# predict.pspfit: Predict function for 'psNormal', 'psBinomial', 'psPoisson' In JOPS: Practical Smoothing with P-Splines

 predict.pspfit R Documentation

## Predict function for `psNormal`, `psBinomial`, `psPoisson`

### Description

Prediction function which returns both linear predictor and inverse link predictions at arbitrary data locations (using `psNormal`, `psBinomial`, `psPoisson` with `class pspfit`).

### Usage

``````## S3 method for class 'pspfit'
predict(object, ..., x, type = "mu")
``````

### Arguments

 `object` an object using `psNormal`, `psBinomial`, or `psPoisson` . `...` other parameters. `x` a scalar or vector of arbitrary `x` locations for desired prediction. `type` the mean value `type = "mu"` (default) or linear predictor `type = "eta"`.

### Value

 `pred` the estimated mean (inverse link function) (default) or the linear predictor prediction with ```type = "eta"```, at arbitary `x` locations.

### Author(s)

Paul Eilers and Brian Marx

### References

Eilers, P.H.C., Marx, B.D., and Durban, M. (2015). Twenty years of P-splines, SORT, 39(2): 149-186.

Eilers, P.H.C. and Marx, B.D. (2021). Practical Smoothing, The Joys of P-splines. Cambridge University Press.

### Examples

``````library(JOPS)
library(boot)

# Extract the data
Count <- hist(boot::coal\$date, breaks = c(1851:1963), plot = FALSE)\$counts
Year <- c(1851:1962)
xl <- min(Year)
xr <- max(Year)

# Poisson smoothing
nseg <- 20
bdeg <- 3
fit1 <- psPoisson(Year, Count, xl, xr, nseg, bdeg, pord = 2, lambda = 1)
names(fit1)
plot(fit1, xlab = "Year", ylab = "Count", se = 2)
predict(fit1, x = fit1\$x[1:5])
predict(fit1, x = fit1\$x[1:5], type = "eta")
``````

JOPS documentation built on Sept. 8, 2023, 5:42 p.m.