R/dlm_trends.R
dlm_trends.RdSummarize and plot time varying coefficients from the fitted model
dlm_trends(fitted_model)A fitted model object
A list containing the plot and data used
to fit the model. These include plot and b_varying
# \donttest{
set.seed(123)
N = 20
data = data.frame("y" = runif(N),
"cov1" = rnorm(N),
"cov2" = rnorm(N),
"year" = 1:N,
"season" = sample(c("A","B"), size=N, replace=TRUE))
b_1 = cumsum(rnorm(N))
b_2 = cumsum(rnorm(N))
data$y = data$cov1*b_1 + data$cov2*b_2
time_varying = y ~ cov1 + cov2
formula = NULL
fit <- fit_dlm(formula = formula,
time_varying = time_varying,
time = "year",
est_df = FALSE,
family = c("normal"),
data=data, chains = 1, iter = 20)
#>
#> SAMPLING FOR MODEL 'dlm' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 0.002149 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 21.49 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: WARNING: No variance estimation is
#> Chain 1: performed for num_warmup < 20
#> Chain 1:
#> Chain 1: Iteration: 1 / 20 [ 5%] (Warmup)
#> Chain 1: Iteration: 2 / 20 [ 10%] (Warmup)
#> Chain 1: Iteration: 4 / 20 [ 20%] (Warmup)
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#> Chain 1: Iteration: 11 / 20 [ 55%] (Sampling)
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#> Chain 1: Iteration: 14 / 20 [ 70%] (Sampling)
#> Chain 1: Iteration: 16 / 20 [ 80%] (Sampling)
#> Chain 1: Iteration: 18 / 20 [ 90%] (Sampling)
#> Chain 1: Iteration: 20 / 20 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 1.491 seconds (Warm-up)
#> Chain 1: 1.372 seconds (Sampling)
#> Chain 1: 2.863 seconds (Total)
#> Chain 1:
#> Warning: The largest R-hat is NA, indicating chains have not mixed.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#r-hat
#> Warning: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#bulk-ess
#> Warning: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.
#> Running the chains for more iterations may help. See
#> https://mc-stan.org/misc/warnings.html#tail-ess
dlm_trends(fit)
#> Error in dlm_trends(fit): object 'fit' not found
# }