# gp_fit: Fit a GP model In gplite: General Purpose Gaussian Process Modelling

 gp_fit R Documentation

## Fit a GP model

### Description

Function `gp_fit` fits a GP model with the current hyperparameters. Notice that this function does not optimize the hyperparameters in any way, but only finds the analytical posterior approximation (depending on chosen `approx`) for the latent values with the current hyperparameters. For optimizing the hyperparameter values, see `gp_optim`.

### Usage

```gp_fit(gp, x, y, trials = NULL, offset = NULL, jitter = NULL, ...)
```

### Arguments

 `gp` The gp model object to be fitted. `x` n-by-d matrix of input values (n is the number of observations and d the input dimension). Can also be a vector of length n if the model has only a single input. `y` Vector of n output (target) values. `trials` Vector of length n giving the number of trials for each observation in binomial (and beta binomial) model. `offset` Vector of constant values added to the latent values f_i (i = 1,...,n). For Poisson models, this is the logarithm of the exposure time in each observation. `jitter` Magnitude of diagonal jitter for covariance matrices for numerical stability. Default is 1e-6. `...` Currently ignored

### Value

An updated GP model object.

### References

Rasmussen, C. E. and Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.

### Examples

```
# Generate some toy data
set.seed(32004)
n <- 150
sigma <- 0.1
x <- rnorm(n)
ycont <- sin(3 * x) * exp(-abs(x)) + rnorm(n) * sigma
y <- rep(0, n)
y[ycont > 0] <- 1
trials <- rep(1, n)

# Fit the model using Laplace approximation (with the specified hyperparameters)
cf <- cf_sexp(lscale = 0.3, magn = 3)
gp <- gp_init(cf, lik_binomial())
gp <- gp_fit(gp, x, y, trials = trials)

```

gplite documentation built on Aug. 24, 2022, 9:07 a.m.