booteval.yhat: Evaluate bootstrap metrics produced from /codecalc.yhat

In yhat: Interpreting Regression Effects

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")
     }

yhat documentation built on Oct. 11, 2023, 1:08 a.m.