stan_clogit.Rd
A model for case-control studies with optional prior distributions for the coefficients, intercept, and auxiliary parameters.
stan_clogit(formula, data, subset, na.action = NULL, ..., strata, prior = normal(), prior_covariance = decov(), prior_PD = FALSE, algorithm = c("sampling", "optimizing", "meanfield", "fullrank"), adapt_delta = NULL, QR = FALSE, sparse = FALSE)
formula, data, subset, na.action | Same as for |
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... | Further arguments passed to the function in the rstan
package ( |
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strata | A factor indicating the groups in the data where the number of
successes (possibly one) is fixed by the research design. It may be useful
to use |
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prior | The prior distribution for the regression coefficients.
See the priors help page for details on the families and
how to specify the arguments for all of the functions in the table above.
To omit a prior ---i.e., to use a flat (improper) uniform prior---
Note: Unless |
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prior_covariance | Cannot be |
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prior_PD | A logical scalar (defaulting to |
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algorithm | A string (possibly abbreviated) indicating the
estimation approach to use. Can be |
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adapt_delta | Only relevant if |
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QR | A logical scalar defaulting to |
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sparse | A logical scalar (defaulting to |
A stanreg object is returned
for stan_clogit
.
The stan_clogit
function is mostly similar in syntax to
clogit
but rather than performing maximum
likelihood estimation of generalized linear models, full Bayesian
estimation is performed (if algorithm
is "sampling"
) via
MCMC. The Bayesian model adds priors (independent by default) on the
coefficients of the GLM.
The data.frame
passed to the data
argument must be sorted by
the variable passed to the strata
argument.
The formula
may have group-specific terms like in
stan_glmer
but should not allow the intercept to vary by the
stratifying variable, since there is no information in the data with which
to estimate such deviations in the intercept.
stanreg-methods
and
clogit
.
The vignette for Bernoulli and binomial models, which has more
details on using stan_clogit
.
http://mc-stan.org/rstanarm/articles/
dat <- infert[order(infert$stratum), ] # order by strata post <- stan_clogit(case ~ spontaneous + induced + (1 | education), strata = stratum, data = dat, subset = parity <= 2, QR = TRUE, chains = 2, iter = 500) # for speed only#> #> SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1). #> Chain 1: #> Chain 1: Gradient evaluation took 0.000226 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 2.26 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: #> Chain 1: Iteration: 1 / 500 [ 0%] (Warmup) #> Chain 1: Iteration: 50 / 500 [ 10%] (Warmup) #> Chain 1: Iteration: 100 / 500 [ 20%] (Warmup) #> Chain 1: Iteration: 150 / 500 [ 30%] (Warmup) #> Chain 1: Iteration: 200 / 500 [ 40%] (Warmup) #> Chain 1: Iteration: 250 / 500 [ 50%] (Warmup) #> Chain 1: Iteration: 251 / 500 [ 50%] (Sampling) #> Chain 1: Iteration: 300 / 500 [ 60%] (Sampling) #> Chain 1: Iteration: 350 / 500 [ 70%] (Sampling) #> Chain 1: Iteration: 400 / 500 [ 80%] (Sampling) #> Chain 1: Iteration: 450 / 500 [ 90%] (Sampling) #> Chain 1: Iteration: 500 / 500 [100%] (Sampling) #> Chain 1: #> Chain 1: Elapsed Time: 0.393 seconds (Warm-up) #> Chain 1: 0.112695 seconds (Sampling) #> Chain 1: 0.505695 seconds (Total) #> Chain 1: #> #> SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2). #> Chain 2: #> Chain 2: Gradient evaluation took 5.3e-05 seconds #> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.53 seconds. #> Chain 2: Adjust your expectations accordingly! #> Chain 2: #> Chain 2: #> Chain 2: Iteration: 1 / 500 [ 0%] (Warmup) #> Chain 2: Iteration: 50 / 500 [ 10%] (Warmup) #> Chain 2: Iteration: 100 / 500 [ 20%] (Warmup) #> Chain 2: Iteration: 150 / 500 [ 30%] (Warmup) #> Chain 2: Iteration: 200 / 500 [ 40%] (Warmup) #> Chain 2: Iteration: 250 / 500 [ 50%] (Warmup) #> Chain 2: Iteration: 251 / 500 [ 50%] (Sampling) #> Chain 2: Iteration: 300 / 500 [ 60%] (Sampling) #> Chain 2: Iteration: 350 / 500 [ 70%] (Sampling) #> Chain 2: Iteration: 400 / 500 [ 80%] (Sampling) #> Chain 2: Iteration: 450 / 500 [ 90%] (Sampling) #> Chain 2: Iteration: 500 / 500 [100%] (Sampling) #> Chain 2: #> Chain 2: Elapsed Time: 0.454144 seconds (Warm-up) #> Chain 2: 0.140407 seconds (Sampling) #> Chain 2: 0.594551 seconds (Total) #> Chain 2:#> 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 #> http://mc-stan.org/misc/warnings.html#bulk-essnd <- dat[dat$parity > 2, c("case", "spontaneous", "induced", "education", "stratum")] # next line would fail without case and stratum variables pr <- posterior_linpred(post, newdata = nd, transform = TRUE) # transform=TRUE gives probabilities # not a random variable b/c probabilities add to 1 within strata all.equal(rep(sum(nd$case), nrow(pr)), rowSums(pr))#> [1] TRUE