Hi all,
I used R code in this topic
Overall Predictive Check - Modelling and Simulation - Certara Forums
to replicate the VPC plot in R and compare with the plot in phoenix
They look slightly different. Dose Phoenix further uses any smoothing function for the interval (shade area)?
For custom VPCs you may want to invest some time in learning the tidyvpc package in R.
You can easily use Phoenix VPC output (as well as RsNLME, NONMEM, any engine really as the only required inputs are the observed data and simulation data)
Here’s an example of how to use output from a Phoenix VPC run. You will need to locate the required files in your Phoenix Temporary Output Folder, the path of which can be found in the compiler output text file from the run.
library(xpose)
#see https://certara.github.io/R-model-results/articles/phoenix.html
obs_data ← read.csv(“C:/Users/username/AppData/Local/Temp/Phoenix/DME_PredCheck_11-21-24.845__63bc3ae3-ebfd-452a-b2ab-2dc9ccfd44ee/predcheck0.csv”)
sim_data ← read.csv(“C:/Users/username/AppData/Local/Temp/Phoenix/DME_PredCheck_11-21-24.845__63bc3ae3-ebfd-452a-b2ab-2dc9ccfd44ee/predout.csv”)
library(tidyvpc)
vpc ← observed(obs_data, x = IVAR, y = DV) %>%
simulated(sim_data, ysim = DV) %>%
stratify(~ Strat1) %>%
binning(bin = “jenks”, nbins = 5) %>%
vpcstats()
plot(vpc)
[attachment=3852:Rplot1.png]
#to add labels to panels: Need to convert numeric variables to factors with labels
obs_data ← obs_data %>%
mutate(SEXfactor = factor(Strat1, levels=c(0,1), labels=c(“M”,“F”)))
sim_data ← sim_data %>%
mutate(SEXfactor = factor(STRAT1, levels=c(0,1), labels=c(“M”,“F”)))
vpc ← observed(obs_data, x = IVAR, y = DV) %>%
simulated(sim_data, ysim = DV) %>%
stratify(~ SEXfactor) %>%
binning(bin = “jenks”, nbins = 5) %>%
vpcstats()
plot(vpc)
[attachment=3853:Rplot2.png]
library(Certara.VPCResults)
vpcResultsUI(observed = obs_data, simulated = sim_data)
Hi, the old post you referred to is quite old many things changed since.
if your design is rich day1, troughs , rich day ss:
you might want to startify by Visit and then do an interval for the troughs.
this require a good knowledge of R and ggplot2 and not part of automatic plotting in tidyvpc.
once you have the intervals computed you can do any plot you want:
