R/endoj.h.R
In ENDOjamovi/ENDOj: Endogenity analysis based on 2SLS
Defines functions
endoj
# This file is automatically generated, you probably don't want to edit this
endojOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"endojOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
.caller = "lm",
.interface = "jamovi",
dep = NULL,
exo = NULL,
endo = NULL,
iv = NULL,
es = list(
"beta"),
estimates_ci = TRUE,
betas_ci = FALSE,
ci_width = 95,
ci_method = "wald",
se_method = "wald",
boot_r = 1000,
vcov = FALSE, ...) {
super$initialize(
package="endoj",
name="endoj",
requiresData=TRUE,
...)
private$...caller <- jmvcore::OptionString$new(
".caller",
.caller,
default="lm",
hidden=TRUE)
private$...interface <- jmvcore::OptionString$new(
".interface",
.interface,
default="jamovi",
hidden=TRUE)
private$..dep <- jmvcore::OptionVariable$new(
"dep",
dep,
default=NULL,
permitted=list(
"factor",
"numeric"))
private$..exo <- jmvcore::OptionVariables$new(
"exo",
exo,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric"),
default=NULL)
private$..endo <- jmvcore::OptionVariables$new(
"endo",
endo,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric"),
default=NULL)
private$..iv <- jmvcore::OptionVariables$new(
"iv",
iv,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric"),
default=NULL)
private$..es <- jmvcore::OptionNMXList$new(
"es",
es,
options=list(
"beta"),
default=list(
"beta"))
private$..estimates_ci <- jmvcore::OptionBool$new(
"estimates_ci",
estimates_ci,
default=TRUE)
private$..betas_ci <- jmvcore::OptionBool$new(
"betas_ci",
betas_ci,
default=FALSE)
private$..ci_width <- jmvcore::OptionNumber$new(
"ci_width",
ci_width,
min=50,
max=99.9,
default=95)
private$..ci_method <- jmvcore::OptionList$new(
"ci_method",
ci_method,
default="wald",
options=list(
"wald",
"perc",
"bca"))
private$..se_method <- jmvcore::OptionList$new(
"se_method",
se_method,
default="wald",
options=list(
"wald",
"robust"))
private$..boot_r <- jmvcore::OptionNumber$new(
"boot_r",
boot_r,
min=1,
default=1000)
private$..vcov <- jmvcore::OptionBool$new(
"vcov",
vcov,
default=FALSE)
private$..residuals <- jmvcore::OptionOutput$new(
"residuals")
private$..predicted <- jmvcore::OptionOutput$new(
"predicted")
self$.addOption(private$...caller)
self$.addOption(private$...interface)
self$.addOption(private$..dep)
self$.addOption(private$..exo)
self$.addOption(private$..endo)
self$.addOption(private$..iv)
self$.addOption(private$..es)
self$.addOption(private$..estimates_ci)
self$.addOption(private$..betas_ci)
self$.addOption(private$..ci_width)
self$.addOption(private$..ci_method)
self$.addOption(private$..se_method)
self$.addOption(private$..boot_r)
self$.addOption(private$..vcov)
self$.addOption(private$..residuals)
self$.addOption(private$..predicted)
}),
active = list(
.caller = function() private$...caller$value,
.interface = function() private$...interface$value,
dep = function() private$..dep$value,
exo = function() private$..exo$value,
endo = function() private$..endo$value,
iv = function() private$..iv$value,
es = function() private$..es$value,
estimates_ci = function() private$..estimates_ci$value,
betas_ci = function() private$..betas_ci$value,
ci_width = function() private$..ci_width$value,
ci_method = function() private$..ci_method$value,
se_method = function() private$..se_method$value,
boot_r = function() private$..boot_r$value,
vcov = function() private$..vcov$value,
residuals = function() private$..residuals$value,
predicted = function() private$..predicted$value),
private = list(
...caller = NA,
...interface = NA,
..dep = NA,
..exo = NA,
..endo = NA,
..iv = NA,
..es = NA,
..estimates_ci = NA,
..betas_ci = NA,
..ci_width = NA,
..ci_method = NA,
..se_method = NA,
..boot_r = NA,
..vcov = NA,
..residuals = NA,
..predicted = NA)
)
endojResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"endojResults",
inherit = jmvcore::Group,
active = list(
info = function() private$.items[["info"]],
main = function() private$.items[["main"]],
diagnostics = function() private$.items[["diagnostics"]],
residuals = function() private$.items[["residuals"]],
predicted = function() private$.items[["predicted"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Instrumental Variables Linear Models")
self$add(jmvcore::Table$new(
options=options,
name="info",
title="Some Info",
columns=list(
list(
`name`="info",
`type`="text",
`title`=""),
list(
`name`="specs",
`type`="text",
`title`="")),
refs=list(
"endoj",
"ivreg")))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
r2 = function() private$.items[["r2"]],
anova = function() private$.items[["anova"]],
coefficients = function() private$.items[["coefficients"]],
vcov = function() private$.items[["vcov"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="main",
title="Model Results")
self$add(jmvcore::Table$new(
options=options,
name="r2",
title="Model Fit",
clearWith=list(
"dep",
"endo",
"exo",
"iv"),
columns=list(
list(
`name`="r2",
`title`="R\u00B2",
`type`="number"),
list(
`name`="ar2",
`title`="Adj. R\u00B2",
`type`="number"),
list(
`name`="df1",
`title`="df",
`type`="integer"),
list(
`name`="df2",
`title`="df (res)",
`type`="integer"),
list(
`name`="test",
`title`="Wald test",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue")),
rows=1))
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="Omnibus tests",
clearWith=list(
"dep",
"endo",
"exo",
"iv"),
columns=list(
list(
`name`="source",
`title`="",
`type`="text"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="f",
`title`="F",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coefficients",
title="Parameter Estimates (Coefficients)",
clearWith=list(
"dep",
"endo",
"exo",
"iv",
"ci_method",
"boot_r",
"se_method"),
columns=list(
list(
`name`="source",
`title`="Names",
`type`="text"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="est.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(estimates_ci)"),
list(
`name`="est.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(estimates_ci)"),
list(
`name`="beta",
`type`="number",
`title`="\u03B2",
`visible`="(es:beta)",
`format`="zto"),
list(
`name`="beta.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(betas_ci)"),
list(
`name`="beta.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(betas_ci)"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="t",
`title`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="vcov",
title="Coefficients Covariances",
visible="(vcov)",
clearWith=list(
"dep",
"model_terms",
"contrasts",
"covs_scale",
"dep_scale",
"fixed_intercept"),
columns=list(
list(
`name`="source",
`title`="Coefficient",
`type`="text"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
standard = function() private$.items[["standard"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="diagnostics",
title="Diagnostics")
self$add(jmvcore::Table$new(
options=options,
name="standard",
title="Model diagnostics",
clearWith=list(
"dep",
"endo",
"exo",
"iv"),
rows=3,
columns=list(
list(
`name`="source",
`title`="Test",
`type`="text"),
list(
`name`="df1",
`title`="df1",
`type`="integer"),
list(
`name`="df2",
`title`="df2",
`type`="integer"),
list(
`name`="test",
`title`="Statistic",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))}))$new(options=options))
self$add(jmvcore::Output$new(
options=options,
name="residuals",
title="Residuals Values",
varTitle="`IVRES_${ dep }`",
varDescription="ivreg res values"))
self$add(jmvcore::Output$new(
options=options,
name="predicted",
title="Predicted Values",
varTitle="`IVPRED_${ dep }`",
varDescription="ivreg pred values"))}))
endojBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"endojBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "endoj",
name = "endoj",
version = c(0,1,0),
options = options,
results = endojResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE)
}))
#' Instrumental variables model
#'
#' Something here
#'
#'
#' @examples
#' \dontrun{
#' some code here
#'}
#' @param data the data as a data frame
#' @param .caller .
#' @param .interface .
#' @param dep a string naming the dependent variable from \code{data}; the
#' variable must be numeric. Not needed if \code{formula} is used.
#' @param exo a vector of strings naming the covariates from \code{data}. Not
#' needed if \code{formula} is used.
#' @param endo a vector of strings naming the covariates from \code{data}. Not
#' needed if \code{formula} is used.
#' @param iv a vector of strings naming the covariates from \code{data}. Not
#' needed if \code{formula} is used.
#' @param es a list of effect sizes to print out. They can be: \code{"eta"}
#' for eta-squared, \code{'partEta'} for partial eta-squared, \code{'omega'}
#' for partial omega-squared, \code{'epsilon'} for partial epsilon-squared,
#' and \code{'beta'} for standardized coefficients (betas). Default is
#' \code{"beta"} and \code{"parEta"}.
#' @param estimates_ci \code{TRUE} (default) or \code{FALSE} , parameters CI
#' in table
#' @param betas_ci \code{TRUE} (default) or \code{FALSE} , parameters CI in
#' table
#' @param ci_width a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width for the plots.
#' @param ci_method .
#' @param se_method .
#' @param boot_r a number bootstrap repetitions.
#' @param vcov \code{TRUE} or \code{FALSE} (default), shows coefficients
#' covariances
#' @param formula (optional) the formula to use, see the examples
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$info} \tab \tab \tab \tab \tab a table \cr
#' \code{results$main$r2} \tab \tab \tab \tab \tab a table of R \cr
#' \code{results$main$anova} \tab \tab \tab \tab \tab a table of ANOVA results \cr
#' \code{results$main$coefficients} \tab \tab \tab \tab \tab a table \cr
#' \code{results$main$vcov} \tab \tab \tab \tab \tab a table \cr
#' \code{results$diagnostics$standard} \tab \tab \tab \tab \tab a table \cr
#' \code{results$residuals} \tab \tab \tab \tab \tab an output \cr
#' \code{results$predicted} \tab \tab \tab \tab \tab an output \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$info$asDF}
#'
#' \code{as.data.frame(results$info)}
#'
#' @export
endoj <- function(
data,
.caller = "lm",
.interface = "jamovi",
dep = NULL,
exo = NULL,
endo = NULL,
iv = NULL,
es = list(
"beta"),
estimates_ci = TRUE,
betas_ci = FALSE,
ci_width = 95,
ci_method = "wald",
se_method = "wald",
boot_r = 1000,
vcov = FALSE,
formula) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("endoj requires jmvcore to be installed (restart may be required)")
if ( ! missing(formula)) {
if (missing(dep))
dep <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="lhs",
subset="1",
required=TRUE)
if (missing(covs))
covs <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="vars",
permitted="numeric")
if (missing(model_terms))
model_terms <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="terms")
}
if ( ! missing(dep)) dep <- jmvcore::resolveQuo(jmvcore::enquo(dep))
if ( ! missing(exo)) exo <- jmvcore::resolveQuo(jmvcore::enquo(exo))
if ( ! missing(endo)) endo <- jmvcore::resolveQuo(jmvcore::enquo(endo))
if ( ! missing(iv)) iv <- jmvcore::resolveQuo(jmvcore::enquo(iv))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(dep), dep, NULL),
`if`( ! missing(exo), exo, NULL),
`if`( ! missing(endo), endo, NULL),
`if`( ! missing(iv), iv, NULL))
options <- endojOptions$new(
.caller = .caller,
.interface = .interface,
dep = dep,
exo = exo,
endo = endo,
iv = iv,
es = es,
estimates_ci = estimates_ci,
betas_ci = betas_ci,
ci_width = ci_width,
ci_method = ci_method,
se_method = se_method,
boot_r = boot_r,
vcov = vcov)
analysis <- endojClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
ENDOjamovi/ENDOj documentation built on May 10, 2022, 7:32 p.m.
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Defines functions endoj
# This file is automatically generated, you probably don't want to edit this
endojOptions <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"endojOptions",
inherit = jmvcore::Options,
public = list(
initialize = function(
.caller = "lm",
.interface = "jamovi",
dep = NULL,
exo = NULL,
endo = NULL,
iv = NULL,
es = list(
"beta"),
estimates_ci = TRUE,
betas_ci = FALSE,
ci_width = 95,
ci_method = "wald",
se_method = "wald",
boot_r = 1000,
vcov = FALSE, ...) {
super$initialize(
package="endoj",
name="endoj",
requiresData=TRUE,
...)
private$...caller <- jmvcore::OptionString$new(
".caller",
.caller,
default="lm",
hidden=TRUE)
private$...interface <- jmvcore::OptionString$new(
".interface",
.interface,
default="jamovi",
hidden=TRUE)
private$..dep <- jmvcore::OptionVariable$new(
"dep",
dep,
default=NULL,
permitted=list(
"factor",
"numeric"))
private$..exo <- jmvcore::OptionVariables$new(
"exo",
exo,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric"),
default=NULL)
private$..endo <- jmvcore::OptionVariables$new(
"endo",
endo,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric"),
default=NULL)
private$..iv <- jmvcore::OptionVariables$new(
"iv",
iv,
suggested=list(
"continuous",
"ordinal"),
permitted=list(
"numeric"),
default=NULL)
private$..es <- jmvcore::OptionNMXList$new(
"es",
es,
options=list(
"beta"),
default=list(
"beta"))
private$..estimates_ci <- jmvcore::OptionBool$new(
"estimates_ci",
estimates_ci,
default=TRUE)
private$..betas_ci <- jmvcore::OptionBool$new(
"betas_ci",
betas_ci,
default=FALSE)
private$..ci_width <- jmvcore::OptionNumber$new(
"ci_width",
ci_width,
min=50,
max=99.9,
default=95)
private$..ci_method <- jmvcore::OptionList$new(
"ci_method",
ci_method,
default="wald",
options=list(
"wald",
"perc",
"bca"))
private$..se_method <- jmvcore::OptionList$new(
"se_method",
se_method,
default="wald",
options=list(
"wald",
"robust"))
private$..boot_r <- jmvcore::OptionNumber$new(
"boot_r",
boot_r,
min=1,
default=1000)
private$..vcov <- jmvcore::OptionBool$new(
"vcov",
vcov,
default=FALSE)
private$..residuals <- jmvcore::OptionOutput$new(
"residuals")
private$..predicted <- jmvcore::OptionOutput$new(
"predicted")
self$.addOption(private$...caller)
self$.addOption(private$...interface)
self$.addOption(private$..dep)
self$.addOption(private$..exo)
self$.addOption(private$..endo)
self$.addOption(private$..iv)
self$.addOption(private$..es)
self$.addOption(private$..estimates_ci)
self$.addOption(private$..betas_ci)
self$.addOption(private$..ci_width)
self$.addOption(private$..ci_method)
self$.addOption(private$..se_method)
self$.addOption(private$..boot_r)
self$.addOption(private$..vcov)
self$.addOption(private$..residuals)
self$.addOption(private$..predicted)
}),
active = list(
.caller = function() private$...caller$value,
.interface = function() private$...interface$value,
dep = function() private$..dep$value,
exo = function() private$..exo$value,
endo = function() private$..endo$value,
iv = function() private$..iv$value,
es = function() private$..es$value,
estimates_ci = function() private$..estimates_ci$value,
betas_ci = function() private$..betas_ci$value,
ci_width = function() private$..ci_width$value,
ci_method = function() private$..ci_method$value,
se_method = function() private$..se_method$value,
boot_r = function() private$..boot_r$value,
vcov = function() private$..vcov$value,
residuals = function() private$..residuals$value,
predicted = function() private$..predicted$value),
private = list(
...caller = NA,
...interface = NA,
..dep = NA,
..exo = NA,
..endo = NA,
..iv = NA,
..es = NA,
..estimates_ci = NA,
..betas_ci = NA,
..ci_width = NA,
..ci_method = NA,
..se_method = NA,
..boot_r = NA,
..vcov = NA,
..residuals = NA,
..predicted = NA)
)
endojResults <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"endojResults",
inherit = jmvcore::Group,
active = list(
info = function() private$.items[["info"]],
main = function() private$.items[["main"]],
diagnostics = function() private$.items[["diagnostics"]],
residuals = function() private$.items[["residuals"]],
predicted = function() private$.items[["predicted"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="",
title="Instrumental Variables Linear Models")
self$add(jmvcore::Table$new(
options=options,
name="info",
title="Some Info",
columns=list(
list(
`name`="info",
`type`="text",
`title`=""),
list(
`name`="specs",
`type`="text",
`title`="")),
refs=list(
"endoj",
"ivreg")))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
r2 = function() private$.items[["r2"]],
anova = function() private$.items[["anova"]],
coefficients = function() private$.items[["coefficients"]],
vcov = function() private$.items[["vcov"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="main",
title="Model Results")
self$add(jmvcore::Table$new(
options=options,
name="r2",
title="Model Fit",
clearWith=list(
"dep",
"endo",
"exo",
"iv"),
columns=list(
list(
`name`="r2",
`title`="R\u00B2",
`type`="number"),
list(
`name`="ar2",
`title`="Adj. R\u00B2",
`type`="number"),
list(
`name`="df1",
`title`="df",
`type`="integer"),
list(
`name`="df2",
`title`="df (res)",
`type`="integer"),
list(
`name`="test",
`title`="Wald test",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue")),
rows=1))
self$add(jmvcore::Table$new(
options=options,
name="anova",
title="Omnibus tests",
clearWith=list(
"dep",
"endo",
"exo",
"iv"),
columns=list(
list(
`name`="source",
`title`="",
`type`="text"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="f",
`title`="F",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="coefficients",
title="Parameter Estimates (Coefficients)",
clearWith=list(
"dep",
"endo",
"exo",
"iv",
"ci_method",
"boot_r",
"se_method"),
columns=list(
list(
`name`="source",
`title`="Names",
`type`="text"),
list(
`name`="estimate",
`title`="Estimate",
`type`="number"),
list(
`name`="se",
`title`="SE",
`type`="number"),
list(
`name`="est.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(estimates_ci)"),
list(
`name`="est.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(estimates_ci)"),
list(
`name`="beta",
`type`="number",
`title`="\u03B2",
`visible`="(es:beta)",
`format`="zto"),
list(
`name`="beta.ci.lower",
`type`="number",
`title`="Lower",
`visible`="(betas_ci)"),
list(
`name`="beta.ci.upper",
`type`="number",
`title`="Upper",
`visible`="(betas_ci)"),
list(
`name`="df",
`title`="df",
`type`="integer"),
list(
`name`="t",
`title`="t",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))
self$add(jmvcore::Table$new(
options=options,
name="vcov",
title="Coefficients Covariances",
visible="(vcov)",
clearWith=list(
"dep",
"model_terms",
"contrasts",
"covs_scale",
"dep_scale",
"fixed_intercept"),
columns=list(
list(
`name`="source",
`title`="Coefficient",
`type`="text"))))}))$new(options=options))
self$add(R6::R6Class(
inherit = jmvcore::Group,
active = list(
standard = function() private$.items[["standard"]]),
private = list(),
public=list(
initialize=function(options) {
super$initialize(
options=options,
name="diagnostics",
title="Diagnostics")
self$add(jmvcore::Table$new(
options=options,
name="standard",
title="Model diagnostics",
clearWith=list(
"dep",
"endo",
"exo",
"iv"),
rows=3,
columns=list(
list(
`name`="source",
`title`="Test",
`type`="text"),
list(
`name`="df1",
`title`="df1",
`type`="integer"),
list(
`name`="df2",
`title`="df2",
`type`="integer"),
list(
`name`="test",
`title`="Statistic",
`type`="number"),
list(
`name`="p",
`title`="p",
`type`="number",
`format`="zto,pvalue"))))}))$new(options=options))
self$add(jmvcore::Output$new(
options=options,
name="residuals",
title="Residuals Values",
varTitle="`IVRES_${ dep }`",
varDescription="ivreg res values"))
self$add(jmvcore::Output$new(
options=options,
name="predicted",
title="Predicted Values",
varTitle="`IVPRED_${ dep }`",
varDescription="ivreg pred values"))}))
endojBase <- if (requireNamespace("jmvcore", quietly=TRUE)) R6::R6Class(
"endojBase",
inherit = jmvcore::Analysis,
public = list(
initialize = function(options, data=NULL, datasetId="", analysisId="", revision=0) {
super$initialize(
package = "endoj",
name = "endoj",
version = c(0,1,0),
options = options,
results = endojResults$new(options=options),
data = data,
datasetId = datasetId,
analysisId = analysisId,
revision = revision,
pause = NULL,
completeWhenFilled = TRUE,
requiresMissings = FALSE)
}))
#' Instrumental variables model
#'
#' Something here
#'
#'
#' @examples
#' \dontrun{
#' some code here
#'}
#' @param data the data as a data frame
#' @param .caller .
#' @param .interface .
#' @param dep a string naming the dependent variable from \code{data}; the
#' variable must be numeric. Not needed if \code{formula} is used.
#' @param exo a vector of strings naming the covariates from \code{data}. Not
#' needed if \code{formula} is used.
#' @param endo a vector of strings naming the covariates from \code{data}. Not
#' needed if \code{formula} is used.
#' @param iv a vector of strings naming the covariates from \code{data}. Not
#' needed if \code{formula} is used.
#' @param es a list of effect sizes to print out. They can be: \code{"eta"}
#' for eta-squared, \code{'partEta'} for partial eta-squared, \code{'omega'}
#' for partial omega-squared, \code{'epsilon'} for partial epsilon-squared,
#' and \code{'beta'} for standardized coefficients (betas). Default is
#' \code{"beta"} and \code{"parEta"}.
#' @param estimates_ci \code{TRUE} (default) or \code{FALSE} , parameters CI
#' in table
#' @param betas_ci \code{TRUE} (default) or \code{FALSE} , parameters CI in
#' table
#' @param ci_width a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width for the plots.
#' @param ci_method .
#' @param se_method .
#' @param boot_r a number bootstrap repetitions.
#' @param vcov \code{TRUE} or \code{FALSE} (default), shows coefficients
#' covariances
#' @param formula (optional) the formula to use, see the examples
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$info} \tab \tab \tab \tab \tab a table \cr
#' \code{results$main$r2} \tab \tab \tab \tab \tab a table of R \cr
#' \code{results$main$anova} \tab \tab \tab \tab \tab a table of ANOVA results \cr
#' \code{results$main$coefficients} \tab \tab \tab \tab \tab a table \cr
#' \code{results$main$vcov} \tab \tab \tab \tab \tab a table \cr
#' \code{results$diagnostics$standard} \tab \tab \tab \tab \tab a table \cr
#' \code{results$residuals} \tab \tab \tab \tab \tab an output \cr
#' \code{results$predicted} \tab \tab \tab \tab \tab an output \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$info$asDF}
#'
#' \code{as.data.frame(results$info)}
#'
#' @export
endoj <- function(
data,
.caller = "lm",
.interface = "jamovi",
dep = NULL,
exo = NULL,
endo = NULL,
iv = NULL,
es = list(
"beta"),
estimates_ci = TRUE,
betas_ci = FALSE,
ci_width = 95,
ci_method = "wald",
se_method = "wald",
boot_r = 1000,
vcov = FALSE,
formula) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("endoj requires jmvcore to be installed (restart may be required)")
if ( ! missing(formula)) {
if (missing(dep))
dep <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="lhs",
subset="1",
required=TRUE)
if (missing(covs))
covs <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="vars",
permitted="numeric")
if (missing(model_terms))
model_terms <- jmvcore::marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
from="rhs",
type="terms")
}
if ( ! missing(dep)) dep <- jmvcore::resolveQuo(jmvcore::enquo(dep))
if ( ! missing(exo)) exo <- jmvcore::resolveQuo(jmvcore::enquo(exo))
if ( ! missing(endo)) endo <- jmvcore::resolveQuo(jmvcore::enquo(endo))
if ( ! missing(iv)) iv <- jmvcore::resolveQuo(jmvcore::enquo(iv))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(dep), dep, NULL),
`if`( ! missing(exo), exo, NULL),
`if`( ! missing(endo), endo, NULL),
`if`( ! missing(iv), iv, NULL))
options <- endojOptions$new(
.caller = .caller,
.interface = .interface,
dep = dep,
exo = exo,
endo = endo,
iv = iv,
es = es,
estimates_ci = estimates_ci,
betas_ci = betas_ci,
ci_width = ci_width,
ci_method = ci_method,
se_method = se_method,
boot_r = boot_r,
vcov = vcov)
analysis <- endojClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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