Description Usage Arguments Value
View source: R/SRSA_StratUttOptimization.R
Simple RSA
The function calculates the optimal parameter values of the free parameters by estimating the log-likelihood of the RSA model given model parameters and data. It also determines the actual RSA model Kullback-Leibler divergence.
3 parameter optimization considering all feature values (also the ones not present in the scene), i.e. feature values of shape, texture and color.
1 | RSAModelKLDiv3paramsAllValuesConsidered_simpleRSA(data, par1, par2)
|
data |
A matrix with data rows. column structure: [1:OC1,OC2,OC3,4:UUFeat, 5:Q1Feat,6:Q2Feat] [7:Q1AnswerV1,V2,V3, 10:Q2AnswerV1,V2,V3] 1:OC1 Object 1. A value between 1 and 27. 2:OC2 Object 2. A value between 1 and 27. 3:OC3 Object 3. A value between 1 and 27. 4:UUFeat Uttered feature. A number between 1 and 3. (1: shape, 2: pattern, 3: color) 5:Q1Feat Questioned feature 1. A number between 1 and 3. (1: shape, 2: pattern, 3: color). Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences. 6:Q2Feat Questioned feature 2. A number between 1 and 3. (1: shape, 2: pattern, 3: color). Example: If you utter "blue" (feature: color), then you can learn something about shape and texture preferences. 7:Q1AnswerV1, V2, V3 The columns 7-9 contain the participants' slider values for the first questioned feature. 10:Q2AnswerV1, V2, C3 The columns 10-12 contain the participants' slider values for the second questioned feature. |
par1 |
|
par2 |
|
Minimized Kullback-Leibler divergence and the optimal parameters.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.