plot.SMI: Result functions for the Similarity of Matrices Index (SMI) In MatrixCorrelation: Matrix Correlation Coefficients

Description

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

Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## 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 N<c3><a6>s, Kristian Hovde Liland

`SMI`, `PCAcv (cross-validated PCA)`.
 ```1 2 3 4 5 6 7 8 9``` ```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 ```