Function reference • stenR

Basic normalization

Functions for basic (ungrouped) normalization in procedural workflow. They allow calculating table objects from raw scale/factor results and normalizing them (or new raw data) into quantiles, Z-scores or some standard score.

StandardScale() print(<StandardScale>) plot(<StandardScale>): Specify standard scale

FrequencyTable() print(<FrequencyTable>) plot(<FrequencyTable>) summary(<FrequencyTable>): Create a FrequencyTable

ScoreTable() print(<ScoreTable>) plot(<ScoreTable>): Create a ScoreTable

SimFrequencyTable(): Generate FrequencyTable using simulated distribution

normalize_score(): Normalize raw scores

normalize_scores_df(): Normalize raw scores for multiple variables

Grouping normalization

Functions for grouped normalization in procedural workflow. There are functions for creating groups/strata based on some conditions, extracting their data from whole dataset, and creating table objects for each of these groups. Similarly to basic normalization, grouped normalization can be then made based on these tables.

GroupConditions() print(<GroupConditions>) as.data.frame(<GroupConditions>): Conditions for observation grouping

GroupAssignment() print(<GroupAssignment>) summary(<GroupAssignment>): Assign to groups based on GroupConditions

intersect_GroupAssignment(): Intersect two GroupAssignment

extract_observations(): Extract observations from data

GroupedFrequencyTable() print(<GroupedFrequencyTable>) summary(<GroupedFrequencyTable>): Create GroupedFrequencyTable

GroupedScoreTable() print(<GroupedScoreTable>): Create GroupedScoreTable

normalize_scores_grouped(): Normalize scores using GroupedFrequencyTables or GroupedScoreTables

plot(<GroupedFrequencyTable>): Gerenic plot of the GroupedFrequencyTable

plot(<GroupedScoreTable>): Gerenic plot of the GroupedScoreTable

ScoringTable - portable object for normalization

FrequencyTable and ScoreTable objects (and their grouped brethren) are easily created from raw data. They aren’t as easile exported or imported, though. ScoringTable allows keeping the standardized scores for one scale in easily transferrable way, exporting them into json or csv objects and also importing them from these files. You can also create file to import yourself.

to_ScoringTable() summary(<ScoringTable>): Create ScoringTable

import_ScoringTable(): Import ScoringTable

export_ScoringTable(): Export ScoringTable

normalize_scores_scoring(): Normalize scores using ScoringTables

S3 classes manipulators

Simple helper functions for manipulation and transforming basic S3 classes.

attach_scales(): Attach additional StandardScale to already created ScoreTable

strip_ScoreTable(): Revert the ScoreTable back to FrequencyTable object.

Item score preprocessors

Data is most often available not as a raw scores of scales, but individual items. These functions are created to make your jouney from items to scales as painless as possible!

ScaleSpec() print(<ScaleSpec>) summary(<ScaleSpec>): Scale Specification object

CombScaleSpec() print(<CombScaleSpec>) summary(<CombScaleSpec>): Combined Scale Specification

sum_items_to_scale(): Sum up discrete raw data

export_ScaleSpec(): Export scale specification

import_ScaleSpec(): Import scale specification

Object Oriented workflow

Complex R6 object mirroring the ungrouped procedural workflow, allowing for much faster and expressive calculations and continuous recalculation of tables based on new data. Object for grouped tables coming soon!

CompScoreTable: R6 class for producing easily re-computable ScoreTable

Varia

Various additional simple functions, default StandardScale objects available within the package and some prepackaged data for learning.

is.GroupConditions() is.GroupAssignment() is.intersected() is.ScaleSpec() is.CombScaleSpec() is.FrequencyTable() is.GroupedFrequencyTable() is.Simulated() is.ScoreTable() is.GroupedScoreTable() is.ScoringTable() is.StandardScale(): Checkers for stenR S3 and R6 classes

default_scales STEN STANINE TANINE TETRONIC WECHSLER_IQ: Default Standard Scales

HEXACO_60: Sample data of HEXACO-60 questionnaire results

SLCS: Sample data of SLCS questionnaire results

IPIP_NEO_300: Sample data of IPIP-NEO-300 questionnaire results