Description Usage Arguments Details Value Note Author(s) References See Also Examples
Tis function, additionally to estimating the
effective dimension reduction space (EDR), see also function edr
, estimates the Mean Squared Error of Prediction (MSEP) and the Mean Absolute Error of Prediction (MAEP) when using the estimated EDR by Cross-Validation. Estimates of the regression function are produced using function sm.regression
from package sm
.
1 2 3 4 |
x |
|
y |
|
m |
Rank of matrix M in case of |
rho0 |
Initial value for the regularization parameter ρ. |
h0 |
Initial bandwidth. |
ch |
Factor for indecreasing h with iterations. |
crhomin |
Factor to in(de)crease the default value of rhomin. This is just added to explore properties of the algorithms. Defaults to 1. |
cm |
Factor in the definition of Π_k=C_m*ρ_k^2 I_L + \hat{M}_{k-1}. Only used if |
method |
Secifies the algoritm to use. The default |
fit |
Specifies the method for estimating and predicting values of the link function. This can either be |
basis |
Specifies the set of basis functions. Options are |
cw |
|
graph |
If |
show |
If |
trace |
|
seed |
Seed for generating random groups for CV |
cvsize |
Groupsize k in leave-k-out CV |
m0 |
Dimension of the dimension reduction space to use when fitting the data. Should be either 1 or 2. |
hsm |
If |
This function performs a leave-k-out cross-validation to estimate the risk
in terms of Mean Squared Error of Prediction (MSEP) and Mean Absolute Error of Prediction (MAEP) when using function edr
to estimate an
effective dimension reduction space of dimension m0
and using this estimated space to predict values of the response. Smoothing within the dimension reduction space is performed using the function sm.regression
from package sm
. The bandwidth for sm.regression
is
chosen by Cross-Validation.
Object of class "edr"
with components.
x |
The design matrix. |
y |
The values of the response. |
bhat |
Matrix \hat{B} characterizing the effective dimension space. For a specified dimension |
fhat |
an highly oversmoothed estimate of the values of the regression function at the design points. This is provided
as a backup only for the case that package |
cumlam |
Cummulative amount of information explained by the first components of \hat{B}. |
nmean |
Mean numbers of observations used in each iteration. |
h |
Final bandwidth |
rho |
Final value of ρ |
h0 |
Initial bandwidth |
rho0 |
Initial value of ρ |
cm |
The factor |
call |
Arguments of the call to edrcv |
cvres |
Residuals from cross-validation. |
cvmseofh |
Estimates of MSEP for bandwidths |
cvmaeofh |
Estimates of MAEP for bandwidths |
cvmse |
Estimate of MSEP |
cvmae |
Estimate of MAEP |
hsm |
Set of bandwidths specified for use with |
hsmopt |
Bandwidth selected for use with |
This function requires package sm
if fit="sm"
.
Joerg Polzehl, polzehl@wias-berlin.de
M. Hristache, A. Juditsky, J. Polzehl and V. Spokoiny (2001). Structure adaptive approach for dimension reduction, The Annals of Statistics. Vol.29, pp. 1537-1566.
J. Polzehl, S. Sperlich (2008). A Note on Stuctural Adaptive Dimension Reduction, Journal of Statistical Computation and Simulation, DOI: 10.1080/00949650801959699
edr
,plot.edr
, summary.edr
, print.edr
, edr.R
, predict.edr
1 2 | require(EDR)
## Not run: demo(edr_ex4)
|
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