Contents
What is GAM package r?
Description gam is used to fit generalized additive models, specified by giving a symbolic description of the additive predictor and a description of the error distribution. gam uses the backfitting algorithm to combine different smoothing or fitting methods.
What is a GAM plot?
gam. It is the workhorse of the mgcViz package, and allows plotting (almost) any type of smooth, parametric or random effects. It is basically a wrapper around plotting methods that are specific to individual smooth effect classes (such as plot.
Is GAM a GLM?
Another person pointed out that a GAM does a different type of regression analysis than a GLM, and that a GLM is preferred when linearity can be assumed. In the past I have been using a GAM for ecological data, e.g.: continuous time-series. when the data did not have a linear shape.
What are 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.
Performs hypothesis tests relating to one or more fitted gam objects. For a single fitted gam object, Wald tests of the significance of each parametric and smooth term are performed. Otherwise the fitted models are compared using an analysis of deviance table. The tests are usually approximate, unless the models are un-penalized.
How is the p value of a fitted GAM model calculated?
The p-value is computed by simulation: the residuals are randomly re-shuffled k.rep times to obtain the null distribution of the differencing variance estimator, if there is no pattern in the residuals. For models fitted to more than k.sample data, the tests are based of k.sample randomly sampled data.
How are generalized additive models fitting in GAM?
The gam model is fit using the local scoring algorithm, which iteratively fits weighted additive models by backfitting. The backfitting algorithm is a Gauss-Seidel method for fitting additive models, by iteratively smoothing partial residuals.