Paths linking latent variables to the observed variables which index them haveĮrror covariances for observed variables have ~~ as the op. The lhs (left hand side, or outcome), rhs (right hand side, or predictor)Īnd op (operation) columns specify what modification should be made. Modificationindices(hz.fit) %>% as_data_frame() %>% arrange( -mi) %>% filter(mi > 11) %>% select(lhs, op, rhs, mi, epc) %>% pander( caption= "Largest MI values for hz.fit") Largest MI values for hz.fit lhs Selects only the lhs, op, rhs, mi, and epc columns.‘improved’, and the p value for the added parameter would be 10 Values bigger than 3.84 indicate that the model would be Model fit would improve if a particular path was added or constraint freed. Modification index is the \(\chi^2\) value, with 1 degree of freedom, by which Parameter constraints or adding paths to our models would help improve it. Modification indices help us answer ‘what if?’ questions about whether freeing Like maps, models are imperfect but useful.Dichotomising continuous predictors (or outcomes).Forgetting that the CI depends on sample size.‘Poking around’ with $ and Save time: use a broom.Making prdictions for margins ( effects of predictors).For multilevel or generalised linear models.Baysian fitting of linear models via MCMC methods.Mediation example after Baron and Kenny.Convergence problems and simplifying the random effects structure.Contrasts and followup tests using lmer.3 level models with ‘partially crossed’ random effects.Variance partition coefficients and intraclass correlations.Allow the effect of sleep deprivation to vary for different participants.Repeated measures or ‘split plot’ designs.Describing statistical models using formulae.Aggregating and reshaping at the same time.Which package should you use to reshape data?.Differences in quantity: numeric variables.
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