genetics (version 1.3.8.1.3)

print.LD: Textual and graphical display of linkage disequilibrium (LD) objects

Description

Textual and graphical display of linkage disequilibrium (LD) objects

Usage

# S3 method for LD
print(x, digits = getOption("digits"), …)
# S3 method for LD.data.frame
print(x, …)
# S3 method for data.frame
summary.LD(object, digits = getOption("digits"),
                      which = c("D", "D'", "r", "X^2", "P-value", "n", " "),
                      rowsep, show.all = FALSE, …)
# S3 method for summary.LD.data.frame
print(x, digits = getOption("digits"), …)
# S3 method for LD.data.frame
plot(x,digits=3, colorcut=c(0,0.01, 0.025, 0.5, 0.1, 1),
                   colors=heat.colors(length(colorcut)), textcol="black",
                   marker, which="D'", distance,  …)

LDtable(x, colorcut=c(0,0.01, 0.025, 0.5, 0.1, 1),
        colors=heat.colors(length(colorcut)), textcol="black",
        digits=3, show.all=FALSE, which=c("D", "D'", "r", "X^2",
        "P-value", "n"), colorize="P-value", cex, …)
LDplot(x, digits=3, marker, distance, which=c("D", "D'", "r", "X^2",
       "P-value", "n", " "), … )

Arguments

x,object

LD or LD.data.frame object

digits

Number of significant digits to display

which

Name(s) of LD information items to be displayed

rowsep

Separator between rows of data, use NULL for no separator.

colorcut

P-value cutoffs points for colorizing LDtable

colors

Colors for each P-value cutoff given in colorcut for LDtable

textcol

Color for text labels for LDtable

marker

Marker used as 'comparator' on LDplot. If omitted separate lines for each marker will be displayed

distance

Marker location, used for locating of markers on LDplot.

show.all

If TRUE, show all rows/columns of matrix. Otherwise omit completely blank rows/columns.

colorize

LD parameter used for determining table cell colors

cex

Scaling factor for table text. If absent, text will be scaled to fit within the table cells.

…

Optional arguments (plot.LD.data.frame passes these to LDtable and LDplot)

Value

None.

See Also

LD, genotype, HWE.test

Examples

# NOT RUN {

g1 <- genotype( c('T/A',    NA, 'T/T',    NA, 'T/A',    NA, 'T/T', 'T/A',
                  'T/T', 'T/T', 'T/A', 'A/A', 'T/T', 'T/A', 'T/A', 'T/T',
                     NA, 'T/A', 'T/A',   NA) )

g2 <- genotype( c('C/A', 'C/A', 'C/C', 'C/A', 'C/C', 'C/A', 'C/A', 'C/A',
                  'C/A', 'C/C', 'C/A', 'A/A', 'C/A', 'A/A', 'C/A', 'C/C',
                  'C/A', 'C/A', 'C/A', 'A/A') )


g3 <- genotype( c('T/A', 'T/A', 'T/T', 'T/A', 'T/T', 'T/A', 'T/A', 'T/A',
                  'T/A', 'T/T', 'T/A', 'T/T', 'T/A', 'T/A', 'T/A', 'T/T',
                  'T/A', 'T/A', 'T/A', 'T/T') )
data <- makeGenotypes(data.frame(g1,g2,g3))

# Compute & display  LD for one marker pair
ld <- LD(g1,g2)
print(ld)

# Compute LD table for all 3 genotypes
ldt <- LD(data)

# display the results
print(ldt)                               # textual display
LDtable(ldt)                            # graphical color-coded table
LDplot(ldt, distance=c(124, 834, 927))  # LD plot vs distance

# more markers makes prettier plots!
data <- list()
nobs <- 1000
ngene <- 20
s <- seq(0,1,length=ngene)
a1 <- a2 <- matrix("", nrow=nobs, ncol=ngene)
for(i in 1:length(s) )
{

  rallele <- function(p) sample( c("A","T"), 1, p=c(p, 1-p))

  if(i==1)
    {
      a1[,i] <- sample( c("A","T"), 1000, p=c(0.5,0.5), replace=TRUE)
      a2[,i] <- sample( c("A","T"), 1000, p=c(0.5,0.5), replace=TRUE)
    }
  else
    {
      p1 <- pmax( pmin( 0.25 + s[i] * as.numeric(a1[,i-1]=="A"),1 ), 0 )
      p2 <- pmax( pmin( 0.25 + s[i] * as.numeric(a2[,i-1]=="A"),1 ), 0 )
      a1[,i] <- sapply(p1, rallele )
      a2[,i] <- sapply(p2, rallele )
    }

  data[[paste("G",i,sep="")]] <- genotype(a1[,i],a2[,i])
}
data <- data.frame(data)
data <- makeGenotypes(data)

ldt <- LD(data)
plot(ldt, digits=2, marker=19) # do LDtable & LDplot on in a single
                               # graphics window
# }