regressProjections: Regression on image set projection

Description

Perform regression on a training set of images, projected onto (provided) eigenvectors, and test on testing images.

Usage

regressProjections(input.train, input.test, demog.train, demog.test, 
           eigenvectors, mask, outcome, covariates="1", model.function=glm, 
	   which.eigenvectors="all", ...)

Arguments

input.train

Masked imaging data from training set of type antsImage or matrix. This will typically be read from a train.mha file generated by, e.g., sccan --imageset-to-matrix.

input.test

Masked imaging data from testing set of type antsImage or matrix. This will typically be read from a test.mha file generated by, e.g., sccan --imageset-to-matrix.

demog.train

Data frame of demographics information for training images.

demog.test

Data frame of demographics information for testing images.

eigenvectors

List of eigenvector images for dimensionality reduction.

mask

Mask image of type antsImage.

outcome

Name of outcome variable to be predicted. Must be present in demog.train and demog.test.

covariates

List of names of covariates to be used for the prediction. All names must be present in demog.train and demog.test.

model.function

Modeling function for predicting outcome from input data. Can be any function that has formula and data arguments and a predict method. Defaults to glm, but svm, randomForest, etc. will also work (assuming necessary libraries are loaded).

which.eigenvectors

Method for selecting eigenvectors. Can be either "all" (uses all eigenvectors in vector.names) or "optimal" (uses BIC stepwise model selection to select eigenvectors). "optimal" only works for model.function arguments that include an extractAIC method and a coefficients attribute.

...

Additional arguments for input to model.function. Example input would be family=binomial for classification instead of regression when using glm.

Value

A list of diagnostic and statistical information generated from the prediction, including: stats: Statistics on computed fits. For numeric outcomes, mean squared error, correlation coefficients, and p-value of prediction for training and testing data. For factor outcomes, misclassification rate and p-value of classification model. outcome.comparison: Data frame comparing real vs. predicted values for testing data. eigenvectors: List of eigenvectors retained in model building.

Details

regressProjections is a convenient way to perform training and testing of predictions of demographic information from imaging data. It takes as input demographics information, imaging data, and eigenvectors, and performs prediction of the outcome variable from the projection of the imaging data on the eigenvectors.

Examples

Run this code

# NOT RUN {
# generate simulated outcome
nsubjects <- 100
x1 <- seq(1, 10, length.out=nsubjects) + rnorm(nsubjects, sd=2)
x2 <- seq(25, 15, length.out=nsubjects) + rnorm(nsubjects, sd=2)
outcome <- 3 * x1 + 4 * x2 + rnorm(nsubjects, sd=1)
# generate simulated images with outcome predicted 
# by sparse subset of voxels
voxel.1 <- 3 * x1 + rnorm(nsubjects, sd=2)
voxel.2 <- rnorm(nsubjects, sd=2)
voxel.3 <- 2 * x2 + rnorm(nsubjects, sd=2)
voxel.4 <- rnorm(nsubjects, sd=3)
input   <- cbind(voxel.1, voxel.2, voxel.3, voxel.4)
# simulate eigenvectors and mask
mydecom <- sparseDecom(input, sparseness=0.25, nvecs=4) 
mask    <- as.antsImage(matrix(c(1,1,1,1), nrow=2))
# generate sample demographics that do not explain outcome
age <- runif(nsubjects, 50, 75)
demog <- data.frame(outcome=outcome, age=age)
# randomly divide data into training and testing 
data.split <- splitData(demog, 2/3, return.rows=TRUE)
result <- regressProjections(input[data.split$rows.in, ], input[data.split$rows.out, ], 
            data.split$data.in, data.split$data.out, mydecom$eigenanatomyimages, 
	    mask, "outcome")
# }