Obtain summary statistics of each parameter from the BKMR fit
Usage
ExtractEsts(fit, q = c(0.025, 0.25, 0.5, 0.75, 0.975), sel = NULL)Arguments
- fit
An object containing the results returned by a the
kmbayesfunction- q
vector of quantiles
- sel
logical expression indicating samples to keep; defaults to keeping the second half of all samples
Value
a list where each component is a data frame containing the summary statistics of the posterior distribution of one of the parameters (or vector of parameters) being estimated
Examples
## First generate dataset
set.seed(111)
dat <- SimData(n = 50, M = 4)
y <- dat$y
Z <- dat$Z
X <- dat$X
## Fit model with component-wise variable selection
## Using only 100 iterations to make example run quickly
## Typically should use a large number of iterations for inference
set.seed(111)
fitkm <- kmbayes(y = y, Z = Z, X = X, iter = 100, verbose = FALSE, varsel = TRUE)
#> Iteration: 10 (10% completed; 0.00503 secs elapsed)
#> Iteration: 20 (20% completed; 0.01084 secs elapsed)
#> Iteration: 30 (30% completed; 0.01624 secs elapsed)
#> Iteration: 40 (40% completed; 0.02185 secs elapsed)
#> Iteration: 50 (50% completed; 0.02704 secs elapsed)
#> Iteration: 60 (60% completed; 0.03231 secs elapsed)
#> Iteration: 70 (70% completed; 0.03753 secs elapsed)
#> Iteration: 80 (80% completed; 0.04381 secs elapsed)
#> Iteration: 90 (90% completed; 0.04999 secs elapsed)
#> Iteration: 100 (100% completed; 0.05631 secs elapsed)
ests <- ExtractEsts(fitkm)
names(ests)
#> [1] "sigsq.eps" "beta" "lambda" "r"
ests$beta
#> mean sd q_2.5 q_25 q_50 q_75 q_97.5
#> beta 1.889308 0.08175852 1.72388 1.839835 1.889967 1.941782 2.046596