Bayesian regularized linear models via Stan

Bayesian inference for linear modeling with regularizing priors on the model parameters that are driven by prior beliefs about \(R^2\), the proportion of variance in the outcome attributable to the predictors. See priors for an explanation of this critical point. stan_glm with family="gaussian" also estimates a linear model with normally-distributed errors and allows for various other priors on the coefficients.

Usage

stan_aov(
  formula,
  data,
  projections = FALSE,
  contrasts = NULL,
  ...,
  prior = R2(stop("'location' must be specified")),
  prior_PD = FALSE,
  algorithm = c("sampling", "meanfield", "fullrank"),
  adapt_delta = NULL
)

stan_lm(
  formula,
  data,
  subset,
  weights,
  na.action,
  model = TRUE,
  x = FALSE,
  y = FALSE,
  singular.ok = TRUE,
  contrasts = NULL,
  offset,
  ...,
  prior = R2(stop("'location' must be specified")),
  prior_intercept = NULL,
  prior_PD = FALSE,
  algorithm = c("sampling", "meanfield", "fullrank"),
  adapt_delta = NULL
)

stan_lm.wfit(
  x,
  y,
  w,
  offset = NULL,
  singular.ok = TRUE,
  ...,
  prior = R2(stop("'location' must be specified")),
  prior_intercept = NULL,
  prior_PD = FALSE,
  algorithm = c("sampling", "meanfield", "fullrank"),
  adapt_delta = NULL
)

stan_lm.fit(
  x,
  y,
  offset = NULL,
  singular.ok = TRUE,
  ...,
  prior = R2(stop("'location' must be specified")),
  prior_intercept = NULL,
  prior_PD = FALSE,
  algorithm = c("sampling", "meanfield", "fullrank"),
  adapt_delta = NULL
)

Arguments

formula, data, subset

Same as lm, but we strongly advise against omitting the data argument. Unless data is specified (and is a data frame) many post-estimation functions (including update, loo, kfold) are not guaranteed to work properly.

projections

For stan_aov, a logical scalar (defaulting to FALSE) indicating whether proj should be called on the fit.

...

Further arguments passed to the function in the rstan package (sampling, vb, or optimizing), corresponding to the estimation method named by algorithm. For example, if algorithm is "sampling" it is possible to specify iter, chains, cores, and other MCMC controls.

Another useful argument that can be passed to rstan via ... is refresh, which specifies how often to print updates when sampling (i.e., show the progress every refresh iterations). refresh=0 turns off the iteration updates.

prior

Must be a call to R2 with its location argument specified or NULL, which would indicate a standard uniform prior for the \(R^2\).

prior_PD

A logical scalar (defaulting to FALSE) indicating whether to draw from the prior predictive distribution instead of conditioning on the outcome.

algorithm

A string (possibly abbreviated) indicating the estimation approach to use. Can be "sampling" for MCMC (the default), "optimizing" for optimization, "meanfield" for variational inference with independent normal distributions, or "fullrank" for variational inference with a multivariate normal distribution. See rstanarm-package for more details on the estimation algorithms. NOTE: not all fitting functions support all four algorithms.

adapt_delta

Only relevant if algorithm="sampling". See the adapt_delta help page for details.

na.action, singular.ok, contrasts

Same as lm, but rarely specified.

model, offset, weights

Same as lm, but rarely specified.

x, y

In stan_lm, stan_aov, logical scalars indicating whether to return the design matrix and response vector. In stan_lm.fit or stan_lm.wfit, a design matrix and response vector.

prior_intercept

Either NULL (the default) or a call to normal. If a normal prior is specified without a scale, then the standard deviation is taken to be the marginal standard deviation of the outcome divided by the square root of the sample size, which is legitimate because the marginal standard deviation of the outcome is a primitive parameter being estimated.

Note: If using a dense representation of the design matrix —i.e., if the sparse argument is left at its default value of FALSE— then the prior distribution for the intercept is set so it applies to the value when all predictors are centered. If you prefer to specify a prior on the intercept without the predictors being auto-centered, then you have to omit the intercept from the formula and include a column of ones as a predictor, in which case some element of prior specifies the prior on it, rather than prior_intercept. Regardless of how prior_intercept is specified, the reported estimates of the intercept always correspond to a parameterization without centered predictors (i.e., same as in glm).

w

Same as in lm.wfit but rarely specified.

Value

A stanreg object is returned for stan_lm, stan_aov.

A stanfit object (or a slightly modified stanfit object) is returned if stan_lm.fit or stan_lm.wfit is called directly.

Details

The stan_lm function is similar in syntax to the lm function but rather than choosing the parameters to minimize the sum of squared residuals, samples from the posterior distribution are drawn using MCMC (if algorithm is "sampling"). The stan_lm function has a formula-based interface and would usually be called by users but the stan_lm.fit and stan_lm.wfit functions might be called by other functions that parse the data themselves and are analogous to lm.fit and lm.wfit respectively.

In addition to estimating sigma — the standard deviation of the normally-distributed errors — this model estimates a positive parameter called log-fit_ratio. If it is positive, the marginal posterior variance of the outcome will exceed the sample variance of the outcome by a multiplicative factor equal to the square of fit_ratio. Conversely if log-fit_ratio is negative, then the model underfits. Given the regularizing nature of the priors, a slight underfit is good.

Finally, the posterior predictive distribution is generated with the predictors fixed at their sample means. This quantity is useful for checking convergence because it is reasonably normally distributed and a function of all the parameters in the model.

The stan_aov function is similar to aov, but does a Bayesian analysis of variance that is basically equivalent to stan_lm with dummy variables. stan_aov has a somewhat customized print method that prints an ANOVA-like table in addition to the output printed for stan_lm models.

References

Lewandowski, D., Kurowicka D., and Joe, H. (2009). Generating random correlation matrices based on vines and extended onion method. Journal of Multivariate Analysis. 100(9), 1989–2001.

Examples

if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") {
# \donttest{
op <- options(contrasts = c("contr.helmert", "contr.poly"))
fit_aov <- stan_aov(yield ~ block + N*P*K, data = npk,
         prior = R2(0.5), seed = 12345)
options(op)
print(fit_aov)
# }
}
#> 
#> SAMPLING FOR MODEL 'lm' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
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#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.093 seconds (Warm-up)
#> Chain 1:                0.102 seconds (Sampling)
#> Chain 1:                0.195 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'lm' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
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#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.144 seconds (Warm-up)
#> Chain 2:                0.07 seconds (Sampling)
#> Chain 2:                0.214 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'lm' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 7e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
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#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.098 seconds (Warm-up)
#> Chain 3:                0.099 seconds (Sampling)
#> Chain 3:                0.197 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'lm' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 9e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.105 seconds (Warm-up)
#> Chain 4:                0.101 seconds (Sampling)
#> Chain 4:                0.206 seconds (Total)
#> Chain 4: 
#> stan_aov
#>  family:       gaussian [identity]
#>  formula:      yield ~ block + N * P * K
#>  observations: 24
#>  predictors:   12
#> ------
#>             Median MAD_SD
#> (Intercept) 54.9    0.8  
#> block1       1.2    1.3  
#> block2       1.2    0.7  
#> block3      -1.3    0.5  
#> block4      -0.7    0.4  
#> block5       0.2    0.3  
#> N1           2.0    0.7  
#> P1          -0.4    0.7  
#> K1          -1.4    0.7  
#> N1:P1       -0.6    0.7  
#> N1:K1       -0.8    0.7  
#> P1:K1        0.1    0.7  
#> 
#> Auxiliary parameter(s):
#>               Median MAD_SD
#> R2            0.5    0.1   
#> log-fit_ratio 0.0    0.1   
#> sigma         4.2    0.7   
#> 
#> ANOVA-like table:
#>               Median MAD_SD
#> Mean Sq block 43.8   20.9  
#> Mean Sq N     48.6   26.7  
#> Mean Sq P     13.7   11.0  
#> Mean Sq K     29.5   19.6  
#> Mean Sq N:P   12.0   16.0  
#> Mean Sq N:K   17.6   23.0  
#> Mean Sq P:K    5.5    7.6  
#> 
#> ------
#> * For help interpreting the printed output see ?print.stanreg
#> * For info on the priors used see ?prior_summary.stanreg
if (.Platform$OS.type != "windows" || .Platform$r_arch !="i386") {
(fit <- stan_lm(mpg ~ wt + qsec + am, data = mtcars, prior = R2(0.75), 
                # the next line is only to make the example go fast enough
                chains = 1, iter = 300, seed = 12345, refresh = 0))
plot(fit, "hist", pars = c("wt", "am", "qsec", "sigma"), 
     transformations = list(sigma = "log"))
}
#> Warning: The largest R-hat is 1.09, 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
#> Warning: Markov chains did not converge! Do not analyze results!
#> `stat_bin()` using `bins = 30`. Pick better value `binwidth`.