Generalized linear mixed model (GLMM) - Statistica General Discussion - Statistica - Dell Community

Generalized linear mixed model (GLMM)

Generalized linear mixed model (GLMM)

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Hi,

Can anyone direct me as to how to analyse my data using GLMM and which package in GLMM is appropriate? Any idea on where to find the GLMM and the correct setting to use. My data is non normally distributed, consist of one continuous dependent variables and three independent categorical variables. The different effects or the levels of one of the categorical variables seem to be dependent on each other.

Nana 

Verified Answer
  • Hello, Nana. In Statistica,  here below are some modules you may want to consider for your analysis:

    1. General Linear Models that  provides a comprehensive set of techniques for analyzing any univariate or multivariate Analysis of Variance (ANOVA), regression, or Analysis of Covariance (ANCOVA) design.

    2. Generalized Linear/Nonlinear Models that both linear and nonlinear effects for any number and type of predictor variables on a discrete or continuous dependent variable can be analyzed

    3. Variance Components models that provides a comprehensive set of techniques for analyzing research designs that include both fixed and random effects.

    You can click the hyperlinks above to access corresponding online help document, which also includes various examples.In addition, here links our online statistics text book to learn more technical details of various modules in Statistica.

    Unfortunately, we are not legally defended to offer advice of which model you should consider for your data analysis. 

All Replies
  • Hello, Nana. In Statistica,  here below are some modules you may want to consider for your analysis:

    1. General Linear Models that  provides a comprehensive set of techniques for analyzing any univariate or multivariate Analysis of Variance (ANOVA), regression, or Analysis of Covariance (ANCOVA) design.

    2. Generalized Linear/Nonlinear Models that both linear and nonlinear effects for any number and type of predictor variables on a discrete or continuous dependent variable can be analyzed

    3. Variance Components models that provides a comprehensive set of techniques for analyzing research designs that include both fixed and random effects.

    You can click the hyperlinks above to access corresponding online help document, which also includes various examples.In addition, here links our online statistics text book to learn more technical details of various modules in Statistica.

    Unfortunately, we are not legally defended to offer advice of which model you should consider for your data analysis. 

  • Alright thanks