httk (version 1.4)

monte_carlo: Monte Carlo for pharmacokinetic models

Description

This function performs Monte Carlo to assess uncertainty and variability for pharmacokinetic models.

Usage

monte_carlo(params,which.quantile=0.95,cv.params=NULL,censored.params=NULL,samples=1000, name.model='calc_analytic_css',output.col.model=NA,return.samples=F,...)

Arguments

params

All parameters needed by the function indicated by the argument "name.model". These paramters that are also listed in either cv.params or censored.params are sampled using Monte Carlo.

which.quantile

This argument specifies which quantiles are to be calculated. It can be a vector or a single value. It defaults to the 0.95 quantile (95%).

cv.params

The parameters listed in cv.params are sampled from a normal distribution that is truncated at zero. This argument should be a list of coefficients of variation (cv) for the normal distribution. Each entry in the list is named for a parameter in "params". New values are sampled with mean equal to the value in "params" and standard deviation equal to the mean times the cv.

censored.params

The parameters listed in censored.params are sampled from a normal distribution that is censored for values less than the limit of detection (specified separately for each paramter). This argument should be a list of sub-lists. Each sublist is named for a parameter in "params" and contains two elements: "cv" (coefficient of variation) and "LOD" (limit of detection), below which parameter values are censored. New values are sampled with mean equal to the value in "params" and standard deviation equal to the mean times the cv.Censored values are sampled on a uniform distribution between 0 and the limit of detection.

samples

This argument is the number of samples to be generated for calculating quantiles.

name.model

This argument is a character vector giving the name of the model to be sampled. Defaults to 'calc_analytic_css'.

output.col.model

If the evaluation of the function indicated by "model" returns a list, then model.output.col is the element from that list that is sampled and is used for calculating quantiles. Defaults to NA (i.e., the function returns a single value).

return.samples

Whether or not to return the vector containing the samples from the simulation instead of the selected quantile.

...

Additional arguments passed to name.model.

Examples

# Fig 1 SimCYP vs. our predictions:
## Not run: 
# library(httk)
# library(ggplot2)
# 
# vary.params <- list(BW=0.3)
# vary.params[["Vliverc"]]<-0.3
# vary.params[["Qgfrc"]]<-0.3
# vary.params[["Qtotal.liverc"]]<-0.3
# vary.params[["million.cells.per.gliver"]]<-0.3
# vary.params[["Clint"]]<-0.3
# censored.params<-list(Funbound.plasma=list(cv=0.3,lod=0.01))
# 
# pValues <- get_cheminfo(c("Compound","CAS","Clint.pValue"))
# pValues.rat <- get_cheminfo(c("Compound","CAS","Clint.pValue"),species="Rat")
# 
# 
#  
# Wetmore.table <- NULL
# for (this.CAS in get_cheminfo(model="3compartmentss"))
#   if (this.CAS %in% get_wetmore_cheminfo())
#   {
#     print(this.CAS)
#     these.params <- parameterize_steadystate(chem.cas=this.CAS)
#     if (these.params[["Funbound.plasma"]] == 0.0) 
#     {
#       these.params[["Funbound.plasma"]] <- 0.005
#     }
#     vLiver.human.values <- monte_carlo(these.params,
#                                        cv.params=vary.params,
#                                        censored.params=censored.params,
#                                        which.quantile=c(0.05,0.5,0.95),
#                                        output.units="mg/L",
#                                        model='3compartmentss',
#                                        suppress.messages=T,
#                                        fu.hep.correct=F)
#     percentiles <- c("5","50","95")
#     for (this.index in 1:3)
#     {
#       this.row <- as.data.frame(get_wetmore_css(chem.cas=this.CAS,
#                                 which.quantile=as.numeric(percentiles[this.index])/100))
#       this.row <- cbind(this.row, as.data.frame(vLiver.human.values[this.index]))
#       this.row <- cbind(this.row, as.data.frame(percentiles[this.index]))
#       this.row <- cbind(this.row, as.data.frame("Human"))
#       this.row <- cbind(this.row, as.data.frame(this.CAS))
#       this.row <- cbind(this.row, as.data.frame(pValues[pValues$CAS==this.CAS,
#                                                 "Human.Clint.pValue"]<0.05))
#       colnames(this.row) <- c("Wetmore", "Predicted", "Percentile", "Species",
#                               "CAS", "Systematic")
#       if (is.na(this.row["Systematic"])) this.row["Systematic"] <- F
#       Wetmore.table <- Wetmore.table <- rbind(Wetmore.table,this.row)
#     }
#   }
# 
# scientific_10 <- function(x) {
#   out <- gsub("1e", "10^", scientific_format()(x))
#   out <- gsub("\+","",out)
#   out <- gsub("10\^01","10",out)
#   out <- parse(text=gsub("10\^00","1",out))
# }
# 
# 
# Fig1 <- ggplot(Wetmore.table, aes(Predicted,Wetmore,group = CAS)) +
#   geom_line() +
#   geom_point(aes(colour=factor(Percentile),shape=factor(Percentile))) +
#   scale_colour_discrete(name="Percentile") +
#   scale_shape_manual(name="Percentile", values=c("5"=21, "50"=22,"95"=24)) +
#   scale_x_log10(expression(paste(C[ss]," Predicted (mg/L) with Refined Assumptions")),
#                 label=scientific_10) + 
#   scale_y_log10(expression(paste(C[ss]," Wetmore ",italic("et al.")," (2012) (mg/L)")),
#                 label=scientific_10) +
#   geom_abline(intercept = 0, slope = 1,linetype="dashed")+
#   theme_bw()+
#   theme(legend.position="bottom", text  = element_text(size=18))
# 
# print(Fig1)
# 
# Fig1a.fit <- lm(log(Wetmore) ~ log(Predicted)*Percentile, Wetmore.table)
# ## End(**Not run**)
# ## End(Not run)