Package 'gamsel' reference manual

Title:	Fit Regularization Path for Generalized Additive Models
Description:	Using overlap grouped-lasso penalties, 'gamsel' selects whether a term in a 'gam' is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families. See <doi:10.48550/arXiv.1506.03850> for more details.
Authors:	Alexandra Chouldechova [aut], Trevor Hastie [aut, cre], Balasubramanian Narasimhan [ctb], Vitalie Spinu [ctb], Matt Wand [ctb]
Maintainer:	Trevor Hastie <[email protected]>
License:	GPL-2
Version:	1.8-5
Built:	2024-11-24 06:53:35 UTC
Source:	CRAN

Fit Regularization Path for Generalized Additive Models

Description

Using overlap grouped lasso penalties, gamsel selects whether a term in a gam is nonzero, linear, or a non-linear spline (up to a specified max df per variable). It fits the entire regularization path on a grid of values for the overall penalty lambda, both for gaussian and binomial families. Key functions are gamsel, predict.gamsel, plot.gamsel, print.gamsel, summary.gamsel, cv.gamsel, plot.cv.gamsel

Author(s)

Alexandra Chouldechova, Trevor Hastie Maintainer: Trevor Hastie [email protected]

Generate basis

Description

Generate basis

Usage

basis.gen(x, df = 6, thresh = 0.01, degree = 8, parms = NULL, ...)
basis.gen(x, df = 6, thresh = 0.01, degree = 8, parms = NULL, ...)

Arguments

`x`	A vector of values for `basis.gen`, or a matrix for `pseudo.bases`
`df`	The degrees of freedom of the smoothing spline.
`thresh`	If the next eigenvector improves the approximation by less than threshold, a truncated bases is returned. For `pseudo.bases` this can be a single value or a vector of values, which are recycled sequentially for each column of `x`
`degree`	The nominal number of basis elements. The basis returned has no more than `degree` columns. For `pseudo.bases` this can be a single value or a vector of values, which are recycled sequentially for each column of `x`
`parms`	A parameter set. If included in the call, these are used to define the basis. This is used for prediction.
`...`	other arguments

Value

the basis

Cross-validation Routine for Gamsel

Description

A routine for performing K-fold cross-validation for gamsel.

Usage

cv.gamsel(
  x,
  y,
  lambda = NULL,
  family = c("gaussian", "binomial"),
  degrees = rep(10, p),
  dfs = rep(5, p),
  bases = pseudo.bases(x, degrees, dfs, parallel = parallel, ...),
  type.measure = c("mse", "mae", "deviance", "class"),
  nfolds = 10,
  foldid,
  keep = FALSE,
  parallel = FALSE,
  ...
)
cv.gamsel(
  x,
  y,
  lambda = NULL,
  family = c("gaussian", "binomial"),
  degrees = rep(10, p),
  dfs = rep(5, p),
  bases = pseudo.bases(x, degrees, dfs, parallel = parallel, ...),
  type.measure = c("mse", "mae", "deviance", "class"),
  nfolds = 10,
  foldid,
  keep = FALSE,
  parallel = FALSE,
  ...
)

Arguments

`x`	`x` matrix as in `gamsel`
`y`	response `y` as in `gamsel`
`lambda`	Optional use-supplied lambda sequence. If `NULL`, default behaviour is for `gamsel` routine to automatically select a good lambda sequence.
`family`	`family` as in `gamsel`
`degrees`	`degrees` as in `gamsel`
`dfs`	`dfs` as in `gamsel`
`bases`	`bases` as in `gamsel`
`type.measure`	Loss function for cross-validated error calculation. Currently there are four options: `mse` (mean squared error), `mae` (mean absolute error), `deviance` (deviance, same as `mse` for `family="gaussian"`), `class` (misclassification error, for use with `family="binomial"`).
`nfolds`	Numer of folds (default is 10). Maximum value is `nobs`. Small values of `nfolds` are recommended for large data sets.
`foldid`	Optional vector of length `nobs` with values between 1 and `nfolds` specifying what fold each observation is in.
`keep`	If `keep=TRUE`, a prevalidated array is returned containing fitted values for each observation and each value of `lambda`. This means these fits are computed with this observation and the rest of its fold omitted. The `folid` vector is also returned. Default is keep=FALSE
`parallel`	If `TRUE`, use parallel `foreach` to fit each fold. See the example below for usage details.
`...`	Other arguments that can be passed to `gamsel`.

Details

This function has the effect of running gamsel nfolds+1 times. The initial run uses all the data and gets the lambda sequence. The remaining runs fit the data with each of the folds omitted in turn. The error is accumulated, and the average error and standard deviation over the folds is computed. Note that cv.gamsel does NOT search for values for gamma. A specific value should be supplied, else gamma=.4 is assumed by default. If users would like to cross-validate gamma as well, they should call cv.gamsel with a pre-computed vector foldid, and then use this same fold vector in separate calls to cv.gamsel with different values of gamma. Note also that the results of cv.gamsel are random, since the folds are selected at random. Users can reduce this randomness by running cv.gamsel many times, and averaging the error curves.

Value

an object of class "cv.gamsel" is returned, which is a list with the ingredients of the cross-validation fit.

`lambda`	the values of `lambda` used in the fits.
`cvm`	The mean cross-validated error - a vector of length `length(lambda)`.
`cvsd`	estimate of standard error of `cvm`.
`cvup`	upper curve = `cvm+cvsd`.
`cvlo`	lower curve = `cvm-cvsd`.
`nzero`	number of non-zero coefficients at each `lambda`.
`name`	a text string indicating type of measure (for plotting purposes).
`gamsel.fit`	a fitted gamsel object for the full data.
`lambda.min`	value of `lambda` that gives minimum `cvm`.
`lambda.1se`	largest value of `lambda` such that error is within 1 standard error of the minimum.
`fit.preval`	if `keep=TRUE`, this is the array of prevalidated fits. Some entries can be `NA`, if that and subsequent values of `lambda` are not reached for that fold
`foldid`	if `keep=TRUE`, the fold assignments used
`index.min`	the sequence number of the minimum lambda.
`index.1se`	the sequence number of the 1se lambda value.

Author(s)

Alexandra Chouldechova and Trevor Hastie
Maintainer: Trevor Hastie [email protected]

References

Chouldechova, A. and Hastie, T. (2015) Generalized Additive Model Selection

Examples


##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
par(mfrow=c(1,2),mar=c(5,4,3,1))
summary(gamsel.out)
gamsel.cv=cv.gamsel(X,y,bases=bases)
par(mfrow=c(1,1))
plot(gamsel.cv)
par(mfrow=c(3,4))
plot(gamsel.out,newx=X,index=20)

##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
par(mfrow=c(1,2),mar=c(5,4,3,1))
summary(gamsel.out)
gamsel.cv=cv.gamsel(X,y,bases=bases)
par(mfrow=c(1,1))
plot(gamsel.cv)
par(mfrow=c(3,4))
plot(gamsel.out,newx=X,index=20)

Fit Regularization Path for Gaussian or Binomial Generalized Additive Model

Description

Usage

gamsel(
  x,
  y,
  num_lambda = 50,
  lambda = NULL,
  family = c("gaussian", "binomial"),
  degrees = rep(10, p),
  gamma = 0.4,
  dfs = rep(5, p),
  bases = pseudo.bases(x, degrees, dfs, parallel = parallel, ...),
  tol = 1e-04,
  max_iter = 2000,
  traceit = FALSE,
  parallel = FALSE,
  ...
)
gamsel(
  x,
  y,
  num_lambda = 50,
  lambda = NULL,
  family = c("gaussian", "binomial"),
  degrees = rep(10, p),
  gamma = 0.4,
  dfs = rep(5, p),
  bases = pseudo.bases(x, degrees, dfs, parallel = parallel, ...),
  tol = 1e-04,
  max_iter = 2000,
  traceit = FALSE,
  parallel = FALSE,
  ...
)

Arguments

`x`	Input (predictor) matrix of dimension `nobs` x `nvars`. Each observation is a row.
`y`	Response variable. Quantitative for `family="gaussian"` and with values in `{0,1}` for `family="binomial"`
`num_lambda`	Number of `lambda` values to use. (Length of `lambda` sequence.)
`lambda`	User-supplied `lambda` sequence. For best performance, leave as `NULL` and allow the routine to automatically select `lambda`. Otherwise, supply a (preferably gradually) decreasing sequence.
`family`	Response type. `"gaussian"` for linear model (default). `"binomial"` for logistic model.
`degrees`	An integer vector of length `nvars` specifying the maximum number of spline basis functions to use for each variable.
`gamma`	Penalty mixing parameter $0 \le\gamma\le 1$ . Values $\gamma < 0.5$ penalize linear fit less than non-linear fit. The default is $\gamma = 0.4$ , which encourages a linear term over a nonlinear term.
`dfs`	Numeric vector of length `nvars` specifying the maximum (end-of-path) degrees of freedom for each variable.
`bases`	A list of orthonormal bases for the non-linear terms for each variable. The function `pseudo.bases` generates these, using the parameters `dfs` and `degrees`. See the documentation for `pseudo.bases`.
`tol`	Convergence threshold for coordinate descent. The coordinate descent loop continues until the total change in objective after a pass over all variables is less than `tol`. Default is `1e-4`.
`max_iter`	Maximum number of coordinate descent iterations over all the variables for each `lambda` value. Default is 2000.
`traceit`	If `TRUE`, various information is printed during the fitting process.
`parallel`	passed on to the `pseudo.bases()` function. Uses multiple process if available.
`...`	additional arguments passed on to `pseudo.bases()`

Details

The sequence of models along the lambda path is fit by (block) cordinate descent. In the case of logistic regression the fitting routine may terminate before all num_lambda values of lambda have been used. This occurs when the fraction of null deviance explained by the model gets too close to 1, at which point the fit becomes numerically unstable. Each of the smooth terms is computed using an approximation to the Demmler-Reinsch smoothing spline basis for that variable, and the accompanying diagonal pernalty matrix.

Value

An object with S3 class gamsel. %% If it is a LIST, use

`intercept`	Intercept sequence of length `num_lambda`
`alphas`	`nvars` x `num_lambda` matrix of linear coefficient estimates
`betas`	`sum(degrees)` x `num_lambda` matrix of non-linear coefficient estimates
`lambdas`	The sequence of lambda values used
`degrees`	Number of basis functions used for each variable
`parms`	A set of parameters that capture the bases used. This allows for efficient generation of the bases elements for `predict.gamsel`

, the predict method for this class.

`family`	`"gaussian"` or `"binomial"`
`nulldev`	Null deviance (deviance of the intercept model)
`dev.ratio`	Vector of length `num_lambda` giving fraction of (null) deviance explained by each model along the `lambda` sequence
`call`	The call that produced this object

%% ...

Author(s)

Alexandra Chouldechova and Trevor Hastie
Maintainer: Trevor Hastie [email protected]

References

Chouldechova, A. and Hastie, T. (2015) Generalized Additive Model Selection, https://arxiv.org/abs/1506.03850

Examples


##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
par(mfrow=c(1,2),mar=c(5,4,3,1))
summary(gamsel.out)
gamsel.cv=cv.gamsel(X,y,bases=bases)
par(mfrow=c(1,1))
plot(gamsel.cv)
par(mfrow=c(3,4))
plot(gamsel.out,newx=X,index=20)
# Binomial model
gamsel.out=gamsel(X,yb,family="binomial")
par(mfrow=c(1,2),mar=c(5,4,3,1))
summary(gamsel.out)
par(mfrow=c(3,4))
plot(gamsel.out,newx=X,index=30)

##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
par(mfrow=c(1,2),mar=c(5,4,3,1))
summary(gamsel.out)
gamsel.cv=cv.gamsel(X,y,bases=bases)
par(mfrow=c(1,1))
plot(gamsel.cv)
par(mfrow=c(3,4))
plot(gamsel.out,newx=X,index=20)
# Binomial model
gamsel.out=gamsel(X,yb,family="binomial")
par(mfrow=c(1,2),mar=c(5,4,3,1))
summary(gamsel.out)
par(mfrow=c(3,4))
plot(gamsel.out,newx=X,index=30)

Returns active variables

Description

Extract active variables of different kinds from a gamsel object

Usage

getActive(
  object,
  index = NULL,
  type = c("nonzero", "linear", "nonlinear"),
  EPS = 0
)
getActive(
  object,
  index = NULL,
  type = c("nonzero", "linear", "nonlinear"),
  EPS = 0
)

Arguments

`object`	gamsel object
`index`	index or vector of indices at which to obtain active information. `NULL` returns all.
`type`	type of active variables to report. One of `c("nonzero", "linear", "nonlinear")`
`EPS`	threshold for what is nonzero; default is 0

Details

Returns a vector of variables indices of variables having the desired properties.

Value

vector of indices

Plotting Routine for Gamsel Cross-Validation Object

Description

Produces a cross-validation curve with standard errors for a fitted gamsel objecty.

Usage

## S3 method for class 'cv.gamsel'
plot(x, sign.lambda = 1, ...)
## S3 method for class 'cv.gamsel'
plot(x, sign.lambda = 1, ...)

Arguments

`x`	`cv.gamsel` object
`sign.lambda`	Either plot against `log(lambda)` (default) against `-lambda` if `sign.lambda=-1`.
`...`	Optional graphical parameters to plot.

Details

A plot showing cross-validation error is produced. Nothing is returned.

Author(s)

Alexandra Chouldechova and Trevor Hastie
Maintainer: Trevor Hastie [email protected]

References

Chouldechova, A. and Hastie, T. (2015) Generalized Additive Model Selection

Examples


##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
gamsel.cv=cv.gamsel(X,y,bases=bases)
par(mfrow=c(1,1))
plot(gamsel.cv)
##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
gamsel.cv=cv.gamsel(X,y,bases=bases)
par(mfrow=c(1,1))
plot(gamsel.cv)

Plotting Routine `gamsel` Object

Description

Produces plots of the estimated functions for specified variables at a given value of lambda.

Usage

## S3 method for class 'gamsel'
plot(x, newx, index, which = 1:p, rugplot = TRUE, ylims, ...)
## S3 method for class 'gamsel'
plot(x, newx, index, which = 1:p, rugplot = TRUE, ylims, ...)

Arguments

`x`	Fitted `gamsel` object.
`newx`	`nobs_new` x `p` matrix giving values of each predictor at which to plot.
`index`	Index of lambda value (i.e., model) for which plotting is desired.
`which`	Which values to plot. Default is all variables, i.e. `{1,2,...,nvars}`. Besides indices, which can take two special values: `"nonzero"` will plot only the nonzero functions, and `"nonlinear"` only the nonlinear functions.
`rugplot`	If `TRUE`, a rugplot showing values of `x` is shown at the bottom of each fitted function plot.
`ylims`	`ylim` argument for plotting each curve, which overides the default which is the range of all the functions.
`...`	Optional graphical parameters to plot.

Details

A plot of the specified fitted functions is produced. Nothing is returned.

Author(s)

Alexandra Chouldechova and Trevor Hastie
Maintainer: Trevor Hastie [email protected]

References

Chouldechova, A. and Hastie, T. (2015) Generalized Additive Model Selection

Examples


##set.seed(1211)
##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
par(mfrow=c(3,4))
plot(gamsel.out,newx=X,index=20)

##set.seed(1211)
##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
par(mfrow=c(3,4))
plot(gamsel.out,newx=X,index=20)

Gamsel Prediction Routine

Description

Make predictions from a gamsel object.

Usage

## S3 method for class 'gamsel'
predict(
  object,
  newdata,
  index = NULL,
  type = c("link", "response", "terms", "nonzero"),
  ...
)
## S3 method for class 'gamsel'
predict(
  object,
  newdata,
  index = NULL,
  type = c("link", "response", "terms", "nonzero"),
  ...
)

Arguments

`object`	Fitted `gamsel` object.
`newdata`	`nobs_new` x `p` matrix of new data values at which to predict.
`index`	Index of model in the sequence for which plotting is desired. Note, this is NOT a lambda value.
`type`	Type of prediction desired. Type `link` gives the linear predictors for `"binomial"`, and fitted values for `"gaussian"`. Type `response` gives fitted probabilities for `"binomial"` and fitted values for `"gaussian"`. Type `"terms"` returns a matrix of fitted functions, with as many columns as there are variables. Type `nonzero` returns a list of the indices of nonzero coefficients at the given `lambda` index.
`...`	Not used

Value

Either a vector aor a matrix is returned, depending on type.

Author(s)

Alexandra Chouldechova and Trevor Hastie
Maintainer: Trevor Hastie [email protected]

References

Chouldechova, A. and Hastie, T. (2015) Generalized Additive Model Selection

Examples


##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
preds=predict(gamsel.out,X,index=20,type="terms")

##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
preds=predict(gamsel.out,X,index=20,type="terms")

print a gamsel object

Description

Print a summary of the gamsel path at each step along the path

Usage

## S3 method for class 'gamsel'
print(x, digits = max(3, getOption("digits") - 3), ...)
## S3 method for class 'gamsel'
print(x, digits = max(3, getOption("digits") - 3), ...)

Arguments

`x`	fitted gamsel object
`digits`	significant digits in printout
`...`	additional print arguments

Details

The call that produced the object x is printed, followed by a five-column matrix with columns NonZero, Lin, NonLin, ⁠%Dev⁠ and Lambda. The first three columns say how many nonzero, linear and nonlinear terms there are. ⁠%Dev⁠ is the percent deviance explained (relative to the null deviance).

Value

The matrix above is silently returned

Author(s)

Alexandra Chouldechova and Trevor Hastie
Maintainer: Trevor Hastie [email protected]

References

Chouldechova, A. and Hastie, T. (2015) Generalized Additive Model Selection

Generate pseudo-spline bases

Description

Generate an approximation to the Demmler-Reinsch orthonormal bases for smoothing splines, using orthogonal polynomials. basis.gen generates a basis for a single x, and pseudo.bases generates a list of bases for each column of the matrix x.

Usage

pseudo.bases(x, degree = 8, df = 6, parallel = FALSE, ...)
pseudo.bases(x, degree = 8, df = 6, parallel = FALSE, ...)

Arguments

`x`	A vector of values for `basis.gen`, or a matrix for `pseudo.bases`
`degree`	The nominal number of basis elements. The basis returned has no more than `degree` columns. For `pseudo.bases` this can be a single value or a vector of values, which are recycled sequentially for each column of `x`
`df`	The degrees of freedom of the smoothing spline.
`parallel`	if TRUE, parallelize
`...`	other arguments for `basis.gen` can be passed through to `[basis.gen]`

Details

basis.gen starts with a basis of orthogonal polynomials of total degree. These are each smoothed using a smoothing spline, which allows for a one-step approximation to the Demmler-Reinsch basis for a smoothing spline of rank equal to the degree. See the reference for details. The function also approximates the appropriate diagonal penalty matrix for this basis, so that the a approximate smoothing spline (generalized ridge regression) has the target df.

Value

An orthonormal basis is returned (a list for pseudo.bases). This has an attribute parms, which has elements coefsCoefficients needed to generate the orthogonal polynomials rotateTransformation matrix for transforming the polynomial basis dpenalty values for the diagonal penalty dfdf used degreenumber of columns

Author(s)

Alexandra Chouldechova and Trevor Hastie
Maintainer: Trevor Hastie [email protected]

References

T. Hastie Pseudosplines. (1996) JRSSB 58(2), 379-396.
Chouldechova, A. and Hastie, T. (2015) Generalized Additive Model Selection

Examples


##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
## Not run: 
     require(doMC)
     registerDoMC(cores=4)
     bases=pseudo.bases(X,degree=10,df=6,parallel=TRUE)

## End(Not run)

##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
## Not run: 
     require(doMC)
     registerDoMC(cores=4)
     bases=pseudo.bases(X,degree=10,df=6,parallel=TRUE)

## End(Not run)

Gamsel summary routine

Description

This makes a two-panel plot of the gamsel object.

Usage

## S3 method for class 'gamsel'
summary(object, label = FALSE, ...)
## S3 method for class 'gamsel'
summary(object, label = FALSE, ...)

Arguments

`object`	`gamsel` object
`label`	if `TRUE`, annotate the plot with variable labels. Default is `FALSE`
`...`	additional arguments to summary

Details

A two panel plot is produced, that summarizes the linear components and the nonlinear components, as a function of lambda. For the linear components, it is the coefficient for each variable. For the nonlinear, we see the norm of the nonlinear coefficients.

Value

Nothing is returned.

Author(s)

Alexandra Chouldechova and Trevor Hastie
Maintainer: Trevor Hastie [email protected]

References

Chouldechova, A. and Hastie, T. (2015) Generalized Additive Model Selection

Examples


##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
par(mfrow=c(1,2),mar=c(5,4,3,1))
summary(gamsel.out)

##data=gamsel:::gendata(n=500,p=12,k.lin=3,k.nonlin=3,deg=8,sigma=0.5)
data = readRDS(system.file("extdata/gamsel_example.RDS", package = "gamsel"))
attach(data)
bases=pseudo.bases(X,degree=10,df=6)
# Gaussian gam
gamsel.out=gamsel(X,y,bases=bases)
par(mfrow=c(1,2),mar=c(5,4,3,1))
summary(gamsel.out)

Package 'gamsel'

Help Index

Fit Regularization Path for Generalized Additive Models

Description

Author(s)

See Also

Generate basis

Description

Usage

Arguments

Value

Cross-validation Routine for Gamsel

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Fit Regularization Path for Gaussian or Binomial Generalized Additive Model

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Returns active variables

Description

Usage

Arguments

Details

Value

Plotting Routine for Gamsel Cross-Validation Object

Description

Usage

Arguments

Details

Author(s)

References

Examples

Plotting Routine gamsel Object

Description

Usage

Arguments

Details

Author(s)

References

See Also

Examples

Gamsel Prediction Routine

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

print a gamsel object

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Generate pseudo-spline bases

Description

Usage

Arguments

Details

Value

Author(s)

References

Plotting Routine `gamsel` Object