Hello,
I need to estimate a generalized linear mixed model (GLMM).
When using R, I need to choose a specific fixed effect (one of the independent variables in the model) to have a random slope according to a random variable such as subject ID.
But when using SPSS, I can't choose a specific fixed effect to have a random slope.
I asked chatGPT about it and it responded as follows:
The difference you're observing in the specification of random slopes for specific fixed effects between SPSS and R might be related to how the two software packages handle mixed-effects modeling. This difference is not necessarily due to the capabilities of the software but rather the way they present the options to the user. Here's why this might be the case:
SPSS:
In SPSS, the interface for specifying mixed-effects models is designed to be user-friendly and may abstract away some of the technical details. It allows users to specify random slopes and intercepts for subjects using the Repeated option or Mixed Linear Models (MIXED) procedure.
SPSS may automatically set up random slopes for all fixed effects by default, which is a more automated approach, but it doesn't give you explicit control over which fixed effects have random slopes.
R (lme4 package):
R, specifically with the lme4 package, provides more flexibility and control in specifying mixed-effects models. This can be both an advantage and a challenge for users.
In R, you have to explicitly specify which fixed effect(s) should have random slopes by including them in the model formula. This explicit control allows for more customized modeling but can be more complex and requires users to have a good understanding of their data and the modeling process.
1. Can someone please confirm this answer?
2. Is there a way to estimate a mixed-effect logistic regression model in R that would be the same as the estimated model in SPSS?
I would appreciate any clues about this issue! Thanks!
See cross-posting at: https://stats.stackexchange.com/questions/628631/what-is-the-difference-between-r-and-spss-in-estimating-random-effects
Beyond that, practically speaking, you might make a small data set available, be specific about what model you are using in SPSS, present the SPSS output, and then I'm sure someone can offer the equivalent model in R.
Mixed effects models can have nested or crossed random effects. They can also have random intercepts or random slopes. And some software allows you to specify correlation structure. ... So, it's important to be specific about the model you are interested in.
The main difference between R and SPSS in estimating random effects is the level of flexibility and control that each software package offers.
R
R is a programming language that provides a high degree of flexibility and control over the specification of mixed-effects models. This can be both an advantage and a disadvantage, depending on the user's experience and expertise. In R, you must explicitly specify which fixed effect(s) should have random slopes by including them in the model formula. You can also specify different variance structures for the random effects.
SPSS
SPSS is a statistical software package that provides a more user-friendly interface for fitting mixed-effects models. However, SPSS is less flexible than R in terms of the types of mixed-effects models that can be fit. For example, SPSS does not allow you to specify random slopes for fixed effects.
Here is a table that summarizes the key differences between R and SPSS in estimating random effects:
FeatureRSPSSFlexibilityHighLowControlHighLowSpecification of random slopesExplicitNot possibleSpecification of variance structuresFlexibleLimitedUser-friendlinessSteep learning curveMore user-friendly interface"
Which software package to choose?
The best software package for you will depend on your specific needs and expertise. If you need to fit a complex mixed-effects model, or if you need more control over the specification of the model, then R is the better choice. If you are new to mixed-effects modeling or if you need a more user-friendly interface, then SPSS is the better choice.
Here are some additional things to consider when choosing between R and SPSS for estimating random effects:
- R is a free and open-source software package, while SPSS is a commercial software package.
- R has a large and active community of users and developers, which means that there is a lot of support available online.
- SPSS is a more widely used software package in academia and industry, so it may be easier to find collaborators or job opportunities if you are proficient in SPSS.
Ultimately, the best way to decide which software package to choose is to try both R and SPSS and see which one you prefer
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