Description Usage Arguments Details Value References Examples
It computes the functional variance of the estimated functional
parameters obtained by funcreg
1 2 3 |
object |
An object of class "funcreg" |
which |
The variance of which functional parameters shall we compute? 0 for the intercept, 1 for the first functional slope and so on. |
type |
What type of variance to compute (see details). |
s |
If type is "beta_s", the variance is computed at each element
of |
t |
If type is "beta_t", the variance is computed at each element
of |
... |
Argument to pass to other objects |
The function computed the variance of the integral of the functional
parameters (single for the intercept and double for the slopes) when
type='beta'
.
If type='beta_t'
, it is the pointwise variance of the functional
intercept if which=0
and the pointwise variance of the integral
of the slopes which respect to s
. By default (t=NULL
) the
time units of the observed data is used. Alternatively, we can provide
a vector of points.
If type='beta_s'
, (not available for which=0
) it is the
pointwise variance of the integral of the slopes which respect to
y
. By default (s=NULL
), equally spaced grid points are
generated using the range of s-basis and the number of time units equals
to the one from the observed observations.
If type='beta_st'
(not available for which=0
), a two
dimensional pointwise variance is computed.
For the last three cases, vcov
returns either a one dimension
functional data object or a two-dimensional one.
It returns a list containing at least:
V |
Either a numeric value when |
Ramsay, James O., & Silverman, Bernard W. (2005), Functional Data Analysis, Springer, New York.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | data(GDPv56)
## We just create response and a covariate artificialy from the GDP
## series
y <- GDPv56[,1:30]
x <- GDPv56[,31:60]
t <- seq(0,1,len=nrow(y))
## First we create the "myfda" objects
yfd <- coefEst(y, t, .0004, 15, 2)
xfd <- coefEst(x, t, .0004, 15, 2)
## we just set lambda and k to arbitrary values
res <- funcreg(yfd~xfd, k=c(5,5), lambda=c(.001,.001,.001))
vcov(res, which=0)
plot(vcov(res, 1, type="beta_t")$V)
|
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