predict.ggmncv {GGMncv}

Predict method for ggmncv Objects

Description

There is a direct correspondence between the inverse covariance matrix and multiple regression (Stephens 1998; Kwan 2014). This readily allows for converting the off diagonal elements to regression coefficients, opening the door to out-of-sample prediction in multiple regression.

Usage

## S3 method for class 'ggmncv'
predict(object, train_data = NULL, newdata = NULL, ...)

Arguments

object

An object of class ggmncv.

train_data

Data used for model fitting (defaults to NULL).

newdata

An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.

...

Currently ignored.

Value

A matrix of predicted values, of dimensions rows (in the training/test data) by the number of nodes (columns).

References

Kwan CC (2014). “A regression-based interpretation of the inverse of the sample covariance matrix.” Spreadsheets in Education, 7(1), 4613.

Stephens G (1998). “On the Inverse of the Covariance Matrix in Portfolio Analysis.” The Journal of Finance, 53(5), 1821–1827.

Examples

# data
Y <- scale(Sachs)

# test data
Ytest <- Y[1:100,]

# training data
Ytrain <- Y[101:nrow(Y),]

fit <- ggmncv(cor(Ytrain), n = nrow(Ytrain),
              progress = FALSE)

pred <- predict(fit, newdata = Ytest)

round(apply((pred - Ytest)^2, 2, mean), 2)