dfa_cv: Apply cross validation to DFA model

In bayesdfa: Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

Apply cross validation to DFA model

Description

Apply cross validation to DFA model

Usage

dfa_cv(
  stanfit,
  cv_method = c("loocv", "lfocv"),
  fold_ids = NULL,
  n_folds = 10,
  estimation = c("sampling", "optimizing", "vb"),
  iter = 2000,
  chains = 4,
  thin = 1,
  ...
)

Arguments

stanfit	A stanfit object, to preserve the model structure from a call to fit_dfa()
cv_method	The method used for cross validation. The options are 'loocv', where time is ignored and each data point is assigned randomly to a fold. The method 'ltocv' is leave time out cross validation, and time slices are iteratively held out out. Finally the method 'lfocv' implements leave future out cross validation to do one-step ahead predictions.
fold_ids	A vector whose length is the same as the number of total data points. Elements are the fold id of each data point. If not all data points are used (e.g. the lfocv or ltocv approach might only use 10 time steps) the value can be something other than a numbber, e.g. NA
n_folds	Number of folds, defaults to 10
estimation	Character string. Should the model be sampled using rstan::sampling() ("sampling",default), rstan::optimizing() ("optimizing"), variational inference rstan::vb() ("vb").
iter	Number of iterations in Stan sampling, defaults to 2000.
chains	Number of chains in Stan sampling, defaults to 4.
thin	Thinning rate in Stan sampling, defaults to 1.
...	Any other arguments to pass to rstan::sampling().

Examples

## Not run: 
set.seed(42)
s <- sim_dfa(num_trends = 1, num_years = 20, num_ts = 3)
obs <- c(s$y_sim[1, ], s$y_sim[2, ], s$y_sim[3, ])
long <- data.frame("obs" = obs, "ts" = sort(rep(1:3, 20)),
"time" = rep(1:20, 3))
m <- fit_dfa(y = long, data_shape = "long", estimation="none")
# random folds
fit_cv <- dfa_cv(m, cv_method = "loocv", n_folds = 5, iter = 50,
chains = 1, estimation="sampling")

# folds can also be passed in
fold_ids <- sample(1:5, size = nrow(long), replace = TRUE)
m <- fit_dfa(y = long, data_shape = "long", estimation="none")
fit_cv <- dfa_cv(m, cv_method = "loocv", n_folds = 5, iter = 50, chains = 1,
fold_ids = fold_ids, estimation="sampling")

# do an example of leave-time-out cross validation where years are dropped
fold_ids <- long$time
m <- fit_dfa(y = long, data_shape = "long", estimation="none")
fit_cv <- dfa_cv(m, cv_method = "loocv", iter = 100, chains = 1,
fold_ids = fold_ids)

# example with covariates and long format data
obs_covar <- expand.grid("time" = 1:20, "timeseries" = 1:3,
"covariate" = 1:2)
obs_covar$value <- rnorm(nrow(obs_covar), 0, 0.1)
obs <- c(s$y_sim[1, ], s$y_sim[2, ], s$y_sim[3, ])
m <- fit_dfa(y = long, obs_covar = obs_covar,
data_shape = "long", estimation="none")
fit_cv <- dfa_cv(m, cv_method = "loocv", n_folds = 5,
iter = 50, chains = 1, estimation="sampling")

## End(Not run)

bayesdfa documentation built on Oct. 11, 2023, 5:14 p.m.