Evaluate bootstrap metrics produced from /codecalc.yhat
This function evaluates the bootstrap metrics produced from /codeboot.yhat.
booteval.yhat(regrOut, boot.out, bty, level, prec)
regrOut |
Output from |
boot.out |
Output from |
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. |
This function evaluates the bootstrap metrics produced from boot.yhat.
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 |
Kim Nimon <kim.nimon@gmail.com>
Nimon, K., & Oswald, F. L. (2013). Understanding the results of multiple linear regression: Beyond standardized regression coefficients. Organizational Research Methods, 16, 650-674.
## 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")
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.