coef.pffr: Get estimated coefficients from a pffr fit

Description Usage Arguments Details Value Author(s) References See Also

View source: R/pffr-methods.R

Description

Returns estimated coefficient functions/surfaces β(t), β(s,t) and estimated smooth effects f(z), f(x,z) or f(x, z, t) and their point-wise estimated standard errors. Not implemented for smooths in more than 3 dimensions.

Usage

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## S3 method for class 'pffr'
coef(
  object,
  raw = FALSE,
  se = TRUE,
  freq = FALSE,
  sandwich = FALSE,
  seWithMean = TRUE,
  n1 = 100,
  n2 = 40,
  n3 = 20,
  Ktt = NULL,
  ...
)

Arguments

object

a fitted pffr-object

raw

logical, defaults to FALSE. If TRUE, the function simply returns object$coefficients

se

logical, defaults to TRUE. Return estimated standard error of the estimates?

freq

logical, defaults to FALSE. If FALSE, use posterior variance object$Vp for variability estimates, else use object$Ve. See gamObject

sandwich

logical, defaults to FALSE. Use a Sandwich-estimator for approximate variances? See Details. THIS IS AN EXPERIMENTAL FEATURE, USE A YOUR OWN RISK.

seWithMean

logical, defaults to TRUE. Include uncertainty about the intercept/overall mean in standard errors returned for smooth components?

n1

see below

n2

see below

n3

n1, n2, n3 give the number of gridpoints for 1-/2-/3-dimensional smooth terms used in the marginal equidistant grids over the range of the covariates at which the estimated effects are evaluated.

Ktt

(optional) an estimate of the covariance operator of the residual process ε_i(t) \sim N(0, K(t,t')), evaluated on yind of object. If not supplied, this is estimated from the crossproduct matrices of the observed residual vectors. Only relevant for sandwich CIs.

...

other arguments, not used.

Details

The seWithMean-option corresponds to the "iterms"-option in predict.gam. The sandwich-options works as follows: Assuming that the residual vectors ε_i(t), i=1,…,n are i.i.d. realizations of a mean zero Gaussian process with covariance K(t,t'), we can construct an estimator for K(t,t') from the n replicates of the observed residual vectors. The covariance matrix of the stacked observations vec(Y_i(t)) is then given by a block-diagonal matrix with n copies of the estimated K(t,t') on the diagonal. This block-diagonal matrix is used to construct the "meat" of a sandwich covariance estimator, similar to Chen et al. (2012), see reference below.

Value

If raw==FALSE, a list containing

Author(s)

Fabian Scheipl

References

Chen, H., Wang, Y., Paik, M.C., and Choi, A. (2013). A marginal approach to reduced-rank penalized spline smoothing with application to multilevel functional data. Journal of the American Statistical Association, 101, 1216–1229.

See Also

plot.gam, predict.gam which this routine is based on.


refund documentation built on July 1, 2021, 9:06 a.m.