R: Approximate hypothesis tests related to SCAM fits

anova.scam {scam} | R Documentation

## Approximate hypothesis tests related to SCAM fits

### Description

Performs hypothesis tests relating to one or more fitted `scam` objects. The function is a clone of `anova.gam` of the `mgcv` package.

The documentation below is similar to that of object `anova.gam`.

### Usage

    ## S3 method for class 'scam'
    anova(object, ..., dispersion = NULL, test = NULL,
                        freq = FALSE,p.type=0)
    ## S3 method for class 'anova.scam'
    print(x, digits = max(3, getOption("digits") - 3),...)

### Arguments

`object,...` | fitted model objects of class `scam` as produced by `scam()`.
`x` | an `anova.scam` object produced by a single model call to `anova.scam()`.
`dispersion` | a value for the dispersion parameter: not normally used.
`test` | what sort of test to perform for a multi-model call. One of `"Chisq"`, `"F"` or `"Cp"`.
`freq` | whether to use frequentist or Bayesian approximations for parametric term p-values. See `summary.gam` for details.
`p.type` | selects exact test statistic to use for single smooth term p-values. See `summary.scam` for details.
`digits` | number of digits to use when printing output.

### Details

see `anova.gam` for details.

### Value

In the multi-model case `anova.scam` produces output identical to `anova.glm`, which it in fact uses.

In the single model case an object of class `anova.scam` is produced, which is in fact an object returned from `summary.scam`.

`print.anova.scam` simply produces tabulated output.

### WARNING

If models 'a' and 'b' differ only in terms with no un-penalized components then p values from anova(a,b) are unreliable, and usually much too low.

Default P-values will usually be wrong for parametric terms penalized using ‘paraPen’: use freq=TRUE to obtain better p-values when the penalties are full rank and represent conventional random effects.

For a single model, interpretation is similar to drop1, not anova.lm.

### Author(s)

Simon N. Wood simon.wood@r-project.org

### References

Scheipl, F., Greven, S. and Kuchenhoff, H. (2008) Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Comp. Statist. Data Anal. 52, 3283-3299

Wood, S.N. (2013a) On p-values for smooth components of an extended generalized additive model. Biometrika 100:221-228

Wood, S.N. (2013b) A simple test for random effects in regression models. Biometrika 100:1005-1010

### See Also

`scam`, `predict.scam`, `scam.check`, `summary.scam`, `anova.gam`

### Examples

    library(scam)
    set.seed(0)
    fac <- rep(1:4,20)
    x1 <- runif(80)*5
    x2 <- runif(80,-1,2)
    x3 <- runif(80, 0, 1)
    y <- fac+log(x1)/5
    y <- y + exp(-1.3*x2) +rnorm(80)*0.1
    fac <- factor(fac)
    b <- scam(y ~ fac+s(x1,bs="mpi")+s(x2,bs="mpd")+s(x3))

    b1 <- scam(y ~ fac+s(x1,bs="mpi")+s(x2,bs="mpd"))
    anova(b,b1,test="F")

    ## b2 <- scam(y ~ fac +s(x1)+s(x2)+te(x1,x2))

[Package scam version 1.2-12 Index]