Change the correlated_rw default from TRUE to FALSE

- this is a little buggy and seems to have challenges converging

Author: ericward-noaa

R/fitting.R

@@ -8,7 +8,7 @@
 #' @param time String describing the name of the variable corresponding to time, defaults to "year"
 #' @param est_df Whether or not to estimate deviations of B as Student - t with estimated degrees of freedom, defaults to `FALSE`
 #' @param family, The name of the family used for the response; can be one of "normal","binomial","possion","nbinom2","gamma","lognormal"
-#' @param correlated_rw, Whether to estimate time-varying parameters as correlated random walk, defaults to TRUE
+#' @param correlated_rw, Whether to estimate time-varying parameters as correlated random walk, defaults to FALSE
 #' @param data The data frame including response and covariates for all model components
 #' @param chains Number of mcmc chains, defaults to 3
 #' @param iter Number of mcmc iterations, defaults to 2000
@@ -75,7 +75,7 @@ fit_dlm <- function(formula = NULL,
                     time = "year",
                     est_df = FALSE,
                     family = c("normal", "binomial", "poisson", "nbinom2", "gamma", "lognormal"),
-                    correlated_rw = TRUE,
+                    correlated_rw = FALSE,
                     data,
                     chains = 3,
                     iter = 2000,

inst/stan/dlm.stan

@@ -97,15 +97,15 @@ transformed parameters {
   }
 }
 model {
-  sigma ~ cauchy(0, 5); // prior for sigma
+  sigma ~ student_t(3,0,1); // prior for sigma
   Lcorr ~ lkj_corr_cholesky(2.0); // prior for cholesky factor of a correlation matrix
-  phi ~ student_t(3,0,2); // obseervation variance
+  phi ~ student_t(3,0,1); // obseervation variance
   b_fixed ~ normal(0,1);
-  nu ~ student_t(3,0,2);
+  nu ~ student_t(3,0,1);
 
   missing_fixed ~ normal(0,1); // estimates of missing Xs for fixed model
   missing_varying ~ normal(0,1); // estimates of missing Xs for time varying model
-  b_devs0 ~ normal(0,1); // initial values of B at time t
+  //b_devs0 ~ normal(0,1); // initial values of B at time t
   if(est_df == 0) {
     if(correlated_rw == 1) {
       for(t in 1:(nT-1)) {