions",
visible=TRUE))
self$add(jmvcore::Preformatted$new(
options=options,
name="text",
title="Clustering vector"))
self$add(jmvcore::Table$new(
options=options,
name="ss",
title="Sum of squares Table",
rows=0,
refs="snowCluster",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand"),
columns=list(
list(
`name`="source",
`title`="",
`type`="text"),
list(
`name`="value",
`title`="Value"))))
self$add(jmvcore::Table$new(
options=options,
name="clustering",
title="Clustering Table",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand"),
refs="snowCluster",
columns=list(
list(
`name`="cluster",
`title`="Cluster No",
`type`="text"),
list(
`name`="count",
`title`="Count",
`type`="integer"))))
self$add(jmvcore::Table$new(
options=options,
name="centroids",
title="Centroids of clusters Table",
rows="(k)",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand"),
refs="snowCluster",
columns=list(
list(
`name`=".name[x]",
`title`="",
`type`="text",
`content`="($key)"))))
self$add(jmvcore::Image$new(
options=options,
name="plot",
title="Plot of means across clusters",
visible="(plot)",
renderFun=".plot",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand",
"angle",
"width",
"height")))
self$add(jmvcore::Image$new(
options=options,
name="plot1",
title="Optimal number of clusters",
refs="factoextra",
visible="(plot1)",
renderFun=".plot1",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand",
"width1",
"height1")))
self$add(jmvcore::Image$new(
options=options,
name="plot2",
title="Cluster plot",
requiresData=TRUE,
refs="factoextra",
visible="(plot2)",
renderFun=".plot2",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand",
"width2",
"height2")))
self$add(jmvcore::Image$new(
options=options,
name="plot3",
title="Variables-PCA",
requiresData=TRUE,
refs="factoextra",
visible="(plot3)",
renderFun=".plot3",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand",
"width3",
"height3")))
self$add(jmvcore::Output$new(
options=options,
name="clust",
title="Clustering",
varTitle="Clustering",
measureType="nominal",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand")))
self$add(jmvcore::Output$new(
options=options,
name="clust1",
title="Gower",
varTitle="Gower",
measureType="nominal",
clearWith=list(
"vars",
"factors",
"k1")))
self$add(jmvcore::Table$new(
options=options,
name="oc",
title="Silhouette index",
clearWith=list(
"vars",
"factors",
"K1"),
refs="clustMixType",
columns=list(
list(
`name`="name",
`title`="Cluster No.",
`type`="text",
`content`="($key)"),
list(
`name`="value",
`title`="Silhouette index",
`type`="number"))))
self$add(jmvcore::Table$new(
options=options,
name="kp",
title="Gower distance",
visible="(kp)",
refs="clustMixType",
clearWith=list(
"vars",
"factors",
"k1"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text",
`content`="($key)"))))
self$add(jmvcore::Image$new(
options=options,
name="plot4",
title="Silhouette plot",
visible="(plot4)",
renderFun=".plot4",
clearWith=list(
"vars",
"factors",
"k1")))}))
kmeansBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"kmeansBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "snowCluster",
name = "kmeans",
version = c(1,0,0),
options = options,
results = kmeansResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'none')
}))
#' K-means Clustering
#'
#'
#' @param data The data as a data frame.
#' @param vars .
#' @param factors .
#' @param k .
#' @param k1 .
#' @param kp .
#' @param oc .
#' @param algo .
#' @param nstart .
#' @param stand .
#' @param plot .
#' @param angle a number from 0 to 90 defining the angle of the x-axis labels,
#' where 0 degrees represents completely horizontal labels.
#' @param plot1 .
#' @param plot2 .
#' @param plot3 .
#' @param width .
#' @param height .
#' @param width1 .
#' @param height1 .
#' @param width2 .
#' @param height2 .
#' @param width3 .
#' @param height3 .
#' @param plot4 .
#' @param width4 .
#' @param height4 .
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$instructions} \tab \tab \tab \tab \tab a html \cr
#' \code{results$text} \tab \tab \tab \tab \tab a preformatted \cr
#' \code{results$ss} \tab \tab \tab \tab \tab a table \cr
#' \code{results$clustering} \tab \tab \tab \tab \tab a table \cr
#' \code{results$centroids} \tab \tab \tab \tab \tab a table \cr
#' \code{results$plot} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plot1} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plot2} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plot3} \tab \tab \tab \tab \tab an image \cr
#' \code{results$clust} \tab \tab \tab \tab \tab an output \cr
#' \code{results$clust1} \tab \tab \tab \tab \tab an output \cr
#' \code{results$oc} \tab \tab \tab \tab \tab a table \cr
#' \code{results$kp} \tab \tab \tab \tab \tab a table \cr
#' \code{results$plot4} \tab \tab \tab \tab \tab an image \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$ss$asDF}
#'
#' \code{as.data.frame(results$ss)}
#'
#' @export
kmeans <- function(
data,
vars,
factors,
k = 2,
k1 = 2,
kp = FALSE,
oc = FALSE,
algo = "Hartigan-Wong",
nstart = 10,
stand = FALSE,
plot = FALSE,
angle = 0,
plot1 = FALSE,
plot2 = FALSE,
plot3 = FALSE,
width = 500,
height = 500,
width1 = 500,
height1 = 500,
width2 = 500,
height2 = 500,
width3 = 500,
height3 = 500,
plot4 = FALSE,
width4 = 500,
height4 = 500) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("kmeans requires jmvcore to be installed (restart may be required)")
if ( ! missing(vars)) vars <- jmvcore::resolveQuo(jmvcore::enquo(vars))
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(vars), vars, NULL),
`if`( ! missing(factors), factors, NULL))
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- kmeansOptions$new(
vars = vars,
factors = factors,
k = k,
k1 = k1,
kp = kp,
oc = oc,
algo = algo,
nstart = nstart,
stand = stand,
plot = plot,
angle = angle,
plot1 = plot1,
plot2 = plot2,
plot3 = plot3,
width = width,
height = height,
width1 = width1,
height1 = height1,
width2 = width2,
height2 = height2,
width3 = width3,
height3 = height3,
plot4 = plot4,
width4 = width4,
height4 = height4)
analysis <- kmeansClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
hyunsooseol/snowCluster documentation built on April 5, 2025, 2:06 a.m.
R Package Documentation
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Defines functions kmeans
Documented in kmeans
# This file is automatically generated, you probably don't want to edit this
kmeansOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"kmeansOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
vars = NULL,
factors = NULL,
k = 2,
k1 = 2,
kp = FALSE,
oc = FALSE,
algo = "Hartigan-Wong",
nstart = 10,
stand = FALSE,
plot = FALSE,
angle = 0,
plot1 = FALSE,
plot2 = FALSE,
plot3 = FALSE,
width = 500,
height = 500,
width1 = 500,
height1 = 500,
width2 = 500,
height2 = 500,
width3 = 500,
height3 = 500,
plot4 = FALSE,
width4 = 500,
height4 = 500, ...) {
super$initialize(
package="snowCluster",
name="kmeans",
requiresData=TRUE,
...)
private$..vars <- jmvcore::OptionVariables$new(
"vars",
vars,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..factors <- jmvcore::OptionVariables$new(
"factors",
factors,
suggested=list(
"nominal"),
permitted=list(
"factor"))
private$..k <- jmvcore::OptionInteger$new(
"k",
k,
default=2,
min=2)
private$..k1 <- jmvcore::OptionInteger$new(
"k1",
k1,
default=2,
min=2)
private$..kp <- jmvcore::OptionBool$new(
"kp",
kp,
default=FALSE)
private$..oc <- jmvcore::OptionBool$new(
"oc",
oc,
default=FALSE)
private$..clust1 <- jmvcore::OptionOutput$new(
"clust1")
private$..algo <- jmvcore::OptionList$new(
"algo",
algo,
options=list(
"Hartigan-Wong",
"Lloyd",
"Forgy",
"MacQueen"),
default="Hartigan-Wong")
private$..nstart <- jmvcore::OptionInteger$new(
"nstart",
nstart,
default=10)
private$..stand <- jmvcore::OptionBool$new(
"stand",
stand,
default=FALSE)
private$..plot <- jmvcore::OptionBool$new(
"plot",
plot,
default=FALSE)
private$..angle <- jmvcore::OptionNumber$new(
"angle",
angle,
min=0,
max=90,
default=0)
private$..plot1 <- jmvcore::OptionBool$new(
"plot1",
plot1,
default=FALSE)
private$..plot2 <- jmvcore::OptionBool$new(
"plot2",
plot2,
default=FALSE)
private$..plot3 <- jmvcore::OptionBool$new(
"plot3",
plot3,
default=FALSE)
private$..clust <- jmvcore::OptionOutput$new(
"clust")
private$..width <- jmvcore::OptionInteger$new(
"width",
width,
default=500)
private$..height <- jmvcore::OptionInteger$new(
"height",
height,
default=500)
private$..width1 <- jmvcore::OptionInteger$new(
"width1",
width1,
default=500)
private$..height1 <- jmvcore::OptionInteger$new(
"height1",
height1,
default=500)
private$..width2 <- jmvcore::OptionInteger$new(
"width2",
width2,
default=500)
private$..height2 <- jmvcore::OptionInteger$new(
"height2",
height2,
default=500)
private$..width3 <- jmvcore::OptionInteger$new(
"width3",
width3,
default=500)
private$..height3 <- jmvcore::OptionInteger$new(
"height3",
height3,
default=500)
private$..plot4 <- jmvcore::OptionBool$new(
"plot4",
plot4,
default=FALSE)
private$..width4 <- jmvcore::OptionInteger$new(
"width4",
width4,
default=500)
private$..height4 <- jmvcore::OptionInteger$new(
"height4",
height4,
default=500)
self$.addOption(private$..vars)
self$.addOption(private$..factors)
self$.addOption(private$..k)
self$.addOption(private$..k1)
self$.addOption(private$..kp)
self$.addOption(private$..oc)
self$.addOption(private$..clust1)
self$.addOption(private$..algo)
self$.addOption(private$..nstart)
self$.addOption(private$..stand)
self$.addOption(private$..plot)
self$.addOption(private$..angle)
self$.addOption(private$..plot1)
self$.addOption(private$..plot2)
self$.addOption(private$..plot3)
self$.addOption(private$..clust)
self$.addOption(private$..width)
self$.addOption(private$..height)
self$.addOption(private$..width1)
self$.addOption(private$..height1)
self$.addOption(private$..width2)
self$.addOption(private$..height2)
self$.addOption(private$..width3)
self$.addOption(private$..height3)
self$.addOption(private$..plot4)
self$.addOption(private$..width4)
self$.addOption(private$..height4)
}),
active = list(
vars = function() private$..vars$value,
factors = function() private$..factors$value,
k = function() private$..k$value,
k1 = function() private$..k1$value,
kp = function() private$..kp$value,
oc = function() private$..oc$value,
clust1 = function() private$..clust1$value,
algo = function() private$..algo$value,
nstart = function() private$..nstart$value,
stand = function() private$..stand$value,
plot = function() private$..plot$value,
angle = function() private$..angle$value,
plot1 = function() private$..plot1$value,
plot2 = function() private$..plot2$value,
plot3 = function() private$..plot3$value,
clust = function() private$..clust$value,
width = function() private$..width$value,
height = function() private$..height$value,
width1 = function() private$..width1$value,
height1 = function() private$..height1$value,
width2 = function() private$..width2$value,
height2 = function() private$..height2$value,
width3 = function() private$..width3$value,
height3 = function() private$..height3$value,
plot4 = function() private$..plot4$value,
width4 = function() private$..width4$value,
height4 = function() private$..height4$value),
private = list(
..vars = NA,
..factors = NA,
..k = NA,
..k1 = NA,
..kp = NA,
..oc = NA,
..clust1 = NA,
..algo = NA,
..nstart = NA,
..stand = NA,
..plot = NA,
..angle = NA,
..plot1 = NA,
..plot2 = NA,
..plot3 = NA,
..clust = NA,
..width = NA,
..height = NA,
..width1 = NA,
..height1 = NA,
..width2 = NA,
..height2 = NA,
..width3 = NA,
..height3 = NA,
..plot4 = NA,
..width4 = NA,
..height4 = NA)
)
kmeansResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"kmeansResults",
inherit = jmvcore::Group,
active = list(
instructions = function() private$.items[["instructions"]],
text = function() private$.items[["text"]],
ss = function() private$.items[["ss"]],
clustering = function() private$.items[["clustering"]],
centroids = function() private$.items[["centroids"]],
plot = function() private$.items[["plot"]],
plot1 = function() private$.items[["plot1"]],
plot2 = function() private$.items[["plot2"]],
plot3 = function() private$.items[["plot3"]],
clust = function() private$.items[["clust"]],
clust1 = function() private$.items[["clust1"]],
oc = function() private$.items[["oc"]],
kp = function() private$.items[["kp"]],
plot4 = function() private$.items[["plot4"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="K-means Clustering",
refs="snowCluster")
self$add(jmvcore::Html$new(
options=options,
name="instructions",
title="Instructions",
visible=TRUE))
self$add(jmvcore::Preformatted$new(
options=options,
name="text",
title="Clustering vector"))
self$add(jmvcore::Table$new(
options=options,
name="ss",
title="Sum of squares Table",
rows=0,
refs="snowCluster",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand"),
columns=list(
list(
`name`="source",
`title`="",
`type`="text"),
list(
`name`="value",
`title`="Value"))))
self$add(jmvcore::Table$new(
options=options,
name="clustering",
title="Clustering Table",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand"),
refs="snowCluster",
columns=list(
list(
`name`="cluster",
`title`="Cluster No",
`type`="text"),
list(
`name`="count",
`title`="Count",
`type`="integer"))))
self$add(jmvcore::Table$new(
options=options,
name="centroids",
title="Centroids of clusters Table",
rows="(k)",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand"),
refs="snowCluster",
columns=list(
list(
`name`=".name[x]",
`title`="",
`type`="text",
`content`="($key)"))))
self$add(jmvcore::Image$new(
options=options,
name="plot",
title="Plot of means across clusters",
visible="(plot)",
renderFun=".plot",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand",
"angle",
"width",
"height")))
self$add(jmvcore::Image$new(
options=options,
name="plot1",
title="Optimal number of clusters",
refs="factoextra",
visible="(plot1)",
renderFun=".plot1",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand",
"width1",
"height1")))
self$add(jmvcore::Image$new(
options=options,
name="plot2",
title="Cluster plot",
requiresData=TRUE,
refs="factoextra",
visible="(plot2)",
renderFun=".plot2",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand",
"width2",
"height2")))
self$add(jmvcore::Image$new(
options=options,
name="plot3",
title="Variables-PCA",
requiresData=TRUE,
refs="factoextra",
visible="(plot3)",
renderFun=".plot3",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand",
"width3",
"height3")))
self$add(jmvcore::Output$new(
options=options,
name="clust",
title="Clustering",
varTitle="Clustering",
measureType="nominal",
clearWith=list(
"vars",
"k",
"algo",
"nstart",
"stand")))
self$add(jmvcore::Output$new(
options=options,
name="clust1",
title="Gower",
varTitle="Gower",
measureType="nominal",
clearWith=list(
"vars",
"factors",
"k1")))
self$add(jmvcore::Table$new(
options=options,
name="oc",
title="Silhouette index",
clearWith=list(
"vars",
"factors",
"K1"),
refs="clustMixType",
columns=list(
list(
`name`="name",
`title`="Cluster No.",
`type`="text",
`content`="($key)"),
list(
`name`="value",
`title`="Silhouette index",
`type`="number"))))
self$add(jmvcore::Table$new(
options=options,
name="kp",
title="Gower distance",
visible="(kp)",
refs="clustMixType",
clearWith=list(
"vars",
"factors",
"k1"),
columns=list(
list(
`name`="name",
`title`="",
`type`="text",
`content`="($key)"))))
self$add(jmvcore::Image$new(
options=options,
name="plot4",
title="Silhouette plot",
visible="(plot4)",
renderFun=".plot4",
clearWith=list(
"vars",
"factors",
"k1")))}))
kmeansBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"kmeansBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "snowCluster",
name = "kmeans",
version = c(1,0,0),
options = options,
results = kmeansResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'none')
}))
#' K-means Clustering
#'
#'
#' @param data The data as a data frame.
#' @param vars .
#' @param factors .
#' @param k .
#' @param k1 .
#' @param kp .
#' @param oc .
#' @param algo .
#' @param nstart .
#' @param stand .
#' @param plot .
#' @param angle a number from 0 to 90 defining the angle of the x-axis labels,
#' where 0 degrees represents completely horizontal labels.
#' @param plot1 .
#' @param plot2 .
#' @param plot3 .
#' @param width .
#' @param height .
#' @param width1 .
#' @param height1 .
#' @param width2 .
#' @param height2 .
#' @param width3 .
#' @param height3 .
#' @param plot4 .
#' @param width4 .
#' @param height4 .
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$instructions} \tab \tab \tab \tab \tab a html \cr
#' \code{results$text} \tab \tab \tab \tab \tab a preformatted \cr
#' \code{results$ss} \tab \tab \tab \tab \tab a table \cr
#' \code{results$clustering} \tab \tab \tab \tab \tab a table \cr
#' \code{results$centroids} \tab \tab \tab \tab \tab a table \cr
#' \code{results$plot} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plot1} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plot2} \tab \tab \tab \tab \tab an image \cr
#' \code{results$plot3} \tab \tab \tab \tab \tab an image \cr
#' \code{results$clust} \tab \tab \tab \tab \tab an output \cr
#' \code{results$clust1} \tab \tab \tab \tab \tab an output \cr
#' \code{results$oc} \tab \tab \tab \tab \tab a table \cr
#' \code{results$kp} \tab \tab \tab \tab \tab a table \cr
#' \code{results$plot4} \tab \tab \tab \tab \tab an image \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$ss$asDF}
#'
#' \code{as.data.frame(results$ss)}
#'
#' @export
kmeans <- function(
data,
vars,
factors,
k = 2,
k1 = 2,
kp = FALSE,
oc = FALSE,
algo = "Hartigan-Wong",
nstart = 10,
stand = FALSE,
plot = FALSE,
angle = 0,
plot1 = FALSE,
plot2 = FALSE,
plot3 = FALSE,
width = 500,
height = 500,
width1 = 500,
height1 = 500,
width2 = 500,
height2 = 500,
width3 = 500,
height3 = 500,
plot4 = FALSE,
width4 = 500,
height4 = 500) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("kmeans requires jmvcore to be installed (restart may be required)")
if ( ! missing(vars)) vars <- jmvcore::resolveQuo(jmvcore::enquo(vars))
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(vars), vars, NULL),
`if`( ! missing(factors), factors, NULL))
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- kmeansOptions$new(
vars = vars,
factors = factors,
k = k,
k1 = k1,
kp = kp,
oc = oc,
algo = algo,
nstart = nstart,
stand = stand,
plot = plot,
angle = angle,
plot1 = plot1,
plot2 = plot2,
plot3 = plot3,
width = width,
height = height,
width1 = width1,
height1 = height1,
width2 = width2,
height2 = height2,
width3 = width3,
height3 = height3,
plot4 = plot4,
width4 = width4,
height4 = height4)
analysis <- kmeansClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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