R: Correlation Analysis Utilities

cor.util {mt}

R Documentation

Correlation Analysis Utilities

Description

Functions to handle correlation analysis on data set.

Usage

cor.cut(mat,cutoff=0.75,abs.f = FALSE,
        use = "pairwise.complete.obs", method = "pearson",...)
  
cor.hcl(mat, cutoff=0.75, use = "pairwise.complete.obs", 
        method = "pearson",fig.f=TRUE, hang=-1,
        horiz = FALSE, main = "Cluster Dendrogram", 
        ylab = ifelse(!horiz, "1 - correlation",""), 
        xlab = ifelse(horiz, "1 - correlation",""),...)
  
cor.heat(mat, use = "pairwise.complete.obs", method = "pearson",
         dend = c("right", "top", "none"),...)
  
corrgram.circle(co, 
                col.regions = colorRampPalette(c("red", "white", "blue")),
                scales = list(x = list(rot = 90)), ...) 

corrgram.ellipse(co,label=FALSE,
                 col.regions = colorRampPalette(c("red", "white", "blue")),
                 scales = list(x = list(rot = 90)), ...) 

cor.heat.gram(mat.1, mat.2, use = "pairwise.complete.obs", 
              method = "pearson", main="Heatmap of correlation", 
              cex=0.75, ...)
              
hm.cols(low = "green", high = "red", n = 123)

Arguments

`mat`, `mat.1`, `mat.2`	A data frame or matrix. It should be noticed that `mat.1` and `mat.2` must have the same number of row.
`cutoff`	A scalar value of threshold.
`abs.f`	Logical flag indicating whether the absolute values should be used.
`fig.f`	Logical flag indicating whether the dendrogram of correlation matrix should be plotted.
`hang`	The fraction of the plot height by which labels should hang below the rest of the plot. A negative value will cause the labels to hang down from 0. See `plot.hclust`.
`horiz`	Logical indicating if the dendrogram should be drawn horizontally or not.
`main`, `xlab`, `ylab`	Graphical parameters, see `plot.default`.
`dend`	Character string indicating whether to draw 'right', 'top' or 'none' dendrograms

`use`	Argument for `cor`. An optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings `"everything"`, `"all.obs"`, `"complete.obs"`, `"na.or.complete"`, or `"pairwise.complete.obs"`.
`method`	Argument for `cor`. A character string indicating which correlation coefficient (or covariance) is to be computed. One of `"pearson"`, `"kendall"`, or `"spearman"`, can be abbreviated.
`co`	Correlation matrix
`label`	A logical value indicating whether the correlation coefficient should be plotted.
`...`	Additional parameters to lattice.
`col.regions`	Color vector to be used
`scales`	A list determining how the x- and y-axes (tick marks and labels) are drawn. More details, see `xyplot`.
`cex`	A numeric multiplier to control character sizes for axis labels

`low`	Colour for low value
`high`	Colour for high value
`n`	The number of colors (>= 1) to be in the palette

Details

cor.cut returns the pairs with correlation coefficient larger than cutoff.

cor.hcl computes hierarchical cluster analysis based on correlation coefficient. For other graphical parameters, see plot.dendrogram.

cor.heat display correlation heatmap using lattice.

corrgram.circle and corrgram.ellipse display corrgrams with circle and ellipse. The functions are modified from codes given in Deepayan Sarkar's Lattice: Multivariate Data Visualization with R, 13.3.3 Corrgrams as customized level plots, pp:238-241.

cor.heat.gram handles the correlation of two data sets which have the same row number. The best application is correlation between MS data (metabolites) and meta/clinical data.

hm.cols creates a vector of n contiguous colors for heat map.

Value

cor.cut returns a data frame with three columns, in which the first and second columns are variable names and their correlation (lager than cutoff) are given in the third column.

cor.hcl returns a list with components of each cluster group and all correlation coefficients.

cor.heat returns an object of class "trellis".

corrgram.circle returns an object of class "trellis".

corrgram.ellipse returns an object of class "trellis".

cor.heat.gram returns a list including the components:

cor.heat: An object of class "trellis" for correlation heatmap ordered by the hierarchical clustering.
cor.gram: An object of class "trellis" for corrgrams with circle ordered by the hierarchical clustering.
cor.short: A matrix of correlation coefficient in short format.
cor.long: A matrix of correlation coefficient in long format.

Author(s)

Wanchang Lin

References

Michael Friendly (2002). Corrgrams: Exploratory displays for correlation matrices. The American Statistician, 56, 316–324.

D.J. Murdoch, E.D. Chow (1996). A graphical display of large correlation matrices. The American Statistician, 50, 178–180.

Deepayan Sarkar (2008). Lattice: Multivariate Data Visualization with R. Springer.

Examples

data(iris)
cor.cut(iris[,1:4],cutoff=0.8, use="pairwise.complete.obs")
cor.hcl(iris[,1:4],cutoff=0.75,fig.f = TRUE)
ph <- cor.heat(iris[,1:4], dend="top")
ph
update(ph, scales = list(x = list(rot = 45)))

## change heatmap color scheme
cor.heat(iris[,1:4], dend="right", xlab="", ylab="",
  col.regions = colorRampPalette(c("green", "black", "red")))

## or use hm.cols
cor.heat(iris[,1:4], dend="right", xlab="", ylab="", col.regions = hm.cols())

## prepare data set
data(abr1)
cls <- factor(abr1$fact$class)
dat <- preproc(abr1$pos[,110:1930], method="log10")  

## feature selection
res <- fs.rf(dat,cls)
## take top 20 features
fs  <- res$fs.order[1:20]
## construct the data set for correlation analysis
mat <- dat[,fs]

cor.cut(mat,cutoff=0.9)
ch <- cor.hcl(mat,cutoff=0.75,fig.f = TRUE, xlab="Peaks")
## plot dendrogram horizontally with coloured labels.
ch <- cor.hcl(mat,cutoff=0.75,fig.f = TRUE, horiz=TRUE,center=TRUE, 
              nodePar = list(lab.cex = 0.6, lab.col = "forest green", pch = NA),
              xlim=c(2,0))

names(ch)
cor.heat(mat,dend="right")
cor.heat(mat,dend="right",col.regions = colorRampPalette(c("yellow", "red")))

## use corrgram with order by the hierarchical clustering
co  <- cor(mat, use="pairwise.complete.obs")
ord <- order.dendrogram(as.dendrogram(hclust(as.dist(1-co)))) 
corrgram.circle(co[ord,ord], main="Corrgrams with circle")
corrgram.ellipse(co[ord,ord], label = TRUE, main = "Corrgrams with circle",
                 col.regions = hm.cols())

## if without ordering
corrgram.circle(co, main="Corrgrams with circle")

## example of cor.heat.gram 
fs.1  <- res$fs.order[21:50]
mat.1 <- dat[,fs.1]

res.cor <- 
  cor.heat.gram(mat, mat.1, main="Heatmap of correlation between mat.1 and mat.2")
names(res.cor)
res.cor$cor.heat
res.cor$cor.gram

[Package mt version 2.0-1.20 Index]