1d:T772,# Plots object of class flexgam. Plots resulting response (link) function or estimated smooth effects of objects of class `flexgam`. ```r ## 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. ## Returns Plots ## 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 ## Author(s) Elmar Spiegel ## 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. ## See Also `flexgam` ## Examples ```r 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") ```

plot_flexgam function