View source: R/CalcR2CvCorrected.R
CalcR2CvCorrected | R Documentation |
Calculates the Young correction for integration, using bootstrap resampling Warning: CalcEigenVar is strongly preferred and should probably be used in place of this function..
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, ...)
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. |
List with adjusted integration indexes, fitted models and simulated distributions of integration indexes and mean coefficient of variation.
Diogo Melo, Guilherme Garcia
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
MeanMatrixStatistics
, CalcR2
## 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)
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