predict.flexgam {FlexGAM}

Predicts values for the object of class flexgam

Description

Predicts values for the given object and a given dataset.

Usage

## 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.

Value

Numeric vector or matrix of fitted values

Note

The sum of the 'terms' is not the 'linear.predictor' since the 'terms' misses the scaling.

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, response.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_2 <- predict(model_2, newdata=newData)