This article describes how to run a Generalized Linear Model. There are seven types of regression analyses to choose from. The linear regression model is the default.
Requirements
- Familiarity with the Structure and Value Attributes of Variable Sets, and how they are used in regression models per our Driver Analysis ebook.
- An Outcome variable to be predicted.
- Predictors variables will be considered as predictors of the outcome variable.
Method
- In the Anything menu select Advanced Analysis > Regression > Generalized Linear Model.
- In the object inspector go to the Inputs tab.
- In the Output menu select the variable to be predicted by the predictor variables.
- Select the predictor variable(s) from the Predictor(s) list.
- OPTIONAL: Select the Regression type. There are seven types of regression analysis to choose from. The linear regression model is the default.
- OPTIONAL: Select the desired Output type:
- Summary: The default; as shown in the example above.
- Detail: Typical R output, some additional information compared to Summary, but without the pretty formatting.
- ANOVA: Analysis of variance table containing the results of Chi-squared likelihood ratio tests for each predictor.
- Shapley Regression output.
- Jaccard Coefficient output.
- Correlation output.
- Relative Importance Analysis: The results of a relative importance analysis.
- Effects Plot Plots the relationship between each of the Predictors and the Outcome.
- OPTIONAL: Select the desired Missing Data treatment. (See Missing Data Options).
- OPTIONAL: Select Variable names to display variable names in the output instead of labels.
- OPTIONAL: Select Correction. Choose between None (the default), False Discovery Rate, Bonferroni.
- OPTIONAL: Select Robust standard errors to compute standard errors that are robust to violations of the assumption of constant variance (eg, heteroscedasticity).
- OPTIONAL: Select Absolute importance scores to display the absolute value of Relative Importance Analysis scores.
- OPTIONAL: Specify the Automated outlier removal percentage to remove possible outliers.
- OPTIONAL: Select Stack data to stack the input data prior to analysis. Stacking can be desirable when each individual in the data set has multiple cases and an aggregate model is desired.
See Also
How to Run Binary Logit Regression
How to Run a Multinomial Logit Regression
How to Run NBD Regression in Displayr
How to Run Ordered Logit Regression
How to Run Quasi-Poisson Regression
How to Run a Stepwise Regression
How to Create Regression Multicollinearity Table (VIF)
How to Create a Prediction-Accuracy Table
How to Create a Goodness-of-Fit Plot
How to Test Residual Heteroscedasticity of Regression Models
How to Save Predicted Values of Regression Models
How to Save Fitted Values of Regression Models
How to Save Probabilities of Each Response of Regression Models
How to Test Residual Normality (Shapiro-Wilk) of Regression Models
How to Test Residual Serial Correlation (Durbin-Watson) of Regression Models