predict.CCM: Classification from a CCM correlation matrix

Description Usage Arguments Details Value Note Author(s) See Also Examples

View source: R/predict.CCM.R

Description

Classification as a function of a CCM correlation matrix that contains the correlations between test and training samples

Usage

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## S3 method for class 'CCM'
predict(object, y, func = mean, ret.scores = FALSE, ...)

Arguments

object

a CCM correlation matrix object obtained from create.CCM

y

classes corresponding to the training samples (columns) of ‘object’

func

the function that determines how a test sample is classified, defaulting to mean. See details.

ret.scores

If set to TRUE then a matrix of results by class are returned (see details); otherwise a vector of classifications/predictions is returned (the default)

...

Additional arguments to func

Details

The function func can be any R function whose first argument is a vector of correlations (x). The CCM assigns each test sample the class that maximizes func(x). If func is mean (the default), the classification is the class with the highest mean correlation. Other useful values for func include median and max.

If ret.scores is TRUE, then a matrix of results by class is returned, where the i(th) column corresponds to the i(th) test sample and each row corresponds to a possible class. Entry (i,j) contains func(x), where x is a vector of correlations between the i(th) test sample and all training samples with the class in row j.

Value

The test sample classifications as a vector or a matrix of results by class.

Note

If the max function is used for func, then the CCM is identical to a 1-nearest neighbor classifier with distance = 1 - r, where 'r' is the correlation (pearson or spearman) specified in the call to create.CCM

Author(s)

Garrett M. Dancik and Yuanbin Ru

See Also

create.CCM

Examples

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data(data.expr)
data(data.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

## CCM using pearson correlation ##
K = create.CCM(test.expr, train.expr, method = "pearson")
## predict based on the class with the maximum correlation
p = predict(K, train.gender, func = max)
table(pred = p, true = test.gender) # check accuracy

CCM documentation built on April 12, 2018, 5:03 p.m.