data.datasets.LSTM

<p>This dataset is a subset of the full datasets, consisting of 1,000 samples
from the original 1,000,000-sample datasets.</p>

Provides a collection of standard factor retention methods in Exploratory Factor
Analysis (EFA), making it easier to determine the number of factors. Traditional
methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>,
Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and
Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis
(PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA
are readily available. This package also implements several newer methods, such as
the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017)
<doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012)
<doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011)
<doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like
Comparison Data Forest (CDF) by Goretzko and Ruscio (2024)
<doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner
(2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network
(DNN) trained on large-scale datasets that can efficiently and reliably determine
the number of factors.

Haijiang Qin

EFAfactors

Determining the Number of Factors in Exploratory Factor Analysis

Lei Guo

data.datasets.LSTM function

<p>A 1,000×21 matrix, where the firt column represents the labels and
the last 20 columns represent feature values, which correspond to the number of
factors associated with the features.</p>

data.datasets.LSTM: Subset Dataset for Training the Long Short Term Memory (LSTM) Network

Description

Arguments

Format

See Also

Examples