R: Extract samples from a fitted Stan model

extract {rstan} | R Documentation

## Extract samples from a fitted Stan model

### Description

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

### Usage

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

### 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).

### Methods

extract

  `signature(object = "stanfit")`

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

### See Also

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

### Examples

    ## 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)

    ## End(Not run)

    # 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))

    for (name in names(ee1)) {
      cat(name, "\n")
      print(dim(ee1[[name]]))
    }

    ee2 <- extract(exfit, permuted = FALSE)
    print(dim(ee2))
    print(dimnames(ee2))

[Package rstan version 2.21.5 Index]