diff --git a/DESCRIPTION b/DESCRIPTION index 5cfb4fd..ebf7193 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: dgo Title: Dynamic Estimation of Group-Level Opinion -Version: 0.2.12 -Date: 2017-11-13 +Version: 0.2.13 +Date: 2017-12-01 Description: Fit dynamic group-level IRT and MRP models from individual or aggregated item response data. This package handles common preprocessing tasks and extends functions for inspecting results, poststratification, and diff --git a/NEWS.md b/NEWS.md index 9431966..9116180 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,3 +1,9 @@ +## dgo 0.2.13 + +* Fix an issue introduced in v0.2.12 that led to an unexpected error in + `shape()` when 1) at least two `group_names` are specified in an order other + than alphabetic and 2) geographic `modifier_data` is used. + ## dgo 0.2.12 * Allow modeling of unobserved groups using aggregated data. The previous @@ -5,8 +11,8 @@ don't represent item responses.) Preserving them has the effect that unobserved groups, defined partially or entirely by the values of the grouping variables in zero-trial rows in `aggregate_data`, can be included in a model. -* Fix an unexpected error when 1) `aggregate_data` is used without `item_data`, - 2) no demographic groups are specified via `group_names`, and 3) geographic +* Fix an unexpected error when 1) `aggregate_data` is used without `item_data`, 2) + no demographic groups are specified via `group_names`, and 3) geographic `modifier_data` is used. * Fix the check for missing `modifier_data`. Geographic `modifier_data` must cover all combinations of the geo and time variables in the item response data diff --git a/R/shape_hierarchical.r b/R/shape_hierarchical.r index 0cdb629..b7522ef 100644 --- a/R/shape_hierarchical.r +++ b/R/shape_hierarchical.r @@ -42,8 +42,7 @@ zero_unmodeled <- function(hierarchical, modifier_names, group_grid_t, ctrl) { paste0(x, unique(group_grid_t[[x]]))[-1] })) unmodeled_frame <- expand.grid(c(list(unmodeled_param_levels, - ctrl@time_filter), rep(list(0L), length(modifier_names))), - stringsAsFactors = FALSE) + ctrl@time_filter), rep(list(0L), length(modifier_names)))) unmodeled_frame <- setNames(unmodeled_frame, c(ctrl@geo_name, ctrl@time_name, modifier_names)) data.table::setDT(unmodeled_frame, key = c(ctrl@geo_name, ctrl@time_name)) diff --git a/cran-comments.md b/cran-comments.md index ea150db..aad72e2 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -7,5 +7,7 @@ ## R CMD check results -There were no ERRORs, WARNINGs, or NOTEs. +There were no ERRORs or WARNINGs. There was one NOTE. (Days since last update: +1). This update fixes a bug introduced in the most recent version and undetected +until today. diff --git a/data/toy_dgirt_in.rda b/data/toy_dgirt_in.rda index b045321..a14352a 100644 Binary files a/data/toy_dgirt_in.rda and b/data/toy_dgirt_in.rda differ diff --git a/data/toy_dgirtfit.rda b/data/toy_dgirtfit.rda index a2555ea..ec01515 100644 Binary files a/data/toy_dgirtfit.rda and b/data/toy_dgirtfit.rda differ diff --git a/docs/articles/index.html b/docs/articles/index.html index 97491fd..fcecaf9 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -82,7 +82,7 @@
-

Articles version 0.2.12

+

Articles version 0.2.13

diff --git a/docs/reference/dgirt_fit-class.html b/docs/reference/dgirt_fit-class.html index 82d4e00..2878abb 100644 --- a/docs/reference/dgirt_fit-class.html +++ b/docs/reference/dgirt_fit-class.html @@ -114,48 +114,48 @@

Examp 
data(toy_dgirtfit)
 # summarize the fitted results
 summary(toy_dgirtfit, pars = 'xi')
#> dgirt samples from 4 chains of 400 iterations, 200 warmup, thinned every 1 
-#>   Drawn Mon Nov 13 18:15:19 2017 
-#>   Package version 0.2.12 
+#>   Drawn Fri Dec  1 11:54:14 2017 
+#>   Package version 0.2.13 
 #>   Model version 2017_01_04 
 #>   43 parameters; 12 theta_bars (year state race3)
 #>   2 periods 2009 to 2010 
 #> 
 #> n_eff
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>   17.37  156.60  255.35  302.78  364.78  800.00 
#> 
+#>   94.08  149.33  174.47  270.79  363.04  800.00 
#> 
 #> Rhat
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
-#>  0.9958  1.0058  1.0128  1.0161  1.0208  1.1404 
#> 
+#>  0.9971  1.0082  1.0225  1.0202  1.0291  1.0520 
#> 
 #> Elapsed time
#>    chain warmup sample total
-#> 1:     1     3S     3S    6S
-#> 2:     2     4S     3S    7S
-#> 3:     3     4S     3S    7S
-#> 4:     4     4S     4S    8S

+#> 1:     1     5S     3S    8S
+#> 2:     2     5S     5S   10S
+#> 3:     3     5S     6S   11S
+#> 4:     4     5S     3S    8S

 # get posterior means with a convenience function
 get_posterior_mean(toy_dgirtfit, pars = 'theta_bar')
#>         param state race3 year        mean
-#>  1: theta_bar    SC black 2009  2.35662560
-#>  2: theta_bar    SC black 2010  1.22256665
-#>  3: theta_bar    SC other 2009 -1.36379358
-#>  4: theta_bar    SC other 2010  0.04227331
-#>  5: theta_bar    SC white 2009 -1.63196374
-#>  6: theta_bar    SC white 2010 -1.47489402
-#>  7: theta_bar    VA black 2009  1.90274077
-#>  8: theta_bar    VA black 2010  1.52339960
-#>  9: theta_bar    VA other 2009 -0.95920551
-#> 10: theta_bar    VA other 2010  0.28518022
-#> 11: theta_bar    VA white 2009 -0.92697350
-#> 12: theta_bar    VA white 2010 -0.83419892

+#>  1: theta_bar    SC black 2009  2.24053065
+#>  2: theta_bar    SC black 2010  1.27549292
+#>  3: theta_bar    SC other 2009 -1.40019273
+#>  4: theta_bar    SC other 2010  0.04084414
+#>  5: theta_bar    SC white 2009 -1.67983508
+#>  6: theta_bar    SC white 2010 -1.42533238
+#>  7: theta_bar    VA black 2009  2.01710000
+#>  8: theta_bar    VA black 2010  1.48835032
+#>  9: theta_bar    VA other 2009 -1.02559194
+#> 10: theta_bar    VA other 2010  0.30256578
+#> 11: theta_bar    VA white 2009 -0.93711250
+#> 12: theta_bar    VA white 2010 -0.82327290

 # generally apply functions to posterior samples after warmup; n.b.
 # `as.array` is iterations x chains x parameters so `MARGIN = 3` applies
 # `FUN` over iterations and chains
-apply(as.array(toy_dgirtfit, pars = 'xi'), 3, mean)
#>      xi[1]      xi[2] 
-#> 1.35546402 0.04828843 

+apply(as.array(toy_dgirtfit, pars = 'xi'), 3, mean)
#>     xi[1]     xi[2] 
+#> 1.3134883 0.1604207 

 # access the posterior samples
-head(as.data.frame(toy_dgirtfit, pars = 'theta_bar'))
#>        param state race3 year iteration     value
-#> 1: theta_bar    SC black 2009         1 0.7183680
-#> 2: theta_bar    SC black 2009         2 0.7609363
-#> 3: theta_bar    SC black 2009         3 0.7942637
-#> 4: theta_bar    SC black 2009         4 1.4685053
-#> 5: theta_bar    SC black 2009         5 1.1297978
-#> 6: theta_bar    SC black 2009         6 0.6581956
+head(as.data.frame(toy_dgirtfit, pars = 'theta_bar'))

#> param state race3 year iteration value +#> 1: theta_bar SC black 2009 1 1.813383 +#> 2: theta_bar SC black 2009 2 2.187389 +#> 3: theta_bar SC black 2009 3 4.876919 +#> 4: theta_bar SC black 2009 4 1.638984 +#> 5: theta_bar SC black 2009 5 3.594305 +#> 6: theta_bar SC black 2009 6 4.129328