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)

object | An object of class |
---|---|

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 |

permuted | A logical scalar indicating whether the draws
after the |

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

include | A logical scalar indicating whether the parameters
named in |

- extract
`signature(object = "stanfit")`

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

`stanfit`

.
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).

S4 class `stanfit`

, `as.array.stanfit`

, and
`monitor`

# NOT RUN { 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] 400ee2 <- extract(exfit, permuted = FALSE) print(dim(ee2))#> [1] 100 4 10print(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__" #>