Exposed minCharacterizationMean param to createAggregateCovariateSett…

…ings.

Updated examples to demonstrate createAggregateCovariateSettings$minCharacterizationMean param.
Updated package version to 0.2.0.
Updated News.md with changes.
Regenerated site with pkgdown.

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: Characterization
 Type: Package
 Title: Characterizations of Cohorts
-Version: 0.1.5
+Version: 0.2.0
 Date: 2024-04-03
 Authors@R: c(
 	person("Jenna", "Reps", , "[email protected]", role = c("aut", "cre")),

NEWS.md

@@ -1,3 +1,8 @@
+Characterization 0.2.0
+======================
+Updated dependency to FeatureExtraction (>= 3.5.0) to support minCharacterizationMean paramater.
+
+
 Characterization 0.1.5
 ======================
 Changed export to csv approach to use batch export from SQLite (#41)

R/AggregateCovariates.R

@@ -21,6 +21,9 @@
 #' @param minPriorObservation The minimum time in the database a patient in the target cohorts must be observed prior to index
 #' @template timeAtRisk
 #' @param covariateSettings   An object created using \code{FeatureExtraction::createCovariateSettings}
+#' @param minCharacterizationMean The minimum mean value for characterization output. Values below this will be cut off from output. This
+#'                                will help reduce the file size of the characterization output, but will remove information
+#'                                on covariates that have very low values. The default is 0.
 #'
 #' @return
 #' A list with the settings
@@ -34,7 +37,8 @@ createAggregateCovariateSettings <- function(
     startAnchor = "cohort start",
     riskWindowEnd = 365,
     endAnchor = "cohort start",
-    covariateSettings) {
+    covariateSettings,
+    minCharacterizationMean = 0) {
   errorMessages <- checkmate::makeAssertCollection()
   # check targetIds is a vector of int/double
   .checkCohortIds(
@@ -81,7 +85,8 @@ createAggregateCovariateSettings <- function(
     startAnchor = startAnchor,
     riskWindowEnd = riskWindowEnd,
     endAnchor = endAnchor,
-    covariateSettings = covariateSettings
+    covariateSettings = covariateSettings,
+    minCharacterizationMean = minCharacterizationMean
   )
 
   class(result) <- "aggregateCovariateSettings"
@@ -163,7 +168,8 @@ computeAggregateCovariateAnalyses <- function(
     cohortId = -1,
     covariateSettings = aggregateCovariateSettings$covariateSettings,
     cdmVersion = cdmVersion,
-    aggregated = T
+    aggregated = T,
+    minCharacterizationMean = aggregateCovariateSettings$minCharacterizationMean
   )
   # adding counts as a new table
   result$cohortCounts <- counts

README.md

@@ -62,7 +62,7 @@ targetIds <- c(1,2,4)
       useDemographicsAge = T,
       useDemographicsRace = T
     )
-    )
+  )
 
   aggregateCovariateSettings2 <- createAggregateCovariateSettings(
     targetIds = targetIds,