CalcR2CvCorrected {evolqg}

Corrected integration value

Description

Calculates the Young correction for integration, using bootstrap resampling Warning: CalcEigenVar is strongly preferred and should probably be used in place of this function..

Usage

CalcR2CvCorrected(ind.data, ...)

## Default S3 method:
CalcR2CvCorrected(
  ind.data,
  cv.level = 0.06,
  iterations = 1000,
  parallel = FALSE,
  ...
)

## S3 method for class 'lm'
CalcR2CvCorrected(ind.data, cv.level = 0.06, iterations = 1000, ...)

Arguments

ind.data

Matrix of individual measurments, or adjusted linear model

...

additional arguments passed to other methods

cv.level

Coefficient of variation level chosen for integration index adjustment in linear model. Defaults to 0.06.

iterations

Number of resamples to take

parallel

if TRUE computations are done in parallel. Some foreach backend must be registered, like doParallel or doMC.

Value

List with adjusted integration indexes, fitted models and simulated distributions of integration indexes and mean coefficient of variation.

Author(s)

Diogo Melo, Guilherme Garcia

References

Young, N. M., Wagner, G. P., and Hallgrimsson, B. (2010). Development and the evolvability of human limbs. Proceedings of the National Academy of Sciences of the United States of America, 107(8), 3400-5. doi:10.1073/pnas.0911856107

See Also

MeanMatrixStatistics, CalcR2

Examples

## Not run: 
integration.dist = CalcR2CvCorrected(iris[,1:4])

#adjusted values
integration.dist[[1]]

#ploting models
library(ggplot2)
ggplot(integration.dist$dist, aes(r2, mean_cv)) + geom_point() +
       geom_smooth(method = 'lm', color= 'black') + theme_bw()

ggplot(integration.dist$dist, aes(eVals_cv, mean_cv)) + geom_point() +
       geom_smooth(method = 'lm', color= 'black') + theme_bw()

## End(Not run)