Merge pull request #46 from sfcheung/devel

Update to 0.1.3.5

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: modelbpp
 Title: Model BIC Posterior Probability
-Version: 0.1.3.4
+Version: 0.1.3.5
 Authors@R:
     c(person(given = "Shu Fai",
              family = "Cheung",

NEWS.md

@@ -1,4 +1,4 @@
-# modelbpp 0.1.3.4
+# modelbpp 0.1.3.5
 
 ## New Features
 
@@ -9,15 +9,15 @@
   covariances involving a predictor
   and an outcome variable (including
   those linked by indirect paths).
-  Default values are `FALSE` for now
-  to maintain backward compatibility but
-  will be changed to `TRUE` in the
-  future. (0.1.3.2)
+  Default values has been changed
+  to `TRUE` since 0.1.3.5. To
+  reproduce results from previous version,
+  set them to `FALSE`. (0.1.3.2, 0.1.3.5)
 
 - Added `min_bpp_labelled` to
   `model_graph()`, to hide the labels
   of models with small BPPs.
-  (0.1.3.4)
+  (0.1.3.5)
 
 ## Bug Fixes
 
@@ -26,6 +26,10 @@
   not be recognized as interchangeable.
   (0.1.3.1)
 
+- Fixed a bug in `must_not_drop` and
+  `must_drop` of `get_drop()`. They
+  should work properly now. (0.1.3.5)
+
 - Fixed a bug in `model_graph()`.
   Short names should now be properly
   constructed. (0.1.3.3)

R/get_drop.R

@@ -128,11 +128,11 @@ get_drop <- function(sem_out,
     # User specified parameters
     if (!is.null(must_drop)) {
         id_must_drop <- syntax_to_id(must_drop, ptable = pt)
-        id_to_drop <- union(id_to_drop, id_must_drop)
+        id_to_drop[id_must_drop] <- TRUE
       }
     if (!is.null(must_not_drop)) {
         id_must_not_drop <- syntax_to_id(must_not_drop, ptable = pt)
-        id_to_drop <- setdiff(id_to_drop, id_must_not_drop)
+        id_to_drop[id_must_not_drop] <- FALSE
       }
     if (any(id_to_drop)) {
         # Determine the sets of changes

R/model_set.R

@@ -104,12 +104,14 @@
 #' loop. For example, if there is
 #' path from `x` through `m` to `y`,
 #' then the path `x ~ y` will create
-#' a feedback loop. Default is `FALSE`
-#' for now, to maintain backward
-#' compatibility.
-#' Do not rely on the default value
-#' because it will be changed to `TRUE`
-#' in a future major version.
+#' a feedback loop.  Default has been
+#' changed to
+#' `TRUE` since Version 0.1.3.5 because
+#' feedback loops are usually
+#' not included except when theoretically
+#' justified. To reproduce results
+#' based on previous version, set this
+#' argument to `FALSE`.
 #'
 #' @param exclude_xy_cov Exclude
 #' covariance between two variables,
@@ -120,11 +122,12 @@
 #' which denotes the covariance between
 #' `x` and the error term of `y`, will
 #' be excluded if this argument is
-#' `TRUE`. Default is `FALSE` for now,
-#' to maintain backward compatibility.
-#' Do not rely on the default value
-#' because it will be changed to `TRUE`
-#' in a future major version.
+#' `TRUE`. Default has been changed to
+#' `TRUE` since Version 0.1.3.5 because
+#' these covariances rarely are
+#' interpretable. To reproduce results
+#' based on previous version, set this
+#' argument to `FALSE`.
 #'
 #' @param must_drop A character vector
 #' of parameters, named in
@@ -319,8 +322,8 @@ model_set <- function(sem_out,
                       must_not_drop = NULL,
                       remove_constraints = TRUE,
                       exclude_error_cov = TRUE,
-                      exclude_feedback = FALSE,
-                      exclude_xy_cov = FALSE,
+                      exclude_feedback = TRUE,
+                      exclude_xy_cov = TRUE,
                       df_change_add = 1,
                       df_change_drop = 1,
                       remove_duplicated = TRUE,

R/print.model_set.R

@@ -300,25 +300,30 @@ print.model_set <- function(x,
         cat("- Cumulative: Cumulative BIC posterior probability.\n")
       }
     if (gt_max_models) {
-        x <- paste(fit_n,
+        tmp <- paste(fit_n,
                    "models were fitted but",
                    max_models,
                    "were printed. Call print() and",
                    "set 'max_models' to a larger number",
                    "to print more models, or set it to",
                    "NA to print all models.")
-        catwrap(x, initial = "- ", exdent = 2)
+        catwrap(tmp, initial = "- ", exdent = 2)
       }
     if (models_fitted &&
         (k_converged != fit_n) &&
         any(is.na(postprob_tmp))) {
-        x <- "BPP and/or prior not computed because one or more models not converged."
-        catwrap(x, initial = "- ", exdent = 2)
+        tmp <- "BPP and/or prior not computed because one or more models not converged."
+        catwrap(tmp, initial = "- ", exdent = 2)
       }
     if (models_fitted &&
         (k_post_check != fit_n)) {
-        x <- "Interpret with caution. One or more models failed lavaan's post.check."
-        catwrap(x, initial = "- ", exdent = 2)
+        tmp <- "Interpret with caution. One or more models failed lavaan's post.check."
+        catwrap(tmp, initial = "- ", exdent = 2)
       }
+    tmp <- paste0("Since Version 0.1.3.5, the default values of ",
+                  "exclude_feedback and exclude_xy_cov changed to TRUE. ",
+                  "Set them to FALSE to reproduce results from previous versions.")
+    catwrap(tmp, initial = "- ", exdent = 2)
+
     invisible(x)
   }

README.md

@@ -10,7 +10,7 @@
 
 # modelbpp: Model BIC Posterior Probability <img src="man/figures/logo.png" align="right" />
 
-(Version 0.1.3.4 updated on 2024-04-06, [release history](https://sfcheung.github.io/modelbpp/news/index.html))
+(Version 0.1.3.5 updated on 2024-04-07, [release history](https://sfcheung.github.io/modelbpp/news/index.html))
 
 This package is for assessing model uncertainty in structural
 equation modeling (SEM) by the BIC posterior

vignettes/modelbpp.Rmd

@@ -138,7 +138,12 @@ out1_bpp_2 <- sort(out1_bpp, decreasing = TRUE)[2]
 ```
 
 The total number of models examined,
-including the original model, is 9.
+including the original model, is `r  length(out1$models)`.
+
+(Note: The total number of models
+was 9 in previous version. Please
+refer to the Note in the printout for
+the changes.)
 
 The BIC posterior probabilities
 (BPPs) suggest that
@@ -186,17 +191,17 @@ equally probable before having data
 prior probabilities). A researcher
 fits the original model because
 
-  - its
-    prior probability is higher than other
-    models, at least other neighboring
-    models (otherwise, it is not worthy
-    of collecting data assess thi original
-    model), but
+- its
+  prior probability is higher than other
+  models, at least other neighboring
+  models (otherwise, it is not worthy
+  of collecting data assess thi original
+  model), but
 
-  - the prior probability
-    is not so high to eliminate the need
-    for collecting data to see how much it is
-    supported by data.
+- the prior probability
+  is not so high to eliminate the need
+  for collecting data to see how much it is
+  supported by data.
 
 Suppose we decide that the prior probability
 of the original model is .50: probable, but
@@ -258,15 +263,15 @@ out1_df2 <- model_set(fit1,
 ```
 
 This is the printout. By default, when there
-are more than 10 models, only the top 10
+are more than 20 models, only the top 20
 models on BPP will be printed:
 
 ```{r}
 out1_df2
 ```
 
 The number of models examined, including
-the original model, is 18.
+the original model, is `r  length(out1_df2$models)`.
 
 This is the output of `model_graph()`:
 
@@ -289,32 +294,32 @@ specify parameters that must be excluded
 from the list to be added (`must_not_add`),
 or must not be dropped (`must_not_drop`).
 
-For example, it may not make sense To
-add the error covariance between `m1`
-and `y` (`m1~~y`). We can exclude it
+For example, suppose it is well
+established that `m1~x` exists and should
+not be dropped, we can exclude it
 when calling `model_set()`
 
 ```{r results = FALSE}
-out1_no_cov <- model_set(fit1,
-                         must_not_add = "m1~~y")
+out1_no_m1_x <- model_set(fit1,
+                          must_not_drop = "m1~x")
 ```
 
 This is the output:
 
 ```{r}
-out1_no_cov
+out1_no_m1_x
 ```
 
-The number of models reduced to 8.
+The number of models reduced to `r  length(out1_df2$models)`.
 
 This is the plot:
 
-```{r graph1_no_cov, ig.height = 8, fig.width = 8, eval = FALSE}
-graph1_no_cov <- model_graph(out1_no_cov)
-plot(graph1_no_cov)
+```{r out1_no_m1_x, ig.height = 8, fig.width = 8, eval = FALSE}
+out1_no_m1_x <- model_graph(out1_no_m1_x)
+plot(out1_no_m1_x)
 ```
 
-![](graph1_no_cov-1.png)
+![](out1_no_m1_x-1.png)
 
 ## Models With Constraints