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HLM 7
階層線性模型與非線性模型軟體
Hierarchical Linear Modeling
軟體代號:878
瀏覽次數:15271
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Features

HLM 6 greatly broadens the range of hierarchical models that can be estimated. It also offers greater convenience of use than previous versions. Here is a quick overview of key new features and options:

  • All new graphical displays of data.
  • Greater expanded graphics for fitted models.
  • Model equations displayed in hierarchical or mixed-model format with or without subscripts - easy to save for publication. Distribution assumptions and link functions are presented in detail.
  • Cross-classified random effects models for linear models and non-linear link functions with convenient Windows interface.
  • High-order Laplace approximation with EM algorithm for stable convergence and accurate estimation in two-level hierarchical generalized linear models (HGLM).
  • Multinomial and ordinal models for three-level data. Also see the types of models.
  • New flexible and accurate sample design weighting for two- and three-level HLMs and HGLMs.
  • Easier automated input from a wide variety of software packages, including the current versions of SAS, SPSS, and STATA.
  • Residual files can be saved directly as SPSS (*.sav) or STATA (*.dta) files.
  • Analyses are based on MDM files, replacing the older less flexible SSM format.     

Overview of modeling options in HLM modules

Interface option

 

HLM2

HLM3

HMLM

HMLM2

HCM2

Basic Settings: Distribution of outcome

Normal outcome

Y

Y

-

-

-

Bernoulli outcome

Y

Y

-

-

-

Poisson outcome (constant exposure)

Y

Y

-

-

-

Poisson outcome (variable exposure)

Y

Y

-

-

-

Binomial outcome

Y

Y

-

-

-

Multinomial outcome

Y

Y

-

-

-

Ordinal outcome

Y

Y

-

-

-

Over-dispersion

Y

Y

-

-

-

Basic Settings: Residual files, title and file names

Level-1 residual file

Y

Y

-

-

Y

Level-2 residual file

Y

Y

-

-

-

Level-3 residual file

-

Y

-

-

-

Row-residual file

-

-

-

-

Y

Column-residual file

-

-

-

-

Y

Title

Y

Y

Y

Y

Y

Output filename

Y

Y

Y

Y

Y

Graph filename

Y

Y

Y

Y

Y

Basic Settings: Treatment of level-1 variance

Unrestricted

-

-

Y

Y

-

Skip unrestricted

-

-

Y

Y

-

Homogeneous

-

-

Y

Y

-

Heterogeneous

-

-

Y

Y

-

Log-linear

-

-

Y

Y

-

Predictor of level-1 var

-

-

Y

Y

-

1-st order autoregressive

-

-

Y

Y

-

Iteration Settings

Number of  iterations

Y

Y

Y

Y

Y

Frequency of accelerator

Y

Y

Y

Y

Y

% change to stop iterating

Y

Y

Y

Y

Y

How to handle bad tau

Y

Y

Y

Y

Y

How to handle bad delta

-

-

-

-

Y

What to do when convergence not reached

Y

Y

Y

Y

Y

Mode of acceleration

-

Y

-

-

-

Estimation Settings

REML

Y

-

-

-

-

FML

Y

Y

Y

Y

Y

PQL

Y

Y

-

-

-

 (HGLM)

(HGLM)

LaPlace iteration control

Y

Y

-

-

-

(HGLM)

(HGLM)

EM Laplace iteration control

Y

-

-

-

-

(HGLM)

Constraint of fixed effects

Y

Y

-

-

-

Heterogeneous sigma^2

Y

-

-

-

-

Plausible values

Y

Y

-

-

-

Multiple imputation

Y

Y

-

-

-

Latent variable regression

Y

Y

Y

-

-

Weighting

Y

Y

-

-

-

Level-1 deletion variables

Y

Y

-

-

-

Fix sigma^2 to specified value

Y

Y

-

-

Y

Hypothesis Testing

Multivariate hypothesis tests

Y

Y

Y

Y

Y

Deviance of models comparison

Y

Y

Y

Y

Y

Test homogeneity of level-1 var

Y

-

-

-

-

Output Settings

No of OLS estimates shown

Y

-

-

-

-

Reduced output

Y

Y

Y

Y

Y

Print variance-covariance matrices

Y

Y

-

-

-

Exploratory Analysis (level-2)

Y

Y

-

-

-

Exploratory Analysis (level-3)

-

Y

-

-

-

Graph Equations (model based)

Model graphs

Y

Y

Y

Y

Y

Level-1 equation graphing

Y

Y

-

-

-

Level-1 residual box-whisker plots

Y

Y

-

-

-

Level-1 residual vs predicted values

Y

Y

-

-

-

Level-2 EB/OLS coefficient confidence intervals

Y

Y

-

-

-

Graph Data

line plots, scatter plots

Y

Y

-

-

-

box-whisker plots

Y

Y

-

-

-