adhd


A data set consists of 221 patients with ADHD


datasets

An exploratory and heuristic approach for specification search
in Structural Equation Modeling. The basic idea is to subsample
the original data, and for each subset we search the
optimal models. Here the criteria of an optimal model is defined
by two objectives: to fit the data well and is simple (parsimonious) model.
As these objectives are conflicting, we apply NSGA-II to optimize,
such that to get optimal models for the whole range of model complexities.
From these optimal models, we observe the model specifications (structures)
that are both stable and parsimonious, called relevant. At the end we infer
causal model from these relevant structures.

Ridho Rahmadi

stablespec

Stable Specification Search in Structural Equation Models

adhd function

A data frame with 221 rows and 10 variables, which is a
mixture of both discrete and continuous variables:
<dl>
<dt>Gender</dt><dd>gender of patients</dd> <dt>AD</dt><dd>the attention deficit measure</dd> <dt>HI</dt><dd>the assessment of hyperactivity/impulsivity</dd> <dt>Aggression</dt><dd>the measure of aggressive behavior</dd> <dt>Medication</dt><dd>the medication status of patients</dd> <dt>Handedness</dt><dd>wheteher a patient uses the right or the left hand</dd> </dl>

adhd: Attention Deficit Hyperactivity Disorder (ADHD)

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