Type: Package
Title: Sparse Additive Modelling
Version: 1.2
Maintainer: Tuo Zhao <tourzhao@gatech.edu>
Depends: R (≥ 2.14), splines
Description: Computationally efficient tools for high dimensional predictive modeling (regression and classification). SAM is short for sparse additive modeling, and adopts the computationally efficient basis spline technique. We solve the optimization problems by various computational algorithms including the block coordinate descent algorithm, fast iterative soft-thresholding algorithm, and newton method. The computation is further accelerated by warm-start and active-set tricks.
License: GPL-2
Encoding: UTF-8
NeedsCompilation: yes
RoxygenNote: 7.3.3
LinkingTo: Rcpp, RcppEigen
Packaged: 2026-02-19 03:51:01 UTC; tourzhao
Author: Haoming Jiang [aut], Yukun Ma [aut], Han Liu [aut], Kathryn Roeder [aut], Xingguo Li [aut], Tuo Zhao [aut, cre]
Repository: CRAN
Date/Publication: 2026-02-19 10:50:02 UTC

Sparse Additive Modelling

Description

SAM provides sparse additive models for high-dimensional prediction tasks (regression and classification). It uses spline basis expansion and efficient optimization routines to compute full regularization paths.

Details

The package exposes four model families:

All models share a common spline representation and return regularization paths, allowing model selection after one fit.

Author(s)

Tuo Zhao, Xingguo Li, Haoming Jiang, Han Liu, and Kathryn Roeder
Maintainer: Tuo Zhao <tourzhao@gatech.edu>

References

P. Ravikumar, J. Lafferty, H.Liu and L. Wasserman. "Sparse Additive Models", Journal of Royal Statistical Society: Series B, 2009.
T. Zhao and H.Liu. "Sparse Additive Machine", International Conference on Artificial Intelligence and Statistics, 2012.

See Also

samQL,samHL,samLL,samEL

Plot function for S3 class "samEL"

Description

Plot the regularization path (regularization parameter versus functional norm).

Usage

## S3 method for class 'samEL'
plot(x, ...)

Arguments

x

An object with S3 class "samEL"

...

Additional arguments passed to methods; currently unused.

Details

The x-axis shows regularization parameters on a log scale. The y-axis shows the functional norm of each component function.

See Also

samEL

Plot function for S3 class "samHL"

Description

Plot the regularization path (regularization parameter versus functional norm).

Usage

## S3 method for class 'samHL'
plot(x, ...)

Arguments

x

An object with S3 class "samHL"

...

Additional arguments passed to methods; currently unused.

Details

The x-axis shows regularization parameters on a log scale. The y-axis shows the functional norm of each component function.

See Also

samHL

Plot function for S3 class "samLL"

Description

Plot the regularization path (regularization parameter versus functional norm).

Usage

## S3 method for class 'samLL'
plot(x, ...)

Arguments

x

An object with S3 class "samLL"

...

Additional arguments passed to methods; currently unused.

Details

The x-axis shows regularization parameters on a log scale. The y-axis shows the functional norm of each component function.

See Also

samLL

Plot function for S3 class "samQL"

Description

Plot the regularization path (regularization parameter versus functional norm).

Usage

## S3 method for class 'samQL'
plot(x, ...)

Arguments

x

An object with S3 class "samQL"

...

Additional arguments passed to methods; currently unused.

Details

The x-axis shows regularization parameters on a log scale. The y-axis shows the functional norm of each component function.

See Also

samQL

Prediction function for S3 class "samEL"

Description

Predict expected counts for test data.

Usage

## S3 method for class 'samEL'
predict(object, newdata, ...)

Arguments

object

An object with S3 class "samEL".

newdata

Numeric test matrix with n rows and d columns.

...

Additional arguments passed to methods; currently unused.

Details

The test matrix is rescaled using the training X.min/X.ran, truncated to [0, 1], and expanded with the same spline basis used during training.

Value

expectations

Estimated expected counts as an n by length(lambda) matrix.

expectation

Alias of expectations kept for backward compatibility.

See Also

samEL

Prediction function for S3 class "samHL"

Description

Predict decision values and class labels for test data.

Usage

## S3 method for class 'samHL'
predict(object, newdata, thol = 0, ...)

Arguments

object

An object with S3 class "samHL".

newdata

Numeric test matrix with n rows and d columns.

thol

Decision-value threshold used to convert scores to labels. The default value is 0.

...

Additional arguments passed to methods; currently unused.

Details

The test matrix is rescaled using the training X.min/X.ran, truncated to [0, 1], and expanded with the same spline basis used during training.

Value

values

Predicted decision values as an n by length(lambda) matrix.

labels

Predicted class labels (-1/1) as an n by length(lambda) matrix.

See Also

samHL

Prediction function for S3 class "samLL"

Description

Predict class probabilities and labels for test data.

Usage

## S3 method for class 'samLL'
predict(object, newdata, thol = 0.5, ...)

Arguments

object

An object with S3 class "samLL".

newdata

Numeric test matrix with n rows and d columns.

thol

Decision-value threshold used to convert probabilities to labels. The default value is 0.5.

...

Additional arguments passed to methods; currently unused.

Details

The test matrix is rescaled using the training X.min/X.ran, truncated to [0, 1], and expanded with the same spline basis used during training.

Value

probs

Estimated posterior probabilities as an n by length(lambda) matrix.

labels

Predicted class labels (0/1) as an n by length(lambda) matrix.

See Also

samLL

Prediction function for S3 class "samQL"

Description

Predict responses for test data.

Usage

## S3 method for class 'samQL'
predict(object, newdata, ...)

Arguments

object

An object with S3 class "samQL".

newdata

Numeric test matrix with n rows and d columns.

...

Additional arguments passed to methods; currently unused.

Details

The test matrix is rescaled using the training X.min/X.ran, truncated to [0, 1], and expanded with the same spline basis used during training.

Value

values

Predicted responses as an n by length(lambda) matrix.

See Also

samQL

Printing function for S3 class "samEL"

Description

Print a summary of an object of class "samEL".

Usage

## S3 method for class 'samEL'
print(x, ...)

Arguments

x

An object with S3 class "samEL"

...

Additional arguments passed to methods; currently unused.

Details

The output includes the regularization path length and its degrees of freedom.

See Also

samEL

Printing function for S3 class "samHL"

Description

Print a summary of an object of class "samHL".

Usage

## S3 method for class 'samHL'
print(x, ...)

Arguments

x

An object with S3 class "samHL"

...

Additional arguments passed to methods; currently unused.

Details

The output includes the regularization path length and its degrees of freedom.

See Also

samHL

Printing function for S3 class "samLL"

Description

Print a summary of an object of class "samLL".

Usage

## S3 method for class 'samLL'
print(x, ...)

Arguments

x

An object with S3 class "samLL"

...

Additional arguments passed to methods; currently unused.

Details

The output includes the regularization path length and its degrees of freedom.

See Also

samLL

Printing function for S3 class "samQL"

Description

Print a summary of an object of class "samQL".

Usage

## S3 method for class 'samQL'
print(x, ...)

Arguments

x

An object with S3 class "samQL"

...

Additional arguments passed to methods; currently unused.

Details

The output includes the regularization path length and its degrees of freedom.

See Also

samQL

Training function of Sparse Additive Poisson Regression

Description

Fit a sparse additive Poisson regression model on training data.

Usage

samEL(
  X,
  y,
  p = 3,
  lambda = NULL,
  nlambda = NULL,
  lambda.min.ratio = 0.25,
  thol = 1e-05,
  max.ite = 1e+05,
  regfunc = "L1"
)

Arguments

X

Numeric training matrix with n rows (samples) and d columns (features).

y

Response vector of length n; values must be non-negative integers.

p

The number of basis spline functions. The default value is 3.

lambda

Optional user-supplied regularization sequence. If provided, use a decreasing sequence; warm starts are used along the path and are usually much faster than fitting a single value.

nlambda

The number of lambda values. The default value is 20.

lambda.min.ratio

Smallest lambda as a fraction of lambda.max (the smallest value that keeps all component functions at zero). The default is 0.25.

thol

Stopping tolerance. The default value is 1e-5.

max.ite

Maximum number of iterations. The default value is 1e5.

regfunc

A string indicating the regularizer. The default value is "L1". You can also assign "MCP" or "SCAD" to it.

Details

The solver combines block coordinate descent, fast iterative soft-thresholding, and Newton updates. Computation is accelerated by warm starts and active-set screening.

Value

p

The number of basis spline functions used in training.

X.min

Per-feature minimums from training data (used to rescale test data).

X.ran

Per-feature ranges from training data (used to rescale test data).

lambda

Sequence of regularization parameters used in training.

w

Solution path matrix with size d*p+1 by length(lambda); each column corresponds to one regularization parameter.

df

Degrees of freedom along the solution path (number of non-zero component functions).

knots

The p-1 by d matrix. Each column contains the knots applied to the corresponding variable.

Boundary.knots

The 2 by d matrix. Each column contains the boundary points applied to the corresponding variable.

func_norm

Functional norm matrix (d by length(lambda)); each column corresponds to one regularization parameter.

See Also

SAM,plot.samEL,print.samEL,predict.samEL

Examples


## generating training data
n = 200
d = 100
X = 0.5*matrix(runif(n*d),n,d) + matrix(rep(0.5*runif(n),d),n,d)
u = exp(-2*sin(X[,1]) + X[,2]^2-1/3 + X[,3]-1/2 + exp(-X[,4])+exp(-1)-1+1)
y = rep(0,n)
for(i in 1:n) y[i] = rpois(1,u[i])

## Training
out.trn = samEL(X,y)
out.trn

## plotting solution path
plot(out.trn)

## generating testing data
nt = 1000
Xt = 0.5*matrix(runif(nt*d),nt,d) + matrix(rep(0.5*runif(nt),d),nt,d)
ut = exp(-2*sin(Xt[,1]) + Xt[,2]^2-1/3 + Xt[,3]-1/2 + exp(-Xt[,4])+exp(-1)-1+1)
yt = rep(0,nt)
for(i in 1:nt) yt[i] = rpois(1,ut[i])

## predicting response
out.tst = predict(out.trn,Xt)

Training function of Sparse Additive Hinge-Loss Classifier

Description

Fit a sparse additive classifier with hinge loss.

Usage

samHL(
  X,
  y,
  p = 3,
  lambda = NULL,
  nlambda = NULL,
  lambda.min.ratio = 0.4,
  thol = 1e-05,
  mu = 0.05,
  max.ite = 1e+05,
  w = NULL
)

Arguments

X

Numeric training matrix with n rows (samples) and d columns (features).

y

Training labels of length n. Labels must be coded as -1 and 1.

p

The number of basis spline functions. The default value is 3.

lambda

Optional user-supplied regularization sequence. If provided, use a decreasing sequence; warm starts are used along the path and are usually much faster than fitting a single value.

nlambda

The number of lambda values. The default value is 20.

lambda.min.ratio

Smallest lambda as a fraction of lambda.max (the smallest value that keeps all component functions at zero). The default is 0.4.

thol

Stopping tolerance. The default value is 1e-5.

mu

Smoothing parameter used to approximate hinge loss. The default value is 0.05.

max.ite

Maximum number of iterations. The default value is 1e5.

w

Optional positive observation weights of length n. The default is 1 for all observations.

Details

The solver combines block coordinate descent, fast iterative soft-thresholding, and Newton updates. Computation is accelerated by warm starts and active-set screening.

Value

p

The number of basis spline functions used in training.

X.min

Per-feature minimums from training data (used to rescale test data).

X.ran

Per-feature ranges from training data (used to rescale test data).

lambda

Sequence of regularization parameters used in training.

w

Solution path matrix with size d*p+1 by length(lambda); each column corresponds to one regularization parameter.

df

Degrees of freedom along the solution path (number of non-zero component functions).

knots

The p-1 by d matrix. Each column contains the knots applied to the corresponding variable.

Boundary.knots

The 2 by d matrix. Each column contains the boundary points applied to the corresponding variable.

func_norm

Functional norm matrix (d by length(lambda)); each column corresponds to one regularization parameter.

See Also

SAM,plot.samHL,print.samHL,predict.samHL

Examples


## generating training data
n = 200
d = 100
X = 0.5*matrix(runif(n*d),n,d) + matrix(rep(0.5*runif(n),d),n,d)
y = sign(((X[,1]-0.5)^2 + (X[,2]-0.5)^2)-0.06)

## flipping about 5 percent of y
y = y*sign(runif(n)-0.05)

## Training
out.trn = samHL(X,y)
out.trn

## plotting solution path
plot(out.trn)

## generating testing data
nt = 1000
Xt = 0.5*matrix(runif(nt*d),nt,d) + matrix(rep(0.5*runif(nt),d),nt,d)

yt = sign(((Xt[,1]-0.5)^2 + (Xt[,2]-0.5)^2)-0.06)

## flipping about 5 percent of y
yt = yt*sign(runif(nt)-0.05)

## predicting response
out.tst = predict(out.trn,Xt)

Training function of Sparse Additive Logistic Regression

Description

Fit a sparse additive logistic regression model on training data.

Usage

samLL(
  X,
  y,
  p = 3,
  lambda = NULL,
  nlambda = NULL,
  lambda.min.ratio = 0.1,
  thol = 1e-05,
  max.ite = 1e+05,
  regfunc = "L1"
)

Arguments

X

Numeric training matrix with n rows (samples) and d columns (features).

y

Binary training labels of length n. Labels must be coded as 0 and 1.

p

The number of basis spline functions. The default value is 3.

lambda

Optional user-supplied regularization sequence. If provided, use a decreasing sequence; warm starts are used along the path and are usually much faster than fitting a single value.

nlambda

The number of lambda values. The default value is 20.

lambda.min.ratio

Smallest lambda as a fraction of lambda.max (the smallest value that keeps all component functions at zero). The default is 0.1.

thol

Stopping tolerance. The default value is 1e-5.

max.ite

Maximum number of iterations. The default value is 1e5.

regfunc

A string indicating the regularizer. The default value is "L1". You can also assign "MCP" or "SCAD" to it.

Details

The solver combines block coordinate descent, fast iterative soft-thresholding, and Newton updates. Computation is accelerated by warm starts and active-set screening.

Value

p

The number of basis spline functions used in training.

X.min

Per-feature minimums from training data (used to rescale test data).

X.ran

Per-feature ranges from training data (used to rescale test data).

lambda

Sequence of regularization parameters used in training.

w

Solution path matrix with size d*p+1 by length(lambda); each column corresponds to one regularization parameter.

df

Degrees of freedom along the solution path (number of non-zero component functions).

knots

The p-1 by d matrix. Each column contains the knots applied to the corresponding variable.

Boundary.knots

The 2 by d matrix. Each column contains the boundary points applied to the corresponding variable.

func_norm

Functional norm matrix (d by length(lambda)); each column corresponds to one regularization parameter.

See Also

SAM,plot.samLL,print.samLL,predict.samLL

Examples


## generating training data
n = 200
d = 100
X = 0.5*matrix(runif(n*d),n,d) + matrix(rep(0.5*runif(n),d),n,d)
y = sign(((X[,1]-0.5)^2 + (X[,2]-0.5)^2)-0.06)

## flipping about 5 percent of y
y = y*sign(runif(n)-0.05)
y = sign(y==1)

## Training
out.trn = samLL(X,y)
out.trn

## plotting solution path
plot(out.trn)

## generating testing data
nt = 1000
Xt = 0.5*matrix(runif(nt*d),nt,d) + matrix(rep(0.5*runif(nt),d),nt,d)

yt = sign(((Xt[,1]-0.5)^2 + (Xt[,2]-0.5)^2)-0.06)

## flipping about 5 percent of y
yt = yt*sign(runif(nt)-0.05)
yt = sign(yt==1)

## predicting response
out.tst = predict(out.trn,Xt)

Training function of Sparse Additive Regression with Quadratic Loss

Description

Fit a sparse additive regression model with quadratic loss.

Usage

samQL(
  X,
  y,
  p = 3,
  lambda = NULL,
  nlambda = NULL,
  lambda.min.ratio = 0.005,
  thol = 1e-05,
  max.ite = 1e+05,
  regfunc = "L1"
)

Arguments

X

Numeric training matrix with n rows (samples) and d columns (features).

y

Numeric response vector of length n.

p

The number of basis spline functions. The default value is 3.

lambda

Optional user-supplied regularization sequence. If provided, use a decreasing sequence; warm starts are used along the path and are usually much faster than fitting a single value.

nlambda

The number of lambda values. The default value is 30.

lambda.min.ratio

Smallest lambda as a fraction of lambda.max (the smallest value that keeps all component functions at zero). The default is 5e-3.

thol

Stopping tolerance. The default value is 1e-5.

max.ite

Maximum number of iterations. The default value is 1e5.

regfunc

A string indicating the regularizer. The default value is "L1". You can also assign "MCP" or "SCAD" to it.

Details

The solver combines block coordinate descent, fast iterative soft-thresholding, and Newton updates. Computation is accelerated by warm starts and active-set screening.

Value

p

The number of basis spline functions used in training.

X.min

Per-feature minimums from training data (used to rescale test data).

X.ran

Per-feature ranges from training data (used to rescale test data).

lambda

Sequence of regularization parameters used in training.

w

Solution path matrix with size d*p by length(lambda); each column corresponds to one regularization parameter.

intercept

The solution path of the intercept.

df

Degrees of freedom along the solution path (number of non-zero component functions).

knots

The p-1 by d matrix. Each column contains the knots applied to the corresponding variable.

Boundary.knots

The 2 by d matrix. Each column contains the boundary points applied to the corresponding variable.

func_norm

Functional norm matrix (d by length(lambda)); each column corresponds to one regularization parameter.

sse

Sums of square errors of the solution path.

See Also

SAM,plot.samQL,print.samQL,predict.samQL

Examples


## generating training data
n = 100
d = 500
X = 0.5*matrix(runif(n*d),n,d) + matrix(rep(0.5*runif(n),d),n,d)

## generating response
y = -2*sin(X[,1]) + X[,2]^2-1/3 + X[,3]-1/2 + exp(-X[,4])+exp(-1)-1

## Training
out.trn = samQL(X,y)
out.trn

## plotting solution path
plot(out.trn)

## generating testing data
nt = 1000
Xt = 0.5*matrix(runif(nt*d),nt,d) + matrix(rep(0.5*runif(nt),d),nt,d)

yt = -2*sin(Xt[,1]) + Xt[,2]^2-1/3 + Xt[,3]-1/2 + exp(-Xt[,4])+exp(-1)-1

## predicting response
out.tst = predict(out.trn,Xt)