# Textual and graphical display of linkage disequilibrium (LD) objects ```r ## S3 method for class 'LD' print(x, digits = getOption("digits"), ...) ## S3 method for class 'LD.data.frame' print(x, ...) ## S3 method for class '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 class 'summary.LD.data.frame' print(x, digits = getOption("digits"), ...) ## S3 method for class '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`) ## Returns None. ## Author(s) Gregory R. Warnes greg@warnes.net ## See Also `LD`, `genotype`, `HWE.test` ## Examples ```r 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 ```

print.LD function