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While interpretations of regression parameters from traditional finite mixture models are specific to unobserved subpopulations or latent classes, investigators are often interested in making inferences about the marginal mean of a count variable in the overall population.
As example, a significant effect for Origin Location factor means that the marginal mean of at least one area is different from the others.
On day 15 the estimated marginal mean of 58Fe incorporation was 14.4% (95% CI: 9.7 21.1) in the malaria group versus 25.9% 955% CI: 17.5 38.4) in the non-malaria anaemic group.
The estimated marginal mean of 57Fe incorporation in the malaria group on day 1 was 8.3% (95% CI: 6.2 11.3) versus 27.9% (95% CI: 20.6 37.9) in the non malaria anaemic group.
We estimated the marginal mean of the GHQ and the association between the GHQ and a fully observed binary outcome, comparing the results with complete data statistics.
The estimates of the marginal mean of the partially observed normally distributed outcome variable using complete case analysis, MI under MAR, and the sensitivity analysis using the weighting approach under MNAR are shown in Table 2.
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Fig. 2 Marginal means of gendered programming involvement (with SE bars), by a competition and b age.
Figure 3 shows the estimated marginal means of the scores on the LCs and quiz for the mathematics participants.
In this article, marginal means of counts with unexplained heterogeneity were modeled using two-component finite mixture distributions.
Finally, estimated marginal means of Log coliform was shown in Fig. 5 which presents changed number of FC according to the week and type of treatment.
Fig. 24 Estimated marginal means of cation exchange capacity in the 0 15 cm soil layer across different land use/land cover systems and altitudes.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com