#' Plot method for the imputed data contained in the objects of class \code{missingHE}
#'
#' Produces a plot of the observed and imputed values (with credible intervals) for the effect and cost outcomes from a
#' Bayesian cost-effectiveness analysis model with two treatment arms, implemented using the function \code{\link{selection}}, \code{\link{selection_long}}, \code{\link{pattern}} or \code{\link{hurdle}}.
#' The graphical layout is obtained from the functions contained in the package \strong{ggplot2} and \strong{ggthemes}.
#' @keywords plot missing data
#' @param x A \code{missingHE} object containing the results of the Bayesian model for cost-effectiveness analysis.
#' @param prob A numeric vector of probabilities representing the upper and lower CI sample quantiles to be calculated and returned for the imputed values.
#' @param class Type of the plot comparing the observed and imputed outcome data. Available choices are
#' 'histogram' and 'scatter' for a histogram or a scatter plot of the observed and imputed outcome data, respectively.
#' @param outcome The outcome variables that should be displayed. Options are: 'all' (default) which shows the plots
#' for both treatment arms, time points (only for longitudinal models) and types of outcome variables; 'effects' and 'costs' which show the plots for the corresponding
#' outcome variables in both arms; 'arm1' and 'arm2' which show the plots by the selected treatment arm. To select
#' the plots for a specific outcome in a specific treatment arm the options that can be used are 'effects_arm1',
#' 'effects_arm2', 'costs_arm1' or 'costs_arm2'.
#' @param time_plot Time point for which plots should be displayed (only for longitudinal models).
#' @param theme Type of ggplot theme among some pre-defined themes, mostly taken from the package \strong{ggthemes}. For a full list of available themes see details.
#' @param ... Additional parameters that can be provided to manage the output of \code{plot.missingHE}.
#' @return A \code{ggplot} object containing the plots specified in the argument \code{class}.
#' @seealso \code{\link{selection}} \code{\link{selection_long}} \code{\link{pattern}} \code{\link{hurdle}} \code{\link{diagnostic}}
#' @details The function produces a plot of the observed and imputed effect and cost data in a two-arm based
#' cost-effectiveness model implemented using the function \code{\link{selection}}, \code{\link{selection_long}}, \code{\link{pattern}} or \code{\link{hurdle}}. The purpose of this graph
#' is to visually compare the outcome values for the fully-observed individuals with those imputed by the model for the missing individuals.
#' For the scatter plot, imputed values are also associated with the credible intervals specified in the argument \code{prob}.
#' The argument \code{theme} allows to customise the graphical aspect of the plots generated by \code{plot.missingHE} and
#' allows to choose among a set of possible pre-defined themes taken form the package \strong{ggtheme}. For a complete list of the available character names
#' for each theme and scheme set, see \strong{ggthemes} and \strong{bayesplot}.
#' @author Andrea Gabrio
#' @references
#' Daniels, MJ. Hogan, JW. (2008) \emph{Missing Data in Longitudinal Studies: strategies for Bayesian modelling and sensitivity analysis}, CRC/Chapman Hall.
#'
#' Molenberghs, G. Fitzmaurice, G. Kenward, MG. Tsiatis, A. Verbeke, G. (2015) \emph{Handbook of Missing Data Methodology}, CRC/Chapman Hall.
#' @import ggplot2 gridExtra grid
#' @export
#' @examples
#' # For examples see the function \code{\link{selection}}, \code{\link{selection_long}},
#' # \code{\link{pattern}} or \code{\link{hurdle}}
#' #
#' #
<- (x, prob = (0.025, 0.975), = "scatter", outcome = "all", time_plot = , theme = , ) {
(!(("gridExtra")) | !(("grid")) | !(("ggplot2")) | !(("ggthemes"))) {
("You need to install the R packages 'gridExtra', 'grid' and 'ggplot2'. Please run in your R terminal:\n install.packages('gridExtra', 'grid', 'ggplot2', 'ggthemes')")
}
((theme) == ) {theme = "base" }
(!theme %in% ("base", "calc", "economist", "excel", "few", "538", "gdocs", "hc", "par", "pander", "solarized", "stata", "tufte", "wsj")) {
("You must provide a valid theme type among those available")
}
(! %in% ("scatter", "histogram")) {
("You must provide a plot class among those available")
}
(!outcome %in% ("all", "effects", "costs", "arm1", "arm2", "effects_arm1", "costs_arm1", "effects_arm2", "costs_arm2")) {
("You must provide a plot outcome among those available")
}
exArgs <- ()
(!(x, what = "missingHE")) {
("Only objects of class 'missingHE' can be used")
}
((prob) != 2 | (prob) == | (prob < 0) != | (prob > 1) != ) {
("You must provide valid lower/upper quantiles for the imputed data distribution")
}
(x$data_format == "wide") {
(x$data_set$`N missing in comparator arm`[1]==0 & x$data_set$`N missing in comparator arm`[2]==0 &
x$data_set$`N missing in reference arm`[1]==0 & x$data_set$`N missing in reference arm`[2]==0) {
("No missing value found in the data")
}
eff1_pos <- (x$data_set$[1]], x$data_set$`N in reference arm`, 3)
cost1_pos <- (x$data_set$costs[1]], x$data_set$`N in reference arm`, 3)
eff2_pos <- (x$data_set$[2]], x$data_set$`N in comparator arm`, 3)
cost2_pos <- (x$data_set$costs[2]], x$data_set$`N in comparator arm`, 3)
eff1_pos,1] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff1, 2, )
eff1_pos,2] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff1, 2, , probs = prob[1])
eff1_pos,3] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff1, 2, , probs = prob[2])
eff2_pos,1] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff2, 2, )
eff2_pos,2] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff2, 2, , probs = prob[1])
eff2_pos,3] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff2, 2, , probs = prob[2])
cost1_pos,1] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost1, 2, )
cost1_pos,2] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost1, 2, , probs = prob[1])
cost1_pos,3] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost1, 2, , probs = prob[2])
cost2_pos,1] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost2, 2, )
cost2_pos,2] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost2, 2, , probs = prob[1])
cost2_pos,3] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost2, 2, , probs = prob[2])
(eff1_pos) <- ("mean", "LB", "UB")
(eff2_pos) <- ("mean", "LB", "UB")
(cost1_pos) <- ("mean", "LB", "UB")
(cost2_pos) <- ("mean", "LB", "UB")
complete_labels <- complete <- ("effects1" = eff1_pos, "effects2" = eff2_pos, "costs1" = cost1_pos, "costs2" = cost2_pos)
observed<-("effects1" = x$data_set$[1]], "effects2" = x$data_set$[2]],
"costs1" = x$data_set$costs[1]], "costs2" = x$data_set$costs[2]])
eff1_imp <- (, (complete$effects1, 1]), 3)
cost1_imp <- (, (complete$costs1, 1]), 3)
eff2_imp <- (, (complete$effects2, 1]), 3)
cost2_imp <- (, (complete$costs2, 1]), 3)
eff1_imputed <- (1:3, (j) {
((1:(complete$effects1, j]), (i) {
((observed$effects1[i]) == ) {eff1_imp[i] <- complete$effects1[i, j]} {eff1_imp[i] <- } } )) })
eff2_imputed <- (1:3, (j){
((1:(complete$effects2, j]), (i) {
((observed$effects2[i]) == ) {eff2_imp[i] <- complete$effects2[i, j]} {eff2_imp[i] <- } })) })
cost1_imputed <- (1:3, (j) {
((1:(complete$costs1, j]), (i) {
((observed$costs1[i]) == ){cost1_imp[i] <- complete$costs1[i, j]} {cost1_imp[i] <- } })) })
cost2_imputed <- (1:3, (j) {
((1:(complete$costs2, j]), (i) {
((observed$costs2[i]) == ) {cost2_imp[i] <- complete$costs2[i, j]} {cost2_imp[i] <- } })) })
eff1_imp <- (1:3, (j) {eff1_imp, j] <- (eff1_imputed[j]) })
eff2_imp <- (1:3, (j) {eff2_imp, j] <- (eff2_imputed[j]) })
cost1_imp <- (1:3, (j) {cost1_imp, j] <- (cost1_imputed[j]) })
cost2_imp <- (1:3, (j) {cost2_imp, j] <- (cost2_imputed[j]) })
imputed <- ("effects1"= eff1_imp, "effects2" = eff2_imp, "costs1" = cost1_imp, "costs2" = cost2_imp)
(eff1_imp) <- ("mean", "LB", "UB")
(eff2_imp) <- ("mean", "LB", "UB")
(cost1_imp) <- ("mean", "LB", "UB")
(cost2_imp) <- ("mean", "LB", "UB")
imputed_labels <- ("effects1"= eff1_imp, "effects2" = eff2_imp, "costs1" = cost1_imp, "costs2" = cost2_imp)
<- ("observed" = observed, "imputed" = imputed, "complete" = complete)
indic <- (x) {
w =
if ((x) == )
w = "missing"
w = "observed"
(w)
}
m_eff1 <- m_eff2 <- m_cost1 <- m_cost2 <- ()
bound_m_eff1 <- (0, (observed$effects1), 3)
m_eff1 <- (observed$effects1, indic)
bound_m_eff1 <- (2:3, (j) {
(1:(m_eff1), (i) {
(m_eff1[i] == "observed") {
bound_m_eff1[i, j] = 0
} (m_eff1[i] == "missing") {
bound_m_eff1[i, j] = imputed$effects1[i, j] } }) })
bound_m_eff2 <- (0, (observed$effects2), 3)
m_eff2 <- (observed$effects2, indic)
bound_m_eff2 <- (2:3, (j) {
(1:(m_eff2), (i) {
(m_eff2[i] == "observed") {
bound_m_eff2[i, j] = 0
} (m_eff2[i] == "missing") {
bound_m_eff2[i, j] = imputed$effects2[i,j] } }) })
bound_m_cost1 <- (0, (observed$costs1), 3)
m_cost1 <- (observed$costs1, indic)
bound_m_cost1 <- (2:3, (j) {
(1:(m_cost1), (i) {
(m_cost1[i] == "observed") {
bound_m_cost1[i, j] = 0
} (m_cost1[i] == "missing") {
bound_m_cost1[i, j] = imputed$costs1[i, j] } }) })
bound_m_cost2 <- (0, (observed$costs2), 3)
m_cost2 <- (observed$costs2, indic)
bound_m_cost2 <- (2:3, (j) {
(1:(m_cost2), (i) {
(m_cost2[i] == "observed") {
bound_m_cost2[i, j] = 0
} (m_cost2[i] == "missing") {
bound_m_cost2[i, j] = imputed$costs2[i, j] } }) })
<- costs <- upper <- lower <- Individuals <- <-
( == "scatter") {
pd <- ggplot2::position_dodge(0.1)
(((x$data_set$[1]])) == ) {
plot.eff1.data <- (Individuals = (1:(complete$effects1, 1])), = observed$effects1, = imputed$effects1, 1],
lower = imputed$effects1, 2], upper = imputed$effects1, 3], = m_eff1)
plot_eff1 <- ggplot2::ggplot(plot.eff1.data,ggplot2::aes(colour = , x = Individuals, y = )) + ggplot2::geom_point(position = pd,na.rm = ) +
ggplot2::geom_pointrange( = plot.eff1.data, mapping = ggplot2::aes(x = Individuals, y = , ymin = upper, ymax = lower), na.rm = ) +
ggplot2::scale_colour_manual(values = ("red", "black"), guide = ggplot2::guide_legend(override.aes = (shape = (16, 16), linetype = (1, 0)))) +
ggplot2::ggtitle("effects (control)")
} (((x$data_set$[1]])) == ) {
plot.eff1.data<-(Individuals = (1:(complete$effects1, 1])), = observed$effects1, = m_eff1)
plot_eff1 <- ggplot2::ggplot(plot.eff1.data,ggplot2::aes(colour = , x = Individuals, y = )) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::scale_colour_manual(values = ("black"), guide = ggplot2::guide_legend(override.aes = (shape = (16), linetype = (0)))) +
ggplot2::ggtitle("effects (control)")
}
(((x$data_set$[2]])) == ) {
plot.eff2.data <- (Individuals = (1:(complete$effects2, 1])), = observed$effects2, = imputed$effects2, 1],
lower = imputed$effects2,2], upper = imputed$effects2, 3], = m_eff2)
plot_eff2 <- ggplot2::ggplot(plot.eff2.data,ggplot2::aes(colour = , x = Individuals, y = )) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::geom_pointrange( = plot.eff2.data, mapping = ggplot2::aes(x = Individuals, y = , ymin = upper, ymax = lower), na.rm = ) +
ggplot2::scale_colour_manual(values = ("red", "black"), guide = ggplot2::guide_legend(override.aes = (shape = (16, 16), linetype = (1, 0)))) +
ggplot2::ggtitle("effects (intervention)")
} (((x$data_set$[2]])) == ) {
plot.eff2.data <- (Individuals = (1:(complete$effects2, 1])), = observed$effects2, = m_eff2)
plot_eff2 <- ggplot2::ggplot(plot.eff2.data,ggplot2::aes(colour = , x = Individuals, y = )) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::scale_colour_manual(values = ("black"), guide = ggplot2::guide_legend(override.aes = (shape = (16), linetype = (0)))) +
ggplot2::ggtitle("effects (intervention)")
}
(((x$data_set$costs[1]])) == ) {
plot.cost1.data <- (Individuals = (1:(complete$costs1, 1])), costs = observed$costs1, = imputed$costs1, 1],
lower = imputed$costs1, 2], upper = imputed$costs1, 3], = m_cost1)
plot_cost1 <- ggplot2::ggplot(plot.cost1.data, ggplot2::aes(colour = , x = Individuals, y = costs)) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::geom_pointrange( = plot.cost1.data, mapping = ggplot2::aes(x = Individuals, y = , ymin = upper, ymax = lower), na.rm = ) +
ggplot2::scale_colour_manual(values = ("red", "black"), guide = ggplot2::guide_legend(override.aes = (shape = (16, 16), linetype=(1, 0)))) +
ggplot2::ggtitle("costs (control)")
} (((x$data_set$costs[1]])) == ) {
plot.cost1.data <- (Individuals = (1:(complete$costs1, 1])), costs = observed$costs1, = m_cost1)
plot_cost1 <- ggplot2::ggplot(plot.cost1.data, ggplot2::aes(colour = , x = Individuals, y = costs)) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::scale_colour_manual(values = ("black"), guide = ggplot2::guide_legend(override.aes = (shape = (16), linetype = (0)))) +
ggplot2::ggtitle("costs (control)")
}
(((x$data_set$costs[2]])) == ) {
plot.cost2.data <- (Individuals = (1:(complete$costs2, 1])),costs = observed$costs2, = imputed$costs2, 1],
lower = imputed$costs2, 2], upper = imputed$costs2, 3], = m_cost2)
plot_cost2 <- ggplot2::ggplot(plot.cost2.data, ggplot2::aes(colour = , x = Individuals, y = costs)) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::geom_pointrange( = plot.cost2.data, mapping = ggplot2::aes(x = Individuals, y = , ymin = upper, ymax = lower), na.rm = ) +
ggplot2::scale_colour_manual(values = ("red", "black"), guide = ggplot2::guide_legend(override.aes = (shape = (16, 16), linetype = (1, 0)))) +
ggplot2::ggtitle("costs (intervention)")
} (((x$data_set$costs[2]])) == ) {
plot.cost2.data <- (Individuals = (1:(complete$costs2,1])), costs = observed$costs2, = m_cost2)
plot_cost2 <- ggplot2::ggplot(plot.cost2.data,ggplot2::aes(colour = , x = Individuals, y = costs)) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::scale_colour_manual(values = ("black"), guide = ggplot2::guide_legend(override.aes = (shape = (16), linetype = (0)))) +
ggplot2::ggtitle("costs (intervention)")
}
} ( == "histogram") {
(((x$data_set$[1]])) == ) {
plot.eff1.data <- (Individuals = (1:(complete$effects1, 1])), = complete$effects1, 1],
lower = complete$effects1, 2], upper = complete$effects1, 3], = m_eff1)
plot_eff1 <- ggplot2::ggplot(plot.eff1.data, ggplot2::aes(x = , color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("red","black")) + ggplot2::ggtitle("effects (control)")
} (((x$data_set$[1]])) == ) {
plot.eff1.data <- (Individuals = (1:(complete$effects1,1])), = complete$effects1,1], = m_eff1)
plot_eff1 <- ggplot2::ggplot(plot.eff1.data, ggplot2::aes(x = , color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("black")) + ggplot2::ggtitle("effects (control)")
}
(((x$data_set$[2]])) == ) {
plot.eff2.data <- (Individuals = (1:(complete$effects2, 1])), = complete$effects2, 1],
lower = complete$effects2, 2], upper = complete$effects2, 3], = m_eff2)
plot_eff2 <- ggplot2::ggplot(plot.eff2.data, ggplot2::aes(x = , color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("red", "black")) + ggplot2::ggtitle("effects (intervention)")
} (((x$data_set$[2]])) == ) {
plot.eff2.data <- (Individuals = (1:(complete$effects1, 2])), = complete$effects1, 2], = m_eff1)
plot_eff2 <- ggplot2::ggplot(plot.eff2.data, ggplot2::aes(x = , color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("black")) + ggplot2::ggtitle("effects (intervention)")
}
(((x$data_set$costs[1]])) == ) {
plot.cost1.data <- (Individuals = (1:(complete$costs1, 1])), costs = complete$costs1, 1],
lower = complete$costs1, 2], upper = complete$costs1, 3], = m_cost1)
plot_cost1 <- ggplot2::ggplot(plot.cost1.data, ggplot2::aes(x = costs, color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("red", "black")) + ggplot2::ggtitle("costs (control)")
} (((x$data_set$costs[1]])) == ) {
plot.cost1.data <- (Individuals = (1:(complete$costs1, 1])), costs = complete$costs1, 1], = m_cost1)
plot_cost1 <- ggplot2::ggplot(plot.cost1.data, ggplot2::aes(x = costs, color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("black")) + ggplot2::ggtitle("costs (control)")
}
(((x$data_set$costs[2]])) == ) {
plot.cost2.data <- (Individuals = (1:(complete$costs2, 1])), costs = complete$costs2, 1], lower = complete$costs2, 2], upper = complete$costs2, 3], = m_cost2)
plot_cost2 <- ggplot2::ggplot(plot.cost2.data, ggplot2::aes(x = costs, color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("red", "black")) + ggplot2::ggtitle("costs (intervention)")
} (((x$data_set$costs[2]])) == ) {
plot.cost2.data <- (Individuals = (1:(complete$costs2, 1])), costs = complete$costs2, 1], = m_cost2)
plot_cost2 <- ggplot2::ggplot(plot.cost2.data, ggplot2::aes(x = costs, color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("black")) + ggplot2::ggtitle("costs (intervention)")
}
}
(theme == "base") {
plot_eff1 <- plot_eff1 + ggplot2::theme(panel.border = ggplot2::element_rect(colour = "black", fill = ), panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(), plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggplot2::theme(panel.border = ggplot2::element_rect(colour = "black", fill = ), panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(), plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggplot2::theme(panel.border = ggplot2::element_rect(colour = "black", fill = ), panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),panel.grid.minor = ggplot2::element_blank(), plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggplot2::theme(panel.border = ggplot2::element_rect(colour = "black", fill = ), panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(), plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "calc") {
plot_eff1 <- plot_eff1 + ggthemes::theme_calc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_calc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_calc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_calc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "economist") {
plot_eff1 <- plot_eff1 + ggthemes::theme_economist() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_economist() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_economist() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_economist() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "excel") {
plot_eff1 <- plot_eff1 + ggthemes::theme_excel() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_excel() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_excel() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_excel() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "few") {
plot_eff1 <- plot_eff1 + ggthemes::theme_few() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_few() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_few() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_few() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "538") {
plot_eff1 <- plot_eff1 + ggthemes::theme_fivethirtyeight() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_fivethirtyeight() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_fivethirtyeight() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_fivethirtyeight() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "gdocs"){
plot_eff1 <- plot_eff1 + ggthemes::theme_gdocs() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_gdocs() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_gdocs() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_gdocs() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "hc") {
plot_eff1 <- plot_eff1 + ggthemes::theme_hc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_hc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_hc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_hc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "par") {
plot_eff1 <- plot_eff1 + ggthemes::theme_par() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_par() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_par() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_par() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "solarized") {
plot_eff1 <- plot_eff1 + ggthemes::theme_solarized() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_solarized() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_solarized() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_solarized() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "pander") {
plot_eff1 <- plot_eff1 + ggthemes::theme_pander() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_pander() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_pander() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_pander() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "stata") {
plot_eff1 <- plot_eff1 + ggthemes::theme_stata() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_stata() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_stata() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_stata() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "tufte") {
plot_eff1 <- plot_eff1 + ggthemes::theme_tufte() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_tufte() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_tufte() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_tufte() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
} (theme == "wsj") {
plot_eff1 <- plot_eff1 + ggthemes::theme_wsj() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2 <- plot_eff2 + ggthemes::theme_wsj() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1 <- plot_cost1 + ggthemes::theme_wsj() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2 <- plot_cost2 + ggthemes::theme_wsj() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
grid_arrange_shared_legend <- (, = 1, = (()), position = ("bottom", "right"), n = 1) {
plots <- ()
position <- (position)
g <- ggplot2::ggplotGrob(plots[n]] + ggplot2::theme(legend.position = position))$grobs
<- g[which((g, (x) x$) == "guide-box")]]
lheight <- ($)
lwidth <- ($)
<- (plots, (x) x + ggplot2::theme(legend.position = "none"))
<- (, = , = )
combined <- (position,"bottom" = gridExtra::arrangeGrob((gridExtra::arrangeGrob, ), , = 1,
heights = ::(::(1, "npc") - lheight, lheight)),
"right" = gridExtra::arrangeGrob((gridExtra::arrangeGrob, ), , = 2, widths = ::(::(1, "npc") - lwidth, lwidth)))
::()
::(combined)
}
(outcome == "all") {
(check_missing <- (((x$data_set$[1]])), ((x$data_set$[2]])),
((x$data_set$costs[1]])), ((x$data_set$costs[2]]))))
((check_missing == )) {n <- (, check_missing) } {n = 1 }
(plot_output <- grid_arrange_shared_legend(plot_eff1, plot_eff2, plot_cost1, plot_cost2, = 2, = 2, n = n))
} (outcome == "effects") {
(check_missing <- (((x$data_set$[1]])), ((x$data_set$[2]]))))
((check_missing == )) {n <- (, check_missing) } {n = 1 }
(plot_output <- grid_arrange_shared_legend(plot_eff1, plot_eff2, = 1, = 2, n = n))
} (outcome == "costs") {
(check_missing <- (((x$data_set$costs[1]])), ((x$data_set$costs[2]]))))
((check_missing==)) {n <- (, check_missing) } {n = 1 }
(plot_output <- grid_arrange_shared_legend(plot_cost1, plot_cost2, = 1, = 2, n = n))
} (outcome == "arm1") {
(check_missing <- (((x$data_set$[1]])), ((x$data_set$costs[1]]))))
((check_missing == )) {n <- (, check_missing) } {n = 1 }
(plot_output <- grid_arrange_shared_legend(plot_eff1, plot_cost1, = 1, = 2, n = n))
} (outcome == "arm2") {
(check_missing <- (((x$data_set$[2]])), ((x$data_set$costs[2]]))))
((check_missing == )) {n <- (, check_missing) } {n = 1 }
(plot_output <- grid_arrange_shared_legend(plot_eff2, plot_cost2, = 1, = 2, n = n))
} (outcome == "effects_arm1") {
(check_missing <- ((x$data_set$[1]])))
((check_missing == )) {n <- (, check_missing) } {n = 1 }
(plot_output <- grid_arrange_shared_legend(plot_eff1, = 1, = 1, n = n))
} (outcome == "effects_arm2") {
(check_missing <- ((x$data_set$[2]])))
((check_missing == )) {n <- (, check_missing) } {n = 1 }
(plot_output <- grid_arrange_shared_legend(plot_eff2, = 1, = 1))
} (outcome == "costs_arm1") {
(check_missing <- ((x$data_set$costs[1]])))
((check_missing == )) {n <- (, check_missing) } {n = 1 }
(plot_output <- grid_arrange_shared_legend(plot_cost1, = 1, = 1))
} (outcome == "costs_arm2") {
(check_missing <- ((x$data_set$costs[1]])))
((check_missing==)) {n <- (, check_missing) } {n = 1 }
(plot_output <- grid_arrange_shared_legend(plot_cost2, = 1, = 1))
}
}
(x$data_format == "long") {
((time_plot) == ) {
(time_plot < 1 | time_plot > (x$data_set$$)[2] | (time_plot) == | ((time_plot, (time_plot))) == ) {
("Time must be a numeric integer. Minimum time is 1 (baseline) and maximum time is the latest follow-up time in the data")
}
}
max_time <- (x$data_set$$)[2]
n_mis_comparator_u <- n_mis_comparator_c <- (, max_time)
n_mis_reference_u <- n_mis_reference_c <- (, max_time)
( 1:max_time) {
n_mis_comparator_u[time] <- x$data_set$`N missing in comparator arm`[1, ]
n_mis_comparator_c[time] <- x$data_set$`N missing in comparator arm`[2, ]
n_mis_reference_u[time] <- x$data_set$`N missing in reference arm`[1, ]
n_mis_reference_c[time] <- x$data_set$`N missing in reference arm`[2, ]
}
((n_mis_comparator_u) == 0 & (n_mis_comparator_c) == 0 & (n_mis_reference_u) == 0 & (n_mis_reference_c) == 0) {
("No missing value found in the data")
}
eff1_pos <- cost1_pos <- (max_time, ())
eff2_pos <- cost2_pos <- (max_time, ())
( 1:max_time) {
eff1_pos[time]] <- (x$data_set$[1]], ], x$data_set$`N in reference arm`, 3)
cost1_pos[time]] <- (x$data_set$costs[1]], ], x$data_set$`N in reference arm`, 3)
eff2_pos[time]] <- (x$data_set$[2]], ], x$data_set$`N in comparator arm`, 3)
cost2_pos[time]] <- (x$data_set$costs[2]], ], x$data_set$`N in comparator arm`, 3)
eff1_pos[time]],1] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff1, , ], 2, )
eff1_pos[time]],2] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff1, , ], 2, , probs = prob[1])
eff1_pos[time]],3] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff1, , ], 2, , probs = prob[2])
eff2_pos[time]],1] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff2, , ], 2, )
eff2_pos[time]],2] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff2, , ], 2, , probs = prob[1])
eff2_pos[time]],3] <- (x$model_output$`model summary`$BUGSoutput$sims.list$eff2, , ], 2, , probs = prob[2])
cost1_pos[time]],1] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost1, , ], 2, )
cost1_pos[time]],2] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost1, , ], 2, , probs = prob[1])
cost1_pos[time]],3] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost1, , ], 2, , probs = prob[2])
cost2_pos[time]],1] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost2, , ], 2, )
cost2_pos[time]],2] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost2, , ], 2, , probs = prob[1])
cost2_pos[time]],3] <- (x$model_output$`model summary`$BUGSoutput$sims.list$cost2, , ], 2, , probs = prob[2])
(eff1_pos[time]]) <- ("mean", "LB", "UB")
(eff2_pos[time]]) <- ("mean", "LB", "UB")
(cost1_pos[time]]) <- ("mean", "LB", "UB")
(cost2_pos[time]]) <- ("mean", "LB", "UB")
}
complete_labels <- complete <- ("effects1" = eff1_pos, "effects2" = eff2_pos, "costs1" = cost1_pos, "costs2" = cost2_pos)
observed <- ("effects1" = x$data_set$[1]], "effects2" = x$data_set$[2]],
"costs1" = x$data_set$costs[1]], "costs2" = x$data_set$costs[2]])
eff1_imp <- cost1_imp <- eff1_imputed <- cost1_imputed <- (max_time, ())
eff2_imp <- cost2_imp <- eff2_imputed <- cost2_imputed <- (max_time, ())
( 1:max_time) {
eff1_imp[time]] <- eff1_imputed[time]] <- (, (complete$effects1[time]], 1]), 3)
cost1_imp[time]] <- eff2_imputed[time]] <- (, (complete$costs1[time]], 1]), 3)
eff2_imp[time]] <- cost1_imputed[time]] <- (, (complete$effects2[time]], 1]), 3)
cost2_imp[time]] <- cost2_imputed[time]] <- (, (complete$costs2[time]], 1]), 3)
}
( 1:max_time) {
eff1_imputed[time]] <- (1:3, (j) {
((1:(complete$effects1[time]], j]), (i) {
((observed$effects1[i, ]) == ) {eff1_imp[time]][i, ] <- complete$effects1[time]][i, j]} {eff1_imp[time]][i, ] <- } } )) })
eff2_imputed[time]] <- (1:3, (j){
((1:(complete$effects2[time]], j]), (i) {
((observed$effects2[i, ]) == ) {eff2_imp[time]][i, ] <- complete$effects2[time]][i, j]} {eff2_imp[time]][i, ] <- } })) })
cost1_imputed[time]] <- (1:3, (j) {
((1:(complete$costs1[time]], j]), (i) {
((observed$costs1[i, ]) == ){cost1_imp[time]][i, ] <- complete$costs1[time]][i, j]} {cost1_imp[time]][i, ] <- } })) })
cost2_imputed[time]] <- (1:3, (j) {
((1:(complete$costs2[time]], j]), (i) {
((observed$costs2[i, ]) == ) {cost2_imp[time]][i, ] <- complete$costs2[time]][i, j]} {cost2_imp[time]][i, ] <- } })) })
eff1_imp[time]] <- (1:3, (j) {eff1_imp[time]], j] <- (eff1_imputed[time]][j]) })
eff2_imp[time]] <- (1:3, (j) {eff2_imp[time]], j] <- (eff2_imputed[time]][j]) })
cost1_imp[time]] <- (1:3, (j) {cost1_imp[time]], j] <- (cost1_imputed[time]][j]) })
cost2_imp[time]] <- (1:3, (j) {cost2_imp[time]], j] <- (cost2_imputed[time]][j]) })
(eff1_imp[time]]) <- ("mean", "LB", "UB")
(eff2_imp[time]]) <- ("mean", "LB", "UB")
(cost1_imp[time]]) <- ("mean", "LB", "UB")
(cost2_imp[time]]) <- ("mean", "LB", "UB")
}
imputed <- ("effects1"= eff1_imp, "effects2" = eff2_imp, "costs1" = cost1_imp, "costs2" = cost2_imp)
imputed_labels <- ("effects1"= eff1_imp, "effects2" = eff2_imp, "costs1" = cost1_imp, "costs2" = cost2_imp)
<- ("observed" = observed, "imputed" = imputed, "complete" = complete)
indic <- (x) {
w =
if ((x) == )
w = "missing"
w = "observed"
(w)
}
m_eff1 <- m_cost1 <- (, = (observed$effects1)[1], = max_time)
m_eff2 <- m_cost2 <- (, = (observed$effects2)[1], = max_time)
bound_m_eff1 <- bound_m_cost1 <- (max_time, ())
bound_m_eff2 <- bound_m_cost2 <- (max_time, ())
( 1:max_time) {
bound_m_eff1[time]] <- (0, (observed$effects1)[1], 3)
bound_m_cost1[time]] <- (0, (observed$costs1)[1], 3)
bound_m_eff2[time]] <- (0, (observed$effects2)[1], 3)
bound_m_cost2[time]] <- (0, (observed$costs2)[1], 3)
}
( 1:max_time) {
m_eff1, ] <- (observed$effects1, ], indic)
bound_m_eff1[time]] <- (2:3, (j) {
(1:(m_eff1, ]), (i) {
(m_eff1[i, ] == "observed") {
bound_m_eff1[time]][i, j] = 0
} (m_eff1[i, ] == "missing") {
bound_m_eff1[time]][i, j] = imputed$effects1[time]][i, j] } }) })
m_eff2, ] <- (observed$effects2, ], indic)
bound_m_eff2[time]] <- (2:3, (j) {
(1:(m_eff2, ]), (i) {
(m_eff2[i, ] == "observed") {
bound_m_eff2[time]][i, j] = 0
} (m_eff2[i, ] == "missing") {
bound_m_eff2[time]][i, j] = imputed$effects2[time]][i,j] } }) })
m_cost1, ] <- (observed$costs1, ], indic)
bound_m_cost1[time]] <- (2:3, (j) {
(1:(m_cost1, ]), (i) {
(m_cost1[i, ] == "observed") {
bound_m_cost1[time]][i, j] = 0
} (m_cost1[i, ] == "missing") {
bound_m_cost1[time]][i, j] = imputed$costs1[time]][i, j] } }) })
m_cost2, ] <- (observed$costs2, ], indic)
bound_m_cost2[time]] <- (2:3, (j) {
(1:(m_cost2, ]), (i) {
(m_cost2[i, ] == "observed") {
bound_m_cost2[time]][i, j] = 0
} (m_cost2[i, ] == "missing") {
bound_m_cost2[time]][i, j] = imputed$costs2[time]][i, j] } }) })
}
<- costs <- upper <- lower <- Individuals <- <-
plot.eff1.data <- plot.cost1.data <- (max_time, ())
plot.eff2.data <- plot.cost2.data <- (max_time, ())
plot_eff1 <- plot_cost1 <- (max_time, ())
plot_eff2 <- plot_cost2 <- (max_time, ())
( == "scatter") {
pd <- ggplot2::position_dodge(0.1)
( 1:max_time) {
(((x$data_set$[1]], ])) == ) {
plot.eff1.data[time]] <- (Individuals = (1:(complete$effects1[time]], 1])), = observed$effects1, ], = imputed$effects1[time]], 1],
lower = imputed$effects1[time]], 2], upper = imputed$effects1[time]], 3], = m_eff1, ])
plot_eff1[time]] <- ggplot2::ggplot(plot.eff1.data[time]], ggplot2::aes(colour = , x = Individuals, y = )) + ggplot2::geom_point(position = pd,na.rm = ) +
ggplot2::geom_pointrange( = plot.eff1.data[time]], mapping = ggplot2::aes(x = Individuals, y = , ymin = upper, ymax = lower), na.rm = ) +
ggplot2::scale_colour_manual(values = ("red", "black"), guide = ggplot2::guide_legend(override.aes = (shape = (16, 16), linetype = (1, 0)))) +
ggplot2::ggtitle(("effects (control), time ", ))
} (((x$data_set$[1]], ])) == ) {
plot.eff1.data[time]] <- (Individuals = (1:(complete$effects1[time]], 1])), = observed$effects1, ], = m_eff1, ])
plot_eff1[time]] <- ggplot2::ggplot(plot.eff1.data[time]], ggplot2::aes(colour = , x = Individuals, y = )) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::scale_colour_manual(values = ("black"), guide = ggplot2::guide_legend(override.aes = (shape = (16), linetype = (0)))) +
ggplot2::ggtitle(("effects (control), time ", ))
}
(((x$data_set$[2]], ])) == ) {
plot.eff2.data[time]] <- (Individuals = (1:(complete$effects2[time]], 1])), = observed$effects2, ], = imputed$effects2[time]], 1],
lower = imputed$effects2[time]],2], upper = imputed$effects2[time]], 3], = m_eff2, ])
plot_eff2[time]] <- ggplot2::ggplot(plot.eff2.data[time]], ggplot2::aes(colour = , x = Individuals, y = )) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::geom_pointrange( = plot.eff2.data[time]], mapping = ggplot2::aes(x = Individuals, y = , ymin = upper, ymax = lower), na.rm = ) +
ggplot2::scale_colour_manual(values = ("red", "black"), guide = ggplot2::guide_legend(override.aes = (shape = (16, 16), linetype = (1, 0)))) +
ggplot2::ggtitle(("effects (intervention), time ", ))
} (((x$data_set$[2]])) == ) {
plot.eff2.data[time]] <- (Individuals = (1:(complete$effects2[time]], 1])), = observed$effects2, ], = m_eff2, ])
plot_eff2[time]] <- ggplot2::ggplot(plot.eff2.data[time]], ggplot2::aes(colour = , x = Individuals, y = )) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::scale_colour_manual(values = ("black"), guide = ggplot2::guide_legend(override.aes = (shape = (16), linetype = (0)))) +
ggplot2::ggtitle(("effects (intervention), time ", ))
}
(((x$data_set$costs[1]], ])) == ) {
plot.cost1.data[time]] <- (Individuals = (1:(complete$costs1[time]], 1])), costs = observed$costs1, ], = imputed$costs1[time]], 1],
lower = imputed$costs1[time]], 2], upper = imputed$costs1[time]], 3], = m_cost1, ])
plot_cost1[time]] <- ggplot2::ggplot(plot.cost1.data[time]], ggplot2::aes(colour = , x = Individuals, y = costs)) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::geom_pointrange( = plot.cost1.data[time]], mapping = ggplot2::aes(x = Individuals, y = , ymin = upper, ymax = lower), na.rm = ) +
ggplot2::scale_colour_manual(values = ("red", "black"), guide = ggplot2::guide_legend(override.aes = (shape = (16, 16), linetype=(1, 0)))) +
ggplot2::ggtitle(("costs (control), time ", ))
} (((x$data_set$costs[1]], ])) == ) {
plot.cost1.data[time]] <- (Individuals = (1:(complete$costs1[time]], 1])), costs = observed$costs1, ], = m_cost1, ])
plot_cost1[time]] <- ggplot2::ggplot(plot.cost1.data[time]], ggplot2::aes(colour = , x = Individuals, y = costs)) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::scale_colour_manual(values = ("black"), guide = ggplot2::guide_legend(override.aes = (shape = (16), linetype = (0)))) +
ggplot2::ggtitle(("costs (control), time ", ))
}
(((x$data_set$costs[2]], ])) == ) {
plot.cost2.data[time]] <- (Individuals = (1:(complete$costs2[time]], 1])),costs = observed$costs2, ], = imputed$costs2[time]], 1],
lower = imputed$costs2[time]], 2], upper = imputed$costs2[time]], 3], = m_cost2, ])
plot_cost2[time]] <- ggplot2::ggplot(plot.cost2.data[time]], ggplot2::aes(colour = , x = Individuals, y = costs)) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::geom_pointrange( = plot.cost2.data[time]], mapping = ggplot2::aes(x = Individuals, y = , ymin = upper, ymax = lower), na.rm = ) +
ggplot2::scale_colour_manual(values = ("red", "black"), guide = ggplot2::guide_legend(override.aes = (shape = (16, 16), linetype = (1, 0)))) +
ggplot2::ggtitle(("costs (intervention), time ", ))
} (((x$data_set$costs[2]], ])) == ) {
plot.cost2.data[time]] <- (Individuals = (1:(complete$costs2[time]], 1])), costs = observed$costs2, ], = m_cost2, ])
plot_cost2[time]] <- ggplot2::ggplot(plot.cost2.data[time]], ggplot2::aes(colour = , x = Individuals, y = costs)) + ggplot2::geom_point(position = pd, na.rm = ) +
ggplot2::scale_colour_manual(values = ("black"), guide = ggplot2::guide_legend(override.aes = (shape = (16), linetype = (0)))) +
ggplot2::ggtitle(("costs (intervention), time ", ))
}
}
} ( == "histogram") {
( 1:max_time) {
(((x$data_set$[1]], ])) == ) {
plot.eff1.data[time]] <- (Individuals = (1:(complete$effects1[time]], 1])), = complete$effects1[time]], 1],
lower = complete$effects1[time]], 2], upper = complete$effects1[time]], 3], = m_eff1, ])
plot_eff1[time]] <- ggplot2::ggplot(plot.eff1.data[time]], ggplot2::aes(x = , color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("red","black")) + ggplot2::ggtitle(("effects (control), time ", ))
} (((x$data_set$[1]], ])) == ) {
plot.eff1.data[time]] <- (Individuals = (1:(complete$effects1[time]], 1])), = complete$effects1[time]], 1], = m_eff1, ])
plot_eff1[time]] <- ggplot2::ggplot(plot.eff1.data[time]], ggplot2::aes(x = , color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("black")) + ggplot2::ggtitle(("effects (control), time ", ))
}
(((x$data_set$[2]], ])) == ) {
plot.eff2.data[time]] <- (Individuals = (1:(complete$effects2[time]], 1])), = complete$effects2[time]], 1],
lower = complete$effects2[time]], 2], upper = complete$effects2[time]], 3], = m_eff2, ])
plot_eff2[time]] <- ggplot2::ggplot(plot.eff2.data[time]], ggplot2::aes(x = , color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("red", "black")) + ggplot2::ggtitle(("effects (intervention), time ", ))
} (((x$data_set$[2]], ])) == ) {
plot.eff2.data[time]] <- (Individuals = (1:(complete$effects1[time]], 2])), = complete$effects1[time]], 2], = m_eff1, ])
plot_eff2[time]] <- ggplot2::ggplot(plot.eff2.data[time]], ggplot2::aes(x = , color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("black")) + ggplot2::ggtitle(("effects (intervention), time ", ))
}
(((x$data_set$costs[1]], ])) == ) {
plot.cost1.data[time]] <- (Individuals = (1:(complete$costs1[time]], 1])), costs = complete$costs1[time]], 1],
lower = complete$costs1[time]], 2], upper = complete$costs1[time]], 3], = m_cost1, ])
plot_cost1[time]] <- ggplot2::ggplot(plot.cost1.data[time]], ggplot2::aes(x = costs, color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("red", "black")) + ggplot2::ggtitle(("costs (control), time ", ))
} (((x$data_set$costs[1]], ])) == ) {
plot.cost1.data[time]] <- (Individuals = (1:(complete$costs1[time]], 1])), costs = complete$costs1[time]], 1], = m_cost1, ])
plot_cost1[time]] <- ggplot2::ggplot(plot.cost1.data[time]], ggplot2::aes(x = costs, color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("black")) + ggplot2::ggtitle(("costs (control), time ", ))
}
(((x$data_set$costs[2]], ])) == ) {
plot.cost2.data[time]] <- (Individuals = (1:(complete$costs2[time]], 1])), costs = complete$costs2[time]], 1],
lower = complete$costs2[time]], 2], upper = complete$costs2[time]], 3], = m_cost2, ])
plot_cost2[time]] <- ggplot2::ggplot(plot.cost2.data[time]], ggplot2::aes(x = costs, color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("red", "black")) + ggplot2::ggtitle(("costs (intervention), time ", ))
} (((x$data_set$costs[2]], ])) == ) {
plot.cost2.data[time]] <- (Individuals = (1:(complete$costs2[time]], 1])), costs = complete$costs2[time]], 1], = m_cost2, ])
plot_cost2[time]] <- ggplot2::ggplot(plot.cost2.data[time]], ggplot2::aes(x = costs, color = )) + ggplot2::geom_histogram(fill = "white", position = "dodge") +
ggplot2::theme(legend.position = "top") + ggplot2::scale_colour_manual(values = ("black")) + ggplot2::ggtitle(("costs (intervention), time ", ))
}
}
}
(theme == "base") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggplot2::theme(panel.border = ggplot2::element_rect(colour = "black", fill = ), panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(), plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggplot2::theme(panel.border = ggplot2::element_rect(colour = "black", fill = ), panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(), plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggplot2::theme(panel.border = ggplot2::element_rect(colour = "black", fill = ), panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(),panel.grid.minor = ggplot2::element_blank(), plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggplot2::theme(panel.border = ggplot2::element_rect(colour = "black", fill = ), panel.background = ggplot2::element_blank(),
panel.grid.major = ggplot2::element_blank(), panel.grid.minor = ggplot2::element_blank(), plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "calc") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_calc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_calc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_calc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_calc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "economist") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_economist() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_economist() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_economist() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_economist() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "excel") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_excel() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_excel() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_excel() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_excel() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "few") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_few() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_few() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_few() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_few() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "538") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_fivethirtyeight() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_fivethirtyeight() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_fivethirtyeight() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_fivethirtyeight() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "gdocs"){
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_gdocs() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_gdocs() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_gdocs() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_gdocs() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "hc") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_hc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_hc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_hc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_hc() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "par") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_par() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_par() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_par() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_par() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "solarized") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_solarized() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_solarized() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_solarized() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_solarized() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "pander") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_pander() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_pander() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_pander() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_pander() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "stata") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_stata() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_stata() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_stata() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_stata() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "tufte") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_tufte() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_tufte() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_tufte() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_tufte() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
} (theme == "wsj") {
( 1:max_time) {
plot_eff1[time]] <- plot_eff1[time]] + ggthemes::theme_wsj() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_eff2[time]] <- plot_eff2[time]] + ggthemes::theme_wsj() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost1[time]] <- plot_cost1[time]] + ggthemes::theme_wsj() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
plot_cost2[time]] <- plot_cost2[time]] + ggthemes::theme_wsj() + ggplot2::theme(plot.title = ggplot2::element_text(hjust = 0.5))
}
}
grid_arrange_shared_legend_plotlist <- (, plotlist = , = (()), = ,
position = ("bottom", "right"), n = 1) {
plots <- ((), plotlist)
if (()) = ((plots)/)
position <- (position)
g <- ggplot2::ggplotGrob(plots[n]] + ggplot2::theme(legend.position = position))$grobs
<- g[which((g, (x) x$) == "guide-box")]]
lheight <- ($)
lwidth <- ($)
<- (plots, (x) x + ggplot2::theme(legend.position = "none"))
<- (, = , = )
combined <- (position, "bottom" = gridExtra::arrangeGrob((gridExtra::arrangeGrob, ), , = 1,
heights = ::(::(1, "npc") - lheight, lheight)),
"right" = gridExtra::arrangeGrob((gridExtra::arrangeGrob, ), , = 2, widths = ::(::(1, "npc") - lwidth, lwidth)))
::()
::(combined)
}
(outcome == "all" & (time_plot) == ) {
check_missing_u <- (, = 2, = max_time)
check_missing_c <- (, = 2, = max_time)
( 1:max_time) {
(check_missing_u , ] <- (((x$data_set$[1]], ])), ((x$data_set$[2]], ]))))
(check_missing_c , ] <- (((x$data_set$costs[1]], ])), ((x$data_set$costs[2]], ]))))
}
(((check_missing_u, check_missing_c) == )) {n <- (, (check_missing_u, check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plotlist = (plot_eff1, plot_eff2, plot_cost1, plot_cost2), = max_time, = 4, n = n))
} (outcome == "all" & (time_plot) == ) {
check_missing_u <- (, = 2, = 1)
check_missing_c <- (, = 2, = 1)
(check_missing_u , 1] <- (((x$data_set$[1]], time_plot])), ((x$data_set$[2]], time_plot]))))
(check_missing_c , 1] <- (((x$data_set$costs[1]], time_plot])), ((x$data_set$costs[2]], time_plot]))))
(((check_missing_u, check_missing_c) == )) {n <- (, (check_missing_u, check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plot_eff1[time_plot]], plot_eff2[time_plot]], plot_cost1[time_plot]], plot_cost2[time_plot]], = 2, = 2, n = n))
} (outcome == "effects" & (time_plot) == ) {
check_missing_u <- (, = 2, = max_time)
( 1:max_time) {
(check_missing_u , ] <- (((x$data_set$[1]], ])), ((x$data_set$[2]], ]))))
}
(((check_missing_u) == )) {n <- (, (check_missing_u)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plotlist = (plot_eff1, plot_eff2), = max_time, = 2, n = n))
} (outcome == "effects" & (time_plot) == ) {
check_missing_u <- (, = 2, = 1)
(check_missing_u , 1] <- (((x$data_set$[1]], time_plot])), ((x$data_set$[2]], time_plot]))))
(((check_missing_u) == )) {n <- (, (check_missing_u)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plot_eff1[time_plot]], plot_eff2[time_plot]], = 1, = 2, n = n))
} (outcome == "costs" & (time_plot) == ) {
check_missing_c <- (, = 2, = max_time)
( 1:max_time) {
(check_missing_c , ] <- (((x$data_set$costs[1]], ])), ((x$data_set$costs[2]], ]))))
}
(((check_missing_c) == )) {n <- (, (check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plotlist = (plot_cost1, plot_cost2), = max_time, = 2, n = n))
} (outcome == "costs" & (time_plot) == ) {
check_missing_c <- (, = 2, = 1)
(check_missing_c , 1] <- (((x$data_set$costs[1]], time_plot])), ((x$data_set$costs[2]], time_plot]))))
(((check_missing_c) == )) {n <- (, (check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plot_cost1[time_plot]], plot_cost2[time_plot]], = 1, = 2, n = n))
} (outcome == "arm1" & (time_plot) == ) {
check_missing_u <- (, = 1, = max_time)
check_missing_c <- (, = 1, = max_time)
( 1:max_time) {
(check_missing_u , ] <- (((x$data_set$[1]], ]))))
(check_missing_c , ] <- (((x$data_set$costs[1]], ]))))
}
(((check_missing_u, check_missing_c) == )) {n <- (, (check_missing_u, check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plotlist = (plot_eff1, plot_cost1), = max_time, = 2, n = n))
} (outcome == "arm1" & (time_plot) == ) {
check_missing_u <- (, = 1, = 1)
check_missing_c <- (, = 1, = 1)
(check_missing_u , 1] <- (((x$data_set$[1]], time_plot]))))
(check_missing_c , 1] <- (((x$data_set$costs[1]], time_plot]))))
(((check_missing_u, check_missing_c) == )) {n <- (, (check_missing_u, check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plot_eff1[time_plot]], plot_cost1[time_plot]], = 1, = 2, n = n))
} (outcome == "arm2" & (time_plot) == ) {
check_missing_u <- (, = 1, = max_time)
check_missing_c <- (, = 1, = max_time)
( 1:max_time) {
(check_missing_u , ] <- (((x$data_set$[2]], ]))))
(check_missing_c , ] <- (((x$data_set$costs[2]], ]))))
}
(((check_missing_u, check_missing_c) == )) {n <- (, (check_missing_u, check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plotlist = (plot_eff2, plot_cost2), = max_time, = 2, n = n))
} (outcome == "arm2" & (time_plot) == ) {
check_missing_u <- (, = 1, = 1)
check_missing_c <- (, = 1, = 1)
(check_missing_u , 1] <- (((x$data_set$[2]], time_plot]))))
(check_missing_c , 1] <- (((x$data_set$costs[2]], time_plot]))))
(((check_missing_u, check_missing_c) == )) {n <- (, (check_missing_u, check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plot_eff2[time_plot]], plot_cost2[time_plot]], = 1, = 2, n = n))
} (outcome == "effects_arm1" & (time_plot) == ) {
check_missing_u <- (, = 1, = max_time)
( 1:max_time) {
(check_missing_u , ] <- (((x$data_set$[1]], ]))))
}
(((check_missing_u) == )) {n <- (, (check_missing_u)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plotlist = plot_eff1, = max_time, = 1, n = n))
} (outcome == "effects_arm1" & (time_plot) == ) {
check_missing_u <- (, = 1, = 1)
(check_missing_u , 1] <- (((x$data_set$[1]], time_plot]))))
(((check_missing_u) == )) {n <- (, (check_missing_u)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plot_eff1[time_plot]], = 1, = 1, n = n))
} (outcome == "effects_arm2" & (time_plot) == ) {
check_missing_u <- (, = 1, = max_time)
( 1:max_time) {
(check_missing_u , ] <- (((x$data_set$[2]], ]))))
}
(((check_missing_u) == )) {n <- (, (check_missing_u)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plotlist = plot_eff2, = max_time, = 1, n = n))
} (outcome == "effects_arm2" & (time_plot) == ) {
check_missing_u <- (, = 1, = 1)
(check_missing_u , 1] <- (((x$data_set$[2]], time_plot]))))
(((check_missing_u) == )) {n <- (, (check_missing_u)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plot_eff2[time_plot]], = 1, = 1, n = n))
} (outcome == "costs_arm1" & (time_plot) == ) {
check_missing_c <- (, = 1, = max_time)
( 1:max_time) {
(check_missing_c , ] <- (((x$data_set$costs[1]], ]))))
}
(((check_missing_c) == )) {n <- (, (check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plotlist = plot_cost1, = max_time, = 1, n = n))
} (outcome == "costs_arm1" & (time_plot) == ) {
check_missing_c <- (, = 1, = 1)
(check_missing_c , 1] <- (((x$data_set$costs[1]], time_plot]))))
(((check_missing_c) == )) {n <- (, (check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plot_cost1[time_plot]], = 1, = 1, n = n))
} (outcome == "costs_arm2" & (time_plot) == ) {
check_missing_c <- (, = 1, = max_time)
( 1:max_time) {
(check_missing_c , ] <- (((x$data_set$costs[2]], ]))))
}
(((check_missing_c) == )) {n <- (, (check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plotlist = plot_cost2, = max_time, = 1, n = n))
} (outcome == "costs_arm2" & (time_plot) == ) {
check_missing_c <- (, = 1, = 1)
(check_missing_c , 1] <- (((x$data_set$costs[2]], time_plot]))))
(((check_missing_c) == )) {n <- (, (check_missing_c)) } {n = 1 }
(plot_output <- grid_arrange_shared_legend_plotlist(plot_cost2[time_plot]], = 1, = 1, n = n))
}
}
res_plot <- ("plot" = plot_output, "complete data" = complete_labels, "observed data" = observed, "imputed data" = imputed_labels)
((res_plot))
}
