| Title: | Conceptual Properties Norming Studies as Parameter Estimation |
| Version: | 1.1.0 |
| 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. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 2.10) |
| NeedsCompilation: | no |
| Packaged: | 2021-10-09 16:13:23 UTC; smorenoa |
| Author: | Sebastian Moreno [aut, cre], Enrique Canessa [ths], Sergio Chaigneau [ths], Rodrigo Lagos [ths], Felipe Medina [ths] |
| Maintainer: | Sebastian Moreno <sebastian.moreno.araya@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2021-10-09 16:30:07 UTC |
Example of the dataset used in the paper.
Description
A real dataset from spanish speakers, translated using automatic algorithms
Usage
data_paper
Format
A data frame with 4364 rows and 3 variables:
- ID
Id of the people
- Concept
Concept being described by the person ID
- Property
A property mentioned for the corresponding concept for the person ID
Test example dataset.
Description
A toy dataset containing the description of ten people, over 3 concept, with multiple properties.
Usage
data_test
Format
A data frame with 65 rows and 3 variables:
- ID
Id of the people
- Concept
Concept being described by the person ID
- Property
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
Description
Estimate the number of people needed and expected number of unique properties for a determined coverage based on the estimated norms
Usage
estimate_participant(est_norms, target_cover)
Arguments
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) |
Value
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
Examples
estimated_norms=generate_norms(data_test)
estimate_participant(estimated_norms,0.8)
Calculate all the norms from a Conceptual properties
Description
Calculate all the norms from a Conceptual properties
Usage
generate_norms(orig_data)
Arguments
orig_data |
a data frame of size nx3 (id, concept, property) |
Value
a data frame with all the estimations
Examples
generate_norms(data_test)
Simulate properties based on the empricial distribution of the original data and new words with frequency one
Description
Simulate properties based on the empricial distribution of the original data and new words with frequency one
Usage
property_simulator(orig_data, new_words, number_subjects)
Arguments
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 |
Value
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
Examples
orig_data=data_paper[data_paper[,2]=="Decision",]
property_simulator(orig_data, 84, 15)