Extract samples from a fitted Stan model

Extract samples from a fitted model represented by an instance of class stanfit.

# S4 method for stanfit
extract(object, pars, permuted = TRUE, inc_warmup = FALSE, 
  include = TRUE)

Methods

extract

signature(object = "stanfit")

Extract samples from a fitted model represented by an instance of class stanfit.

Arguments

object

An object of class stanfit.

pars

An optional character vector providing the parameter names (or other quantity names) of interest. If not specified, all parameters and other quantities are used. The log-posterior with name lp__ is also included by default.

permuted

A logical scalar indicating whether the draws after the warmup period in each chain should be permuted and merged. If FALSE, the original order is kept. For each stanfit object, the permutation is fixed (i.e., extracting samples a second time will give the same sequence of iterations).

inc_warmup

A logical scalar indicating whether to include the warmup draws. This argument is only relevant if permuted is FALSE.

include

A logical scalar indicating whether the parameters named in pars should be included (TRUE) or excluded (FALSE).

Value

When permuted = TRUE, this function returns a named list, every element of which is an array representing samples for a parameter with all chains merged together.

When permuted = FALSE, an array is returned; the first dimension is for the iterations, the second for the number of chains, the third for the parameters. Vectors and arrays are expanded to one parameter (a scalar) per cell, with names indicating the third dimension. See the examples (with comments) below. The monitor function can be applied to the returned array to obtain a summary (similar to the print method for stanfit objects).

See also

S4 class stanfit, as.array.stanfit, and monitor

Examples

if (FALSE) {
ex_model_code <- '
  parameters {
    real alpha[2,3];
    real beta[2]; 
  } 
  model {
    for (i in 1:2) for (j in 1:3) 
      alpha[i, j] ~ normal(0, 1); 
    for (i in 1:2) 
      beta ~ normal(0, 2); 
  } 
'

## fit the model 
fit <- stan(model_code = ex_model_code, chains = 4) 

## extract alpha and beta with 'permuted = TRUE' 
fit_ss <- extract(fit, permuted = TRUE) # fit_ss is a list 
## list fit_ss should have elements with name 'alpha', 'beta', 'lp__'
alpha <- fit_ss$alpha  
beta <- fit_ss$beta 
## or extract alpha by just specifying pars = 'alpha' 
alpha2 <- extract(fit, pars = 'alpha', permuted = TRUE)$alpha 
print(identical(alpha, alpha2)) 

## or extract alpha by excluding beta and lp__
alpha3 <- extract(fit, pars = c('beta', 'lp__'), 
                  permuted = TRUE, include = FALSE)$alpha
print(identical(alpha, alpha3))

## get the samples for alpha[1,1] and beta[2] 
alpha_11 <- alpha[, 1, 1] 
beta_2 <- beta[, 2] 

## extract samples with 'permuted = FALSE' 
fit_ss2 <- extract(fit, permuted = FALSE) # fit_ss2 is an array  

## the dimensions of fit_ss2 should be  
## "# of iterations * # of chains * # of parameters"
dim(fit_ss2) 

## since the third dimension of `fit_ss2` indicates 
## parameters, the names should be 
##  alpha[1,1], alpha[2,1], alpha[1,2], alpha[2,2], 
##  alpha[1,3], alpha[2,3], beta[1], beta[2], and lp__ 
## `lp__` (the log-posterior) is always included 
## in the samples.  
dimnames(fit_ss2) 
}

# Create a stanfit object from reading CSV files of samples (saved in rstan
# package) generated by funtion stan for demonstration purpose from model as follows. 
# 
excode <- '
  transformed data {
    real y[20];
    y[1] <- 0.5796;  y[2]  <- 0.2276;   y[3] <- -0.2959; 
    y[4] <- -0.3742; y[5]  <- 0.3885;   y[6] <- -2.1585;
    y[7] <- 0.7111;  y[8]  <- 1.4424;   y[9] <- 2.5430; 
    y[10] <- 0.3746; y[11] <- 0.4773;   y[12] <- 0.1803; 
    y[13] <- 0.5215; y[14] <- -1.6044;  y[15] <- -0.6703; 
    y[16] <- 0.9459; y[17] <- -0.382;   y[18] <- 0.7619;
    y[19] <- 0.1006; y[20] <- -1.7461;
  }
  parameters {
    real mu;
    real<lower=0, upper=10> sigma;
    vector[2] z[3];
    real<lower=0> alpha;
  } 
  model {
    y ~ normal(mu, sigma);
    for (i in 1:3) 
      z[i] ~ normal(0, 1);
    alpha ~ exponential(2);
  } 
'
# exfit <- stan(model_code = excode, save_dso = FALSE, iter = 200, 
#               sample_file = "rstan_doc_ex.csv")
# 
exfit <- read_stan_csv(dir(system.file('misc', package = 'rstan'),
                       pattern='rstan_doc_ex_[[:digit:]].csv',
                       full.names = TRUE))

ee1 <- extract(exfit, permuted = TRUE)
print(names(ee1))
#> [1] "mu"    "sigma" "z"     "alpha" "lp__" 

for (name in names(ee1)) {
  cat(name, "\n")
  print(dim(ee1[[name]]))
}
#> mu 
#> [1] 400
#> sigma 
#> [1] 400
#> z 
#> [1] 400   3   2
#> alpha 
#> [1] 400
#> lp__ 
#> [1] 400

ee2 <- extract(exfit, permuted = FALSE)
print(dim(ee2))
#> [1] 100   4  10
print(dimnames(ee2))
#> $iterations
#> NULL
#> 
#> $chains
#> [1] "chain:1" "chain:2" "chain:3" "chain:4"
#> 
#> $parameters
#>  [1] "mu"     "sigma"  "z[1,1]" "z[2,1]" "z[3,1]" "z[1,2]" "z[2,2]" "z[3,2]"
#>  [9] "alpha"  "lp__"  
#>