generate_observables: Generate observable emotion scores data from latent variables

In transforEmotion: Sentiment Analysis for Text, Image and Video using Transformer Models

Generate observable emotion scores data from latent variables

Description

Function to generate observable data from 2 latent variables (negative and positive affect). The function takes in the latent variable scores, the number of time steps, the number of observable variables per latent factor, and the measurement error variance. It returns a matrix of observable data. The factor loadings are not the same for all observable variables. They have uniform random noise added to them (between -0.15 and 0.15). The loadings are scaled so that the sum of the loadings for each latent factor is 2, to introduce a ceiling effect and to differentiate the dynamics of specific emotions. This is further empahsized by adding small noise to the measurement error variance for each observed variable (between -0.01 and 0.01).

Usage

generate_observables(X, num_steps, num_obs, error, loadings = 0.8)

Arguments

X	Matrix or Data frame. The (num_steps X 2) matrix of latent variable scores.
num_steps	Numeric integer. Number of time steps.
num_obs	Numeric integer. The number of observable variables per latent factor.
error	Numeric. Measurement error variance.
loadings	Numeric (default = 0.8). The default initial loading of the latent variable on the observable variable.

Value

A (num_steps X num_obs) Matrix or Data frame containing the observable variables.

transforEmotion documentation built on Sept. 11, 2024, 9:26 p.m.