I conducted a study with 28 subjects to measure the effect of an intervention. Thus, I have 3 data points from each subject: pre-treatment, treatment, and post-treatment.
I tried running a generalized mixed-effect linear model, since my data is binomial and non-independent. In this model, I included subject as a random effect (together with three fixed effects I wanted to look at). My problem is that the model would not converge. So I was wondering whether using a generalized linear model instead would be a good alternative, as this one does converge. I know it is not the best solution, but my knowledge of statistics is pretty limited and I cannot think of a better alternative. Even though I would not be able to account for those random effects, would this model still give me some useful information/results?
Note: I tried using different optimizers and none of them worked, since I still got convergence warnings.
More information about my study:
It's a teaching-intervention study. I looked at whether explicit instruction during a language class led to better use of two particular linguistic structures. I also had two groups (corresponding to 2 different classes: intermediate learners and advanced learners).
My code:
lm_general = lme4::glmer(TARGET~CONSTRUCTION*PHASE*TYPE_OF_SPEAKER + (1|SUBJECT) + (1|ITEM), data = my_df, family = "binomial")
My results (for fixed effects):
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.0461 0.3352 3.120 0.00181 **
CONSTRUCTIONpassives -2.8949 0.2050 -14.121 < 2e-16 ***
PHASEpretreatment -0.1810 0.2321 -0.780 0.43548
PHASEposttreatment -0.3527 0.2300 -1.534 0.12504
TYPE_OF_SPEAKERintermediate -2.3805 0.3903 -6.099 1.07e-09 ***
CONSTRUCTIONpassives:PHASEpretreatment -18.0473 1319.5225 -0.014 0.98909
CONSTRUCTIONpassives:PHASEposttreatment -17.8300 1302.0983 -0.014 0.98907
CONSTRUCTIONpassives:TYPE_OF_SPEAKERintermediate 2.6915 0.2687 10.018 < 2e-16 ***
PHASEpretreatment:TYPE_OF_SPEAKERintermediate -0.9765 0.3584 -2.725 0.00643 **
PHASEposttreatment:TYPE_OF_SPEAKERintermediate 0.6715 0.3100 2.166 0.03028 *
CONSTRUCTIONpassives:PHASEpretreatment:TYPE_OF_SPEAKERintermediate 0.4458 1744.3590 0.000 0.99980
CONSTRUCTIONpassives:PHASEposttreatment:TYPE_OF_SPEAKERintermediate -1.2249 1729.7634 -0.001 0.99943
My warning: "Model failed to converge: degenerate Hessian with 1 negative eigenvalues"
head
of the data, the code you used to do this, the warning, and what thesummary
said of the model object? And also some more details about the experiment: What are you measuring? How was it collected? Etc. $\endgroup$