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scam (version 1.1-5)

Shape constrained additive models

Description

Routines for generalized additive modelling under shape constraints on the component functions of the linear predictor. Models can contain multiple shape constrained (univariate and/or bivariate) and unconstrained terms. The routines of mgcv(gam) package are used for setting up the model matrix, printing and plotting the results. Penalized likelihood maximization based on Newton-Raphson method is used to fit a model with multiple smoothing parameter selection by GCV or UBRE/AIC.

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Version

Install

install.packages('scam')

Monthly Downloads

11,282

Version

1.1-5

License

GPL (>= 2)

Maintainer

Natalya Pya

Last Published

September 14th, 2013

Functions in scam (1.1-5)

plot.scam

SCAM plotting
extrapolate.uni.scam

Extrapolation from the fitted SCAM model
print.scam

Print a SCAM object
smooth.construct.mpi.smooth.spec

Constructor for monotone increasing P-splines in SCAMs
residuals.scam

SCAM residuals
smooth.construct.micx.smooth.spec

Constructor for monotone increasing and convex P-splines in SCAMs
shape.constrained.smooth.terms

Shape preserving smooth terms in SCAM
smooth.construct.tesmd1.smooth.spec

Tensor product smoothing constructor for a bivariate function monotone decreasing in the first covariate
smooth.construct.tesmd2.smooth.spec

Tensor product smoothing constructor for a bivariate function monotone decreasing in the second covariate
check.analytical

Checking the analytical gradient of the GCV/UBRE score
smooth.construct.tesmi1.smooth.spec

Tensor product smoothing constructor for a bivariate function monotone increasing in the first covariate
scam-package

Shape constrained additive models
bfgs_gcv.ubre

Multiple Smoothing Parameter Estimation by GCV/UBRE
smooth.construct.tedmi.smooth.spec

Tensor product smoothing constructor for bivariate function subject to double monotone increasing constraint
scam.check

Some diagnostics for a fitted scam object
smooth.construct.micv.smooth.spec

Constructor for monotone increasing and concave P-splines in SCAMs
marginal.matrices.tesmi2.ps

Constructs marginal model matrices for "tesmi2" and "tesmd2" bivariate smooths in case of B-splines basis functions for both unconstrained marginal smooths
smooth.construct.mdcx.smooth.spec

Constructor for monotone decreasing and convex P-splines in SCAMs
scam

Shape constrained additive models (SCAM) and integrated smoothness selection
gcv.ubre_grad

The GCV/UBRE score value and its gradient
summary.scam

Summary for a SCAM fit
predict.scam

Prediction from fitted SCAM model
marginal.matrices.tesmi1.ps

Constructs marginal model matrices for "tesmi1" and "tesmd1" bivariate smooths in case of B-splines basis functions for both unconstrained marginal smooths
smooth.construct.cx.smooth.spec

Constructor for convex P-splines in SCAMs
smooth.construct.tedmd.smooth.spec

Tensor product smoothing constructor for bivariate function subject to double monotone decreasing constraint
smooth.construct.tesmi2.smooth.spec

Tensor product smoothing constructor for a bivariate function monotone increasing in the second covariate
Predict.matrix.mpi.smooth

Predict matrix method functions for SCAMs
derivative.smooth

Derivative of the univariate constrained smooth term
scam.fit

Newton-Raphson method to fit SCAM
smooth.construct.mpd.smooth.spec

Constructor for monotone decreasing P-splines in SCAMs
smooth.construct.mdcv.smooth.spec

Constructor for monotone decreasing and concave P-splines in SCAMs