plot.flexgam {FlexGAM}

Plots object of class flexgam.

Description

Plots resulting response (link) function or estimated smooth effects of objects of class flexgam.

Usage

## S3 method for class 'flexgam'
plot(x, type=c("response","covariate"), ci = TRUE,  rug = TRUE, ...)

Arguments

x

Object of class flexgam.

type

Whether the response function or the smooth covariate effects should be plotted.

ci

Include confidence intervals?

rug

Include rug plots?

...

Standard plot add-ons should work. ylab and xlab check for the length of the smooth predictor.

Details

Plots either the estimated response function or the estimated smooth covariate effects. Valid confidence intervals are currently only plotted for P-splines.

Value

Plots

Note

If other smooth effects than P-splines are used in the formula the plot function internally calls mgcv::plot.gam to show their effect. Then P-splines are plotted twice.

Author(s)

Elmar Spiegel

References

Spiegel, Elmar, Thomas Kneib and Fabian Otto-Sobotka. Generalized additive models with flexible response functions. Statistics and Computing (2017). https://doi.org/10.1007/s11222-017-9799-6

See Also

flexgam

Examples

set.seed(1)
n <- 1000
x1 <- runif(n)
x2 <- runif(n)
x3 <- runif(n)
eta_orig <- -1 + 2*sin(6*x1) + exp(x2) + x3
pi_orig <- pgamma(eta_orig, shape=2, rate=sqrt(2))
y <- rbinom(n,size=1,prob=pi_orig)

Data <- data.frame(y,x1,x2,x3)
formula <- y ~ s(x1,k=20,bs="ps") + s(x2,k=20,bs="ps") + x3

# Fix smoothing parameters to save computational time.
control2 <- list("fix_smooth" = TRUE, "quietly" = TRUE, "sm_par_vec" = 
                     c("lambda" = 100, "s(x1)" = 2000, "s(x2)" = 9000))

set.seed(2)
model_2 <- flexgam(formula=formula, data=Data, type="FlexGAM2", 
                   family=binomial(link=logit), control = control2)

plot(model_2,type="response")
plot(model_2,type="covariate")