The goal of psc is to compare a dataset of observations against a parametric model
InstallationYou 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)
#> Warning in data_match(cls, lev, DC): vi specified as a character in the model, consider respecifying
#> as a factor to ensure categories match between CFM and DC
#> Warning in data_match(cls, lev, DC): allmets specified as a character in the model, consider respecifying
#> as a factor to ensure categories match between CFM and DC
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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: 0x1097f3cd8>
#>
#> Call:
#> CFM model + beta
#>
#> Coefficients:
#> median 2.5% 97.5% Pr(x<0) Pr(x>0)
#> beta 0.3681 0.1639 0.5567 0.0002 0.9998
#> DIC 280.7310 273.4438 292.3749 NA NA⦠and to see a plot of what you have done
In that case, donât forget to commit and push the resulting figure files, so they display on GitHub and CRAN.
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