The proposed method represents error degrees by multiple ordinal ranks and performs error degree estimation for prospective values from known observed data by using the KDA-based ordinal regression.
We used multiple ordinal logistic regression analyses to predict categories of SES and identify explanatory genetic and geographic variables.
It converted continuous data into ordinal classes for statistical analysis.
Simple comparisons and mixed models of multiple ordinal regression.
Simple and multiple ordinal logistic regression models were used for the analyses.
Then, all factors were included in a multiple ordinal logistic regression analysis (proportional odds model) [ 12].
Three almost equally-represented ordinal classes were defined (1 = neutral, 2 = aromatic/light muscat/muscat unstable, 3 = high muscat) and polymorphisms were tested incorporating the Q-matrix as covariate to class ordinal variation (class 1 to class 2 and class 2 to class 3).
The final full-scale model was based on multiple ordinal logistic regression with backward elimination of non-significant covariates.
The multiple ordinal regression model was adjusted for the objectively (GIS-based) assessed number of green neighborhood qualities.
Multiple ordinal logistic regression was conducted to determine factors associated with increasing level of HIV related stigma.
