R/jammglm.f.R
In jamovi-amm/jamm: Advanced Mediational Models
Defines functions
jammGLM
Documented in
jammGLM
# ## fix some option when passed by R console ###
## this code should be added in the jammGLM function generated
## by jamovi compiler after copying it from .h.R to .f.R
#
# >> for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
# >> if (inherits(modelTerms, 'formula')) modelTerms <- jmvcore::decomposeFormula(modelTerms)
#
#
# if (is.something(names(moderatorsTerms))) {
# if (!is.list(moderatorsTerms))
# jmvcore::reject("The `moderatorsTerms` option should be a list")
#
# moderatorsTerms<-lapply(mediators, function(med) {
# ifelse(med %in% names(moderatorsTerms),as.list(moderatorsTerms[med]),list())
# })
# }
# if (is.something(names(scaling)))
# scaling<-lapply(names(scaling), function(a) list(var=a,type=scaling[[a]]))
# if (is.something(names(contrasts)))
# contrasts<-lapply(names(contrasts), function(a) list(var=a,type=contrasts[[a]]))
#
# ############
# ### some check for input passed by R console ###
# moderators<-unlist(moderatorsTerms)
# test1<-intersect(moderators,mediators)
# if (length(test1)>0)
# jmvcore::reject(paste("You specified the variable", test1,"to be both a mediator and a moderator. This is not allowed. You can specify interactions involving moderators and mediators as you like, but do not assign the role of moderator to a mediator."))
#' GLM Mediation Model
#'
#' GLM mediation model
#' @param data the data as a data frame
#' @param dep a string naming the dependent variable from \code{data},
#' variable must be numeric
#' @param mediators a vector of strings naming the mediators from \code{data}
#' @param factors a vector of strings naming the fixed factors from
#' \code{data}
#' @param covs a vector of strings naming the covariates from \code{data}
#' @param modelTerms a list of character vectors describing fixed effects
#' terms
#' @param ciType Choose the confidence interval type
#' @param ciWidth a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width for the parameter estimates
#' @param bootN number of bootstrap samples for estimating confidence
#' intervals
#' @param contrasts a list of lists specifying the factor and type of contrast
#' to use, one of \code{'deviation'}, \code{'simple'}, \code{'difference'},
#' \code{'helmert'}, \code{'repeated'} or \code{'polynomial'}
#' @param showRealNames \code{TRUE} or \code{FALSE} (default), provide raw
#' names of the contrasts variables
#' @param showContrastCode \code{TRUE} or \code{FALSE} (default), provide
#' contrast coefficients tables
#' @param simpleScale \code{'mean_sd'} (default), \code{'custom'} , or
#' \code{'custom_percent'}. Use to condition the covariates (if any)
#' @param cvalue offset value for conditioning
#' @param percvalue offset value for conditioning
#' @param simpleScaleLabels .
#' @param scaling a list of lists specifying the covariates scaling, one of
#' \code{'centered to the mean'}, \code{'standardized'}, or \code{'none'}.
#' \code{'none'} leaves the variable as it is
#' @param tableOptions .
#' @param pathOptions .
#' @param mediatorsTerms a list of lists specifying the models for with the
#' mediators as dependent variables.
#' @param moderatorsTerms a list of lists specifying the the IV which
#' moderatorate each mediated effect.
#' @param missing handle missing values: \code{listwise} or \code{full information max likelihood}.
#' @param diagram Choose the type of diagram: \code{conceptual} or
#' \code{statistical}.
#' @param diag_paths Choose the diagram labels
#' @param diag_labsize Choose the diagram labels
#' @param diag_shape Choose the diagram labels
#' @param diag_abbrev Choose the diagram labels
#' @param diag_offset \code{TRUE} or \code{FALSE} (default), offset labels in
#' diagram
#' @param formula (optional) the formula to use, see the examples
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$info} \tab \tab \tab \tab \tab a table \cr
#' \code{results$pathmodelgroup$pathmodel} \tab \tab \tab \tab \tab a path model \cr
#' \code{results$pathmodelgroup$statmodel} \tab \tab \tab \tab \tab a path model \cr
#' \code{results$pathmodelgroup$pathnotes} \tab \tab \tab \tab \tab a table \cr
#' \code{results$models$moderationEffects} \tab \tab \tab \tab \tab a table \cr
#' \code{results$models$main} \tab \tab \tab \tab \tab a table \cr
#' \code{results$models$contrastCodeTables} \tab \tab \tab \tab \tab an array of contrast coefficients tables \cr
#' \code{results$regressions$overall} \tab \tab \tab \tab \tab a group \cr
#' \code{results$regressions$mediator_regressions} \tab \tab \tab \tab \tab an array of regressions for the mediators \cr
#' \code{results$regressions$full} \tab \tab \tab \tab \tab a group \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$info$asDF}
#'
#' \code{as.data.frame(results$info)}
#'
#' @export
jammGLM <- function(
data,
dep = NULL,
mediators = NULL,
factors = NULL,
covs = NULL,
modelTerms = NULL,
ciType = "standard",
ciWidth = 95,
bootN = 1000,
contrasts = NULL,
showRealNames = TRUE,
showContrastCode = FALSE,
simpleScale = "mean_sd",
cvalue = 1,
percvalue = 25,
simpleScaleLabels = "labels",
scaling = NULL,
tableOptions = list(
"beta",
"component"),
pathOptions = list(
"suggested"),
mediatorsTerms = list(
list()),
moderatorsTerms = list(
list()),
missing = "listwise",
diagram = "conceptual",
diag_paths = "est",
diag_labsize = "medium",
diag_shape = "rectangle",
diag_abbrev = "0",
diag_offset = FALSE,
formula) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("jammGLM requires jmvcore to be installed (restart may be required)")
if ( ! missing(formula)) {
if (missing(dep))
dep <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="dep")
if (missing(factors))
factors <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="factors")
if (missing(covs))
covs <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="covs")
if (missing(mediators))
mediators <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="mediators")
if (missing(mediatorsTerms))
mediatorsTerms <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="mediatorsTerms")
if (missing(modelTerms))
modelTerms <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="modelTerms")
if (missing(moderatorsTerms))
moderatorsTerms <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="moderatorsTerms")
if (missing(simpleScale))
simpleScale <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="simpleScale")
}
if ( ! missing(dep)) dep <- jmvcore::resolveQuo(jmvcore::enquo(dep))
if ( ! missing(mediators)) mediators <- jmvcore::resolveQuo(jmvcore::enquo(mediators))
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if ( ! missing(covs)) covs <- jmvcore::resolveQuo(jmvcore::enquo(covs))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(dep), dep, NULL),
`if`( ! missing(mediators), mediators, NULL),
`if`( ! missing(factors), factors, NULL),
`if`( ! missing(covs), covs, NULL))
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
if (inherits(modelTerms, "formula")) modelTerms <- jmvcore::decomposeFormula(modelTerms)
## fix some option when passed by R console ###
# this code should be added in the jammGLM function generated
# by jamovi compiler after copying it from .h.R to .f.R
if (is.something(names(moderatorsTerms))) {
if (!is.list(moderatorsTerms))
jmvcore::reject("The `moderatorsTerms` option should be a list")
moderatorsTerms<-lapply(mediators, function(med) {
ifelse(med %in% names(moderatorsTerms),as.list(moderatorsTerms[med]),list())
})
}
if (is.something(names(scaling)))
scaling<-lapply(names(scaling), function(a) list(var=a,type=scaling[[a]]))
if (is.something(names(contrasts)))
contrasts<-lapply(names(contrasts), function(a) list(var=a,type=contrasts[[a]]))
############
### some check for input passed by R console ###
moderators<-unlist(moderatorsTerms)
test1<-intersect(moderators,mediators)
if (length(test1)>0)
jmvcore::reject(paste("You specified the variable", test1,"to be both a mediator and a moderator. This is not allowed. You can specify interactions involving moderators and mediators as you like, but do not assign the role of moderator to a mediator."))
options <- jammGLMOptions$new(
dep = dep,
mediators = mediators,
factors = factors,
covs = covs,
modelTerms = modelTerms,
ciType = ciType,
ciWidth = ciWidth,
bootN = bootN,
contrasts = contrasts,
showRealNames = showRealNames,
showContrastCode = showContrastCode,
simpleScale = simpleScale,
cvalue = cvalue,
percvalue = percvalue,
simpleScaleLabels = simpleScaleLabels,
scaling = scaling,
tableOptions = tableOptions,
pathOptions = pathOptions,
mediatorsTerms = mediatorsTerms,
moderatorsTerms = moderatorsTerms,
missing= missing,
diagram = diagram,
diag_paths = diag_paths,
diag_labsize = diag_labsize,
diag_shape = diag_shape,
diag_abbrev = diag_abbrev,
diag_offset = diag_offset)
analysis <- jammGLMClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
jamovi-amm/jamm documentation built on Nov. 15, 2023, 9:18 p.m.
R Package Documentation
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Defines functions jammGLM
Documented in jammGLM
# ## fix some option when passed by R console ###
## this code should be added in the jammGLM function generated
## by jamovi compiler after copying it from .h.R to .f.R
#
# >> for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
# >> if (inherits(modelTerms, 'formula')) modelTerms <- jmvcore::decomposeFormula(modelTerms)
#
#
# if (is.something(names(moderatorsTerms))) {
# if (!is.list(moderatorsTerms))
# jmvcore::reject("The `moderatorsTerms` option should be a list")
#
# moderatorsTerms<-lapply(mediators, function(med) {
# ifelse(med %in% names(moderatorsTerms),as.list(moderatorsTerms[med]),list())
# })
# }
# if (is.something(names(scaling)))
# scaling<-lapply(names(scaling), function(a) list(var=a,type=scaling[[a]]))
# if (is.something(names(contrasts)))
# contrasts<-lapply(names(contrasts), function(a) list(var=a,type=contrasts[[a]]))
#
# ############
# ### some check for input passed by R console ###
# moderators<-unlist(moderatorsTerms)
# test1<-intersect(moderators,mediators)
# if (length(test1)>0)
# jmvcore::reject(paste("You specified the variable", test1,"to be both a mediator and a moderator. This is not allowed. You can specify interactions involving moderators and mediators as you like, but do not assign the role of moderator to a mediator."))
#' GLM Mediation Model
#'
#' GLM mediation model
#' @param data the data as a data frame
#' @param dep a string naming the dependent variable from \code{data},
#' variable must be numeric
#' @param mediators a vector of strings naming the mediators from \code{data}
#' @param factors a vector of strings naming the fixed factors from
#' \code{data}
#' @param covs a vector of strings naming the covariates from \code{data}
#' @param modelTerms a list of character vectors describing fixed effects
#' terms
#' @param ciType Choose the confidence interval type
#' @param ciWidth a number between 50 and 99.9 (default: 95) specifying the
#' confidence interval width for the parameter estimates
#' @param bootN number of bootstrap samples for estimating confidence
#' intervals
#' @param contrasts a list of lists specifying the factor and type of contrast
#' to use, one of \code{'deviation'}, \code{'simple'}, \code{'difference'},
#' \code{'helmert'}, \code{'repeated'} or \code{'polynomial'}
#' @param showRealNames \code{TRUE} or \code{FALSE} (default), provide raw
#' names of the contrasts variables
#' @param showContrastCode \code{TRUE} or \code{FALSE} (default), provide
#' contrast coefficients tables
#' @param simpleScale \code{'mean_sd'} (default), \code{'custom'} , or
#' \code{'custom_percent'}. Use to condition the covariates (if any)
#' @param cvalue offset value for conditioning
#' @param percvalue offset value for conditioning
#' @param simpleScaleLabels .
#' @param scaling a list of lists specifying the covariates scaling, one of
#' \code{'centered to the mean'}, \code{'standardized'}, or \code{'none'}.
#' \code{'none'} leaves the variable as it is
#' @param tableOptions .
#' @param pathOptions .
#' @param mediatorsTerms a list of lists specifying the models for with the
#' mediators as dependent variables.
#' @param moderatorsTerms a list of lists specifying the the IV which
#' moderatorate each mediated effect.
#' @param missing handle missing values: \code{listwise} or \code{full information max likelihood}.
#' @param diagram Choose the type of diagram: \code{conceptual} or
#' \code{statistical}.
#' @param diag_paths Choose the diagram labels
#' @param diag_labsize Choose the diagram labels
#' @param diag_shape Choose the diagram labels
#' @param diag_abbrev Choose the diagram labels
#' @param diag_offset \code{TRUE} or \code{FALSE} (default), offset labels in
#' diagram
#' @param formula (optional) the formula to use, see the examples
#' @return A results object containing:
#' \tabular{llllll}{
#' \code{results$info} \tab \tab \tab \tab \tab a table \cr
#' \code{results$pathmodelgroup$pathmodel} \tab \tab \tab \tab \tab a path model \cr
#' \code{results$pathmodelgroup$statmodel} \tab \tab \tab \tab \tab a path model \cr
#' \code{results$pathmodelgroup$pathnotes} \tab \tab \tab \tab \tab a table \cr
#' \code{results$models$moderationEffects} \tab \tab \tab \tab \tab a table \cr
#' \code{results$models$main} \tab \tab \tab \tab \tab a table \cr
#' \code{results$models$contrastCodeTables} \tab \tab \tab \tab \tab an array of contrast coefficients tables \cr
#' \code{results$regressions$overall} \tab \tab \tab \tab \tab a group \cr
#' \code{results$regressions$mediator_regressions} \tab \tab \tab \tab \tab an array of regressions for the mediators \cr
#' \code{results$regressions$full} \tab \tab \tab \tab \tab a group \cr
#' }
#'
#' Tables can be converted to data frames with \code{asDF} or \code{\link{as.data.frame}}. For example:
#'
#' \code{results$info$asDF}
#'
#' \code{as.data.frame(results$info)}
#'
#' @export
jammGLM <- function(
data,
dep = NULL,
mediators = NULL,
factors = NULL,
covs = NULL,
modelTerms = NULL,
ciType = "standard",
ciWidth = 95,
bootN = 1000,
contrasts = NULL,
showRealNames = TRUE,
showContrastCode = FALSE,
simpleScale = "mean_sd",
cvalue = 1,
percvalue = 25,
simpleScaleLabels = "labels",
scaling = NULL,
tableOptions = list(
"beta",
"component"),
pathOptions = list(
"suggested"),
mediatorsTerms = list(
list()),
moderatorsTerms = list(
list()),
missing = "listwise",
diagram = "conceptual",
diag_paths = "est",
diag_labsize = "medium",
diag_shape = "rectangle",
diag_abbrev = "0",
diag_offset = FALSE,
formula) {
if ( ! requireNamespace("jmvcore", quietly=TRUE))
stop("jammGLM requires jmvcore to be installed (restart may be required)")
if ( ! missing(formula)) {
if (missing(dep))
dep <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="dep")
if (missing(factors))
factors <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="factors")
if (missing(covs))
covs <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="covs")
if (missing(mediators))
mediators <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="mediators")
if (missing(mediatorsTerms))
mediatorsTerms <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="mediatorsTerms")
if (missing(modelTerms))
modelTerms <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="modelTerms")
if (missing(moderatorsTerms))
moderatorsTerms <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="moderatorsTerms")
if (missing(simpleScale))
simpleScale <- jammGLMClass$private_methods$.marshalFormula(
formula=formula,
data=`if`( ! missing(data), data, NULL),
name="simpleScale")
}
if ( ! missing(dep)) dep <- jmvcore::resolveQuo(jmvcore::enquo(dep))
if ( ! missing(mediators)) mediators <- jmvcore::resolveQuo(jmvcore::enquo(mediators))
if ( ! missing(factors)) factors <- jmvcore::resolveQuo(jmvcore::enquo(factors))
if ( ! missing(covs)) covs <- jmvcore::resolveQuo(jmvcore::enquo(covs))
if (missing(data))
data <- jmvcore::marshalData(
parent.frame(),
`if`( ! missing(dep), dep, NULL),
`if`( ! missing(mediators), mediators, NULL),
`if`( ! missing(factors), factors, NULL),
`if`( ! missing(covs), covs, NULL))
for (v in factors) if (v %in% names(data)) data[[v]] <- as.factor(data[[v]])
if (inherits(modelTerms, "formula")) modelTerms <- jmvcore::decomposeFormula(modelTerms)
## fix some option when passed by R console ###
# this code should be added in the jammGLM function generated
# by jamovi compiler after copying it from .h.R to .f.R
if (is.something(names(moderatorsTerms))) {
if (!is.list(moderatorsTerms))
jmvcore::reject("The `moderatorsTerms` option should be a list")
moderatorsTerms<-lapply(mediators, function(med) {
ifelse(med %in% names(moderatorsTerms),as.list(moderatorsTerms[med]),list())
})
}
if (is.something(names(scaling)))
scaling<-lapply(names(scaling), function(a) list(var=a,type=scaling[[a]]))
if (is.something(names(contrasts)))
contrasts<-lapply(names(contrasts), function(a) list(var=a,type=contrasts[[a]]))
############
### some check for input passed by R console ###
moderators<-unlist(moderatorsTerms)
test1<-intersect(moderators,mediators)
if (length(test1)>0)
jmvcore::reject(paste("You specified the variable", test1,"to be both a mediator and a moderator. This is not allowed. You can specify interactions involving moderators and mediators as you like, but do not assign the role of moderator to a mediator."))
options <- jammGLMOptions$new(
dep = dep,
mediators = mediators,
factors = factors,
covs = covs,
modelTerms = modelTerms,
ciType = ciType,
ciWidth = ciWidth,
bootN = bootN,
contrasts = contrasts,
showRealNames = showRealNames,
showContrastCode = showContrastCode,
simpleScale = simpleScale,
cvalue = cvalue,
percvalue = percvalue,
simpleScaleLabels = simpleScaleLabels,
scaling = scaling,
tableOptions = tableOptions,
pathOptions = pathOptions,
mediatorsTerms = mediatorsTerms,
moderatorsTerms = moderatorsTerms,
missing= missing,
diagram = diagram,
diag_paths = diag_paths,
diag_labsize = diag_labsize,
diag_shape = diag_shape,
diag_abbrev = diag_abbrev,
diag_offset = diag_offset)
analysis <- jammGLMClass$new(
options = options,
data = data)
analysis$run()
analysis$results
}
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