seq2feature_mds_stochastic

a <code>"<a rd-options="" href="/link/proc?package=ProcData&version=0.2.5" data-mini-rdoc="ProcData::proc">proc</a>"</code> object or a square matrix. If a squared matrix is 
provided, it is treated as the dissimilary matrix of a group of response processes.

seqs

the number of features to be extracted.

a character string specifies the dissimilarity measure for two
response processes. See 'Details'.

dist_type

the maximum number of epochs for stochastic gradient
descent.

max_epoch

the step size of stochastic gradient descent.

step_size

a logical scalar. If <code>TRUE</code>, the principal components of the
extracted features are returned.

the accuracy tolerance for determining convergence.

logical. If <code>TRUE</code>, the dissimilarity matrix will be
returned. Default is <code>FALSE</code>.

return_dist

seed

L_set

Feature extraction by stochastic mds

General tools for exploratory process data analysis. Process data refers to the data describing
participants' problem solving processes in computer-based assessments. It is often recorded in computer
log files. This package provides two action sequence generators and implements two automatic feature extraction methods that compress the information
stored in process data, which often has a nonstandard format, into standard numerical vectors. This package
also provides recurrent neural network based models that relate response processes with other binary or
scale variables of interest. The functions that involve training and evaluating neural networks are wrappers
of functions in 'keras'.

Xueying Tang

ProcData

Process Data Analysis

Susu Zhang

Zhi Wang

Jingchen Liu

Zhiliang Ying

seq2feature_mds_stochastic function

a <code>"<a rd-options='' href='proc'>proc</a>"</code> object or a square matrix. If a squared matrix is 
provided, it is treated as the dissimilary matrix of a group of response processes.

seq2feature_mds_stochastic: Feature extraction by stochastic mds

Description

Usage

Arguments

Value