| Title: | Conceptual Properties Norming Studies as Parameter Estimation |
|---|---|
| Description: | Implementation of conceptual properties norming studies, including estimates of CPNs parameters with their corresponding variances and estimates for the sampling process, and a sampling property function based on a modified empirical distribution from the original data. |
| Authors: | Sebastian Moreno [aut, cre], Enrique Canessa [ths], Sergio Chaigneau [ths], Rodrigo Lagos [ths], Felipe Medina [ths] |
| Maintainer: | Sebastian Moreno <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.1.0 |
| Built: | 2024-11-10 04:59:28 UTC |
| Source: | https://github.com/cran/CPNCoverageAnalysis |
A real dataset from spanish speakers, translated using automatic algorithms
data_paperdata_paper
A data frame with 4364 rows and 3 variables:
Id of the people
Concept being described by the person ID
A property mentioned for the corresponding concept for the person ID
A toy dataset containing the description of ten people, over 3 concept, with multiple properties.
data_testdata_test
A data frame with 65 rows and 3 variables:
Id of the people
Concept being described by the person ID
A property mentioned for the corresponding concept for the person ID
Estimate the number of people needed and expected number of unique properties for a determined coverage based on the estimated norms
estimate_participant(est_norms, target_cover)estimate_participant(est_norms, target_cover)
est_norms |
a data frame with the estimated norms (generated by generateNorms) |
target_cover |
float between 0 and 1, corresponding to coverage (the fraction of the total incidence probabilities of the reported properties that are in the reference sample) |
a vector with the extra number of participant to achieve the especific coverage, and the estimate of the number of unique properties listed by the new amount of suggested people
estimated_norms=generate_norms(data_test) estimate_participant(estimated_norms,0.8)estimated_norms=generate_norms(data_test) estimate_participant(estimated_norms,0.8)
Calculate all the norms from a Conceptual properties
generate_norms(orig_data)generate_norms(orig_data)
orig_data |
a data frame of size nx3 (id, concept, property) |
a data frame with all the estimations
generate_norms(data_test)generate_norms(data_test)
Simulate properties based on the empricial distribution of the original data and new words with frequency one
property_simulator(orig_data, new_words, number_subjects)property_simulator(orig_data, new_words, number_subjects)
orig_data |
a data frame of size nx3 (id, concept, property). The empriical distribution is generated from this data |
new_words |
integer greater than 0, corresponding to the number of words with frequency one that should be added to the empirical distribution |
number_subjects |
number of subjects to be sampled. Each subject with generates new properties |
a vector with the extra number of participant to achieve the especific coverage, and the estimate of the number of unique properties listed by the new amount of suggested people
orig_data=data_paper[data_paper[,2]=="Decision",] property_simulator(orig_data, 84, 15)orig_data=data_paper[data_paper[,2]=="Decision",] property_simulator(orig_data, 84, 15)