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scam (version 1.2-17)

Shape Constrained Additive Models

Description

Generalized additive models under shape constraints on the component functions of the linear predictor. Models can include multiple shape-constrained (univariate and bivariate) and unconstrained terms. Routines of the package 'mgcv' are used to set up the model matrix, print, and plot the results. Multiple smoothing parameter estimation by the Generalized Cross Validation or similar. See Pya and Wood (2015) for an overview. A broad selection of shape-constrained smoothers, linear functionals of smooths with shape constraints, and Gaussian models with AR1 residuals.

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Version

Install

install.packages('scam')

Monthly Downloads

8,863

Version

1.2-17

License

GPL (>= 2)

Maintainer

Last Published

June 19th, 2024

Functions in scam (1.2-17)

plot.scam

SCAM plotting
residuals.scam

SCAM residuals
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
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-package

tools:::Rd_package_title("scam")
scam

Shape constrained additive models (SCAM) and integrated smoothness selection
print.scam

Print a SCAM object
qq.scam

QQ plots for scam model residuals
scam.check

Some diagnostics for a fitted scam object
predict.scam

Prediction from fitted SCAM model
smooth.construct.mdcv.smooth.spec

Constructor for monotone decreasing and concave P-splines in SCAMs
shape.constrained.smooth.terms

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

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

Constructor for convex P-splines in SCAMs
scam.fit

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

Locally shape-constrained P-spline based constructor (LSCOP-spline): locally increasing splines up to a change point.
scam.control

Setting SCAM fitting defaults
smooth.construct.mdcx.smooth.spec

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

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

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

Constructor for monotone increasing SCOP-splines with an additional 'finish at zero' constraint
smooth.construct.po.smooth.spec

Constructor for SCOP-splines with positivity constraint
smooth.construct.tecvcv.smooth.spec

Tensor product smoothing constructor for bivariate function subject to double concavity constraint
smooth.construct.tecxcv.smooth.spec

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

Tensor product smoothing constructor for bivariate function subject to double convexity constraint
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
smooth.construct.miso.smooth.spec

Constructor for monotone increasing SCOP-splines with an additional 'start at zero' constraint
smooth.construct.mpd.smooth.spec

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

Constructor for monotone increasing P-splines in SCAMs
smooth.construct.tesmi1.smooth.spec

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

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

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

Summary for a SCAM fit
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.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.tedmd.smooth.spec

Tensor product smoothing constructor for bivariate function subject to double monotone decreasing constraint
smooth.construct.tesmd2.smooth.spec

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

Visualization of SCAM objects
smooth.construct.tedmi.smooth.spec

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

Tensor product interaction with decreasing constraint along the first covariate and unconstrained along the second covariate
smooth.construct.tismi.smooth.spec

Tensor product interaction with increasing constraint along the first covariate and unconstrained along the second covariate
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
check.analytical

Checking the analytical gradient of the GCV/UBRE score
bfgs_gcv.ubre

Multiple Smoothing Parameter Estimation by GCV/UBRE
Predict.matrix.mpi.smooth

Predict matrix method functions for SCAMs
formula.scam

SCAM formula
anova.scam

Approximate hypothesis tests related to SCAM fits
derivative.scam

Derivative of the univariate smooth model terms
linear.functional.terms

Linear functionals of a smooth in GAMs
logLik.scam

Log likelihood for a fitted SCAM, for AIC
gcv.ubre_grad

The GCV/UBRE score value and its gradient