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The goal of psc is to compare a dataset of observations against a parametric model
You can install the development version of psc from GitHub with:
# install.packages("devtools")
devtools::install_github("richJJackson/psc")This is a basic example which shows you how to solve a common problem:
library(psc)
library(survival)
## basic example code
### Load model
data("surv.mod")
### Load Data
data("data")
### Use 'pscfit' to compare
surv.psc <- pscfit(surv.mod,data)
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summary(surv.psc)
#> Summary:
#>
#> 100 observations selected from the data cohort for comparison
#> CFM of type flexsurvreg identified
#> linear predictor succesfully obtained with median:
#> trt: 3.15
#> Average expected response:
#> trt: 9.1
#> Average observed response: 6.366
#>
#> Counterfactual Model (CFM):
#> A model of class 'flexsurvreg'
#> Fit with 3 internal knots
#>
#> Formula:
#> Surv(time, cen) ~ vi/age60 + ecog + allmets + logafp + alb +
#> logcreat + logast + aet
#> <environment: 0x11a95d660>
#>
#> Call:
#> CFM model + beta
#>
#> Coefficients:
#> median 2.5% 97.5% Pr(x<0) Pr(x>0)
#> beta 0.3574 0.1204 0.5801 0.0004 0.9996
#> DIC 280.8601 273.6541 291.9444 NA NA… and to see a plot of what you have done
In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.