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m_MR_p3.R
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# sROC plot --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# UI for sROC plot (output the plot)
MR_sroc_plot_UI <- function(id) {
ns <- NS(id)
tagList(
p("NOTE: The dotted line(s) represent the 95% prediction region from the bivariate model;
the greyed out area(s) represent the 95% credible region from the bivariate model"),
uiOutput(outputId = ns("my_tooltip")),
plotOutput(outputId = ns("plot"),
click = ns("plot_click"))
)
}
# UI function to output sROC plot settings menu
MR_sroc_plot_settings_menu_UI <- function(id,
data) {
ns <- NS(id)
tagList(
sliderInput(inputId = ns("plot_dimension_slider"),
label = "Change size of plot",
min = 1,
max = 2000,
value = 500,
ticks = FALSE),
sliderInput(inputId = ns("size_summary"),
label = "Size of summary estimates",
min = 0,
max = 20,
step = 0.5,
ticks = FALSE,
value = 5),
sliderInput(inputId = ns("size_study_specific"),
label = "Size of study-specific estimates",
min = 0,
max = 20,
step = 0.5,
ticks = FALSE,
value = 3),
checkboxGroupInput(inputId = ns("HSROCcheck"),
label = h4("Options"),
choices = list("Observed data Points"=1,
"sROC curve"=2,
"set x and y-axis limits to (0,1)"=3),
selected = list(1,3)),
uiOutput(ns("HSROC_curve_type_ui")),
conditionalPanel(condition = 'output.cov_type_obj == 1',
ns = ns,
awesomeCheckbox(inputId = ns("cts_as_fct"),
"Display covariate as categorical",
FALSE)),
awesomeCheckbox(inputId = ns("prevcheck"),
"Display disease prevalence",
FALSE),
awesomeCheckbox(inputId = ns("weightcheck"),
"Display percentage study weights",
FALSE),
conditionalPanel(condition = 'input.weightcheck == 1', # works
ns=ns,
sliderInput(inputId = ns("weight_scale"),
label = "scale to display study weights",
min = 1,
max = 2000,
value = 500)),
checkboxGroupInput(inputId = ns("cicheck"),
label = "Display 95% study level confidence intervals for observed data",
choices = list("Sensitivity"=1, "Specificity"=2)),
uiOutput(ns("covariate_display_ui")),
# input$covcheck_display comes from the "covariate_display_ui" renderUI object.
conditionalPanel(condition = 'input.covcheck_display != 1',
ns=ns,
uiOutput(ns("covariate_model_ui"))),
# Download plot:
h5("Download plot:"),
radioButtons(inputId = ns("plot_file_type"), label = h5("File type"), choices = c(".png", ".pdf")),
numericInput(inputId = ns("plot_width"), label=h5("Plot width"), value = 5),
numericInput(inputId = ns("plot_height"), label=h5("Plot height"), value = 5),
numericInput(inputId = ns("plot_dpi"), label=h5("Plot DPI"), value = 600),
downloadButton(outputId = ns("plot_download"), label = "Download Plot")
)
}
# Server function to generate and download sROC plot
MR_sroc_plot_server <- function(id,
data,
cts_cov_indicator,
draws) {
moduleServer(
id,
function(input, output, session) {
plot_object <- reactive({
validate(
need(!(is.null(draws())),
"Please run model to display plot")
)
req(data(), draws(), input$covcheck_model, cancelOutput = TRUE)
mod <- draws()
cov_index <- as.integer(as.double(input$covcheck_model)) - 1
X <- data()
N <- nrow(X) # number of studies
C <- ncol(data())
Names <- colnames(data())
if (C > 8 & Names[7] != "rob_PS") { j <<- 6 } else { j <<- 13 }
# extract covariate info from data
cov_names <- num_covariates(X)$combined[-1] # extract names of covariates
cols_covariates <- dplyr::select(X, cov_names, year.cts, prevalence.cts) # columns containing only covariate data
no_cov <- num_covariates(X)$no_covariates # number of covariates
size_study_specific <- input$size_study_specific
size_summary <- input$size_summary
cts_cov_indicator <- cts_cov_indicator$cts_cov_indicator
if (cts_cov_indicator == 0) { # if categorical/discrete covariate ----------------------------------------------------------------------
# count number of levels of the covariate
num_levels <- length(rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,5])
# add study weights to dataset
X <- MA_weights(X, mod)
## credible regions
cred_list <- list()
for (i in 1:num_levels) {
cred_list[[i]] <- tibble(y = (rstan::extract(mod, pars = "lSe")$lSe[,i]) ,
x = (rstan::extract(mod, pars = "lSp")$lSp[,i]))
}
cred <- rbindlist(cred_list, idcol = TRUE) %>%
dplyr::mutate( !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(.id))
# in inv_logit space
g <- ggplot(data = cred,
aes(x = x, y = y,
color = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) )) +
stat_ellipse()
g
# Get ellipse coordinates from plot
pb <- ggplot_build(g)
el = pb$data[[1]][c("x","y", "group")]
credible_region <- tibble(x = plogis(el$x),
y = plogis(el$y),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(el$group))
g <- ggplot(data = credible_region,
aes(x = x, y = y,
color = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)))) +
geom_polygon(data = credible_region, aes(x = 1 - x, y = y), alpha=0.05, size=0.4) +
xlim(0,1) +
ylim(0,1)
## prediction regions
pred_list <- list()
for (i in 1:num_levels) {
pred_list[[i]] <- tibble(y = (rstan::extract(mod, pars = "lSe_pred")$lSe_pred[,i]),
x = (rstan::extract(mod, pars = "lSp_pred")$lSp_pred[,i]))
}
pred <- rbindlist(pred_list, idcol = TRUE) %>%
dplyr::mutate(!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(.id))
# in inv_logit space
g <- ggplot(data = pred,
aes(x = x, y = y,
color = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)))) +
stat_ellipse()
# Get ellipse coordinates from plot
pb <- ggplot_build(g)
el = pb$data[[1]][c("x","y", "group")]
pred_region <- tibble(x = plogis(el$x),
y = plogis(el$y),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(el$group))
g <- ggplot(data = pred_region, aes(x = x,
y = y,
color = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)))) +
geom_polygon(data = pred_region, aes(x = 1 - x,
y = y,
color = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5))),
alpha=0.05,
size=0.4) +
xlim(0,1) +
ylim(0,1)
## medians
median_sens <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,5])
l_sens <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,4])
u_sens <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se"))$summary[,6])
median_spec <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary[,5])
l_spec <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary[,4])
u_spec <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp"))$summary[,6])
medians <- tibble(median_sens = median_sens, l_sens = l_sens, u_sens = u_sens,
median_spec = median_spec, l_spec = l_spec, u_spec = u_spec,
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(seq(from = 1, to = num_levels, by = 1)))
medians2 <- dplyr::mutate(medians, !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) :=
factor(levels(factor(X[, j + cov_index])) ))
pred_region2 <- pred_region
for (i in 1:num_levels) {
pred_region2[, 3] = case_when(pred_region[, 3] == i ~ levels(factor(X[, j + cov_index]))[i] ,
TRUE ~ as.character(c(pred_region2[, 3])[[1]]) )
}
credible_region2 <- credible_region
for (i in 1:num_levels) {
credible_region2[, 3] = case_when(credible_region[, 3] == i ~ levels(factor(X[, j + cov_index]))[i] ,
TRUE ~ as.character(c(credible_region2[, 3])[[1]]) )
}
# observed values
ss<- tibble(
Study =as.numeric(as.factor(X$author)),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(X[, j + cov_index]),
TP=X$TP, FN=X$FN, FP=X$FP, TN=X$TN,
N=(X$TP+X$FN+X$FP+X$TN) ,
Sensitivity= (TP/(TP+FN)) ,
Specificity= (TN/(TN+FP)) ,
prev = round((TP+FN)/N, 2),
pctse = X$pctse,
pctsp = X$pctsp
)
TPR <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("roc_points_tpr"))$summary[,5])
FPR <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("roc_points_fpr"))$summary[,5])
cov <- c()
for (i in 1:num_levels) {
cov[(1+101*(i-1)):(101+101*(i-1))] <- levels(factor(X[, j + cov_index]))[i]
}
roc_points <- tibble(TPR, FPR,
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := cov)
# Calculate min and max values of sens and spec to avoid extrapolation of SROC curve
# need to calculate min and max TPR and FPR for each group
minTPR <- c()
maxTPR <- c()
minFPR <- c()
maxFPR <- c()
roc_points2 <- list()
for (i in 1:num_levels) {
X_sub <- dplyr::filter(X, !!as.name(colnames(X)[j + cov_index]) == as.numeric(levels(factor(X[, j + cov_index]))[i]))
minTPR[i] <- min( X_sub$TP/(X_sub$TP+X_sub$FN) )
maxTPR[i] <- max( X_sub$TP/(X_sub$TP+X_sub$FN) )
minFPR[i] <- min( X_sub$FP/(X_sub$FP+X_sub$TN) )
maxFPR[i] <- max( X_sub$FP/(X_sub$FP+X_sub$TN) )
# Create new data frame which restricts roc_points to being between min and max values
roc_points2[[i]] <- subset(dplyr::filter(roc_points,
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) ==
as.numeric(levels(factor(X[, j + cov_index]))[i])),
FPR<maxFPR[i] & FPR>minFPR[i] & TPR<maxTPR[i] & TPR>minTPR[i])
}
roc_points_restricted <- rbindlist(roc_points2)
roc_points_unrestricted <- roc_points
# Calculate sens and spec confidence intervals at the study level
# Add the confidence intervals to the dataset
foreach (i = 1:N) %do% {
ss$Sens_LCI[i] <- binconf(ss$TP[i], ss$TP[i]+ss$FN[i], method="exact")[2]
ss$Sens_UCI[i] <- binconf(ss$TP[i], ss$TP[i]+ss$FN[i], method="exact")[3]
ss$FPR_LCI[i] <- 1 - binconf(ss$TN[i], ss$FP[i]+ss$TN[i], method="exact")[3]
ss$FPR_UCI[i] <- 1 - binconf(ss$TN[i], ss$FP[i]+ss$TN[i], method="exact")[2]
}
ss <- cbind(ss, cols_covariates) # add covariates to data
# Base plot (default)
g <- ggplot(data = medians2, aes(y=median_sens,
x = 1-median_spec,
colour = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5)))) +
geom_point(size = size_summary) + # summary points
geom_path(data = pred_region2, aes(x= 1 - x, y= y,
colour = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5))),
linetype = 2, size = 0.4, inherit.aes = F) + # prediction region
geom_polygon(data = credible_region2, aes(x= 1 - x, y= y,
colour = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5))),
alpha=0.05, size=0.4, linetype = 2,inherit.aes = F) + # conf region
theme_bw() +
theme(text = element_text(size=15)) +
ylab("Sensitivity") +
xlab("1 - Specificity") +
coord_fixed()
g
# make covariates factors for display
for (i in 1:no_cov) {
ss <- dplyr::mutate(ss, !!as.name(colnames(ss)[16+i]) := factor( !!as.name(colnames(ss)[16+i])))
}
# Plot study level estimates
if ('1' %in% input$HSROCcheck) {
if (input$weightcheck == TRUE) {
g = g + geom_ellipse(data = ss,
inherit.aes = F,
aes(x0 = 1-Specificity,
y0 = Sensitivity,
a=pctsp/input$weight_scale,
b=pctse/input$weight_scale,
angle = 0,
colour = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5))))
g
}
else {
g = g + geom_point(data = ss, aes(y=Sensitivity,
x = 1-Specificity,
colour = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5))),
size = size_study_specific,
alpha=0.7,
inherit.aes = F)
g
}
}
# HSROC curve
if ('2' %in% input$HSROCcheck) {
g = g + geom_path(data = roc_points_restricted,
inherit.aes = FALSE,
aes(x = FPR, y = TPR,
colour = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5))))
g
if (input$HSROCcheck_unrestricted_HSROC_curve == TRUE) {
g = g + geom_path(data = roc_points_unrestricted,
inherit.aes = FALSE,
aes(x = FPR, y = TPR,
colour = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5))))
g
}
}
else { g }
if ('3' %in% input$HSROCcheck) {
g = g + scale_x_continuous(breaks = seq(0,1,0.2), limits = c(0,1)) +
scale_y_continuous(breaks = seq(0,1,0.2), limits = c(0,1))
g
} else { g }
# display disease prevalence's on sROC plot
if ( input$prevcheck== TRUE ) {
g = g + geom_label_repel(data = ss,
inherit.aes = FALSE,
aes(y=Sensitivity, x = 1-Specificity, label = prev),
size = 3.5,
box.padding = 0.25)
g
}
else { g }
# Plot observed sens CI's
if ('1' %in% input$cicheck) {
g = g + geom_errorbar(data = ss,
# inherit.aes = F,
aes(y=Sensitivity,
x = 1-Specificity,
ymin = Sens_LCI,
ymax = Sens_UCI,
colour = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5))),
width = 0.01,
alpha = 0.4)
g
}
else { g }
# Plot observed spec CI's
if ('2' %in% input$cicheck) {
g = g + geom_errorbar(data = ss,
# inherit.aes = F,
aes(y=Sensitivity,
x = 1-Specificity,
xmin = FPR_LCI,
xmax = FPR_UCI,
colour = !!as.name(str_sub(colnames(X)[j + cov_index], end = -5))),
width = 0.01,
alpha = 0.4)
g
}
else { g }
# plot other covariate selected as text label
for (i in 1:(no_cov+2)) {
if (input$covcheck_display == i+1) { # the covariate selected by the user
if (str_sub(colnames(X)[j + cov_index], start = -3) != "cts") { # if selected display covariate is discrete / categorical
if (input$weightcheck == FALSE) { # without study weights
# plot covariates as text labels only
g = g + geom_label_repel(data = ss,
inherit.aes = F,
aes(y=Sensitivity, x = 1-Specificity,
label = !!as.name(colnames(ss)[16+i])) )
g
}
else { g }
if (input$weightcheck == TRUE) { # with study weights
# plot covariates as text only
g = g + geom_label_repel(data = ss,
inherit.aes = F,
aes(y=Sensitivity, x = 1-Specificity,
label = !!as.name(colnames(ss)[16+i]))) +
geom_ellipse(data = ss,
inherit.aes = F,
aes(x0 = 1-Specificity, y0 = Sensitivity,
colour = !!as.name(colnames(ss)[16+cov_index]),
a=pctsp/input$weight_scale,
b=pctse/input$weight_scale,
angle = 0))
}
else { g }
} else { # if selected display covariate is cts.
if (input$weightcheck == FALSE) { # without study weights
# plot covariates as text labels only
g = g + geom_label_repel(data = ss,
inherit.aes = F,
aes(y=Sensitivity,
x = 1-Specificity,
label = as.factor(!!as.name(colnames(ss)[16+i]))) ) # make cts cov. discrete for the labels
g
}
else { g }
if (input$weightcheck == TRUE) { # with study weights
# plot covariates as text only
g = g + geom_label_repel(data = ss,
inherit.aes = F,
aes(y=Sensitivity,
x = 1-Specificity,
label = as.factor(!!as.name(colnames(ss)[16+i])))) +
geom_ellipse(data = ss,
inherit.aes = F,
aes(x0 = 1-Specificity, y0 = Sensitivity,
colour = (!!as.name(colnames(ss)[16+cov_index])),
a=pctsp/input$weight_scale,
b=pctse/input$weight_scale,
angle = 0))
}
else { g }
}
}
} # end of covariate label plotting
g
}
# Meta-regression - sROC plot - cts covariate ----------------------------------------------------------------
else {
X <- MA_weights(X, mod) # extract study weights
## credible region
cred <- tibble(y = (rstan::extract(mod, pars = "lSe_at_cov_input")$lSe_at_cov_input) ,
x = (rstan::extract(mod, pars = "lSp_at_cov_input")$lSp_at_cov_input))
# in inv_probit space
g <- ggplot(data = cred, aes(x = x, y = y)) +
stat_ellipse()
# Get ellipse coordinates from plot
pb <- ggplot_build(g)
el = pb$data[[1]][c("x","y", "group")]
credible_region <- tibble(x = plogis(el$x), y = plogis(el$y))
g <- ggplot(data = credible_region, aes(x = x, y = y)) +
geom_polygon(data = credible_region, aes(x = 1 - x, y = y), alpha=0.05, size=0.4) +
xlim(0,1) +
ylim(0,1)
## prediction region
pred <- tibble(y = (rstan::extract(mod, pars = "lSe_pred_at_cov_input")$lSe_pred_at_cov_input),
x = (rstan::extract(mod, pars = "lSp_pred_at_cov_input")$lSp_pred_at_cov_input))
# in inv_probit space
g <- ggplot(data = pred, aes(x = x, y = y)) +
stat_ellipse()
# Get ellipse coordinates from plot
pb <- ggplot_build(g)
el = pb$data[[1]][c("x","y", "group")]
pred_region <- tibble(x = plogis(el$x), y = plogis(el$y))
## medians
median_sens <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Se_at_cov_input"))$summary[,5])
median_spec <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("Sp_at_cov_input"))$summary[,5])
medians <- tibble(median_sens = median_sens,
median_spec = median_spec)
# observed values
ss<- tibble(
Study =as.numeric(as.factor(X$author)),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := factor(X[, j + cov_index]),
TP=X$TP, FN=X$FN, FP=X$FP, TN=X$TN,
N=(X$TP+X$FN+X$FP+X$TN) ,
Sensitivity= (TP/(TP+FN)) ,
Specificity= (TN/(TN+FP)) ,
prev = round((TP+FN)/N, 2),
pctse = X$pctse,
pctsp = X$pctsp
)
# Add the HSROC curve
TPR <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("roc_points_tpr"))$summary[,5])
FPR <- (rstan::summary(mod, probs = c(0.025, 0.5, 0.975), pars = c("roc_points_fpr"))$summary[,5])
# Calculate min and max values of sens and spec to avoid extrapolation of SROC curve
minTPR <- min( X$TP/(X$TP+X$FN) )
maxTPR <- max( X$TP/(X$TP+X$FN) )
minFPR <- min( X$FP/(X$FP+X$TN) )
maxFPR <- max( X$FP/(X$FP+X$TN) )
roc_points <- tibble(TPR, FPR)
# Create new data frame which restricts roc_points to being between min and max values
roc_points_restricted <- subset(roc_points,
FPR<maxFPR & FPR>minFPR & TPR<maxTPR & TPR>minTPR)
roc_points_unrestricted <- roc_points
# Calculate sens and spec confidence intervals at the study level
# Add the confidence intervals to the dataset
foreach (i = 1:N) %do% {
ss$Sens_LCI[i] <- binconf(ss$TP[i], ss$TP[i]+ss$FN[i], method="exact")[2]
ss$Sens_UCI[i] <- binconf(ss$TP[i], ss$TP[i]+ss$FN[i], method="exact")[3]
ss$FPR_LCI[i] <- 1 - binconf(ss$TN[i], ss$FP[i]+ss$TN[i], method="exact")[3]
ss$FPR_UCI[i] <- 1 - binconf(ss$TN[i], ss$FP[i]+ss$TN[i], method="exact")[2]
}
ss <- cbind(ss, cols_covariates) # add covariates to data
ss_unfct <- ss
# make covariates factors for display
for (i in 1:no_cov) {
ss_unfct <- dplyr::mutate(ss_unfct,
!!as.name(paste0(colnames(ss)[16+i],", categorical")) := !!as.name(colnames(ss)[16+i]))
ss <- dplyr::mutate(ss,
!!as.name(colnames(ss)[16+i]) := factor( !!as.name(colnames(ss)[16+i])))
}
print(colnames(ss)[16+cov_index])
### Base Plot
# Base plot (default)
g <- ggplot(data = medians, aes(y=median_sens,
x = 1-median_spec)) +
geom_point(size = size_summary) + # summary points
geom_path(data = pred_region, aes(x= 1 - x, y= y), linetype = 2, size = 0.4, inherit.aes = F) + # prediction region
geom_polygon(data = credible_region, aes(x= 1 - x, y= y), alpha=0.05, size=0.4, linetype = 2,inherit.aes = F) + # conf region
theme_bw() +
theme(text = element_text(size=15)) +
ylab("Sensitivity") +
xlab("1 - Specificity") +
coord_fixed()
g
# Plot study level estimates
if ('1' %in% input$HSROCcheck) {
if (input$cts_as_fct == TRUE) { # plot cts covariate as categorical
if (input$weightcheck == TRUE) {
g = g + geom_ellipse(data = ss,
aes(x0 = 1-Specificity,
y0 = Sensitivity,
a=pctsp/input$weight_scale,
b=pctse/input$weight_scale,
colour = !!as.name(colnames(ss)[16+cov_index]),
angle = 0))
g
}
else {
g = g + geom_point(data = ss,
aes(y = Sensitivity,
x = 1-Specificity,
colour = !!as.name(colnames(ss)[16+cov_index])),
size = size_study_specific,
alpha=0.7,
inherit.aes = F)
g
}
} else { # plot cts covariate as cts
# ss <- dplyr::mutate(ss,
# !!as.name(paste0(colnames(ss)[16+cov_index],", categorical")) :=
# varhandle::unfactor( !!as.name(colnames(ss)[16+cov_index]) ) )
if (input$weightcheck == TRUE) {
g = g + geom_ellipse(data = ss_unfct,
aes(x0 = 1-Specificity,
y0 = Sensitivity,
a=pctsp/input$weight_scale,
b=pctse/input$weight_scale,
colour = !!as.name(colnames(ss)[16+cov_index]),
angle = 0))
g
}
else {
g = g + geom_point(data = ss_unfct,
aes(y = Sensitivity,
x = 1-Specificity,
colour = !!as.name(colnames(ss)[16+cov_index])),
size = size_study_specific,
alpha=0.7)
g
}
}
}
# HSROC curve
if ('2' %in% input$HSROCcheck) {
g = g + geom_path(data = roc_points_restricted,
aes(x = FPR, y = TPR))
g
if (input$HSROCcheck_unrestricted_HSROC_curve == TRUE) {
g = g + geom_path(data = roc_points_unrestricted,
aes(x = FPR, y = TPR))
g
}
}
else { g }
if ('3' %in% input$HSROCcheck) {
g = g + scale_x_continuous(breaks = seq(0,1,0.2), limits = c(0,1)) +
scale_y_continuous(breaks = seq(0,1,0.2), limits = c(0,1))
g
} else { g }
# display disease prevalence's on sROC plot
if ( input$prevcheck == TRUE ) {
g = g + geom_label_repel(data = ss ,
inherit.aes = F,
aes(y = Sensitivity,
x = 1-Specificity,
label = prev),
size = 3.5,
box.padding = 0.25)
g
}
else { g }
# Plot observed sens CI's
if ('1' %in% input$cicheck) {
g = g + geom_errorbar(data = ss,
inherit.aes = F,
aes(y=Sensitivity,
x = 1-Specificity,
ymin = Sens_LCI,
ymax = Sens_UCI),
width = 0.01,
alpha = 0.4)
g
}
else { g }
# Plot observed spec CI's
if ('2' %in% input$cicheck) {
g = g + geom_errorbar(data = ss,
aes(x = 1-Specificity,
y = Sensitivity,
xmin = FPR_LCI,
xmax = FPR_UCI),
width = 0.01,
alpha = 0.4,
inherit.aes = F)
g
}
else { g }
# If using Cov.csv
if (C > 6 & Names[7] != "rob_PS") {
# Plot observed sens CI's
if ('1' %in% input$cicheck) {
g = g + geom_errorbar(data = ss, aes(x = 1-Specificity,
y = Sensitivity,
ymin = Sens_LCI,
ymax = Sens_UCI),
width = 0.02,
alpha = 1,
inherit.aes = F)
g
}
else { g }
# Plot observed spec CI's
if ('2' %in% input$cicheck) {
g = g + geom_errorbar(data = ss, aes(x = 1-Specificity,
y = Sensitivity,
xmin = FPR_LCI,
xmax = FPR_UCI),
width = 0.02,
alpha = 1,
inherit.aes = F)
g
}
else { g }
}
# plot other covariate selected as text label
for (i in 1:(no_cov+2)) {
if (input$covcheck_display == i+1) { # the covariate selected by the user
if (str_sub(colnames(X)[j + cov_index], start = -3) != "cts") { # if selected display covariate is discrete / categorical
if (input$weightcheck == FALSE) { # without study weights
# plot covariates as text labels only
g = g + geom_label_repel(data = ss,
inherit.aes = F,
aes(y=Sensitivity, x = 1-Specificity,
label = !!as.name(colnames(ss)[16+i])) )
g
}
else { g }
if (input$weightcheck == TRUE) { # with study weights
# plot covariates as text only
g = g + geom_label_repel(data = ss,
inherit.aes = F,
aes(y=Sensitivity,
x = 1-Specificity,
label = !!as.name(colnames(ss)[16+i]))) +
geom_ellipse(data = ss,
inherit.aes = F,
aes(x0 = 1-Specificity,
y0 = Sensitivity,
colour = !!as.name(colnames(ss)[16+cov_index]),
a=pctsp/input$weight_scale,
b=pctse/input$weight_scale,
angle = 0))
}
else { g }
} else { # if selected display covariate is cts.
if (input$weightcheck == FALSE) { # without study weights
# plot covariates as text labels only
g = g + geom_label_repel(data = ss,
inherit.aes = F,
aes(y=Sensitivity,
x = 1-Specificity,
label = as.factor(!!as.name(colnames(ss)[16+i]))) ) # make cts cov. discrete for the labels
g
}
else { g }
if (input$weightcheck == TRUE) { # with study weights
# plot covariates as text only
g = g + geom_label_repel(data = ss,
inherit.aes = F,
aes(y=Sensitivity,
x = 1-Specificity,
label = as.factor(!!as.name(colnames(ss)[16+i])))) +
geom_ellipse(data = ss,
inherit.aes = F,
aes(x0 = 1-Specificity, y0 = Sensitivity,
colour = (!!as.name(colnames(ss)[16+cov_index])),
a=pctsp/input$weight_scale,
b=pctse/input$weight_scale,
angle = 0))
}
else { g }
}
}
} # end of covariate label plotting
g
}
g
})
# Output ggplot object
observe({
output$plot <- renderPlot({
validate(
need(!(is.null(draws())),
"Please run model to display plot")
)
req(draws(), cancelOutput = TRUE)
plot_object()
}, height = as.numeric(input$plot_dimension_slider))
})
# Download ggplot object
output$plot_download <- downloadHandler(
filename = function(){
paste0("sroc_meta_regression", input$plot_file_type)
},
content = function(file) {
ggsave(file,
plot_object(),
width = input$plot_width,
height = input$plot_height,
dpi = input$plot_dpi,
units = "in")
}
)
# tooltips -----------------------------------------------------------------------------------------------
click <- reactive({ input$plot_click })
# values to display on plot
output$vals <- renderPrint({
validate(
need(!(is.null(draws())),
"Please run model to display plot")
)
req(draws(), cancelOutput = TRUE)
X <- data()
cov_index <- as.integer(as.double(input$covcheck_model)) - 1
N <- nrow(X)
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") {
j <<- 6
no_covariates <<- length(colnames(X[,7:C]))
} else {
j <<- 13
no_covariates <<- length(colnames(X[,14:C]))
}
# observed values
ss<- tibble(
Study =as.numeric(as.factor(X$author)),
TP=X$TP, FN=X$FN, FP=X$FP, TN=X$TN,
N=(X$TP+X$FN+X$FP+X$TN) ,
Sensitivity= (TP/(TP+FN)) ,
Specificity= (TN/(TN+FP)) ,
FPR = 1 - Specificity,
prev = round((TP+FN)/N, 2),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := (X[, j + cov_index])
)
ss2 <- left_join(ss, X)
# make cols without .cts and .cat endings for display
for (i in 1:no_covariates) {
ss2 <- dplyr::mutate(ss2,
!!as.name( str_sub(colnames(X)[j + i], end = -5) ) := !!as.name( colnames(X)[j + i]) )
}
# select columns to display
cols <- c("author", "year",
str_sub( colnames(X)[c( (j+1):(j - 1 + no_covariates) )] , end = -5),
"Sensitivity", "Specificity", "FPR")
data <- dplyr::select(ss2, cols)
data[] <- lapply(data, function(x) if (is.numeric(x) ) round(x, 3) else x) # round all numeric cols to 3 s.f.
data <- data.frame(data)
y <- nearPoints(df = data, click() , xvar = "FPR", yvar = "Sensitivity" )
req(nrow(y) != 0)
y
})
# tooltip
output$my_tooltip <- renderUI({
validate(
need(!(is.null(draws())),
"Please run model to display plot")
)
req(draws(), cancelOutput = TRUE)
ns <- session$ns
X <- data()
cov_index <- as.integer(as.double(input$covcheck_model)) - 1
N <- nrow(X)
C <- ncol(X)
Names <- colnames(X)
if (C > 8 & Names[7] != "rob_PS") {
j <<- 6
no_covariates <<- length(colnames(X[,7:C]))
} else {
j <<- 13
no_covariates <<- length(colnames(X[,14:C]))
}
# observed values
ss<- tibble(
Study =as.numeric(as.factor(X$author)),
TP=X$TP, FN=X$FN, FP=X$FP, TN=X$TN,
N=(X$TP+X$FN+X$FP+X$TN) ,
Sensitivity= (TP/(TP+FN)) ,
Specificity= (TN/(TN+FP)) ,
FPR = 1 - Specificity,
prev = round((TP+FN)/N, 2),
!!as.name(str_sub(colnames(X)[j + cov_index], end = -5)) := (X[, j + cov_index])
)
ss2 <- left_join(ss, X)
# make cols without .cts and .cat endings for display
for (i in 1:no_covariates) {
ss2 <- dplyr::mutate(ss2,
!!as.name( str_sub(colnames(X)[j + i], end = -5) ) := !!as.name( colnames(X)[j + i]) )
}
# select columns to display
cols <- c("author", "year",
str_sub( colnames(X)[c( (j+1):(j - 1 + no_covariates) )] , end = -5),
"Sensitivity", "Specificity", "FPR")
data <- dplyr::select(ss2, cols)
data[] <- lapply(data, function(x) if (is.numeric(x) ) round(x, 3) else x) # round all numeric cols to 3 s.f.
data <- data.frame(data)
y <- nearPoints(df = data, click(), xvar = "FPR", yvar = "Sensitivity" )
req(nrow(y) != 0)
verbatimTextOutput(ns("vals"))
})
}
)
}