# R/plotGP.R In ebenmichael/gaussianProcess: Fits Gaussian Processes for Regression

#### Documented in plot.gaussianProcess

```## Function to plot the results from a GP regression

#' Plot the predictive distribution of new data. Only works with 1 dimension
#'
#' @param gp gaussianProcess object
#' @param x.min Lowest x value to plot
#' @param x.max Highest x value to plot
#' @param by Spacing for x values, defaults to 0.1
#' @param plot.points Whether to plot the data as well, defaults to False
#' @param plot.mean Whether to plot the mean of the process
#' @param plot.int Whether to plot an interval around the mean
#' @param plot.samples Whether to plot samples
#' @param plot.predictive Whether to plot the posterior predictive
#' @param int.size Posterior intervals are mean +- int.size * var, defaults to 1.96
#' @param n.samples Number of samples to plot
#'
#' @export
plot.gaussianProcess <- function(gp, x.min, x.max, by=0.1,
plot.points=TRUE, plot.mean=TRUE,
plot.int=TRUE, plot.samples=FALSE,
plot.predictive=FALSE, int.size=1.96,
n.samples=10) {

Xnew <- as.matrix(seq(x.min, x.max, by))
# get the posterior predictive mean and covariance
pred <- predict(gp, Xnew)

# put the posterior predictive in a data frame
vars <- diag(pred\$covariance)
if(plot.predictive) {
vars <- vars + gp\$noise.var
}
upper <- pred\$mean + int.size * vars
lower <- pred\$mean - int.size * vars
df.pred <- data.frame(x=Xnew, mean=pred\$mean, upper=upper, lower=lower)
if(plot.points) {
# plot the original data
df <- data.frame(x=gp\$data, y=gp\$target)
gplt <- ggplot2::ggplot() +
ggplot2::geom_point(data=df, ggplot2::aes(x=x, y=y), alpha=.5)
}
else {
gplt <- ggplot2::ggplot(df.pred, ggplot2::aes(x=x, y=mean))
}

# plot the posterior mean
if(plot.mean) {
gplt <- gplt + ggplot2::geom_line(data=df.pred,
ggplot2::aes(x=x,y=mean),
color='red')
}
if(plot.int) {
gplt <- gplt + ggplot2::geom_ribbon(data=df.pred,
ggplot2::aes(x=x, ymin=lower, ymax=upper),
alpha=.2)
}

# plot the samples if desired
if(plot.samples) {
samples <- data.frame(t(sample(gp, Xnew, n.samples)))
samples\$x <- Xnew
samples <- reshape2::melt(samples, id="x")
# make the plots more transparent if also plotting the mean
if(plot.mean) alpha=.3 else alpha=1
gplt <- gplt + ggplot2::geom_line(data=samples,
ggplot2::aes(x=x, y=value,
color=variable),
alpha=alpha) +
ggplot2::theme(legend.position="none")
}
# show the plot
gplt

}
```
ebenmichael/gaussianProcess documentation built on May 13, 2017, 10:58 a.m.