summaryvgam: Summarizing Vector Generalized Additive Model Fits

Description Usage Arguments Details Value See Also Examples

View source: R/summary.vgam.q

Description

These functions are all methods for class vgam or summary.vgam objects.

Usage

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summaryvgam(object, dispersion = NULL, digits = options()$digits - 2,
            presid = TRUE, nopredictors = FALSE)
## S3 method for class 'summary.vgam'
show(x, quote = TRUE, prefix = "",
                            digits = options()$digits-2, nopredictors = NULL)

Arguments

object

an object of class "vgam", which is the result of a call to vgam with at least one s term.

x

an object of class "summary.vgam", which is the result of a call to summaryvgam().

dispersion, digits, presid

See summaryvglm.

quote, prefix, nopredictors

See summaryvglm.

Details

This methods function reports a summary more similar to summary.gam() from gam than summary.gam from mgcv. It applies to G1-VGAMs using s and vector backfitting. In particular, an approximate score test for linearity is conducted for each s term—see Section 4.3.4 of Yee (2015) for details. The p-values from this type of test tend to be biased upwards (too large).

Value

summaryvgam returns an object of class "summary.vgam"; see summary.vgam-class.

See Also

vgam, summary.glm, summary.lm, summary.gam from mgcv, summarypvgam for P-VGAMs.

Examples

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hfit <- vgam(agaaus ~ s(altitude, df = 2), binomialff, data = hunua)
summary(hfit)
summary(hfit)@anova  # Table for (approximate) testing of linearity

Example output

Loading required package: stats4
Loading required package: splines

Call:
vgam(formula = agaaus ~ s(altitude, df = 2), family = binomialff, 
    data = hunua)

Name of additive predictor: logitlink(prob) 

(Default) Dispersion Parameter for binomialff family:   1

Residual deviance:  394.9298 on 389.167 degrees of freedom

Log-likelihood: -197.4649 on 389.167 degrees of freedom

Number of Fisher scoring iterations:  6 

DF for Terms and Approximate Chi-squares for Nonparametric Effects

                    Df Npar Df Npar Chisq     P(Chi)
(Intercept)          1                              
s(altitude, df = 2)  1     0.8     9.2773 0.00167449
                    Df Npar Df Npar Chisq      P(Chi)
(Intercept)          1      NA         NA          NA
s(altitude, df = 2)  1     0.8   9.277346 0.001674495

VGAM documentation built on Jan. 16, 2021, 5:21 p.m.