Reports beta weights, validity coefficients, structure coefficients, product measures, commonality analysis coefficients, and dominance analysis weights for lm class objects.
Usage
calc.yhat(lm.out,prec=3)
Arguments
lm.out
lm class object
prec
level of precision for rounding, defaults to 3
Details
Takes the lm class object and reports beta weights, validity coefficients,
structure coefficients, product measures, commonality analysis coefficients,
and dominance analysis weights.
Value
PredictorMetrics
Predictor metrics associated with lm class object
OrderedPredictorMetrics
Rank order of predictor metrics
PairedDominanceMetrics
Dominance analysis for predictor pairs
APSRelatedMetrics
APS metrics associated with lm class object
Author(s)
Kim Nimon <kim.nimon@gmail.com>
References
Nimon, K., & Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. Organizational Research Methods, 16,
650-674.
Examples
## Predict paragraph comprehension based on three verbal
## tests: general info, sentence comprehension, & word
## classification
## Use HS dataset in MBESS
require ("MBESS")
data(HS.data)
## Regression
lm.out<-lm(paragrap~general+sentence+wordc,data=HS.data)
## Regression Indices
regr.out<-calc.yhat(lm.out)