Description Usage Arguments Value Author(s) References See Also Examples
Performs a forward variable selection for iterative bias reduction using kernel, thin plate splines or low rank splines. Missing values are not allowed.
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formula 
An object of class 
data 
An optional data frame, list or environment (or object
coercible by 
subset 
An optional vector specifying a subset of observations to be used in the fitting process. 
criterion 
Character string. If the number of iterations
( 
df 
A numeric vector of either length 1 or length equal to the
number of columns of 
Kmin 
The minimum number of bias correction iterations of the search grid considered by the model selection procedure for selecting the optimal number of iterations. 
Kmax 
The maximum number of bias correction iterations of the search grid considered by the model selection procedure for selecting the optimal number of iterations. 
smoother 
Character string which allows to choose between thine plate
splines 
kernel 
Character string which allows to choose between gaussian kernel
( 
rank 
Numeric value that control the rank of low rank splines
(denoted as 
control.par 
a named list that control optional parameters. The
components are

cv.options 
A named list which controls the way to do cross
validation with component 
varcrit 
Character string. Criterion used for variable
selection. The criteria available are GCV,
AIC ( 
Returns an object of class forwardibr
which is a matrix
with p
columns. In the first row, each entry j contains
the value of the chosen criterion for the univariate smoother using
the jth explanatory variable. The variable which realize the minimum
of the first row is included in the model. All the column of this
variable will be Inf
except the first row. In the second row,
each entry j contains the bivariate smoother using the jth
explanatory variable and the variable already included. The variable
which realize the minimum of the second row is included in the
model. All the column of this variable will be Inf
except the
two first row. This forward selection process continue until the
chosen criterion increases.
PierreAndre Cornillon, Nicolas Hengartner and Eric MatznerLober.
Cornillon, P.A.; Hengartner, N.; Jegou, N. and MatznerLober, E. (2012) Iterative bias reduction: a comparative study. Statistics and Computing, 23, 777791.
Cornillon, P.A.; Hengartner, N. and MatznerLober, E. (2013) Recursive bias estimation for multivariate regression smoothers Recursive bias estimation for multivariate regression smoothers. ESAIM: Probability and Statistics, 18, 483502.
Cornillon, P.A.; Hengartner, N. and MatznerLober, E. (2017) Iterative Bias Reduction Multivariate Smoothing in R: The ibr Package. Journal of Statistical Software, 77, 1–26.
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