R/tree.h.R
# This file is automatically generated, you probably don't want to edit this
treeOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"treeOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
vars = NULL,
facs = NULL,
target = NULL,
targetLevel = NULL,
train = NULL,
trainLevel = NULL,
showPlot = FALSE, ...) {
super$initialize(
package="ClinicoPath",
name="tree",
requiresData=TRUE,
...)
private$..vars <- jmvcore::OptionVariables$new(
"vars",
vars,
suggested=list(
"continuous"),
permitted=list(
"numeric"))
private$..facs <- jmvcore::OptionVariables$new(
"facs",
facs,
suggested=list(
"ordinal",
"nominal"),
permitted=list(
"factor"))
private$..target <- jmvcore::OptionVariable$new(
"target",
target,
suggested=list(
"ordinal",
"nominal"),
permitted=list(
"factor"))
private$..targetLevel <- jmvcore::OptionLevel$new(
"targetLevel",
targetLevel,
variable="(target)")
private$..train <- jmvcore::OptionVariable$new(
"train",
train,
suggested=list(
"ordinal",
"nominal"),
permitted=list(
"factor"))
private$..trainLevel <- jmvcore::OptionLevel$new(
"trainLevel",
trainLevel,
variable="(train)")
private$..showPlot <- jmvcore::OptionBool$new(
"showPlot",
showPlot,
default=FALSE)
self$.addOption(private$..vars)
self$.addOption(private$..facs)
self$.addOption(private$..target)
self$.addOption(private$..targetLevel)
self$.addOption(private$..train)
self$.addOption(private$..trainLevel)
self$.addOption(private$..showPlot)
}),
active = list(
vars = function() private$..vars$value,
facs = function() private$..facs$value,
target = function() private$..target$value,
targetLevel = function() private$..targetLevel$value,
train = function() private$..train$value,
trainLevel = function() private$..trainLevel$value,
showPlot = function() private$..showPlot$value),
private = list(
..vars = NA,
..facs = NA,
..target = NA,
..targetLevel = NA,
..train = NA,
..trainLevel = NA,
..showPlot = NA)
)
treeResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"treeResults",
inherit = jmvcore::Group,
active = list(
todo = function() private$.items[["todo"]],
text1 = function() private$.items[["text1"]],
text2 = function() private$.items[["text2"]],
text2a = function() private$.items[["text2a"]],
text2b = function() private$.items[["text2b"]],
text3 = function() private$.items[["text3"]],
text4 = function() private$.items[["text4"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Decision Tree",
refs=list(
"ClinicoPathJamoviModule"))
self$add(jmvcore::Html$new(
options=options,
name="todo",
title="To Do",
clearWith=list(
"vars",
"facs",
"target",
"targetLevel",
"train",
"trainLevel")))
self$add(jmvcore::Preformatted$new(
options=options,
name="text1",
title="Data View 1",
clearWith=list(
"vars",
"facs",
"target",
"targetLevel",
"train",
"trainLevel")))
self$add(jmvcore::Preformatted$new(
options=options,
name="text2",
title="Data View 2",
clearWith=list(
"vars",
"facs",
"target",
"targetLevel",
"train",
"trainLevel")))
self$add(jmvcore::Preformatted$new(
options=options,
name="text2a",
title="Data View 2a",
clearWith=list(
"vars",
"facs",
"target",
"targetLevel",
"train",
"trainLevel")))
self$add(jmvcore::Preformatted$new(
options=options,
name="text2b",
title="Data View 2b",
clearWith=list(
"vars",
"facs",
"target",
"targetLevel",
"train",
"trainLevel")))
self$add(jmvcore::Preformatted$new(
options=options,
name="text3",
title="Decision Tree 3",
clearWith=list(
"vars",
"facs",
"target",
"targetLevel",
"train",
"trainLevel")))
self$add(jmvcore::Html$new(
options=options,
name="text4",
title="Decision Tree 4",
clearWith=list(
"vars",
"facs",
"target",
"targetLevel",
"train",
"trainLevel")))}))
treeBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"treeBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "ClinicoPath",
name = "tree",
version = c(1,0,0),
options = options,
results = treeResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = FALSE,
requiresMissings = FALSE,
weightsSupport = 'auto')
}))
#' Decision Tree
#'
#' Function for making Decision Trees.
#'
#' @examples
#' \dontrun{
#' # example will be added
#'}
#' @param data The data as a data frame.
#' @param vars continuous explanatory variables
#' @param facs categorical explanatory variables
#' @param target target variable
#' @param targetLevel .
#' @param train Variable containing Test/Train information
#' @param trainLevel .
#' @param showPlot .
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$todo} \tab \tab \tab \tab \tab a html \cr
#' \code{results$text1} \tab \tab \tab \tab \tab a preformatted \cr
#' \code{results$text2} \tab \tab \tab \tab \tab a preformatted \cr
#' \code{results$text2a} \tab \tab \tab \tab \tab a preformatted \cr
#' \code{results$text2b} \tab \tab \tab \tab \tab a preformatted \cr
#' \code{results$text3} \tab \tab \tab \tab \tab a preformatted \cr
#' \code{results$text4} \tab \tab \tab \tab \tab a html \cr
#' }
#'
#' @export
tree <- function(
data,
vars,
facs,
target,
targetLevel,
train,
trainLevel,
showPlot = FALSE) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("tree requires jmvcore to be installed (restart may be required)")
if ( ! missing(vars)) vars <- jmvcore::resolveQuo(jmvcore::enquo(vars))
if ( ! missing(facs)) facs <- jmvcore::resolveQuo(jmvcore::enquo(facs))
if ( ! missing(target)) target <- jmvcore::resolveQuo(jmvcore::enquo(target))
if ( ! missing(train)) train <- jmvcore::resolveQuo(jmvcore::enquo(train))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(vars), vars, NULL),
`if`( ! missing(facs), facs, NULL),
`if`( ! missing(target), target, NULL),
`if`( ! missing(train), train, NULL))
for (v in facs) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in target) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
for (v in train) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
options <- treeOptions$new(
vars = vars,
facs = facs,
target = target,
targetLevel = targetLevel,
train = train,
trainLevel = trainLevel,
showPlot = showPlot)
analysis <- treeClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}