Since the effort to measure the predictors increases from DBH to H and to ρ we fitted models for three sets of predictors: (1) just on DBH, (2) DBH and H, (3) all three predictors together.
To evaluate the influence of forest structure on the epiphytic lichen distribution, models with three sets of predictors (topo-climatic, forest-structure and combined predictor sets) were fitted for each species.
We examined the extent to which three sets of predictors, (1) larval host plant (Lupinus perennis, wild lupine) availability, (2) characteristics of the matrix surrounding host plant patches, and (3) factors affecting a patch's thermal environment, accounted for variation in lupine patch use by Karner blues at Indiana Dunes National Lakeshore, Indiana and Fort McCoy, Wisconsin, USA.
It is possible that the information being provided by the two sets of predictors is redundant, resulting from the fact that expression differences among the segregants arise due to the underlying genetic variation.
Models were fit with two sets of predictors: 1) traditional biomarkers, and 2) traditional biomarkers plus RBM analytes; additionally, age, gender, and ApoE genotype were included in all models.
However, by Weeks 3 and 4, there was little difference between the operating characteristics associated with these two sets of predictors.
Is there a way to determine the importance of each predictor when there is more than one type of recurrence?" two sets of predictors and outcomes (see Table 1) were selected by consulting and collaborating with oncologists and studying the literature in the domain.
The predictive model was made of two sets of predictors: (1) variables 'a priori' chosen as confounders (age, gender, severity of gallstones, previous upper abdominal surgery and type of admission); (2) variables empirically tested (comorbidities) which were selected using iterative stepwise statistical procedures.
We investigated two sets of predictor variables, both, independently and in combination.
I used two sets of predictor variables to examine the determinants of focal species' dN/dt, the first focused on network characteristics and the second focused on quantitative genetics variance components (VP and VA).
Statistics of prediction sensitivity, specificity, PPV and NPV obtained from two sets of predictor variables of tumor characteristics, tumor pathological variables and five pairs of hemoglobin predictor variables without and with tumor pathological variables are shown in Figure 3 (training) and Figure 4 (testing), respectively.
