View source: R/baseline_model.R
baseline_model | R Documentation |
construct a baseline model to model noise and other non-event-related sources of variance
baseline_model(
basis = c("constant", "poly", "bs", "ns"),
degree = 1,
sframe,
intercept = c("runwise", "global", "none"),
nuisance_list = NULL
)
basis |
the basis function type |
degree |
the degree of the spline function. |
sframe |
sframe a |
intercept |
whether to include an intercept for each block. Automatically set to |
nuisance_list |
a list of nuisance matrices, one matrix per fMRI block |
## bspline basis with degree = 3. This will produce a design matrix with three
## splines regressor and a constant intercept.
sframe <- sampling_frame(blocklens=c(100,100), TR=2)
bmod <- baseline_model(basis="bs", degree=3, sframe=sframe)
bmod_global <- baseline_model(basis="bs", degree=3,
sframe=sframe, intercept="global")
bmod_nointercept <- baseline_model(basis="bs", degree=3,
sframe=sframe, intercept="none")
stopifnot(ncol(design_matrix(bmod)) == 8)
stopifnot(ncol(design_matrix(bmod_global)) == 7)
stopifnot(ncol(design_matrix(bmod_nointercept)) == 6)
## polynomial with no intercept term
bmod <- baseline_model(basis="poly", degree=3, sframe=sframe, intercept="none")
## a baseline model that only has dummy-coded intercept terms, one per block,
## i.e. to model runwise mean shifts only.
bmod <- baseline_model(basis="constant", degree=1, sframe=sframe)
## global intercept only
bmod <- baseline_model(basis="constant", degree=1, sframe=sframe, intercept="global")
## add an arbitrary nuisance matrix with two columns, i.e. motion regressors,
## physiological noise, etc.
nuismat <- matrix(rnorm(100*2), 100, 2)
bmod <- baseline_model(basis="bs", degree=3, sframe=sframe,
nuisance_list=list(nuismat, nuismat))
stopifnot(ncol(design_matrix(bmod)) == 12)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.