For section 2 of the lab report complete the following analyses and interpret them. Add this work under the GLM2 heading in your lab report. Conduct one multiple logistic regression predicting high selfesteem "SelfesteemHigh" as created in the GLM2 lecture content from two predictors of your choosing. Create a graph of the predicted probabilities of being in the SelfesteemHigh group by each of your two predictors. It should parallel the predicted probability plot here: http://joshuawiley.com/MonashHonoursStatistics/GLM2.html#glms-with-multiple-predictors Report and interpret the odds ratios, 95% confidence intervals for each predictor and present the average marginal effect for each predictor to interpret the results on the probability scale. For section 3 of the lab report, complete the following. Using the missing dataset as in lecture, multiply impute the data, and use the with() and pool() functions to run a linear regression with any of the variables from the imputed dataset (i.e., Female, Age, Stress, PosAff, NegAff) and report pooled results. Code for the data setup is below. Briefly describe and interpret the linear regression results on the multiply imputed data. Section 4: 1. run an intercept only linear mixed model with PosAff ratings as the outcome variable including a fixed and random intercept by person (the id variable is called UserID). 2. Describe the number of observations and the number of participants included in the analysis 3. Report & interpret the intraclass correlation coefficient (ICC). 4. Conduct model diagnostics and briefly comment on whether the homogeneity of variance and normality assumptions appear to be met or not (even if not met, you do not need to address it in this lab report section). 5. Finally, report the intercept with 95% confidence interval. Section 5: 1. Create a between and within version of STRESS using meanDeviations() 2. Fit a linear mixed model using lmer() with both the between and within version of STRESS included as fixed effects and both a random intercept and random slope for the within version of STRESS by UserID. 3. Check model diagnostics and where appropriate use transformations or exclude outliers / extreme values. You only need to do this once (i.e., if after removing extreme values, there are still more extreme values, you do not need to address them, just do it once). Also, deal with any singularity / non convergence issues, simplifying the model if needed to achieve convergence. Keep all code to document your process. 4. Make a summary of the final model (after any cleaning). 5. Briefly report the results from the LMM (interpret the fixed effects along with confidence intervals / p-values). Section 6: 1. Pick a continuous, within person variable as the outcome (any variable measured more than once per person). Also choose at least two variables that you will use as predictors/explanatory variables (may be between and/or within). At least one of your two predictors/explanatory variables must be a continuous variable (e.g., not Female, EDU). 2. Fit a linear mixed model using lmer() and test whether your predictors interact with each other by including at least a fixed effect of the interaction term. You may include random slopes or not. Ensure that you have a model that converges and does not have a singularity warning (if you're unable to solve these, you may need to pick different variables). Note, model diagnostics are not required for this lab report section, only convergence and no singularity issues. 3. Regardless of whether the interaction is statistically significant or not, create a graph of the interaction. Create a graph of the interaction using visreg(). 4. Regardless of whether the interaction is statistically significant or not, calculate the simple slopes, confidence intervals, and p-values for the simple slopes (e.g., using emtrends()). Briefly report the simple slopes. Section 7: 1. Pick a continuous, within person variable as the outcome (any variable measured more than once per person). Also choose a continuous, within person predictor/explanatory variable. 2. Fit a linear mixed model using lmer() and fit models that have an intercept only, linear effect of your predictor, quadratic effect of your predictor, and cubic effect of your predictor. Use AIC and BIC values to compare models. Which model is the best? You may have your explanatory variable as a fixed only or fixed and random effect. Ensure that you have models that converge and do not have a singularity warning (e.g., dropping random effects if needed; if you're unable to solve these, you may need to pick different variables). Note, model diagnostics are not required for this lab report section, only convergence and no singularity issues. 3. Report and interpret the conditional and marginal f2 effect size(s) for your predictor from your final model. If your best model is an intercept only model, then pick a different model (e.g., linear effect of your predictor model) to report and interpret the conditional and marginal f2 effect sizes for your predictor.
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