semipar.mp: Massively parallel semiparametric regression

In vows: Voxelwise Semiparametrics

Description

Fits a possibly very large number of semiparametric models by quadratically penalized least squares. The model may include a combination of parametric terms, smooth terms, varying-coefficient terms, and simple random effect structures.

Usage

semipar.mp(formula, Y, lsp, data = NULL, range.basis = NULL,
  knots = "quantile", rm.constr = FALSE, random = NULL,
  store.reml = FALSE, store.fitted = FALSE)

Arguments

formula	a formula object such as "~ x1 + sf(x2) +sf(x2, effect = x3)" where x1 is a linear (parametric) predictor, x2 is a predictor on which the responses depend smoothly, and x3 is a predictor whose effect is linear but varies smoothly with x2 (i.e., a varying-coefficient predictor).
Y	an n \times V response matrix, where V is the number of models fitted in parallel, e.g., voxels in neuroimaging applications.
lsp	vector of candidate log tuning parameters (log(λ)).
data	an optional data frame containing the variables in the model.
range.basis	a numeric vector of length 2 defining the interval over which the B-spline basis is created. If NULL, it will be set as the range of the variable to be evaluated by the basis.
knots	knot placement for the B-spline bases. The default, "quantile", gives knots at equally spaced quantiles of the data. The alternative, "equispaced", gives equally spaced knots.
rm.constr	logical: should the constraints be removed for varying-coefficient models?
random	a formula or a matrix for random effects.
store.reml	logical: should the pointwise REML criterion at each grid point be included in the output? FALSE by default, as this output can be very large.
store.fitted	logical: should the fitted values be included in the output? FALSE by default.

Details

The basic approach to massively parallel smoothing is described in Reiss et al. (2014). Although simple mixed-effect models are available, semipar.mix.mp is generally preferable for mixed models with a single smooth term.

Each element of list.all corresponding to a nonparametric term of the model is a list with components modmat, penmat, pen.order, start, and end. For each parametric term, the same five components are included, plus basis, argvals, effect, k, and norder.

Value

An object of class "semipar.mp", which is also of class "qplsc.mp" but includes the following additional elements:

where.sf, where.nsf	vectors or scalars identifying where the smooth and non-smooth terms, respectively, appear in the model formula.
list.all	a list of lists, one for each term of the model; see Details.
formula	model formula.
Y	response matrix.
lsp	candidate values for the log smoothing parameter.
data	the supplied data frame, if any.

Author(s)

Yin-Hsiu Chen [e-mail] and Philip Reiss [e-mail]

References

Reiss, P. T., Huang, L., Chen, Y.-H., Huo, L., Tarpey, T., and Mennes, M. (2014). Massively parallel nonparametric regression, with an application to developmental brain mapping. Journal of Computational and Graphical Statistics, Journal of Computational and Graphical Statistics, 23(1), 232–248.

Examples

n<-32
Ys <- matrix(0, n, 5)
for(i in 1:n) Ys[i,]<--2:2+rnorm(5, i^2, i^0.5)+sin(i)
x1 <- rnorm(n,0,5)
x2 <- 1:n+runif(n, 1, 20)
semipar.obj <- semipar.mp(~x1+sf(x2,k=10),Y=Ys,lsp=seq(5,50,,30))

vows documentation built on May 2, 2019, 9:26 a.m.