Description Usage Arguments Value Note Examples
Runs the MCMC for the functional dynamic linear model (with no predictor variables). The dynamic factors are modeled using an AR(1) model.
1 2 3 |
Y |
the |
tau |
the |
K |
the number of factors; if NULL, use SVD-based proportion of variability explained |
nsave |
number of MCMC iterations to record |
nburn |
number of MCMC iterations to discard (burin-in) |
nskip |
number of MCMC iterations to skip between saving iterations, i.e., save every (nskip + 1)th draw |
mcmc_params |
named list of parameters for which we store the MCMC output; must be one or more of
|
use_obs_SV |
logical; when TRUE, include a stochastic volatility model for the observation error variance |
X_Tp1 |
the |
includeBasisInnovation |
logical; when TRUE, include an iid basis coefficient term for residual correlation (i.e., the idiosyncratic error term for a factor model on the full basis matrix) |
Con_mat |
a |
A named list of the nsave
MCMC samples for the parameters named in mcmc_params
If Tm
is large, then storing all posterior samples for Yhat
or Ypred
, which are nsave x T x m
, may be inefficient
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | # Simulate some data (w/o predictors):
sim_data = simulate_dfosr(T = 100, m = 20,
p_0 = 0, p_1 = 0)
Y = sim_data$Y; tau = sim_data$tau
T = nrow(Y); m = ncol(Y); # Dimensions
# Run the MCMC w/ K = 6:
out = fdlm(Y = Y, tau = tau, K = 6,
mcmc_params = list("beta", "fk", "Yhat", "Ypred"))
# Plot the factors:
plot_factors(post_beta = out$beta)
# Plot the loading curves:
plot_flc(post_fk = out$fk, tau = tau)
# Plot a fitted value w/ posterior predictive credible intervals:
i = sample(1:T, 1); # Select a random time i
plot_fitted(y = Y[i,],
mu = colMeans(out$Yhat)[i,],
postY = out$Ypred[,i,],
y_true = sim_data$Y_true[i,],
t01 = tau)
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