loocv_pos: A Positive Network Predication Function

Description Usage Arguments Details Value Author(s) Examples

Description

Use positive brain network to predicate behavior via feature selection and LOOCV

Usage

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loocv_pos(x, y, threshold = 0.01)

Arguments

x

x is a n by p matrix consisting n subjects and p features.

y

y is a n by 1 vector consisting behavior measurement.

threshold

threshold is a number (e.g. 0.01) specifying the significant feature to retain

Details

The codes take two variables x and y, where x is a n by p matrix consisting n subjects and p features, and y is a n by 1 vector consisting behavior measurement. The codes then conduct (1) feature selection, and (2) leave-one-subject-out cross-validation using selected features of x on predication of y.

Value

cor

correlation between observed and predicated behavior

p

p-value (significance) of the correlation between observed and predicated behavior

significant.positive.edges

significant edges predicative of behavior in the positive network

Author(s)

Oliver Y. Chén

Examples

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require(Hmisc)
set.seed(1)
X <- matrix(rnorm(40000, 10, 5),40,1000) ### 40 subjects and 1000 features
Y <- rnorm(40, 20, 15) ### 40 subjects' observed behavior
pos.out <- loocv_pos(x=X, y=Y, threshold=0.01)
str(pos.out)

oliverychen/network.predication documentation built on May 30, 2019, 7:03 p.m.