Hi Prashanti,
I understand that you want a single beta coefficient for a categorical predictor using 'fitlme' function. However, in standard regression analysis, the separate coefficients for the categories of 'x2' are because each level has its own impact on the response variable.
If you want to test the overall significance of the categorical variable 'x2' (i.e., test whether there is any difference among the categories of x2 in their association with y), you can perform an F-test. The 'fitlme' function provides a method 'anova' for this purpose, which will tell you if 'x2' as a whole is statistically significant.
lme = fitlme(...); % Your model fitting with 'DummyVarCoding', 'effects'
anova(lme)
The 'anova' function will give you a p-value for 'x2' indicating whether the differences among the levels of 'x2' are statistically significant. Please refer to the following documentation for more information on 'anova' function.
If you insist on obtaining a single beta coefficient for a categorical variable with multiple levels, you might need to reconsider the coding of your categorical variable or the interpretation of the model
Hope this helps with your query.
