booteval.yhat {yhat}

Evaluate bootstrap metrics produced from /codecalc.yhat

Description

This function evaluates the bootstrap metrics produced from /codeboot.yhat.

Usage

  booteval.yhat(regrOut, boot.out, bty, level, prec)

Arguments

regrOut

Output from calc.yhat

boot.out

Output from boot in conjunction with boot.yhat

bty

Type of confidence interval. Only types "perc", "norm", "basic", and "bca" supported.

level

Confidence level (e.g., .95)

prec

Integer indicating number of decimal places to be used.

Details

This function evaluates the bootstrap metrics produced from boot.yhat.

Value

Confidence intervals are reported for predictor and all possible subset metrics as well as differences between appropriate predictors and all possible subset metrics. The function also output the means, standard errors, probabiltites, and reproducibility metrics for the dominance comparisons. Means and standard deviations are reported for Kendall's tau correlation between sample predictor metrics and the bootstrap statistics of like metrics.

combCIpm

Upper and lower CIs for predictor metrics

lowerCIpm

Lower CIs for predictor metrics

upperCIpm

Upper CIs for predictor metrics

combCIaps

Upper and lower CIs for APS metrics

lowerCIaps

Lower CIs for APS metrics

upperCIaps

Upper CIs for APS metrics

domBoot

Dominance analysis bootstrap results

tauDS

Descriptive statistics for Kendall's tau

combCIpmDiff

Upper and lower CIs for differences between predictor metrics

lowerCIpmDiff

Lower CIs for differences between predictor metrics

upperCIpmDiff

Upper CIs for differences between predictor metrics

combCIapsDiff

Upper and lower CIs for differences between APS metrics

lowerCIapsDiff

Lower CIs for differences between APS metrics

upperCIapsDiff

Upper CIs for differences between APS metrics

combCIincDiff

Upper and lower CIs for differences between incremental validity metrics

lowerCIincDiff

Lower CIs for differences between incremental validity metrics

upperCIincDiff

Upper CIs for differences between incremental validity metrics

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.

See Also

lm calc.yhat boot plotCI.yhat

Examples

  ## Bootstrap regression results predicting paragraph     
  ## comprehension based on four 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)

  ## Calculate regression metrics
     regrOut<-calc.yhat(lm.out)

  ## Bootstrap results
     require ("boot")
     boot.out<-boot(HS,boot.yhat,100,lmOut=lm.out,regrout0=regrOut)

  ## Evaluate bootstrap results
     result<-booteval.yhat(regrOut,boot.out,bty="perc")
     }