response.flexgam {FlexGAM}

Predicts values for the object of class flexgam

Description

Predicts values for the given object, if a new predictor is given.

Usage

## S3 method for class 'flexgam'
response(object, linear.predictor = NULL, ...)
response(object, ...)

Arguments

object

Object of class flexgam.

linear.predictor

New predictor, which has to be centered and scaled properly.

...

Currently not used

Details

Calculates predicted values for the given model. Used to get the estimated response function separately from the covariate effects. For the same predictor predict.flexgam(...,type="response") and response(...) are equal.

Value

Numeric vector of fitted values.

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, deviance.flexgam, predict.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)


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_1 <- predict(model_2, newdata=newData, type="response")
predictor1 <- predict(model_2, newdata=newData, type="linear.predictor")
fitted_2 <- response(model_2, linear.predictor=predictor1)
all.equal(fitted_1,fitted_2)