Description Usage Arguments Details Value Author(s) References See Also
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 pointwise estimated standard errors. Not implemented for smooths in more than 3 dimensions.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 
object 
a fitted 
raw 
logical, defaults to FALSE. If TRUE, the function simply returns 
se 
logical, defaults to TRUE. Return estimated standard error of the estimates? 
freq 
logical, defaults to FALSE. If FALSE, use posterior variance 
sandwich 
logical, defaults to FALSE. Use a Sandwichestimator 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 

Ktt 
(optional) an estimate of the covariance operator of the residual process ε_i(t) \sim N(0, K(t,t')),
evaluated on 
... 
other arguments, not used. 
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 blockdiagonal matrix with n copies of the estimated K(t,t') on the diagonal.
This blockdiagonal matrix is used to construct the "meat" of a sandwich covariance estimator, similar to Chen et al. (2012),
see reference below.
If raw==FALSE
, a list containing
pterms
a matrix containing the parametric / nonfunctional coefficients (and, optionally, their se's)
smterms
a named list with one entry for each smooth term in the model. Each entry contains
coef
a matrix giving the grid values over the covariates, the estimated effect (and, optionally, the se's).
The first covariate varies the fastest.
x, y, z
the unique gridpoints used to evaluate the smooth/coefficient function/coefficient surface
xlim, ylim, zlim
the extent of the x/y/zaxes
xlab, ylab, zlab
the names of the covariates for the x/y/zaxes
dim
the dimensionality of the effect
main
the label of the smooth term (a short label, same as the one used in summary.pffr
)
Fabian Scheipl
Chen, H., Wang, Y., Paik, M.C., and Choi, A. (2013). A marginal approach to reducedrank penalized spline smoothing with application to multilevel functional data. Journal of the American Statistical Association, 101, 1216–1229.
plot.gam
, predict.gam
which this routine is
based on.
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