## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  dev = "png",
  collapse = TRUE,
  message =FALSE,
  warning =FALSE,
  fig.width = 7,
  comment = "#>"
)
if (capabilities(("cairo"))) {
  knitr::opts_chunk$set(dev.args = list(png = list(type = "cairo")))
}
options(rmarkdown.html_vignette.check_title = FALSE)
library(coveffectsplot)
library(mrgsolve)
library(ggplot2)
library(ggstance)
library(ggridges)
library(tidyr)
library(dplyr)
library(table1)
library(egg)
library(data.table)
library(ggh4x)
library(patchwork)
theme_set(theme_bw())
#utility function to simulate varying one covariate at a time keeping the rest at the reference
expand.modelframe <- function(..., rv, covcol="covname") {
  args <- list(...)
  df <- lapply(args, function(x) x[[1]])
  df[names(rv)] <- rv
  res <- lapply(seq_along(rv), function(i) {
    df[[covcol]] <- names(rv)[i]
    df[[names(rv)[i]]] <- args[[names(rv)[i]]]
    as.data.frame(df)
  })
  do.call(rbind, res)
}
round_pad <- function(x, digits = 2, round5up = TRUE) {
  eps <- if (round5up) x * (10^(-(digits + 3))) else 0
  formatC(round(x + eps, digits), digits = digits, format = "f", flag = "0")
}


derive.exposure <- function(time, CP) {
  n <- length(time)
  x <- c(
    Cmax = max(CP),
    Clast = CP[n],
    AUC = sum(diff(time) * (CP[-1] + CP[-n])) / 2
  )
  data.table(paramname=names(x), paramvalue=x)
}

cor2cov <- function (cor, sd) 
{
  if (missing(sd)) {
    sd <- diag(cor)
  }
  diag(cor) <- 1
  n <- nrow(cor)
  diag(sd, n) %*% cor %*% diag(sd, n)
}

stat_sum_df <- function(fun, geom="point", ...) {
  stat_summary(fun.data = fun,  geom=geom,  ...)
}

summary_conc <- function(CP) {
  x <- c(
    Cmed = median(CP),
    Clow = quantile(CP, probs = 0.05),
    Cup =  quantile(CP, probs = 0.95)
  )
  data.table(paramname=names(x), paramvalue=x)
}

nbsvsubjects <- 1000
nsim <- 100 # uncertainty replicates for vignette you might want a higher number



## ----pkmodel, collapse=TRUE---------------------------------------------------
codepkmodelcov <- '
$PARAM @annotated
KA    : 0.5   : Absorption rate constant Ka (1/h)
CL    : 4     : Clearance CL (L/h)
V     : 10    : Central volume Vc (L)
Vp    : 50    : Peripheral volume Vp (L)
Qp    : 10    : Intercompartmental clearance Q (L/h)
CLALB : -0.8  : Ablumin on CL (ref. 45 g/L)
CLSEX : 0.2   : Sex on CL (ref. Female)
CLWT  : 1     : Weight on CL (ref. 85 kg)
VSEX  : 0.07  : Sex on Vc (ref. Female)
VWT   : 1     : Weight on Vc (ref. 85 kg)

$PARAM @annotated // reference values for covariate
WT    : 85    : Weight (kg)
SEX   : 0     : Sex (0=Female, 1=Male)
ALB   : 45    : Albumin (g/L)

$PKMODEL cmt="GUT CENT PER", depot=TRUE, trans=11

$MAIN
double CLi = CL *
    pow((ALB/45.0), CLALB)*
    (SEX == 1.0 ? (1.0+CLSEX) : 1.0)*
    pow((WT/85.0), CLWT)*exp(nCL); 
double V2i = V *
    (SEX == 1.0 ? (1.0+VSEX) : 1.0)*
    pow((WT/85.0), VWT)*exp(nVC);  

double KAi = KA;
double V3i = Vp *pow((WT/85.0),    1);
double Qi = Qp *pow((WT/85.0), 0.75);

$OMEGA @annotated @block
nCL : 0.09       : ETA on CL
nVC : 0.01 0.09  : ETA on Vc

$TABLE
double CP   = CENT/V2i;

$CAPTURE CP KAi CLi V2i V3i Qi WT SEX ALB
'
modcovsim <- mcode("codepkmodelcov", codepkmodelcov)

partab <- setDT(modcovsim@annot$data)[block=="PARAM", .(name, descr, unit)]
partab <- merge(partab, melt(setDT(modcovsim@param@data), meas=patterns("*"), var="name"))
knitr::kable(partab)

## ----pksimulation, fig.width=7, message=FALSE---------------------------------
idata <- data.table(ID=1:nbsvsubjects, WT=85, SEX=0, ALB=45)

ev1 <- ev(time = 0, amt = 100, cmt = 1)
data.dose <- ev(ev1)
data.dose <- setDT(as.data.frame(data.dose))
data.all <- data.table(idata, data.dose)

outputsim <- modcovsim %>%
  data_set(data.all) %>%
  mrgsim(end = 24, delta = 0.25) %>%
  as.data.frame %>%
  as.data.table

outputsim$SEX <- factor(outputsim$SEX, labels="Female")

# Only plot a random sample of N=500
set.seed(678549)
plotdata <- outputsim[ID %in% sample(unique(ID), 500)]

# New facet label names for dose variable
albumin.labs <- c("albumin: 45 ng/mL")
names(albumin.labs) <- c("45")
wt.labs <- c("weight: 85 kg")
names(wt.labs) <- c("85")


p1 <- ggplot(plotdata, aes(time, CP, group = ID)) +
  geom_line(alpha = 0.2, size = 0.1) +
  facet_grid(~ WT + ALB + SEX,
             labeller =  labeller(ALB = albumin.labs,
                                  WT = wt.labs)) +
  labs(y = "Plasma Concentrations", x = "Time (h)")

p2 <- ggplot(plotdata, aes(time, CP, group = ID)) +
  geom_line(alpha = 0.2, size = 0.1) +
  facet_grid(~ WT + ALB + SEX,
             labeller =  labeller(ALB = albumin.labs,
                                  WT = wt.labs)) +
  scale_y_log10() +
  labs(y = "Plasma~Concentrations\n(logarithmic scale)", x = "Time (h)")
  #labs(y = expression(Log[10]~Plasma~Concentrations), x = "Time (h)")+



egg::ggarrange(p1, p2, ncol = 2)

## ----computenca, fig.width=7 , message=FALSE----------------------------------
derive.exposure <- function(time, CP) {
  n <- length(time)
  x <- c(
    Cmax = max(CP),
    Clast = CP[n],
    AUC = sum(diff(time) * (CP[-1] + CP[-n])) / 2
  )
  data.table(paramname=names(x), paramvalue=x)
}
refbsv <- outputsim[, derive.exposure(time, CP), by=.(ID, WT, SEX, ALB)]

p3 <- ggplot(refbsv, aes(
        x      = paramvalue,
        y      = paramname,
        fill   = factor(..quantile..),
        height = ..ndensity..)) +
  facet_wrap(~ paramname, scales="free", ncol=1) +
  stat_density_ridges(
    geom="density_ridges_gradient", calc_ecdf=TRUE,
    quantile_lines=TRUE, rel_min_height=0.001, scale=0.9,
    quantiles=c(0.05, 0.25, 0.5, 0.75, 0.95)) +
  scale_fill_manual(
    name   = "Probability",
    values = c("white", "#FF000050", "#FF0000A0", "#FF0000A0", "#FF000050", "white"),
    labels = c("(0, 0.05]", "(0.05, 0.25]",
               "(0.25, 0.5]", "(0.5, 0.75]",
               "(0.75, 0.95]", "(0.95, 1]")) +
  theme_bw() +
  theme(
    legend.position = "none",
    axis.text.y     = element_blank(),
    axis.ticks.y    = element_blank(),
    axis.title.y    = element_blank()) +
  labs(x="PK Parameters", y="") +
  scale_x_log10() +
  coord_cartesian(expand=FALSE)

# Obtain the standardized parameter value by dividing by the median.
refbsv[, stdparamvalue := paramvalue/median(paramvalue), by=paramname]

p4 <- ggplot(refbsv, aes(
        x      = stdparamvalue,
        y      = paramname,
        fill   = factor(..quantile..),
        height = ..ndensity..)) +
  facet_wrap(~ paramname, scales="free", ncol=1) +
  stat_density_ridges(
    geom="density_ridges_gradient", calc_ecdf=TRUE,
    quantile_lines=TRUE, rel_min_height=0.001, scale=0.9,
    quantiles=c(0.05, 0.25, 0.5, 0.75, 0.95)) +
  scale_fill_manual(
    name="Probability",
    values=c("white", "#FF000050", "#FF0000A0", "#FF0000A0", "#FF000050", "white"),
    labels = c("(0, 0.05]", "(0.05, 0.25]",
             "(0.25, 0.5]", "(0.5, 0.75]",
             "(0.75, 0.95]", "(0.95, 1]")) +
  theme_bw() +
  theme(
    legend.position = "none",
    axis.text.y     = element_blank(),
    axis.ticks.y    = element_blank(),
    axis.title.y    = element_blank()) +
  labs(x="Standardized PK Parameters", y="") +
  scale_x_log10() +
  coord_cartesian(expand=FALSE, xlim = c(0.3,3))

p3+p4

## ----computebsvpk , fig.width=7 , message=FALSE-------------------------------
bsvranges <- refbsv[,list(
    P05 = quantile(stdparamvalue, 0.05),
    P25 = quantile(stdparamvalue, 0.25),
    P50 = quantile(stdparamvalue, 0.5),
    P75 = quantile(stdparamvalue, 0.75),
    P95 = quantile(stdparamvalue, 0.95)), by = paramname]
bsvranges

## ----covcomb , fig.width=7----------------------------------------------------
reference.values <- data.frame(WT = 85, ALB = 45, SEX = 0)   

covcomb <- expand.modelframe( #defined at the top and now expand_modelframe was added
  WT  = c(56, 72, 98, 128), # P05, P25, P50, P75, P95
  ALB = c(40, 50),          # P05, P50, P95
  SEX = c(1),               # Reference is for SEX=0 (female)
  rv = reference.values)

# Add the reference
covcomb <- rbind(covcomb, data.table(reference.values, covname="REF"))

covcomb$ID <- 1:nrow(covcomb)

covcomb

## ----fig.width=8.5, fig.height=7, ,message=FALSE------------------------------
idata <- data.table::copy(covcomb)
idata$covname <-  NULL
ev1 <- ev(time=0, amt=100, cmt=1)
data.dose <- as.data.frame(ev1)
data.all <- data.table(idata, data.dose)

outcovcomb<- modcovsim %>%
  data_set(data.all) %>%
  zero_re() %>% 
  mrgsim(end=24, delta=0.25) %>%
  as.data.frame %>%
  as.data.table

outcovcomb$SEX <- factor(outcovcomb$SEX, labels=c("Female", "Male"))

albumin.labs <- c("albumin: 40 ng/mL","albumin: 45 ng/mL","albumin: 50 ng/mL")
names(albumin.labs) <- c("40","45","50")
wt.labs <- c("weight: 56 kg","weight: 72 kg","weight: 85 kg","weight: 98 kg","weight: 128 kg")
names(wt.labs) <- c("56","72","85","98","128")

pkprofiletypical <- ggplot(outcovcomb, aes(x=time, y=CP, col=factor(WT), linetype=SEX)) +
  geom_line(aes(group=ID), alpha = 1, linewidth = 1) +
  facet_nested(ALB + SEX ~ WT,
               labeller= labeller(ALB = albumin.labs, WT = wt.labs, SEX = label_value),
             switch = "y") +
  labs(
    x        = "Time (h)",
    y        = "Plasma Concentrations",
    linetype = "Sex",
    colour   = "Weight",
    caption  = "Simulation without Uncertainty and without BSV") +
  coord_cartesian(ylim=c(0,4))

pkprofiletypical <- pkprofiletypical +theme_bw(base_size = 13)+
  theme(axis.title.y = element_text(size=15))+
  guides(colour   = guide_legend(override.aes = list(alpha = 1, linewidth = 1)),
         linetype = guide_legend(override.aes = list(linewidth = 1), order = 1))+
  coord_cartesian(ylim=c(0,4))
pkprofiletypical


## ----fig.width=7--------------------------------------------------------------
theta <- unclass(as.list(param(modcovsim)))
theta[c("WT", "SEX", "ALB")] <- NULL
theta <- unlist(theta)
as.data.frame(t(theta))

#note that from RsNLME or NONMEM or Monolix the software will give you the varcov just read it it in.
# example NONMEM  read in your run .cov
# varcov <- read.csv(paste("runxyz",".cov",sep=""), header=TRUE, skip=1, sep="")

varcov <- cor2cov(
  matrix(0.2, nrow=length(theta), ncol=length(theta)),
  sd=theta*0.25)
rownames(varcov) <- colnames(varcov) <- names(theta)
as.data.frame(varcov)

## ----fig.width=7--------------------------------------------------------------
set.seed(678549)
# mvtnorm::rmvnorm is another option that can be explored
# if you have run a bootstrap just use the parameters data generated using all replicates
sim_parameters <- MASS::mvrnorm(nsim, theta, varcov, empirical=T) %>% as.data.table
head(sim_parameters)

## ----fig.width=8.5, fig.height=7, ,message=FALSE------------------------------
idata <- data.table::copy(covcomb)
idata$covname <-  NULL
ev1       <- ev(time=0, amt=100, cmt=1)
data.dose <- as.data.frame(ev1)
iter_sims <- NULL
for(i in 1:nsim) {
  data.all  <- data.table(idata, data.dose, sim_parameters[i])
  out <- modcovsim %>%
    data_set(data.all) %>%
    zero_re() %>% 
    mrgsim(start=0, end=24, delta=0.25) %>%
    as.data.frame %>%
    as.data.table
  out[, rep := i]
  iter_sims <- rbind(iter_sims, out)
}
iter_sims$SEX <- factor(iter_sims$SEX, labels = c("Female", "Male"))


summary_conc <- function(CP) {
  x <- c(
    Cmed = median(CP),
    Clow = quantile(CP, probs = 0.05),
    Cup =  quantile(CP, probs = 0.95)
  )
  data.table(paramname=names(x), paramvalue=x)
}

iter_sims_sum <- iter_sims[, summary_conc(CP), by=.( time, WT, SEX, ALB)]
iter_sims_sum <- spread(iter_sims_sum,paramname,paramvalue)
iter_sims_sum <- as.data.frame(iter_sims_sum)

albumin.labs <- c("albumin: 40 ng/mL","albumin: 45 ng/mL","albumin: 50 ng/mL")
names(albumin.labs) <- c("40","45","50")
wt.labs <- c("weight: 56 kg","weight: 72 kg","weight: 85 kg","weight: 98 kg","weight: 128 kg")
names(wt.labs) <- c("56","72","85","98","128")

pkprofileuncertainty_sum <- ggplot(iter_sims_sum, aes(x=time, col=factor(WT),
                      fill=factor(WT),
                      linetype=SEX)) +
  geom_ribbon((aes(ymin= `Clow.5%` ,ymax=`Cup.95%`)), alpha = 0.4, linetype = 0)+
  geom_line(aes(y=Cmed),alpha=1 , color ="black" , linewidth = 1)+
  facet_nested(ALB +SEX ~ WT,
             labeller= labeller(ALB = albumin.labs,
                                WT = wt.labs,
                                SEX = label_value),switch = "y") +
  labs(
    x        = "Time (h)",
    y        = "Plasma Concentrations",
    linetype = "Sex",
    colour   = "Uncertainty\n5-95%\nWeight",
    fill   = "Uncertainty\n5-95%\nWeight",
    caption  = "Simulation with Uncertainty, without BSV") +
  theme_bw(base_size = 13)+
  theme(axis.title.y = element_text(size=15))+
  guides(fill     = guide_legend(override.aes = list(alpha = 0.4, size = 0.4)),
         linetype = guide_legend(override.aes = list(linewidth = 1), order = 1))+
  coord_cartesian(ylim=c(0,4))

pkprofileuncertainty_sum


## ----fig.width=7, include=TRUE, message=FALSE---------------------------------
out.df.univariatecov.nca <- iter_sims[, derive.exposure(time, CP), by=.(rep, ID, WT, SEX, ALB)]

refvalues <- out.df.univariatecov.nca[
  ALB==45 & WT==85 & SEX=="Female",
  .(medparam = median(paramvalue)), by= .(rep,paramname) ]
head(data.frame(refvalues))


## ----fig.width=7,fig.height=5 ,message=FALSE----------------------------------
covcomb$covvalue[covcomb$covname=="WT"] <- paste(covcomb$WT[covcomb$covname=="WT"],"kg")
covcomb$covvalue[covcomb$covname=="ALB"] <- paste(covcomb$ALB[covcomb$covname=="ALB"],"g/L")                   
covcomb$covvalue[covcomb$covname=="SEX"] <- "Male"
covcomb$covvalue[covcomb$covname=="REF"] <- "85 kg\nFemale\n45 g/L"
#covcomb[covname=="REF", covvalue := "85 kg Female 45 g/L"]
covcomb <- as.data.table(covcomb)

out.df.univariatecov.nca <- merge(
  out.df.univariatecov.nca,
  covcomb[, list(ID, covname, covvalue)]
)

setkey(out.df.univariatecov.nca, paramname,rep)

out.df.univariatecov.nca <- merge(
  out.df.univariatecov.nca,
  refvalues)

out.df.univariatecov.nca[, paramvaluestd := paramvalue/medparam]


boxplotdat <- out.df.univariatecov.nca[covname!="REF"]
boxplotdat[covname=="WT",  covname2 := "Weight"]
boxplotdat[covname=="ALB", covname2 := "Albumin"]
boxplotdat[covname=="SEX", covname2 := "Sex"]

boxplotdatREFWT <- out.df.univariatecov.nca[covname=="REF"]
boxplotdatREFWT[, covname2 := "Weight"]
boxplotdatREFWT[, covvalue := covcomb[covname=="REF", covvalue]]

boxplotdatREFSEX <- out.df.univariatecov.nca[covname=="REF"]
boxplotdatREFSEX[, covname2 := "Sex"]
boxplotdatREFSEX[, covvalue := covcomb[covname=="REF", covvalue]]

boxplotdatREFALB <- out.df.univariatecov.nca[covname=="REF"]
boxplotdatREFALB[, covname2 := "Albumin"]
boxplotdatREFALB[, covvalue := covcomb[covname=="REF", covvalue]]

boxplotdat <- rbind(
  boxplotdat,
  boxplotdatREFWT,
  boxplotdatREFSEX,
  boxplotdatREFALB)

boxplotdat[paramname=="AUC",   paramname2 := "AUC"]
boxplotdat[paramname=="Clast", paramname2 := "C[last]"]
boxplotdat[paramname=="Cmax",  paramname2 := "C[max]"]

boxplotdat[, covname2 := factor(covname2, levels=unique(covname2))]
#boxplotdat[, covvalue := factor(covvalue, levels=unique(covvalue))]
boxplotdat[, covvalue := factor(covvalue,
  levels=c("56 kg", "72 kg", "40 g/L", "Male", "85 kg\nFemale\n45 g/L", "98 kg", "128 kg", "50 g/L"))]

pkparametersboxplot<- ggplot(boxplotdat, aes(x=covvalue, y=paramvalue))+
  facet_grid(paramname2 ~ covname2, scales="free", labeller=label_parsed,
             switch="both") +
  geom_boxplot() +
  labs(y="Parameter Values") +
  theme(axis.title=element_blank(),
        strip.placement = "outside")
pkparametersboxplot

## ----fig.width=7, fig.height=4 ,message=FALSE---------------------------------
out.df.univariatecov.nca[covname=="WT",      covname2 := "Weight"]
out.df.univariatecov.nca[covname=="ALB",     covname2 := "Albumin"]
out.df.univariatecov.nca[covname=="SEX",     covname2 := "Sex"]
out.df.univariatecov.nca[covname=="REF",     covname2 := "Reference"]
out.df.univariatecov.nca[paramname=="AUC",   paramname2 := "AUC"]
out.df.univariatecov.nca[paramname=="Clast", paramname2 := "C[last]"]
out.df.univariatecov.nca[paramname=="Cmax",  paramname2 := "C[max]"]

out.df.univariatecov.nca[, covvalue   := factor(covvalue, levels=unique(covvalue))]
out.df.univariatecov.nca[, covname2   := factor(covname2, levels=unique(covname2))]
out.df.univariatecov.nca[, paramname2 := factor(paramname2, levels=unique(paramname2))]

ggplot(out.df.univariatecov.nca[out.df.univariatecov.nca$covname!="REF",], aes(
    x      = paramvaluestd,
    y      = covvalue,
    fill   = factor(..quantile..),
    height = ..ndensity..)) +
  facet_grid(covname2 ~ paramname2,
    scales   = "free_y",
    space    = "free",
    labeller = label_parsed)+
  annotate("rect",
    xmin  = 0.8,
    xmax  = 1.25,
    ymin  = -Inf,
    ymax  = Inf,
    fill  = "gray",
    alpha = 0.4) +
  stat_density_ridges(
    geom           = "density_ridges_gradient",
    calc_ecdf      = TRUE,
    quantile_lines = TRUE,
    rel_min_height = 0.001,
    scale          = 0.9,
    quantiles      = c(0.05,0.5, 0.95)) +
  scale_x_continuous(
    breaks = c(0.25, 0.5, 0.8, 1/0.8, 1/0.5, 1/0.25),
    trans  = "log") +
  scale_fill_manual(
    name   = "Probability",
    values = c("white", "#0000FFA0", "#0000FFA0", "white"),
    labels = c("(0, 0.05]", "(0.05, 0.5]","(0.5, 0.95]", "(0.95, 1]")) +
  geom_vline(aes(xintercept=1), size=1) +
  theme_bw() +
  labs(x="Effects Relative to Parameter Reference Value", y="")+
  scale_x_continuous(breaks=c(0.25,0.5,0.8,1,1/0.8,1/0.5,1/0.25),
                     labels=c("1/4","1/2","0.8","1","1.25","2","4"),
                     trans ="log" )

## ----fig.width=7, fig.height=6------------------------------------------------
fpdata <- out.df.univariatecov.nca[,
  setNames(as.list(quantile(paramvaluestd, probs=c(0.5, 0.05, 0.95))), c("mid", "lower", "upper")),
  by=.(paramname2, covname2, covvalue)]

bsvranges[paramname=="AUC",   paramname2 := "AUC"]
bsvranges[paramname=="Clast", paramname2 := "C[last]"]
bsvranges[paramname=="Cmax",  paramname2 := "C[max]"]
setkey(bsvranges, paramname2)

fpdataBSV50 <- fpdata[covname2 == "Reference"]
fpdataBSV50$covname2 <- "BSV"
fpdataBSV50$covvalue <- "50% of patients"
setkey(fpdataBSV50, paramname2)

fpdataBSV50$lower    <- bsvranges[,"P25"]
fpdataBSV50$upper    <- bsvranges[,"P75"]

fpdataBSV90 <- fpdata[covname2 == "Reference"]
fpdataBSV90$covname2 <- "BSV"
fpdataBSV90$covvalue <- "90% of patients"
setkey(fpdataBSV90, paramname2)

fpdataBSV90$lower    <- bsvranges[,"P05"]
fpdataBSV90$upper    <- bsvranges[,"P95"]

fpdata <- rbind(fpdata, fpdataBSV90, fpdataBSV50)

fpdata[, LABEL := sprintf("%s [%s, %s]",
  round_pad(mid, 2),
  round_pad(lower, 2),
  round_pad(upper, 2)) ]

setnames(fpdata, "paramname2", "paramname")
setnames(fpdata, "covname2", "covname")
setnames(fpdata, "covvalue", "label")

fpdata[, label := factor(label, levels=unique(label))]

interval_legend_text <- "Median (points)\n90% CI (horizontal lines)"
interval_bsv_text    <- "BSV (points)\nPrediction Intervals (horizontal lines)"
ref_legend_text      <- "Reference (vertical line)\nClinically relevant limits\n(gray area)"
area_legend_text     <- "Reference (vertical line)\nClinically relevant limits\n(gray area)"

png("./Figure4_6.png",width =9 ,height = 6,units = "in",res=72)
coveffectsplot::forest_plot(fpdata[paramname=="AUC" & 
                                     covname!="Reference" &
                                     covname!="BSV",],
                            ref_area               = c(0.8, 1/0.8),
                            x_range                = c(0.5, 2),
                            strip_placement        = "inside",
                            base_size              = 18,
                            y_label_text_size      = 12,
                            y_facet_text_angle     = 0,
                            xlabel                 = "Fold Change Relative to Reference",
                            ref_legend_text        = ref_legend_text,
                            area_legend_text       = area_legend_text,
                            interval_legend_text   = interval_legend_text,
                            interval_bsv_text      = interval_bsv_text,
                            plot_title             = "",
                            facet_formula          = "covname ~ paramname",
                            facet_switch           = "y",
                            facet_scales           = "free_y",
                            facet_space            = "free",
                            paramname_shape        = FALSE,
                            table_position         = "right",
                            table_text_size        = 4,
                            plot_table_ratio       = 3,
                            show_table_facet_strip = "none",
                            logxscale              = TRUE,
                            major_x_ticks          = c(0.5, 0.8,1, 1/0.8, 1/0.5),
                            major_x_labels         = c("1/2", "0.8","1", "1.25", "2"),
                            return_list            = FALSE)
dev.off()

## ----message=FALSE,fig.width=7------------------------------------------------
png("./coveffectsplot4.png",width =9 ,height = 6,units = "in",res=72)
coveffectsplot::forest_plot(fpdata[paramname=="AUC"],
                            ref_area = c(0.8, 1/0.8),
                            x_range = c(0.5,2),
                            xlabel = "Fold Change Relative to Reference",
                            x_label_text_size= 10,
                            y_facet_text_angle     = 0,
                            facet_formula = "covname~paramname",
                            theme_benrich = TRUE,
                            table_title_size = 15,
                            table_title = "Median [90% CI]",
                            interval_legend_text = interval_legend_text,
                            interval_bsv_text = interval_bsv_text,
                            legend_position = "none",
                            strip_placement = "outside",
                            base_size = 12,
                            facet_switch = "y",
                            facet_scales = "free_y",
                            facet_space = "free",
                            paramname_shape = FALSE,
                            table_position = "right",
                            table_text_size=4,
                            plot_table_ratio = 3,
                            show_table_facet_strip = "none",
                            logxscale = TRUE,
                            major_x_ticks =    c(0.25,0.5,0.8,1,1/0.8,1/0.5,1/0.25),
                            major_x_labels = c("1/4","1/2","0.8","1","1.25","2","4"),
                            return_list = FALSE)
dev.off()

## ----message=FALSE,fig.width=7------------------------------------------------
png("./coveffectsplot0.png",width = 9 ,height = 6,units = "in",res=72)
plotlists <- coveffectsplot::forest_plot(fpdata[paramname=="AUC"],
                            ref_area = c(0.8, 1/0.8),
                            xlabel = "Fold Change Relative to Reference",
                            ref_legend_text = "Reference (vertical line)\nClinically
                            relevant limits\n(gray area)",
                            area_legend_text = "Reference (vertical line)\nClinically
                            relevant limits\n(gray area)",
                            interval_legend_text = interval_legend_text,
                            plot_title             = "",
                            interval_bsv_text = interval_bsv_text,
                            facet_formula = "covname~paramname",
                            facet_switch = "y",
                            facet_scales = "free_y",
                            facet_space = "free",
                            paramname_shape = FALSE,
                            table_position = "right",
                            table_text_size=4,
                            plot_table_ratio = 4,
                            show_table_facet_strip = "none",
                            logxscale = TRUE,
                            major_x_ticks = c(0.25,0.5,0.8,1,1/0.8,1/0.5,1/0.25),
                            major_x_labels = c("1/4","1/2","0.8","1","1.25","2","4"),
                            return_list = TRUE)
plotlists
dev.off()

## ----fig.width=7, fig.height=6, warning=FALSE,message=FALSE-------------------
main_plot <- plotlists[[1]] + theme(
               panel.spacing=unit(10, "pt"),
               panel.grid=element_blank(),
               panel.grid.minor=element_blank(),
               legend.position="bottom",
               strip.placement.y="outside",
               strip.background.y=element_blank(),
               strip.text.y=element_text(
                   hjust=1,
                   vjust=1,
                   face="italic",color="gray",
                   size=rel(1)),
               legend.text = element_text(size=rel(0.5)),
               plot.margin = margin(t=0,r=0,b=0,l=5,unit="pt")) +
  scale_y_discrete(
breaks=c("90% of patients",
         "50% of patients",
         "85 kg\nFemale\n45 g/L",
         "40 g/L","50 g/L","Male",
         "56 kg","72 kg","98 kg","128 kg"
),
labels=c("90% of patients",
               "50% of patients",
               "85 kg-Female-45 g/L",
               "40 g/L","50 g/L","Male",
               "56 kg","72 kg","98 kg","128 kg"
)
  )

table_plot <- plotlists[[2]] + theme(
               panel.border=element_blank(),
               panel.spacing=unit(10, "pt"),
               strip.background.y=element_blank(),
               legend.text = element_text(size=rel(0.5)),
               plot.margin = margin(t=0,r=5,b=0,l=0,unit="pt"))

png("./coveffectsplot5.png",width =8.5 ,height = 6,units = "in",res=72)
egg::ggarrange(
  main_plot,
  table_plot,
  nrow = 1,
  widths = c(3, 1)
)
dev.off()

## ----fig.width=7, fig.height=6,message=FALSE----------------------------------
png("./coveffectsplot6.png",width =9.5 ,height = 6,units = "in",res=72)
forest_plot(fpdata,
                            ref_area = c(0.8, 1/0.8),
                            x_range = c(0.5,2),
                            xlabel = "Fold Change Relative to Reference",
                            facet_formula = "covname~paramname",
                            interval_legend_text = interval_legend_text,
                            interval_bsv_text = interval_bsv_text,
                            facet_switch = "y",
                            facet_scales = "free_y",
                            facet_space = "free",
                            facet_labeller = "label_parsed",
                            paramname_shape = FALSE,
                            table_position = "none",
                            table_text_size=4,
                            base_size = 11,
                            plot_table_ratio = 4,
                            show_table_facet_strip = "none",
                            logxscale = TRUE,
                            major_x_ticks = c(0.5,0.8,1,1/0.8,1/0.5),
                            major_x_labels = c("1/2","0.8","1","1.25","2"),
                            x_label_text_size = 10,
                            return_list = FALSE)
dev.off()

## ----fig.width=7, fig.height=6,message=FALSE----------------------------------
png("./coveffectsplot7.png",width =9.5 ,height = 6,units = "in",res=72)
forest_plot(fpdata[paramname!="AUC"],
                            ref_area = c(0.8, 1/0.8),
                            x_range = c(0.35,1/0.35),
                            xlabel = "Fold Change Relative to Reference",
            ref_legend_text = "Reference\nClinically relevant limits\n(0.8-1.25)",
            area_legend_text = "Reference\nClinically relevant limits\n(0.8-1.25)",
            interval_legend_text = "Median\n90% CI",
            interval_bsv_text = "BSV\nPrediction Intervals", 
                            facet_formula = "covname~.",
                            paramname_shape = TRUE,
            legend_order =c("shape","pointinterval","ref", "area"),
                            legend_shape_reverse = TRUE,
                            bsv_col = scales::muted("red"),
                            interval_col = scales::muted("blue"),
                            facet_switch = "y",
                            facet_scales = "free_y",
                            facet_space = "free",
                            table_position = "none",
                            table_text_size=4,
                            base_size = 9,
                            plot_table_ratio = 4,
                            show_table_facet_strip = "none",
                            logxscale = TRUE,
                            major_x_ticks = c(0.5,0.8,1,1/0.8,1/0.5),
                            major_x_labels = c("1/2","0.8","1","1.25","2"),
                            legend_space_x_mult = 0.01,
                            legend_position = "right",
                            return_list = FALSE)
dev.off()

## ----fig.width=7, fig.height=6,message=FALSE----------------------------------
png("./coveffectsplot_color.png",width =9.5 ,height = 6,units = "in",res=72)
forest_plot(fpdata[paramname!="AUC" &
                     covname!="BSV"&
                     covname!="Reference",],
                            ref_area = c(0.8, 1/0.8),
                            x_range = c(0.35,1/0.35),
                            xlabel = "Fold Change Relative to Reference",
            ref_legend_text = "Reference\nClinically relevant limits\n(0.8-1.25)",
            area_legend_text = "Reference\nClinically relevant limits\n(0.8-1.25)",
                            facet_formula = "covname~.",
                            paramname_shape = TRUE,
                            paramname_color = TRUE,
            combine_interval_shape_legend = TRUE,
            legend_order =c("shape","pointinterval","ref", "area"),
                            legend_shape_reverse = TRUE,
                            bsv_col = scales::muted("red"),
                            facet_switch = "y",
                            facet_scales = "free_y",
                            facet_space = "free",
                            table_position = "none",
                            base_size = 12,
                            logxscale = TRUE,
                            major_x_ticks = c(0.5,0.8,1,1/0.8,1/0.5),
                            major_x_labels = c("1/2","0.8","1","1.25","2"),
                            legend_space_x_mult = 0.01,
                            legend_position = "right",
                            return_list = TRUE)[[1]]+
  scale_color_manual(labels = c(expression(C[last]),expression(C[max])),
                       values = c(scales::muted("blue"),scales::muted("red")))+
  scale_shape_discrete(labels = c(expression(C[last]),expression(C[max])))

dev.off()

