tips:model_selection_with_glmulti_and_mumin
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tips:model_selection_with_glmulti_and_mumin [2021/03/16 20:13] – Wolfgang Viechtbauer | tips:model_selection_with_glmulti_and_mumin [2021/03/16 20:16] – Wolfgang Viechtbauer | ||
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{{ tips: | {{ tips: | ||
- | The importance value for a particular predictor is equal to the sum of the weights/ | + | The importance value for a particular predictor is equal to the sum of the weights/ |
==== Multimodel Inference ==== | ==== Multimodel Inference ==== | ||
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</ | </ | ||
- | I rounded the results to 4 digits to make the results easier to interpret. Note that the table again includes the importance values. In addition, we get unconditional estimates of the model coefficients (first column). These are model-averaged parameter estimates, which are weighted averages of the model coefficients across the various models (with weights equal to the model probabilities). These values are called " | + | I rounded the results to 4 digits to make the results easier to interpret. Note that the table again includes the importance values. In addition, we get unconditional estimates of the model coefficients (first column). These are model-averaged parameter estimates, which are weighted averages of the model coefficients across the various models (with weights equal to the model probabilities). These values are called " |
==== Multimodel Predictions ==== | ==== Multimodel Predictions ==== |
tips/model_selection_with_glmulti_and_mumin.txt · Last modified: 2022/10/13 06:07 by Wolfgang Viechtbauer