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scam (version 1.2-2)

Shape Constrained Additive Models

Description

Routines for generalized additive modelling under shape constraints on the component functions of the linear predictor (Pya and Wood, 2015) . Models can contain multiple shape constrained (univariate and/or bivariate) and unconstrained terms. The routines of gam() in package 'mgcv' 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

11,080

Version

1.2-2

License

GPL (>= 2)

Maintainer

Natalya Pya

Last Published

September 24th, 2017

Functions in scam (1.2-2)

plot.scam

SCAM plotting
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
scam.fit

Newton-Raphson method to fit SCAM
anova.scam

Approximate hypothesis tests related to SCAM fits
Predict.matrix.mpi.smooth

Predict matrix method functions for 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
print.scam

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

Constructor for monotone increasing P-splines in SCAMs
bfgs_gcv.ubre

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

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

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

Checking the analytical gradient of the GCV/UBRE score
gcv.ubre_grad

The GCV/UBRE score value and its gradient
linear.functional.terms

Linear functionals of a smooth in GAMs
smooth.construct.micx.smooth.spec

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

SCAM residuals
scam-package

Shape Constrained Additive Models
smooth.construct.mdcx.smooth.spec

Constructor for monotone decreasing and convex P-splines in SCAMs
smooth.construct.micv.smooth.spec

Constructor for monotone increasing and concave P-splines in SCAMs
smooth.construct.mpd.smooth.spec

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

Tensor product smoothing constructor for bivariate function subject to mixed constraints: monotone decreasing constraint wrt the first covariate and concavity wrt the second one
smooth.construct.cx.smooth.spec

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

Tensor product smoothing constructor for bivariate function subject to mixed constraints: monotone increasing constraint wrt the first covariate and concavity wrt the second one
smooth.construct.tedecx.smooth.spec

Tensor product smoothing constructor for bivariate function subject to mixed constraints: monotone decreasing constraint wrt the first covariate and convexity wrt the second one
scam

Shape constrained additive models (SCAM) and integrated smoothness selection
smooth.construct.mdcv.smooth.spec

Constructor for monotone decreasing and concave P-splines in SCAMs
smooth.construct.tescv.smooth.spec

Tensor product smoothing constructor for a bivariate function concave in the second covariate
shape.constrained.smooth.terms

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

Tensor product smoothing constructor for a bivariate function convex in the second covariate
smooth.construct.temicx.smooth.spec

Tensor product smoothing constructor for bivariate function subject to mixed constraints: monotone increasing constraint wrt the first covariate and convexity wrt the second one
smooth.construct.tesmi1.smooth.spec

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

Constructor for concave P-splines in SCAMs
smooth.construct.tesmi2.smooth.spec

Tensor product smoothing constructor for a bivariate function monotone increasing in the second covariate
smooth.construct.tesmd1.smooth.spec

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

Constructor for monotone increasing P-splines in SCAMs
smooth.construct.tesmd2.smooth.spec

Tensor product smoothing constructor for a bivariate function monotone decreasing in the second covariate
summary.scam

Summary for a SCAM fit
vis.scam

Visualization of SCAM objects
formula.scam

SCAM formula
derivative.scam

Derivative of the univariate smooth model terms
logLik.scam

Log likelihood for a fitted SCAM, for AIC
marginal.matrices.tescv.ps

Constructs marginal model matrices for "tescv" and "tescx" bivariate smooths in case of B-splines basis functions for both unconstrained marginal smooths
scam.check

Some diagnostics for a fitted scam object
predict.scam

Prediction from fitted SCAM model