Recursive path analysis is a useful tool for inference on a sequence of three or more response variables in which the causal effects of variables, if any, are in one direction. The primary objective in such analysis is to decompose the total effect of each variable into its direct and ...
Recursive path analysis is a useful tool for inference on a sequence of three or more response variables in which the causal effects of variables, if any, are in one direction. The primary objective in such analysis is to decompose the total effect of each variable into its direct and indire...
variable. Use of a model may more compactly describe the effects of interest but involves assumptions about the way the predictor and outcome variables are related. Perhaps the simplest model is to assume a linear relationship between the outcome and predictor. For example, one could assume that ...
This strategy ignores uncertainty in the estimates of energy availability, which should be propagated into estimates of effects and predicted values of the response variable. I used Bayesian hierarchical models to include uncertainty in site-level covariates when modeling dabbling duck count data during...
and Draper, N. R., Measures of lack of fit for response surface designs and predictor variable transformations. Technometrics , 24 , 1-8 (1982).Box, G. E. P. and Draper, N. R. (1982), "Measures of Lack of Fit for Response Surface Designs and Predictor Variable Transformations," ...
Summary Linear regression is used to model one quantitative variable as a function of one or more other variables. In this chapter we introduce regression modeling with the fitting of a response variable as a linear function of one predictor variable. The topics covered in this chapter include th...
Importance of risk factors from in random forest models (R2 = 0.51) in predicting ΔNPV. The importance was determined by randomly permuting values for each predictor variable (VIMP) (a) and by averaging the depth of the first split within the regression tree for each variable across 1...
In prediction problems with more predictors than observations, it can sometimes be helpful to use a joint probability model, π(Y, X), rather than a purely conditional model, π(Y | X), where Y is a scalar response variable and X is a vector of predictors. This approach is motivated by...
in order to better characterize the important features for children obesity. We tried both RF and GBM models, in order to assess the robustness of the estimated ranks, and derived a consensus variable importance score for all the predictors by combining the predictions of the two models. This ...
statistically because of limited sample size and the low order relationships obtained with Further,there is reason to expect a positive, stable relationship between an individual's judged investigationis that it may prove useful to distinguish between social desirability judgments in...