run_searchlight | R Documentation |
Execute a searchlight analysis.
This function runs a searchlight analysis using a specified MVPA model, radius, and method. It can be customized with a combiner function and permutation options.
This function runs a searchlight analysis using a specified RSA model, radius, and method. It can be customized with permutation options, distance computation methods, and regression methods.
This function runs a searchlight analysis using a specified MANOVA model, radius, and method. It can be customized with permutation options.
This function runs a searchlight analysis using a specified vector RSA model, radius, and method. It can be customized with permutation options, distance computation methods, and regression methods.
run_searchlight(model_spec, radius, method, niter, ...)
## S3 method for class 'mvpa_model'
run_searchlight(
model_spec,
radius = 8,
method = c("randomized", "standard"),
niter = 4,
combiner = "average",
permute = FALSE,
...
)
## S3 method for class 'rsa_model'
run_searchlight(
model_spec,
radius = 8,
method = c("randomized", "standard"),
niter = 4,
permute = FALSE,
distmethod = c("spearman", "pearson"),
regtype = c("pearson", "spearman", "lm", "rfit"),
...
)
## S3 method for class 'manova_model'
run_searchlight(
model_spec,
radius = 8,
method = c("randomized", "standard"),
niter = 4,
permute = FALSE,
...
)
## S3 method for class 'vector_rsa'
run_searchlight(
model_spec,
radius = 8,
method = c("randomized", "standard"),
niter = 4,
permute = FALSE,
...
)
model_spec |
An object of type |
radius |
The radius of the searchlight sphere (default is 8, allowable range: 1-100). |
method |
The method used for the searchlight analysis ("randomized" or "standard"). |
niter |
The number of iterations for randomized searchlight (default is 4). |
... |
Additional arguments to be passed to the function. |
combiner |
A function that combines results into an appropriate output, or one of the following strings: "pool" or "average". |
permute |
Whether to permute the labels (default is FALSE). |
distmethod |
The method used to compute distances between searchlight samples ("spearman" or "pearson"). |
regtype |
The method used to fit response and predictor distance matrices ("pearson", "spearman", "lm", or "rfit"). |
A named list of NeuroVol
objects, where each element contains a performance metric (e.g. AUC) at every voxel location.
Bjornsdotter, M., Rylander, K., & Wessberg, J. (2011). A Monte Carlo method for locally multivariate brain mapping. Neuroimage, 56(2), 508-516.
Kriegeskorte, N., Goebel, R., & Bandettini, P. (2006). Information-based functional brain mapping. Proceedings of the National academy of Sciences of the United States of America, 103(10), 3863-3868.
run_searchlight.randomized
, run_searchlight.standard
# TODO: Add an example
dataset <- gen_sample_dataset(c(4,4,4), 100, blocks=3)
cval <- blocked_cross_validation(dataset$design$block_var)
model <- load_model("sda_notune")
mspec <- mvpa_model(model, dataset$dataset, design=dataset$design, model_type="classification", crossval=cval)
res <- run_searchlight(mspec, radius=8, method="standard")
# A custom "combiner" can be used to post-process the output of the searchlight classifier for special cases.
# In the example below, the supplied "combining function" extracts the predicted probability of the correct class
# for every voxel and every trial and then stores them in a data.frame.
## Not run:
custom_combiner <- function(mspec, good, bad) {
good %>% pmap(function(result, id, ...) {
data.frame(trial=1:length(result$observed), id=id, prob=prob_observed(result))
}) %>% bind_rows()
}
res2 <- run_searchlight(mspec, radius=8, method="standard", combiner=custom_combiner)
## End(Not run)
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