Sentence examples for prior variable from inspiring English sources

There is no requirement for prior variable selection or stepwise regression techniques and the results are easily interpretable in terms of simple plots.

We look at three different prior variable distributions for the same-topology.

We often find ourselves with narratives that tell us how someone gets sick, and it might be that a useful narrative is one which isolates the temporally prior variables and lets us see how later phenomena depend on them.

The phylogenetically correct residuals used in the figures were taken from the PGLM model following the stepwise insertion of prior variables in the order listed in Table 1.

While canonical DA considers all variables simultaneously when building the discriminatory model, the stepwise method discriminates groups by iteratively incorporating variables that maximize the between- versus within-group variance after conditioning on prior variables included.

Interestingly, these features comprised, side-by-side, prior variables (e.g., age), event and injury parameters, immediate bodily responses (e.g., ED pain), symptoms (nightmares, loss of concentration, total PTSD and depression symptoms), clinicians' observations (e.g., CGI) and more elaborated subjective responses (need for help, sense of worthlessness).

Variable selection was undertaken manually, and plots of residuals were examined prior to variable addition to ensure that the variable was included in the model using the least biased functional form.

Plots of residuals were examined prior to variable addition to ensure that the variable was included in the model using the least biased functional form.

Indeed, its inclusion led to a diminished influence of numerous other variables; for example, the co-morbidity HIV/AIDS was not associated with leaving AMA when the prior AMA variable was included in the model (OR 0.93, 95% CI 0.52-1.67), but was strongly associated when the prior AMA variable was not included (OR 2.85, 95% CI 2.04-4.04).

Omitting the prior AMA variable from the model eliminates this sort of confounding, and permits clearer identification of other variables associated with leaving AMA.

Since including the prior AMA variable masks the association with other relevant characteristics, and because of issues with interpreting the prior AMA variable for patients without any past hospitalizations, for identifying other variables associated with leaving AMA we emphasize the model excluding the prior AMA variable (Table  3, left half).