Description Usage Arguments Details Value Examples
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.
1 2 3 4 5 6 7 8 9 10  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"), adapt_delta = NULL)
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"), adapt_delta = NULL,
importance_resampling = TRUE, keep_every = 1)

biglm 
The list output by 
xbar 
A numeric vector of column means in the implicit design matrix excluding the intercept for the observations included in the model. 
ybar 
A numeric scalar indicating the mean of the outcome for the observations included in the model. 
s_y 
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 ( 
prior 
Must be a call to 
prior_intercept 
Either Note: If using a dense representation of the design matrix
—i.e., if the 
prior_PD 
A logical scalar (defaulting to 
algorithm 
A string (possibly abbreviated) indicating the
estimation approach to use. Can be 
adapt_delta 
Only relevant if 
b 
A numeric vector of OLS coefficients, excluding the intercept 
R 
A square uppertriangular matrix from the QR decomposition of the design matrix, excluding the intercept 
SSR 
A numeric scalar indicating the sumofsquared residuals for OLS 
N 
A integer scalar indicating the number of included observations 
has_intercept 
A logical scalar indicating whether to add an intercept to the model when estimating it. 
importance_resampling 
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 
keep_every 
Positive integer, which defaults to 1, but can be higher
in order to thin the importance sampling realizations and also only
apples when 
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.
The output of both stan_biglm
and stan_biglm.fit
is an
object of stanfitclass
rather than
stanregobjects
, 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 stanregobjects
object cannot be calculated,
such as residuals. Thus, the functions in the rstanarm package that
input stanregobjects
, such as
posterior_predict
cannot be used.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  # 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)
cbind(lm = b, stan_lm = rstan::get_posterior_mean(post)[13:15,]) # shrunk

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