# find_dfa_trends: Find the best number of trends according to LOOIC In bayesdfa: Bayesian Dynamic Factor Analysis (DFA) with 'Stan'

## Description

Fit a DFA with different number of trends and return the leave one out (LOO) value as calculated by the loo package.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```find_dfa_trends( y = y, kmin = 1, kmax = 5, iter = 2000, thin = 1, compare_normal = FALSE, convergence_threshold = 1.05, variance = c("equal", "unequal"), ... ) ```

## Arguments

 `y` A matrix of data to fit. Columns represent time element. `kmin` Minimum number of trends, defaults to 1. `kmax` Maximum number of trends, defaults to 5. `iter` Iterations when sampling from each Stan model, defaults to 2000. `thin` Thinning rate when sampling from each Stan model, defaults to 1. `compare_normal` If `TRUE`, does model selection comparison of Normal vs. Student-t errors `convergence_threshold` The maximum allowed value of Rhat to determine convergence of parameters `variance` Vector of variance arguments for searching over large groups of models. Can be either or both of ("equal","unequal") `...` Other arguments to pass to `fit_dfa()`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```set.seed(42) s <- sim_dfa(num_trends = 2, num_years = 20, num_ts = 3) # only 1 chain and 180 iterations used so example runs quickly: m <- find_dfa_trends( y = s\$y_sim, iter = 50, kmin = 1, kmax = 2, chains = 1, compare_normal = FALSE, variance = "equal", convergence_threshold = 1.1, control = list(adapt_delta = 0.95, max_treedepth = 20) ) m\$summary m\$best_model ```

bayesdfa documentation built on May 29, 2021, 1:06 a.m.