print.jmstanBig
print.jmstanBig.Rdprint method for class 'jmstanBig'
Usage
# S3 method for jmstanBig
print(x, ...)Value
prints table containing various parameter estimates, SE, P- value for both survival and longitudinal submodel, if the model is bayesian it includes their credible interval too.
Examples
# \donttest{
##
library(survival)
library(dplyr)
mod1<-jmstanBig(dtlong=long2,
dtsurv = surv2,
longm=y~ x7+visit+(1|id),
survm=Surv(time,status)~x1+visit,
samplesize=200,
time_var='visit',id='id')
#> Fitting a univariate joint model.
#>
#> Please note the warmup may be much slower than later iterations!
#>
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.00881 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 88.1 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 46.036 seconds (Warm-up)
#> Chain 1: 24.14 seconds (Sampling)
#> Chain 1: 70.176 seconds (Total)
#> Chain 1:
#> Fitting a univariate joint model.
#>
#> Please note the warmup may be much slower than later iterations!
#>
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.003599 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 35.99 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 38.053 seconds (Warm-up)
#> Chain 1: 27.137 seconds (Sampling)
#> Chain 1: 65.19 seconds (Total)
#> Chain 1:
#> Fitting a univariate joint model.
#>
#> Please note the warmup may be much slower than later iterations!
#>
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.003973 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 39.73 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 60.363 seconds (Warm-up)
#> Chain 1: 13.105 seconds (Sampling)
#> Chain 1: 73.468 seconds (Total)
#> Chain 1:
#> Fitting a univariate joint model.
#>
#> Please note the warmup may be much slower than later iterations!
#>
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.003774 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 37.74 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 51.011 seconds (Warm-up)
#> Chain 1: 16.763 seconds (Sampling)
#> Chain 1: 67.774 seconds (Total)
#> Chain 1:
#> Fitting a univariate joint model.
#>
#> Please note the warmup may be much slower than later iterations!
#>
#> SAMPLING FOR MODEL 'jm' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.002787 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 27.87 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 46.349 seconds (Warm-up)
#> Chain 1: 23.098 seconds (Sampling)
#> Chain 1: 69.447 seconds (Total)
#> Chain 1:
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
print(mod1)
#>
#> Joint model for Big data using rstanarm
#> Call:
#> jmstanBig(dtlong = long2, dtsurv = surv2, longm = y ~ x7 + visit +
#> (1 | id), survm = Surv(time, status) ~ x1 + visit, samplesize = 200,
#> time_var = "visit", id = "id")
#>
#>
#> Total observation in longitudinal data: 1000
#>
#> Chunk size: 200
#>
#> Longitudinal process:
#> Mean StDev 2.5% 97.5% Zvalue Pvalue
#> (Intercept) 8.863 0.440 7.965 9.699 20.155 0.000
#> x7 -0.023 0.007 -0.036 -0.009 -3.443 0.001
#> visit -0.081 0.024 -0.127 -0.033 -3.409 0.001
#> sigma 0.973 0.023 0.930 1.020 42.632 0.000
#> mean_PPD 7.249 0.041 7.167 7.331 175.646 0.000
#>
#> Survival process:
#> Mean StDev 2.5% 97.5% Zvalue Pvalue
#> (Intercept) -1.667 1.048 -3.672 0.502 -1.591 0.112
#> x11 0.031 0.250 -0.458 0.502 0.125 0.901
#> visit -0.134 0.089 -0.303 0.046 -1.509 0.131
#> b-splines-coef1 -2.904 1.436 -6.197 -0.539 -2.021 0.043
#> b-splines-coef2 -0.461 0.912 -2.277 1.353 -0.506 0.613
#> b-splines-coef3 0.486 0.666 -0.785 1.796 0.730 0.466
#> b-splines-coef4 -0.529 1.013 -2.581 1.434 -0.522 0.602
#> b-splines-coef5 2.411 1.701 -0.919 5.697 1.418 0.156
#> b-splines-coef6 -4.647 3.222 -11.868 0.768 -1.442 0.149
#> Assoc|Long1|etavalue -0.191 0.147 -0.489 0.079 -1.302 0.193
#> Random effects covariance matrix:
#> Groups Name Std.Dev.
#> id Long1|(Intercept) 1.1298
# }