predict_flexgam: Predicts values for the object of class flexgam

In FlexGAM: Generalized Additive Models with Flexible Response Functions

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)

FlexGAM documentation built on July 2, 2020, 2:37 a.m.