This is the same model as with stan_lm but it utilizes the output from biglm in the biglm package in order to proceed when the data is too large to fit in memory.

stan_biglm(
biglm,
xbar,
ybar,
s_y,
...,
prior = R2(stop("'location' must be specified")),
prior_intercept = NULL,
prior_PD = FALSE,
algorithm = c("sampling", "meanfield", "fullrank"),
)

stan_biglm.fit(
b,
R,
SSR,
N,
xbar,
ybar,
s_y,
has_intercept = TRUE,
...,
prior = R2(stop("'location' must be specified")),
prior_intercept = NULL,
prior_PD = FALSE,
algorithm = c("sampling", "meanfield", "fullrank", "optimizing"),
importance_resampling = TRUE,
keep_every = 1
)

## Arguments

biglm The list output by biglm in the biglm package. A numeric vector of column means in the implicit design matrix excluding the intercept for the observations included in the model. A numeric scalar indicating the mean of the outcome for the observations included in the model. A numeric scalar indicating the unbiased sample standard deviation of the outcome for the observations included in the model. 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 possibly to specify iter, chains, cores, refresh, etc. Must be a call to R2 with its location argument specified or NULL, which would indicate a standard uniform prior for the $$R^2$$. 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). A logical scalar (defaulting to FALSE) indicating whether to draw from the prior predictive distribution instead of conditioning on the outcome. 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. Only relevant if algorithm="sampling". See the adapt_delta help page for details. A numeric vector of OLS coefficients, excluding the intercept A square upper-triangular matrix from the QR decomposition of the design matrix, excluding the intercept A numeric scalar indicating the sum-of-squared residuals for OLS A integer scalar indicating the number of included observations A logical scalar indicating whether to add an intercept to the model when estimating it. Logical scalar indicating whether to use importance resampling when approximating the posterior distribution with a multivariate normal around the posterior mode, which only applies when algorithm is "optimizing" but defaults to TRUE in that case Positive integer, which defaults to 1, but can be higher in order to thin the importance sampling realizations and also only apples when algorithm is "optimizing" but defaults to TRUE in that case

## Value

The output of both stan_biglm and stan_biglm.fit is an object of stanfit-class rather than stanreg-objects, which is more limited and less convenient but necessitated by the fact that stan_biglm does not bring the full design matrix into memory. Without the full design matrix,some of the elements of a stanreg-objects object cannot be calculated, such as residuals. Thus, the functions in the rstanarm package that input stanreg-objects, such as posterior_predict cannot be used.

## Details

The stan_biglm function is intended to be used in the same circumstances as the biglm function in the biglm package but with an informative prior on the $$R^2$$ of the regression. Like biglm, the memory required to estimate the model depends largely on the number of predictors rather than the number of observations. However, stan_biglm and stan_biglm.fit have additional required arguments that are not necessary in biglm, namely xbar, ybar, and s_y. If any observations have any missing values on any of the predictors or the outcome, such observations do not contribute to these statistics.

## Examples

# create inputs ols <- lm(mpg ~ wt + qsec + am, data = mtcars, # all row are complete so ... na.action = na.exclude) # not necessary in this case b <- coef(ols)[-1] R <- qr.R(ols$qr)[-1,-1] SSR <- crossprod(ols$residuals)[1] not_NA <- !is.na(fitted(ols)) N <- sum(not_NA) xbar <- colMeans(mtcars[not_NA,c("wt", "qsec", "am")]) y <- mtcars\$mpg[not_NA] ybar <- mean(y) s_y <- sd(y) post <- stan_biglm.fit(b, R, SSR, N, xbar, ybar, s_y, prior = R2(.75), # the next line is only to make the example go fast chains = 1, iter = 500, seed = 12345)
#> #> SAMPLING FOR MODEL 'lm' NOW (CHAIN 1). #> Chain 1: #> Chain 1: Gradient evaluation took 1.9e-05 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 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: 0.636559 seconds (Warm-up) #> Chain 1: 0.289041 seconds (Sampling) #> Chain 1: 0.9256 seconds (Total) #> Chain 1:
#> Warning: There were 1 divergent transitions after warmup. Increasing adapt_delta above 0.99 may help. See #> http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> Warning: The largest R-hat is 1.06, indicating chains have not mixed. #> Running the chains for more iterations may help. See #> http://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 #> http://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 #> http://mc-stan.org/misc/warnings.html#tail-ess
cbind(lm = b, stan_lm = rstan::get_posterior_mean(post)[13:15,]) # shrunk
#> lm stan_lm #> wt -3.916504 -3.776792 #> qsec 1.225886 1.218030 #> am 2.935837 3.002232