another small bug

R/fit_basset.R

@@ -92,11 +92,6 @@ basset <- function(Y=NULL, X, upsilon=NULL, Theta=NULL, Gamma=NULL, Xi=NULL, lin
       stop("Theta and Gamma must be of the same length.")
     }
 
-    ## setting newdata <- X if newdata is null
-    if(is.null(newdata)){
-      newdata <- X
-    }
-
     ## evaluating theta and gamma
     ## theta
     theta_eval <- function(Theta, X, linear){
@@ -139,6 +134,7 @@ basset <- function(Y=NULL, X, upsilon=NULL, Theta=NULL, Gamma=NULL, Xi=NULL, lin
     Gamma_comb <- Reduce('+', Gamma_trans)
 
     ## fitting the joint model
+    ## newdata auto handled by pibble
     collapse_samps <- pibble(Y, X=diag(ncol(X)), upsilon, Theta_comb, Gamma_comb, Xi, init, pars, newdata = newdata, ...)
 
     ## fitting uncollapse using the joint samples
@@ -176,6 +172,11 @@ basset <- function(Y=NULL, X, upsilon=NULL, Theta=NULL, Gamma=NULL, Xi=NULL, lin
       }
     } 
 
+    ## setting newdata <- X if newdata is null
+    if(is.null(newdata)){
+      newdata <- X
+    }
+
     for(i in 1:num.comp){
       ## if num.comp == 1 --> return the samples from Lambda above
       if(num.comp == 1){

vignettes/non-linear-models.Rmd

@@ -145,30 +145,30 @@ predicted <- predict(fit.clr, X_predict, jitter=1)
 Now I am going to create a visual that shows the observed data in CLR coordinates 
 (to do that I will need to add a pseudo-count) along with the smoothed estimates. 
 ```{r fig.height=5, fig.width=7}
-# family_names <- as(mallard_family$tax_table$Family, "vector")
-# Y_clr_tidy <- clr_array(Y+0.65, parts = 1) %>% 
-#   gather_array(mean, coord, sample) %>% 
-#   mutate(time = X[1,sample], 
-#          coord = paste0("CLR(", family_names[coord],")"))
-# 
-# predicted_tidy <- gather_array(predicted, val, coord, sample, iter) %>% 
-#   mutate(time = X_predict[1,sample]) %>% 
-#   filter(!is.na(val)) %>% 
-#   group_by(time, coord) %>% 
-#   summarise_posterior(val, na.rm=TRUE) %>% 
-#   ungroup() %>% 
-#   mutate(coord = paste0("CLR(", family_names[coord],")"))
-# 
-# ggplot(predicted_tidy, aes(x = time, y=mean)) +
-#   geom_ribbon(aes(ymin=p2.5, ymax=p97.5), fill="darkgrey", alpha=0.5) +
-#   geom_ribbon(aes(ymin=p25, ymax=p75), fill="darkgrey", alpha=0.9)+
-#   geom_line(color="blue") +
-#   geom_point(data = Y_clr_tidy, alpha=0.5) +
-#   facet_wrap(~coord, scales="free_y") +
-#   theme_minimal()+
-#   theme(axis.title.y = element_blank(), 
-#         axis.title.x = element_blank(), 
-#         axis.text.x = element_text(angle=45))
+family_names <- as(mallard_family$tax_table$Family, "vector")
+Y_clr_tidy <- clr_array(Y+0.65, parts = 1) %>%
+  gather_array(mean, coord, sample) %>%
+  mutate(time = X[1,sample],
+         coord = paste0("CLR(", family_names[coord],")"))
+
+predicted_tidy <- gather_array(predicted, val, coord, sample, iter) %>%
+  mutate(time = X_predict[1,sample]) %>%
+  filter(!is.na(val)) %>%
+  group_by(time, coord) %>%
+  summarise_posterior(val, na.rm=TRUE) %>%
+  ungroup() %>%
+  mutate(coord = paste0("CLR(", family_names[coord],")"))
+
+ggplot(predicted_tidy, aes(x = time, y=mean)) +
+  geom_ribbon(aes(ymin=p2.5, ymax=p97.5), fill="darkgrey", alpha=0.5) +
+  geom_ribbon(aes(ymin=p25, ymax=p75), fill="darkgrey", alpha=0.9)+
+  geom_line(color="blue") +
+  geom_point(data = Y_clr_tidy, alpha=0.5) +
+  facet_wrap(~coord, scales="free_y") +
+  theme_minimal()+
+  theme(axis.title.y = element_blank(),
+        axis.title.x = element_blank(),
+        axis.text.x = element_text(angle=45))
 
 ```