Last data update: 2014.03.03

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Results 1 - 10 of 121 found.
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smoothCon (Package: mgcv) : Prediction/Construction wrapper functions for GAM smooth terms

Wrapper functions for construction of and prediction from smooth terms in a GAM. The purpose of the wrappers is to allow user-transparant re-parameterization of smooth terms, in order to allow identifiability constraints to be absorbed into the parameterization of each term, if required. The routine also handles ‘by’ variables and construction of identifiability constraints automatically, although this behaviour can be over-ridden.
● Data Source: CranContrib
● Keywords: models
● Alias: PredictMat, smoothCon
● 0 images

uniquecombs (Package: mgcv) : find the unique rows in a matrix

This routine returns a matrix or data frame containing all the unique rows of the matrix or data frame supplied as its argument. That is, all the duplicate rows are stripped out. Note that the ordering of the rows on exit is not the same as on entry. It also returns an index attribute for relating the result back to the original matrix.
● Data Source: CranContrib
● Keywords: models
● Alias: uniquecombs
● 0 images

print.gam (Package: mgcv) : Print a Generalized Additive Model object.

The default print method for a gam object.
● Data Source: CranContrib
● Keywords: models
● Alias: print.gam
● 0 images

mvn (Package: mgcv) : Multivariate normal additive models

Family for use with gam implementing smooth multivariate Gaussian regression. The means for each dimension are given by a separate linear predictor, which may contain smooth components. The Choleski factor of the response precision matrix is estimated as part of fitting.
● Data Source: CranContrib
● Keywords: models
● Alias: mvn
● 0 images

initial.sp (Package: mgcv) : Starting values for multiple smoothing parameter estimation

Finds initial smoothing parameter guesses for multiple smoothing parameter estimation. The idea is to find values such that the estimated degrees of freedom per penalized parameter should be well away from 0 and 1 for each penalized parameter, thus ensuring that the values are in a region of parameter space where the smoothing parameter estimation criterion is varying substantially with smoothing parameter value.
● Data Source: CranContrib
● Keywords: models
● Alias: initial.sp
● 0 images

exclude.too.far (Package: mgcv) : Exclude prediction grid points too far from data

Takes two arrays defining the nodes of a grid over a 2D covariate space and two arrays defining the location of data in that space, and returns a logical vector with elements TRUE if the corresponding node is too far from data and FALSE otherwise. Basically a service routine for vis.gam and plot.gam.
● Data Source: CranContrib
● Keywords: hplot
● Alias: exclude.too.far
● 0 images

formula.gam (Package: mgcv) : GAM formula

Description of gam formula (see Details), and how to extract it from a fitted gam object.
● Data Source: CranContrib
● Keywords: models
● Alias: formula.gam
● 0 images

gam.check (Package: mgcv) : Some diagnostics for a fitted gam model

Takes a fitted gam object produced by gam() and produces some diagnostic information about the fitting procedure and results. The default is to produce 4 residual plots, some information about the convergence of the smoothness selection optimization, and to run diagnostic tests of whether the basis dimension choises are adequate.
● Data Source: CranContrib
● Keywords: models
● Alias: gam.check
● 0 images

s (Package: mgcv) : Defining smooths in GAM formulae

Function used in definition of smooth terms within gam model formulae. The function does not evaluate a (spline) smooth - it exists purely to help set up a model using spline based smooths.
● Data Source: CranContrib
● Keywords: models
● Alias: s
● 0 images

choose.k (Package: mgcv) : Basis dimension choice for smooths

Choosing the basis dimension, and checking the choice, when using penalized regression smoothers.
● Data Source: CranContrib
● Keywords: models
● Alias: choose.k
● 0 images