gencovtest {biotools}  R Documentation 
Testing Genetic Covariance
Description
gencovtest()
tests genetic covariance components from a MANOVA model. Two different approaches can
be used: (I) a test statistic that takes into account the genetic and environmental effects and (II) a test
statistic that only considers the genetic information. The first type refers to tests based on the mean
crossproducts ratio, whose distribution is obtained via Monte Carlo simulation of Wishart matrices. The
second way of testing genetic covariance refers to tests based upon an adaptation of Wilks' and Pillai's
statistics for evaluating independence of two sets of variables. All these tests are described by Silva (2015).
Usage
## S3 method for class 'manova'
gencovtest(obj, geneticFactor, gcov = NULL,
residualFactor = NULL, adjNrep = 1,
test = c("MCPR", "Wilks", "Pillai"),
nsim = 9999,
alternative = c("two.sided", "less", "greater"))
## S3 method for class 'gencovtest'
print(x, digits = 4, ...)
## S3 method for class 'gencovtest'
plot(x, var1, var2, ...)
Arguments
obj 
an object of class 
geneticFactor 
a character indicating the genetic factor from which to test covariance components. It must be declared as a factor in the manova object. 
gcov 
optional; a matrix containing estimates of genetic covariances to be tested. If

residualFactor 
optional; a character indicating a source in the manova model to be used as
error term. If 
adjNrep 
a correction index for dealing with unbalanced data. See details. 
test 
a character indicating the test. It must be on of the following:

nsim 
the number of Monte Carlo simulations. Used only if 
alternative 
the type of alternative hypothesis. Used only if 
x 
an object of class 
digits 
the number of digits to be displayed by the print method. 
var1 
a character of integer indicating one of the two response variable or its position. 
var2 
a character of integer indicating one of the two response variable or its position. 
... 
further arguments. 
Details
The genetic covariance matrix is currently estimated via method of moments, following the equation:
G = (Mg  Me) / (nrep * adjNrep)
where Mg
and Me
are the matrices of mean crossproducts associated with the genetic factor and
the residuals, respectively; nrep
is the number of replications, calculated as the ratio between the
total number of observations and the number of levels of the genetic factor; adjNrep
is supposed to
adjust nrep, specially when estimating G
from unbalanced data.
Value
An object of class gencovtest
, a list of
gcov 
a pdimensional square matrix containing estimates of the genetic covariances. 
gcor 
a pdimensional square matrix containing estimates of the genetic correlations. 
test 
the test (as input). 
statistics 
a pdimensional square matrix containing the 
p.values 
a pdimensional square matrix containing the associated pvalues. 
alternative 
the type of alternative hypothesis (as input). 
X2 
a pdimensional square matrix containing the Chisquare (D.f. = 1) approximation for Wilks's and Pillai's statistics. Stored only if one of these two tests is chosen. 
simRatio 
an array consisting of 
dfg 
the number of degrees of freedom associated with the genetic factor. 
dfe 
the number of degrees of freedom associated with the residual term. 
Warning
When using the MCPR test, be aware that dfg
should be equal or greater than the number of variables (p).
Otherwise the simulation of Wishart matrices may not be done.
A collinearity diagnosis is carried out using the condition number (CN), for the inferences may be affected by the
quality of G
. Thus, if CN > 100, a warning message is displayed.
Author(s)
Anderson Rodrigo da Silva <anderson.agro@hotmail.com>
References
Silva, A.R. (2015) On Testing Genetic Covariance. LAP Lambert Academic Publishing. ISBN 3659716553
See Also
Examples
# MANOVA
data(maize)
M < manova(cbind(NKPR, ED, CD, PH) ~ family + env, data = maize)
summary(M)
# Example 1  MCPR
t1 < gencovtest(obj = M, geneticFactor = "family")
print(t1)
plot(t1, "ED", "PH")
# Example 2  Pillai
t2 < gencovtest(obj = M, geneticFactor = "family", test = "Pillai")
print(t2)
plot(t2, "ED", "PH")
# End (not run)