Computes posterior predictions for each latent component of a
survMixBayes model. By default, predictions are returned on the
linear predictor scale for both components.
Arguments
- object
A
survMixBayesmodel object.- newdata
A numeric matrix of new observations (\(n_{new} \times K\)) with columns aligned to the design matrix used for fitting. If
NULL, the fitted design matrix stored inobject$Xis used.- se.fit
Logical; if
TRUE, also return posterior SD of predictions.- interval
Either
"none"or"credible", indicating whether to compute credible intervals.- level
Probability level for the credible interval (default 0.95).
- ...
Not used.
Value
A list with two components, component1 and component2,
corresponding to the two latent mixture components.
If se.fit = FALSE and interval = "none", each element is a
numeric vector of posterior mean linear predictors.
Otherwise, each element is a matrix containing the fitted values and,
optionally, posterior SDs and credible interval bounds.
Details
Component 1 is interpreted as the correct-match component and component 2 as the incorrect-match component (after label-switching correction).
Examples
# \donttest{
set.seed(301)
n <- 150
trt <- rbinom(n, 1, 0.5)
# Simulate Weibull AFT data
true_time <- rweibull(n, shape = 1.5, scale = exp(1 + 0.8 * trt))
cens_time <- rexp(n, rate = 0.1)
true_obs_time <- pmin(true_time, cens_time)
true_status <- as.integer(true_time <= cens_time)
# Induce linkage mismatch errors in approximately 20% of records
is_mismatch <- rbinom(n, 1, 0.2)
obs_time <- true_obs_time
obs_status <- true_status
mismatch_idx <- which(is_mismatch == 1)
shuffled <- sample(mismatch_idx)
obs_time[mismatch_idx] <- obs_time[shuffled]
obs_status[mismatch_idx] <- obs_status[shuffled]
linked_df <- data.frame(time = obs_time, status = obs_status, trt = trt)
adj <- adjMixBayes(linked.data = linked_df)
fit <- plsurvreg(
survival::Surv(time, status) ~ trt,
dist = "weibull",
adjustment = adj,
control = list(
iterations = 200,
burnin.iterations = 100,
seed = 123
)
)
#>
#> SAMPLING FOR MODEL 'survMixBayes_weibull' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 9.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.94 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: WARNING: There aren't enough warmup iterations to fit the
#> Chain 1: three stages of adaptation as currently configured.
#> Chain 1: Reducing each adaptation stage to 15%/75%/10% of
#> Chain 1: the given number of warmup iterations:
#> Chain 1: init_buffer = 15
#> Chain 1: adapt_window = 75
#> Chain 1: term_buffer = 10
#> Chain 1:
#> Chain 1: Iteration: 1 / 200 [ 0%] (Warmup)
#> Chain 1: Iteration: 20 / 200 [ 10%] (Warmup)
#> Chain 1: Iteration: 40 / 200 [ 20%] (Warmup)
#> Chain 1: Iteration: 60 / 200 [ 30%] (Warmup)
#> Chain 1: Iteration: 80 / 200 [ 40%] (Warmup)
#> Chain 1: Iteration: 100 / 200 [ 50%] (Warmup)
#> Chain 1: Iteration: 101 / 200 [ 50%] (Sampling)
#> Chain 1: Iteration: 120 / 200 [ 60%] (Sampling)
#> Chain 1: Iteration: 140 / 200 [ 70%] (Sampling)
#> Chain 1: Iteration: 160 / 200 [ 80%] (Sampling)
#> Chain 1: Iteration: 180 / 200 [ 90%] (Sampling)
#> Chain 1: Iteration: 200 / 200 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.618 seconds (Warm-up)
#> Chain 1: 0.488 seconds (Sampling)
#> Chain 1: 1.106 seconds (Total)
#> Chain 1:
#> Warning: The largest R-hat is 1.18, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> 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
#>
#> ......................................................................................
#> . Method Time (sec) Status .
#> ......................................................................................
#> . ECR-ITERATIVE-1 0.088 Converged (2 iterations) .
#> ......................................................................................
#>
#> Relabelling all methods according to method ECR-ITERATIVE-1 ... done!
#> Retrieve the 1 permutation arrays by typing:
#> [...]$permutations$"ECR-ITERATIVE-1"
#> Retrieve the 1 best clusterings: [...]$clusters
#> Retrieve the 1 CPU times: [...]$timings
#> Retrieve the 1 X 1 similarity matrix: [...]$similarity
#> Label switching finished. Total time: 0.1 seconds.
# Create a design matrix for new covariate values
newdata <- stats::model.matrix(~ trt, data = data.frame(trt = c(0, 1)))
# Predict posterior mean linear predictors for each latent component
preds <- predict(fit, newdata = newdata, se.fit = TRUE, interval = "credible")
print(preds$component1)
#> fit se.fit 2.5 % 97.5 %
#> 1 0.4407128 0.7432820 -0.74783735 1.989130
#> 2 1.1569081 0.7328594 -0.04520797 2.696848
print(preds$component2)
#> fit se.fit 2.5 % 97.5 %
#> 1 -0.0683974 1.550691 -3.145031 2.640371
#> 2 -0.2061190 2.156831 -4.552259 2.747006
# }