Nov 23, 2014 2:25 PM
| Last Modified: Feb 14, 2017 8:23 AM
This Add-In generates the linear mixed-effects (random- and fixed-effect) model terms for one-way or full factorial repeated-measures designs involving a continuous response variable (categorical responses are not supported at this time)
Download factorial_repeated_measures.jmpaddin at the bottom of this page.
Once downloaded, double-click the file to open in JMP.
You will be prompted asking if you would like to install the add-in. Select Install.
Under the Add-Ins menu, you will have a section for "Repeated Measures"
In repeated measures factorial designs, subjects are measured in each condition of the factorial combination of the within-subject factors, and are measured on only one level of between-subject factors. For example, subjects in a wine tasting experiment could taste and make ratings on several different wines, but are usually measured at one level of gender and expertise, as the levels of these factors are difficult to change in the time-course of an experiment. Full factorial designs involve a complete cross of factors. In the example experiment below, each subject tastes wine from 8 glasses, formed from the factorial combination of four different wines rated under the two conditions of Label ( four of the glasses were placed in front of bottles with expensive-looking labels, and four were placed in front of bottles with cheap-looking labels). The factorial cross of Expertise and Gender, the between-subject factors, involves measuring individuals in all four combinations (F/Expert, F/Novice, M/Expert, M/Novice). These factors are "between-subject" because no subject is measured at more than one level of the factors of expertise and gender.
This add-in generates a linear mixed-effects model analysis. With no missing data, this analysis produces estimates and tests that are identical to a univariate, general linear model repeated-measures analysis assuming compound symmetry. However, mixed-models estimate model terms differently so estimates will be different (and potentially better) in the presence of missing data; univariate GLM repeated-measures analysis requires complete data, and subjects with missing cells are eliminated, whereas mixed-models can tolerate missing data without eliminating an entire subject from the analysis. Missing data (that isn't missing at random) can bias model estimates, so be sure to investigate the cause of missing data before interpreting the results of any model.
Cast columns into the appropriate role and click "Run Model," or click "Launch Dialog" to proceed to the Fit Model dialog to make changes to the model before running. To keep dialog box open after running, check the box for "Keep Dialog Open."
Note: For these models, "Subject ID" should have a Nominal modeling type; this add-in will automatically set the modeling type to Nominal if it is not already.
Model terms are generated, and an Fit Model output is launched with Summary of Fit details and Information Criteria, REML Variance Component Estimates, Fixed Effect Tests, and Effect Details.
In this case (with two within-subject factors, and two-between subject factors), the model generated is as follows:
:Gender *:Expertise *:Wine,
:Gender *:Expertise *:Label,
:Gender *:Wine *:Label,
:Expertise *:Wine *:Label,
:Gender *:Expertise *:Wine *:Label
:Judge *:Wine *:Label[:Gender,:Expertise]
Many options are available under the top-most Red Triangle, including additional regression reports, parameter estimates, diagnostic plots and saving residuals, leverage pairs, etc.
Plots, Means, and Tests
Expand Effect Details to generate plots for individual factors and interactions, and to perform tests on the cell means using all pairwise T-tests, Tukey HSD, and linear contrasts.
To interatively profile the model, select the top-most Red Triangle > Factor Profiling > Profiler
Modify and View Model
Select the top-most Red Triangle, and select Model Dialog to see the model specification and to make changes to the model.
This add-in requires that data be in tall, or long format. This is when repeated observations from a subject are represented across rows, rather than across columns (see below).
If you need to restructure wide data, use Tables > Stack, and cast the columns with repeated observations in to the Stack Columns section. For factorial designs you will need to separate out the factor levels of each factor. By using Col > Recode this can be done quickly.
- This add-in generates terms with the following algorithm:
- Full factorial of all fixed-effect factors (all within- and between-subject factors)
- Full factorial of only within-subject factors and subject-factor (with redundant terms removed)
- All terms involving subject are marked as random effects
- If between subject factors are present, all terms involving subjects are marked as nested in all between-subject factors
- If there are replicates of every cell (each subject was in each condition more than once) the highest order interaction with subjects is estimable. If there are not replicates, this highest order interaction is confounded with the residual (it IS the residual), and will be automatically removed.
- Models are limited to up to 5 within-subject factors, and up to 5 between-subject factors. If you regularly use models with more than 5 within-subject or 5 between-subject factors, please let me know.
I have a two sets of videos with more detail here that might be useful if you are going to us this add-in.