| qb.close {qtlbim} | R Documentation |
Boxplots and summaries of how close MCMC samples of genetic architectures are to target architecture.
qb.close(qbObject, target = NULL, epistasis = TRUE, signed = FALSE,
score.type = c("sq.atten","attenuation","variance","recombination",
"distance"), ...)
## S3 method for class 'qb.close':
plot(x, category = c("pattern", "nqtl"), xlab,
cutoff, sort.pattern = c("percent","score"), ...)
## S3 method for class 'qb.close':
summary(object, cutoff, digits = 0,
show = "score", ... )
qbObject |
Object of class qb. |
x,object |
Object of class qb.close. |
target |
Target architure as data frame with columns chrom
and locus. Extracted from summary if target is
a qb.scanone object. If target = NULL,
score.type is set to "variance" to be compared with
null model. |
epistasis |
Include epistasis in patterns if TRUE. |
signed |
Sign score. Most useful to examine single chromosome. |
score.type |
Type of score to use as distance. |
category |
Boxplots indexed by nqtl or pattern. |
xlab |
Label for X axis (default taken from x object). |
cutoff |
Percent cutoff for pattern inclusion in model
selection. Default is 0.25 (0.5) if epistasis is TRUE
(FALSE). |
sort.pattern |
If type = "pattern", sort by
percent posterior or by median of score. |
digits |
Number of digits displayed for locus. |
show |
Character string with name from object to show. |
... |
Parameters to methods. |
Closeness for each loci is measured as 1-2r, with r the
recombination rate. Thus unlinked loci have measure 0. Loci between
a MCMC sample architecture and the target architecture on the same
chromosome are matched by closest distance in cM between subsets of the
the same length (if target has 2 QTL on chr 3 and sample has 3, consider
all pairs from sample to find closest pair in 2-D). Measure per sample
is sum across all loci. A quick way to generate a target is to use
qb.BestPattern.
The score.type is "recombination" = r, the recombination
rate; "attenuation" = 1-2r; "sq.atten" = squared
attenuation, "distance" in cM, or genetic "variance".
Brian S. Yandell, yandell@stat.wisc.edu
data(qbExample) ## Here target is known for simulated data. target <- data.frame(chrom = c(1,1,2,3), locus = c(15,45,12,15)) temp <- qb.close(qbExample, target) summary(temp) plot(temp) plot(temp, category = "nqtl") ## Or use target from qb.BestPattern target <- summary(qb.BestPattern(qbExample))$best qb.close(qbExample, target)