qb.covar {qtlbim} | R Documentation |
Compare main effects with GxE effects to address correlation of estimates.
qb.covar(qbObject, element = "add", covar = 1, adjust.covar, chr, ...) ## S3 method for class 'qb.covar': summary(object, percent = 5, digits = 3, ...) ## S3 method for class 'qb.covar': print(x, ...) ## S3 method for class 'qb.covar': plot(x, percent = 5, cex, include.zero = TRUE, ...)
qbObject |
Object of class qb . |
object |
Object of class qb.covar . |
x |
Object of class qb.covar . |
element |
Main effect to examine ("add" or "dom"). |
covar |
Index to covariates used in MCMC samples. |
adjust.covar |
Adjustments to covariates. Default is
NA , which adjusts by covariate mean values. Values are
assumed to be in order of fixed covariates. |
chr |
Subset of chromosomes as integer vector. |
percent |
Percentile (0 to 100) for summaries. |
digits |
Number of significant digits to print. |
cex |
Character expansion for plots (default decreases with MCMC sample size). |
include.zero |
Include zero values in plot when TRUE . |
... |
Arguments passed through to inherited routines. |
The diagonal dark green line of points on plots by chromosome indicate adjustment for covariates that have not been centered. Main effects are generally less correlated with GxE when covariates are first centered to have mean zero.
Objects of class qb.covar
have three columns: main effect, GxE
effect and chromosome. Summary objects have eight columns, three for main
effect and GxE (mean, lower and upper percentile), followed by
correlation and p-value. Summaries are done by chromosome.
Brian S. Yandell
http://www.qtlbim.org
data(qbExample) temp <- qb.covar(qbExample) summary(temp) plot(temp)