plot.SMI: Result functions for the Similarity of Matrices Index (SMI)

In khliland/MatrixCorrelation: Matrix Correlation Coefficients

Result functions for the Similarity of Matrices Index (SMI)

Description

Plotting, printing and summary functions for SMI, plus significance testing.

Usage

## S3 method for class 'SMI'
plot(
  x,
  y = NULL,
  x1lab = attr(x, "mat.names")[[1]],
  x2lab = attr(x, "mat.names")[[2]],
  main = "SMI",
  signif = 0.05,
  xlim = c(-(pq[1] + 1)/2, (pq[2] + 1)/2),
  ylim = c(0.5, (sum(pq) + 3)/2),
  B = 10000,
  cex = 1,
  cex.sym = 1,
  frame = NULL,
  frame.col = "red",
  frame.lwd = 2,
  replicates = NULL,
  ...
)

## S3 method for class 'SMI'
print(x, ...)

## S3 method for class 'SMI'
summary(object, ...)

is.signif(x, signif = 0.05, B = 10000, ...)

Arguments

x	object of class SMI.
y	not used.
x1lab	optional label for first matrix.
x2lab	optional label for second matrix.
main	optional heading (default = SMI).
signif	significance level for testing (default=0.05).
xlim	optional plotting limits.
ylim	optional plotting limits.
B	number of permutations (for significant, default=10000).
cex	optional text scaling (default = 1)
cex.sym	optional scaling for significance symbols (default = 1)
frame	two element integer vector indicating framed components.
frame.col	color for framed components.
frame.lwd	line width for framed components.
replicates	vector of replicates for significance testing.
...	additional arguments for plot.
object	object of class SMI.

Details

For plotting a diamonad plot is used. High SMI values are light and low SMI values are dark. If orthogonal projections have been used for calculating SMIs, significance symbols are included in the plot unless signif=NULL.

Value

plot silently returns NULL. print and summary return the printed matrix.

Author(s)

Kristian Hovde Liland

References

Similarity of Matrices Index - Ulf G. Indahl, Tormod Næs, Kristian Hovde Liland

See Also

SMI, PCAcv (cross-validated PCA).

Examples

X1  <- scale( matrix( rnorm(100*300), 100,300), scale = FALSE)
usv <- svd(X1)
X2  <- usv$u[,-3] %*% diag(usv$d[-3]) %*% t(usv$v[,-3])

smi <- SMI(X1,X2,5,5)
plot(smi, B = 1000) # default B = 10000
print(smi)
summary(smi)
is.signif(smi, B = 1000) # default B = 10000

khliland/MatrixCorrelation documentation built on Feb. 5, 2023, 3:13 a.m.