R/CausalMediation.R
In AdrianAntico/RemixAutoML: AutoQuant
Defines functions
CausalMediation
Documented in
CausalMediation
# AutoQuant is a package for quickly creating high quality visualizations under a common and easy api.
# Copyright (C) <year> <name of author>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#' @title CausalMediation
#'
#' @description CausalMediation utilizes models from regmedint package
#'
#' @author Adrian Antico
#' @family Statistical Modeling
#'
#' @param data Data frame containing the following relevant variables.
#' @param OutcomeTargetVariable yvar in underlying model. A character vector of length 1. Outcome variable name. It should be the time variable for the survival outcome.
#' @param TreatmentVariable avar in underlying model. A character vector of length 1. Treatment variable name.
#' @param MediatorVariable mvar in underlying model. A character vector of length 1. Mediator variable name.
#' @param Covariates For main model
#' @param ConfoundingVariables cvar in underlying model. A character vector of length > 0. Covariate names. Use \code{NULL} if there is no covariate. However, this is a highly suspicious situation. Even if \code{avar} is randomized, \code{mvar} is not. Thus, there are usually some confounder(s) to account for the common cause structure (confounding) between \code{mvar} and \code{yvar}.
#' @param MM_TreatmentCovariates emm_ac_mreg in underlying model. A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the mediator model.
#' @param OM_TreatmentCovariates emm_ac_yreg in underlying model. A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the outcome model.
#' @param OM_MediatorCovariates emm_mc_yreg in underlying model. A character vector of length > 0. Effect modifiers names. The covariate vector in mediator-covariate product term in outcome model.
#' @param SurvivalEventVariable eventvar in underlying model. An character vector of length 1. Only required for survival outcome regression models. Note that the coding is 1 for event and 0 for censoring, following the R survival package convention.
#' @param UnTreated_ReferenceIndicator a0 in underlying model. A numeric vector of length 1. The reference level of treatment variable that is considered "untreated" or "unexposed".
#' @param Treated_ReferenceIndicator a1 in underlying model. A numeric vector of length 1.
#' @param Mediator_ControlDirectEffectLevel m_cde in underlying model. A numeric vector of length 1. Mediator level at which controlled direct effect is evaluated at.
#' @param Covariate_NaturalDirectIndirect c_cond in underlying model. A numeric vector of the same length as \code{cvar}. Covariate levels at which natural direct and indirect effects are evaluated at.
#' @param MediatorTargetType mreg in underlying model. A character vector of length 1. Mediator regression type: \code{"linear"} or \code{"logistic"}.
#' @param OutcomeTargetType yreg in underlying model. A character vector of length 1. Outcome regression type: \code{"linear"}, \code{"logistic"}, \code{"loglinear"}, \code{"poisson"}, \code{"negbin"}, \code{"survCox"}, \code{"survAFT_exp"}, or \code{"survAFT_weibull"}.
#' @param TreatmentMediatorInteraction interaction in underlying model. A logical vector of length 1. The presence of treatment-mediator interaction in the outcome model. Default to TRUE.
#' @param CaseControlSourceData casecontrol in underlying model. A logical vector of length 1. Default to FALSE. Whether data comes from a case-control study.
#' @param RemoveNA na_omit in underlying model. A logical vector of length 1. Default to FALSE. Whether to remove NAs in the columns of interest before fitting the models.
#'
#' @examples
#' \dontrun{
#' library(regmedint) # to load vv2015
#' data(vv2015)
#' Output <- AutoQuant::CausalMediation(
#' data = vv2015,
#' OutcomeTargetVariable = 'y', # yvar char length = 0
#' TreatmentVariable = "x", # avar char length = 0 (binary)
#' MediatorVariable = "m", # mvar char length = 0 (binary)
#' Covariates = "c", # cvar char length > 0
#' MM_TreatmentCovariates = NULL, # emm_ac_mreg = NULL char length > 0
#' OM_TreatmentCovariates = NULL, # emm_ac_yreg = NULL char length > 0
#' OM_MediatorCovariates = NULL, # emm_mc_yreg = NULL char length > 0
#' SurvivalEventVariable = "event", # eventvar char length = 0
#' UnTreated_ReferenceIndicator = 0, # ao num length = 1
#' Treated_ReferenceIndicator = 1, # a1 num length = 1
#' Mediator_ControlDirectEffectLevel = 1, # m_cde num length = 1
#' Covariate_NaturalDirectIndirect = 3, # c_cond; same length as Covariates num length = length(Covariates)
#' MediatorTargetType = 'logistic', # mreg "linear" or "logistic",
#' OutcomeTargetType = 'survAFT_weibull', # yreg "linear", "logistic", "loglinear", "poisson", "negbin", "survCox", "survAFT_exp", or "survAFT_weibull"
#' TreatmentMediatorInteraction = TRUE, # interaction = TRUE,
#' CaseControlSourceData = FALSE, # casecontrol = FALSE,
#' RemoveNA = FALSE)
#'
#' # data = vv2015
#' # OutcomeTargetVariable = 'y'
#' # TreatmentVariable = "x"
#' # MediatorVariable = "m"
#' # Covariates = "c"
#' # MM_TreatmentCovariates = NULL
#' # OM_TreatmentCovariates = NULL
#' # OM_MediatorCovariates = NULL
#' # SurvivalEventVariable = "event"
#' # UnTreated_ReferenceIndicator = 0
#' # Treated_ReferenceIndicator = 1
#' # Mediator_ControlDirectEffectLevel = 1
#' # Covariate_NaturalDirectIndirect = 3
#' # MediatorTargetType = 'logistic'
#' # OutcomeTargetType = 'survAFT_weibull'
#' # TreatmentMediatorInteraction = TRUE
#' # CaseControlSourceData = FALSE
#' # RemoveNA = FALSE
#' }
#'
#' @return list with model output object, summary output, effects output, and an effects plot
#'
#' @export
CausalMediation <- function(data,
OutcomeTargetVariable = NULL, # yvar char length = 0
TreatmentVariable = NULL, # avar char length = 0 (binary)
MediatorVariable = NULL, # mvar char length = 0 (binary)
Covariates = NULL, # cvar char length > 0
MM_TreatmentCovariates = NULL, # emm_ac_mreg = NULL char length > 0
OM_TreatmentCovariates = NULL, # emm_ac_yreg = NULL char length > 0
OM_MediatorCovariates = NULL, # emm_mc_yreg = NULL char length > 0
SurvivalEventVariable = NULL, # eventvar char length = 0
UnTreated_ReferenceIndicator = NULL, # ao num length = 1
Treated_ReferenceIndicator = NULL, # a1 num length = 1
Mediator_ControlDirectEffectLevel = NULL, # m_cde num length = 1
Covariate_NaturalDirectIndirect = 0, # c_cond; same length as Covariates num length = length(Covariates)
MediatorTargetType = 'linear', # mreg "linear" or "logistic"
OutcomeTargetType = 'linear', # yreg "linear", "logistic", "loglinear", "poisson", "negbin", "survCox", "survAFT_exp", or "survAFT_weibull"
TreatmentMediatorInteraction = TRUE, # interaction = TRUE,
CaseControlSourceData = FALSE, # casecontrol = FALSE,
RemoveNA = FALSE) { # na_omit = FALSE
print("Causaul Mediation 1")
print(data)
print(OutcomeTargetVariable)
print(TreatmentVariable)
print(MediatorVariable)
print(Covariates)
print(MM_TreatmentCovariates)
print(OM_TreatmentCovariates)
print(OM_MediatorCovariates)
print(SurvivalEventVariable)
print(UnTreated_ReferenceIndicator)
print(Treated_ReferenceIndicator)
print(Mediator_ControlDirectEffectLevel)
print(Covariate_NaturalDirectIndirect)
print(MediatorTargetType)
print(OutcomeTargetType)
print(TreatmentMediatorInteraction)
print(CaseControlSourceData)
print(RemoveNA)
# Model
Output <- regmedint::regmedint(
data = data,
yvar = OutcomeTargetVariable,
avar = TreatmentVariable,
mvar = MediatorVariable,
cvar = Covariates,
emm_ac_mreg = MM_TreatmentCovariates,
emm_ac_yreg = OM_TreatmentCovariates,
emm_mc_yreg = OM_MediatorCovariates,
eventvar = SurvivalEventVariable,
a0 = UnTreated_ReferenceIndicator,
a1 = Treated_ReferenceIndicator,
m_cde = Mediator_ControlDirectEffectLevel,
c_cond = Covariate_NaturalDirectIndirect,
mreg = MediatorTargetType,
yreg = OutcomeTargetType,
interaction = TreatmentMediatorInteraction,
casecontrol = CaseControlSourceData,
na_omit = RemoveNA)
print("Causaul Mediation 2")
# Summary Output
SummaryOutput <- summary(Output)
print("Causaul Mediation 3")
Effects <- data.table::as.data.table(SummaryOutput$summary_myreg, keep.rownames = TRUE)
print("Causaul Mediation 4")
data.table::setnames(Effects, c('est','rn','se','Z','p'), c('Estimate','Measure','Standard Error','Z-Value','P-Value'))
print("Causaul Mediation 5")
Effects[, Description := 'a']
Effects[, Definition := 'a']
data.table::set(Effects, i = 1L, j = 'Description', value = 'Conditional Direct Effect')
data.table::set(Effects, i = 1L, j = 'Definition', value = 'E[Y_treat,mediator | C = c] − E[Y_notreat,mediator | C = c]')
data.table::set(Effects, i = 2L, j = 'Description', value = 'Pure Natural Direct Effect')
data.table::set(Effects, i = 2L, j = 'Definition', value = 'E[Y_treat,no_mediation | C = c] − E[Y_no_treat,no_mediation | C = c]')
data.table::set(Effects, i = 3L, j = 'Description', value = 'Total Natural Indirect Effect')
data.table::set(Effects, i = 3L, j = 'Definition', value = 'E[Y_treat, mediation |C = c] − E[Y_treat,no_mediation | C = c]')
data.table::set(Effects, i = 4L, j = 'Description', value = 'Total Natural Direct Effect')
data.table::set(Effects, i = 4L, j = 'Definition', value = 'E[Y_treat,mediation | C = c] − E[Y_no_treat, mediation | C = c]')
data.table::set(Effects, i = 5L, j = 'Description', value = 'Pure Natural Indirect Effect')
data.table::set(Effects, i = 5L, j = 'Definition', value = 'E[Y_no_treat,mediation | C = c] − E[Y_no_treat,no_mediation | C = c]')
data.table::set(Effects, i = 6L, j = 'Description', value = 'Total Effect')
data.table::set(Effects, i = 6L, j = 'Definition', value = 'E[Y_treat | C = c] − E[Y_no_treat | C = c]')
if(MediatorTargetType == 'linear') {
data.table::set(Effects, i = 7L, j = 'Description', value = 'Proportion Mediated')
data.table::set(Effects, i = 7L, j = 'Definition', value = 'TNIE / (TNIE + PNDE)')
} else {
data.table::set(Effects, i = 7L, j = 'Description', value = 'Proportion Mediated')
data.table::set(Effects, i = 7L, j = 'Definition', value = 'exp(PNDE) * (exp(TNIE) - 1) / (exp(PNDE) * exp(TNIE) - 1)')
}
# Significance stars
Effects[, Significance := data.table::fcase(
`P-Value` < 0.001, '***',
`P-Value` < 0.01, '**',
`P-Value` < 0.05, '*',
default = ''
)]
data.table::setcolorder(Effects, c(8L,1L:7L,9L))
p1 <- AutoQuant::BarPlot(data = Effects, XVar = 'Description', YVar = 'Estimate', AggMethod = 'sum', ColorVar = 'Significance', FillColor = '#66ff10')
MainEffects <- data.table::as.data.table(x = SummaryOutput$summary_yreg_fit$table, keep.rownames = TRUE)
data.table::setnames(x = MainEffects, old = c('rn','z','p'), c('Variable', 'Z-Value', 'P-Value'))
MainEffects[, Significance := data.table::fcase(
`P-Value` < 0.001, '***',
`P-Value` < 0.01, '**',
`P-Value` < 0.05, '*',
default = ''
)]
# Return
return(list(
Output = Output,
SummaryOutput = SummaryOutput,
MainEffects = MainEffects,
Effects = Effects,
EffectsPlot = p1
))
}
AdrianAntico/RemixAutoML documentation built on Feb. 3, 2024, 3:32 a.m.
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Defines functions CausalMediation
Documented in CausalMediation
# AutoQuant is a package for quickly creating high quality visualizations under a common and easy api.
# Copyright (C) <year> <name of author>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
#' @title CausalMediation
#'
#' @description CausalMediation utilizes models from regmedint package
#'
#' @author Adrian Antico
#' @family Statistical Modeling
#'
#' @param data Data frame containing the following relevant variables.
#' @param OutcomeTargetVariable yvar in underlying model. A character vector of length 1. Outcome variable name. It should be the time variable for the survival outcome.
#' @param TreatmentVariable avar in underlying model. A character vector of length 1. Treatment variable name.
#' @param MediatorVariable mvar in underlying model. A character vector of length 1. Mediator variable name.
#' @param Covariates For main model
#' @param ConfoundingVariables cvar in underlying model. A character vector of length > 0. Covariate names. Use \code{NULL} if there is no covariate. However, this is a highly suspicious situation. Even if \code{avar} is randomized, \code{mvar} is not. Thus, there are usually some confounder(s) to account for the common cause structure (confounding) between \code{mvar} and \code{yvar}.
#' @param MM_TreatmentCovariates emm_ac_mreg in underlying model. A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the mediator model.
#' @param OM_TreatmentCovariates emm_ac_yreg in underlying model. A character vector of length > 0. Effect modifiers names. The covariate vector in treatment-covariate product term in the outcome model.
#' @param OM_MediatorCovariates emm_mc_yreg in underlying model. A character vector of length > 0. Effect modifiers names. The covariate vector in mediator-covariate product term in outcome model.
#' @param SurvivalEventVariable eventvar in underlying model. An character vector of length 1. Only required for survival outcome regression models. Note that the coding is 1 for event and 0 for censoring, following the R survival package convention.
#' @param UnTreated_ReferenceIndicator a0 in underlying model. A numeric vector of length 1. The reference level of treatment variable that is considered "untreated" or "unexposed".
#' @param Treated_ReferenceIndicator a1 in underlying model. A numeric vector of length 1.
#' @param Mediator_ControlDirectEffectLevel m_cde in underlying model. A numeric vector of length 1. Mediator level at which controlled direct effect is evaluated at.
#' @param Covariate_NaturalDirectIndirect c_cond in underlying model. A numeric vector of the same length as \code{cvar}. Covariate levels at which natural direct and indirect effects are evaluated at.
#' @param MediatorTargetType mreg in underlying model. A character vector of length 1. Mediator regression type: \code{"linear"} or \code{"logistic"}.
#' @param OutcomeTargetType yreg in underlying model. A character vector of length 1. Outcome regression type: \code{"linear"}, \code{"logistic"}, \code{"loglinear"}, \code{"poisson"}, \code{"negbin"}, \code{"survCox"}, \code{"survAFT_exp"}, or \code{"survAFT_weibull"}.
#' @param TreatmentMediatorInteraction interaction in underlying model. A logical vector of length 1. The presence of treatment-mediator interaction in the outcome model. Default to TRUE.
#' @param CaseControlSourceData casecontrol in underlying model. A logical vector of length 1. Default to FALSE. Whether data comes from a case-control study.
#' @param RemoveNA na_omit in underlying model. A logical vector of length 1. Default to FALSE. Whether to remove NAs in the columns of interest before fitting the models.
#'
#' @examples
#' \dontrun{
#' library(regmedint) # to load vv2015
#' data(vv2015)
#' Output <- AutoQuant::CausalMediation(
#' data = vv2015,
#' OutcomeTargetVariable = 'y', # yvar char length = 0
#' TreatmentVariable = "x", # avar char length = 0 (binary)
#' MediatorVariable = "m", # mvar char length = 0 (binary)
#' Covariates = "c", # cvar char length > 0
#' MM_TreatmentCovariates = NULL, # emm_ac_mreg = NULL char length > 0
#' OM_TreatmentCovariates = NULL, # emm_ac_yreg = NULL char length > 0
#' OM_MediatorCovariates = NULL, # emm_mc_yreg = NULL char length > 0
#' SurvivalEventVariable = "event", # eventvar char length = 0
#' UnTreated_ReferenceIndicator = 0, # ao num length = 1
#' Treated_ReferenceIndicator = 1, # a1 num length = 1
#' Mediator_ControlDirectEffectLevel = 1, # m_cde num length = 1
#' Covariate_NaturalDirectIndirect = 3, # c_cond; same length as Covariates num length = length(Covariates)
#' MediatorTargetType = 'logistic', # mreg "linear" or "logistic",
#' OutcomeTargetType = 'survAFT_weibull', # yreg "linear", "logistic", "loglinear", "poisson", "negbin", "survCox", "survAFT_exp", or "survAFT_weibull"
#' TreatmentMediatorInteraction = TRUE, # interaction = TRUE,
#' CaseControlSourceData = FALSE, # casecontrol = FALSE,
#' RemoveNA = FALSE)
#'
#' # data = vv2015
#' # OutcomeTargetVariable = 'y'
#' # TreatmentVariable = "x"
#' # MediatorVariable = "m"
#' # Covariates = "c"
#' # MM_TreatmentCovariates = NULL
#' # OM_TreatmentCovariates = NULL
#' # OM_MediatorCovariates = NULL
#' # SurvivalEventVariable = "event"
#' # UnTreated_ReferenceIndicator = 0
#' # Treated_ReferenceIndicator = 1
#' # Mediator_ControlDirectEffectLevel = 1
#' # Covariate_NaturalDirectIndirect = 3
#' # MediatorTargetType = 'logistic'
#' # OutcomeTargetType = 'survAFT_weibull'
#' # TreatmentMediatorInteraction = TRUE
#' # CaseControlSourceData = FALSE
#' # RemoveNA = FALSE
#' }
#'
#' @return list with model output object, summary output, effects output, and an effects plot
#'
#' @export
CausalMediation <- function(data,
OutcomeTargetVariable = NULL, # yvar char length = 0
TreatmentVariable = NULL, # avar char length = 0 (binary)
MediatorVariable = NULL, # mvar char length = 0 (binary)
Covariates = NULL, # cvar char length > 0
MM_TreatmentCovariates = NULL, # emm_ac_mreg = NULL char length > 0
OM_TreatmentCovariates = NULL, # emm_ac_yreg = NULL char length > 0
OM_MediatorCovariates = NULL, # emm_mc_yreg = NULL char length > 0
SurvivalEventVariable = NULL, # eventvar char length = 0
UnTreated_ReferenceIndicator = NULL, # ao num length = 1
Treated_ReferenceIndicator = NULL, # a1 num length = 1
Mediator_ControlDirectEffectLevel = NULL, # m_cde num length = 1
Covariate_NaturalDirectIndirect = 0, # c_cond; same length as Covariates num length = length(Covariates)
MediatorTargetType = 'linear', # mreg "linear" or "logistic"
OutcomeTargetType = 'linear', # yreg "linear", "logistic", "loglinear", "poisson", "negbin", "survCox", "survAFT_exp", or "survAFT_weibull"
TreatmentMediatorInteraction = TRUE, # interaction = TRUE,
CaseControlSourceData = FALSE, # casecontrol = FALSE,
RemoveNA = FALSE) { # na_omit = FALSE
print("Causaul Mediation 1")
print(data)
print(OutcomeTargetVariable)
print(TreatmentVariable)
print(MediatorVariable)
print(Covariates)
print(MM_TreatmentCovariates)
print(OM_TreatmentCovariates)
print(OM_MediatorCovariates)
print(SurvivalEventVariable)
print(UnTreated_ReferenceIndicator)
print(Treated_ReferenceIndicator)
print(Mediator_ControlDirectEffectLevel)
print(Covariate_NaturalDirectIndirect)
print(MediatorTargetType)
print(OutcomeTargetType)
print(TreatmentMediatorInteraction)
print(CaseControlSourceData)
print(RemoveNA)
# Model
Output <- regmedint::regmedint(
data = data,
yvar = OutcomeTargetVariable,
avar = TreatmentVariable,
mvar = MediatorVariable,
cvar = Covariates,
emm_ac_mreg = MM_TreatmentCovariates,
emm_ac_yreg = OM_TreatmentCovariates,
emm_mc_yreg = OM_MediatorCovariates,
eventvar = SurvivalEventVariable,
a0 = UnTreated_ReferenceIndicator,
a1 = Treated_ReferenceIndicator,
m_cde = Mediator_ControlDirectEffectLevel,
c_cond = Covariate_NaturalDirectIndirect,
mreg = MediatorTargetType,
yreg = OutcomeTargetType,
interaction = TreatmentMediatorInteraction,
casecontrol = CaseControlSourceData,
na_omit = RemoveNA)
print("Causaul Mediation 2")
# Summary Output
SummaryOutput <- summary(Output)
print("Causaul Mediation 3")
Effects <- data.table::as.data.table(SummaryOutput$summary_myreg, keep.rownames = TRUE)
print("Causaul Mediation 4")
data.table::setnames(Effects, c('est','rn','se','Z','p'), c('Estimate','Measure','Standard Error','Z-Value','P-Value'))
print("Causaul Mediation 5")
Effects[, Description := 'a']
Effects[, Definition := 'a']
data.table::set(Effects, i = 1L, j = 'Description', value = 'Conditional Direct Effect')
data.table::set(Effects, i = 1L, j = 'Definition', value = 'E[Y_treat,mediator | C = c] − E[Y_notreat,mediator | C = c]')
data.table::set(Effects, i = 2L, j = 'Description', value = 'Pure Natural Direct Effect')
data.table::set(Effects, i = 2L, j = 'Definition', value = 'E[Y_treat,no_mediation | C = c] − E[Y_no_treat,no_mediation | C = c]')
data.table::set(Effects, i = 3L, j = 'Description', value = 'Total Natural Indirect Effect')
data.table::set(Effects, i = 3L, j = 'Definition', value = 'E[Y_treat, mediation |C = c] − E[Y_treat,no_mediation | C = c]')
data.table::set(Effects, i = 4L, j = 'Description', value = 'Total Natural Direct Effect')
data.table::set(Effects, i = 4L, j = 'Definition', value = 'E[Y_treat,mediation | C = c] − E[Y_no_treat, mediation | C = c]')
data.table::set(Effects, i = 5L, j = 'Description', value = 'Pure Natural Indirect Effect')
data.table::set(Effects, i = 5L, j = 'Definition', value = 'E[Y_no_treat,mediation | C = c] − E[Y_no_treat,no_mediation | C = c]')
data.table::set(Effects, i = 6L, j = 'Description', value = 'Total Effect')
data.table::set(Effects, i = 6L, j = 'Definition', value = 'E[Y_treat | C = c] − E[Y_no_treat | C = c]')
if(MediatorTargetType == 'linear') {
data.table::set(Effects, i = 7L, j = 'Description', value = 'Proportion Mediated')
data.table::set(Effects, i = 7L, j = 'Definition', value = 'TNIE / (TNIE + PNDE)')
} else {
data.table::set(Effects, i = 7L, j = 'Description', value = 'Proportion Mediated')
data.table::set(Effects, i = 7L, j = 'Definition', value = 'exp(PNDE) * (exp(TNIE) - 1) / (exp(PNDE) * exp(TNIE) - 1)')
}
# Significance stars
Effects[, Significance := data.table::fcase(
`P-Value` < 0.001, '***',
`P-Value` < 0.01, '**',
`P-Value` < 0.05, '*',
default = ''
)]
data.table::setcolorder(Effects, c(8L,1L:7L,9L))
p1 <- AutoQuant::BarPlot(data = Effects, XVar = 'Description', YVar = 'Estimate', AggMethod = 'sum', ColorVar = 'Significance', FillColor = '#66ff10')
MainEffects <- data.table::as.data.table(x = SummaryOutput$summary_yreg_fit$table, keep.rownames = TRUE)
data.table::setnames(x = MainEffects, old = c('rn','z','p'), c('Variable', 'Z-Value', 'P-Value'))
MainEffects[, Significance := data.table::fcase(
`P-Value` < 0.001, '***',
`P-Value` < 0.01, '**',
`P-Value` < 0.05, '*',
default = ''
)]
# Return
return(list(
Output = Output,
SummaryOutput = SummaryOutput,
MainEffects = MainEffects,
Effects = Effects,
EffectsPlot = p1
))
}
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