# Methods: Extract model parameters In FlexGAM: Generalized Additive Models with Flexible Response Functions

## Description

These are the standard functions to extract parameters of the estimated object.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```## S3 method for class 'flexgam' coefficients(object, ...) ## S3 method for class 'flexgam' coef(object, ...) ## S3 method for class 'flexgam' fitted(object, ...) ## S3 method for class 'flexgam' fitted.values(object, ...) ## S3 method for class 'flexgam' residuals(object, ...) ## S3 method for class 'flexgam' resid(object, ...) ```

## Arguments

 `object` Object of class `flexgam`. `...` Currently not used

## Details

These functions extract the coefficients, fitted values or residuals of the given object.

## Value

Coefficients, fitted values or residuals of the given object.

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

`flexgam`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```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) coefficients(model_2) ```