yhat: calc.yhat – R documentation

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calc.yhat

More regression indices for lm class objects

Description

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.

Thomas, D. R., Zumbo, B. D., Kwan, E., & Schweitzer, L. (2014). On Johnson's (2000) relative weights method for assessing variable importance: A reanalysis. Multivariate Behavioral Research, 16, 49(4), 329-338.

Examples

## Predict paragraph comprehension based on three verbal
  ## tests: general info, sentence comprehension, & word
  ## classification
  
  ## Use HS dataset in MBESS 
     if (require("MBESS")){
     data(HS)
  
  ## Regression
     lm.out<-lm(t6_paragraph_comprehension~
                t5_general_information+t7_sentence+t8_word_classification,data=HS)
  
  ## Regression Indices
     regr.out<-calc.yhat(lm.out)
     }

yhat

Interpreting Regression Effects

v2.0-3

GPL (>= 2)

Authors

Kim Nimon <kim.nimon@gmail.com>, Fred Oswald, and J. Kyle Roberts.

Initial release

2020-12-31