CCM-package | R Documentation |
Classification method that classifies an observation based on its correlation with observations having known class labels. There are two main functions. The function create.CCM
creates a correlation matrix of correlations between training and test samples. Both Pearson's and Spearman's rank-based correlations are supported. The function predict.CCM
assigns class labels to test observations according to the class that has the highest mean correlation by default. However, any (user-defined) function in addition to the mean (e.g., median, max) can be specified.
For a complete list of functions, use 'library(help="CCM")'
Package: | CCM |
Type: | Package |
Version: | 1.2 |
Date: | 2018-04-05 |
License: | GPL(>=2) |
LazyLoad: | yes |
Garrett M. Dancik and Yuanbin Ru
Maintainer: Garrett M. Dancik <dancikg@easternct.edu>
create.CCM
;
predict.CCM
;
plot.CCM
## load data ## data(data.expr) data(data.gender) ## check within class correlations ## ## outliers may be caused by poor quality ## ## observations or may indicate CCM is not appropriate ## K = cor.by.class(data.expr, data.gender) ## visualize the results ## boxplot(K, xlab = "gender") ## split dataset into training / testing ## train.expr = data.expr[,1:20] test.expr = data.expr[,21:40] train.gender = data.gender[1:20] test.gender = data.gender[21:40] ## CCM using spearman correlation ## K = create.CCM(test.expr, train.expr, method = "spearman") ## predict based on the class with the highest mean correlation (the default) ## p = predict(K, train.gender) table(pred = p, true = test.gender) # check accuracy ## plot correlations for the 3rd observation ## plot(K, train.gender, index = 3, main = "correlations for obs #3", xlab = "gender", ylab = "correlation")
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