Description Usage Arguments Author(s)
View source: R/cv_vbvs_concurrent.R
Cross validation to choose the tuning parameter v0 in the variational bayes variable selection algorithm for the linear functional concurrent model. Uses five-fold cross validation.
1 2 3 |
formula |
formula for desired regression. should have form |
id.var |
variable giving subject ID vector |
data |
optional data frame |
Kt |
number of spline basis functions for coefficients and FPCs |
Kp |
number of FPCs to estimate |
v0 |
tuning parameter vector; normal spike variance. defaults to 0.01 to 0.1 in increments of 0.01. |
v1 |
tuning parameter; normal slab variance |
SEED |
seed value, used to ensure reproducibility of the training split |
standardized |
logical; are covariates already standardized? |
t.min |
minimum value to be evaluated on the time domain (useful if data are sparse and / or irregular). if 'NULL', taken to be minimum observed value. |
t.max |
maximum value to be evaluated on the time domain (useful if data are sparse and / or irregular). if 'NULL', taken to be maximum observed value. |
Jeff Goldsmith jeff.goldsmith@columbia.edu
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