Description Usage Arguments Details Value Author(s) See Also Examples
Perform regression on a training set of images, projected onto (provided) eigenvectors, and test on testing images.
1 2 3 | regressProjections(input.train, input.test, demog.train, demog.test,
eigenvectors, mask, outcome, covariates="1", model.function=glm,
which.eigenvectors="all", ...)
|
input.train |
Masked imaging data from training set of type |
input.test |
Masked imaging data from testing set of type |
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 |
outcome |
Name of outcome variable to be predicted. Must be present in |
covariates |
List of names of covariates to be used for the prediction. All names must be present in |
model.function |
Modeling function for predicting outcome from input data. Can be any function that has |
which.eigenvectors |
Method for selecting eigenvectors. Can be either |
... |
Additional arguments for input to |
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.
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.
Kandel BM and Avants B
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | # 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")
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