support the extended population

R/imprr_weights.R

@@ -20,6 +20,9 @@
 #' looks for `app_identity_1`, `app_identity_2`, `app_identity_3`, and so on,
 #' with an underbar separator followed by numbers.
 #' @param anc_correct Indicator for passing the anchor question.
+#' @param population Choice of the target population out of
+#' non-random respondents (default) or all respondents.
+#' @param assumption Choice of the identifying assumption if `population` is set to all
 #' @param seed Seed for \code{set.seed} for reproducibility.
 #' @param weight A vector of weights. Defaults to NULL.
 #' @param ranking The name of the column that will store the full ranking
@@ -35,6 +38,8 @@ imprr_weights <- function(data,
                           J = NULL,
                           main_q,
                           anc_correct,
+                          population = "non-random",
+                          assumption = "contaminated",
                           seed = 123456,
                           weight = NULL,
                           ranking = "ranking") {
@@ -59,6 +64,10 @@ imprr_weights <- function(data,
     weight <- rep(1, N)
   }
 
+  if (population == "all" & assumption == "uniform"){
+    data[[anc_correct]] <- rep(1, N) # get naive estimate for theta
+  }
+
   # Check the validity of the input arguments ==================================
   ## Main ranking only
 

vignettes/v2-bias-correction.Rmd

@@ -88,7 +88,7 @@ out_direct_uniform <- imprr_direct(
 Similarly, the contaminated sampling assumption can be specified as follows:
 
 ```{r}
-# Perform bias correction for the entire population with the uniform preference assumption
+# Perform bias correction for the entire population with the contaminated sampling assumption
 
 out_direct_contaminated <- imprr_direct(
   data = identity,
@@ -166,6 +166,36 @@ out_weights <- imprr_weights(
 )
 ```
 
+
+By default, `imprr_weights` assumes that the target population is a set of non-random respondents. When researchers wish to study the entire population as a target group, additional arguments must be specified, including `population` and `assumption`. For example, the uniform preference assumption can be specified as follows:
+
+```{r}
+# Perform bias correction with the uniform preference assumption
+out_weights_uniform <- imprr_weights(
+  data = identity,
+  J = 4,
+  main_q = "app_identity",
+  anc_correct = "anc_correct_identity",
+  population = "all",
+  assumption = "uniform"
+)
+```
+
+Similarly, the contaminated sampling assumption can be specified as follows:
+
+```{r}
+# Perform bias correction with the uniform preference assumption
+out_weights_contaminated <- imprr_weights(
+  data = identity,
+  J = 4,
+  main_q = "app_identity",
+  anc_correct = "anc_correct_identity",
+  population = "all",
+  assumption = "contaminated"
+)
+```
+
+
 ### Results: Estimated Weights
 
 The output of `imprr_weights` contains the set of weights for all possible ranking profiles with `J` items. For example, when `J = 4`, the set has `{1234, 1243, ..., 4321}` and each profile now has an estimated weight.