calc.yhat {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 <[e-mail]>

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)
     }