plot.LSTM

This function generates a bar plot of the classification probabilities predicted by the pre-trained LSTM
for determining the number of factors. The plot displays the probability distribution across different numbers of factors,
with each bar representing the probability for a specific number of factors. The maximum number of factors that the network
can evaluate is 10. The function also annotates each bar with its probability value.

A method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network,
which is originally developed by
Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided.
The sample size of the dataset used to train the LSTM model is 1,000,000.
Each sample is a batch of simulated response data with a specific latent factor structure.
The eigenvalues of these response data will be used as sequential data to train the LSTM.
The pre-trained LSTM is capable of factor retention for real response data with a
true latent factor number ranging from 1 to 10, that is, determining the number of factors.

Haijiang Qin

LSTMfactors

Determining the Number of Factors in Exploratory Factor Analysis
by LSTM

Lei Guo

plot.LSTM function

<dl><dt>x</dt>
<dd>An object of class <code>LSTM</code>, representing the results to be plotted.</dd>
<dt>...</dt>
<dd>Additional arguments to be passed to the plotting function.</dd></dl>

Arguments

Plot LSTM Classification Probability Distribution — plot.LSTM

<dl>

<dt>x</dt>
<dd>An object of class <code>LSTM</code>, representing the results to be plotted.</dd>


<dt>...</dt>
<dd>Additional arguments to be passed to the plotting function.</dd>

</dl>

plot.LSTM: Plot LSTM Classification Probability Distribution

Description

Usage

Value

Arguments

See Also