| calc.penalties {qtl} | R Documentation |
Derive penalties for the penalized LOD scores (used by
stepwiseqtl) on the basis of permutation results
from a two-dimensional, two-QTL scan (obtained by scantwo).
calc.penalties(perms, alpha=0.05, lodcolumn)
perms |
Permutation results from scantwo. |
alpha |
Significance level. |
lodcolumn |
If the scantwo permutation results contain LOD scores for multiple phenotypes, this argument indicates which to use in the summary. This may be a vector. If missing, penalties for all phenotypes are calculated. |
Thresholds derived from scantwo permutations (that
is, for a two-dimensional, two-QTL genome scan) are used to calculate
penalties on main effects and interactions.
The main effect penalty is the 1-alpha quantile of the null
distribution of the genome-wide maximum LOD score from a single-QTL
genome scan (as with scanone).
The "heavy" interaction penalty is the 1-alpha quantile of
the null distribution of the maximum interaction LOD score (that is,
the log10 likelihood ratio comparing the best model
with two interacting QTL to the best model with two additive QTL) from
a two-dimensional, two-QTL genome scan (as with
scantwo).
The "light" interaction penality is the difference between the
"fv1" threshold from the scantwo
permutations (that is, the 1-alpha quantile of the LOD score
comparing the best model with two interacting QTL to the best
single-QTL model) and the main effect penalty.
Vector of three values indicating the penalty on main effects and heavy and light penalties on interactions, or a matrix of such results, with each row corresponding to a different phenotype.
Karl W Broman, kbroman@biostat.wisc.edu
Manichaikul, A., Moon, J. Y., Sen, 'S, Yandell, B. S. and Broman, K. W. (2009) A model selection approach for the identification of quantitative trait loci in experimental crosses, allowing epistasis. Genetics, to appear.
data(fake.f2) fake.f2 <- calc.genoprob(fake.f2, step=5) out.2dim <- scantwo(fake.f2, method="hk") # permutations ## Not run: permo.2dim <- scantwo(fake.f2, method="hk", n.perm=1000) summary(permo.2dim, alpha=0.05) # penalties calc.penalties(permo.2dim)