# Predicts values for the object of class flexgam Predicts values for the given object and a given dataset. ```r ## S3 method for class 'flexgam' predict(object, newdata=NULL, type=c("response","linear.predictor", "terms"), ...) ``` ## Arguments - `object`: Object of class flexgam. - `newdata`: New data to build predicted values. Same behaviour as in standard predict. - `type`: Should the fitted values (`'response'`) or the linear predictor (`'linear.predictor'`) be predicted. Alternatively the linear predictor for each covariate separately is given (`'terms'`). - `...`: Currently not used ## Details Calculates the predicted values for the given model. ## Returns Numeric vector or matrix of fitted values ## Note The sum of the `'terms'` is not the `'linear.predictor'` since the `'terms'` misses the scaling. ## 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 ## See Also `flexgam`, `deviance.flexgam`, `response.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) set.seed(2) 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) newData <- data.frame(y,x1,x2,x3) fitted_2 <- predict(model_2, newdata=newData) ```

predict_flexgam function