# plotObservedEffects: Plot Observed Values Vs. Each Dimension of the Design Matrix In mlegp: Maximum Likelihood Estimates of Gaussian Processes

## Description

Constructs multiple graphs, plotting each parameter from the design matrix on the x-axis and observations on the y-axis

## Usage

 `1` ```plotObservedEffects(x, ...) ```

## Arguments

 `x` an object of class `gp` or a design matrix `...` if x is a design matrix, a vector of observations; if x is of class `gp`, a vector of parameter numbers or parameter names to plot (by default, all parameters will be graphed)

## Details

if `x` is NOT of class `gp` (i.e., `x` is a design matrix), all columns of `x` will be plotted separately against the vector of observations

if `x` is of class `gp`, the specified columns of the design matrix of `x` will be plotted against the the observations

## Note

It is often useful to use this function before fitting the gaussian process, to check that the observations are valid

## Author(s)

Garrett M. Dancik dancikg@easternct.edu

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```## create the design and output matrices ## x1 = kronecker(seq(0,1,by=.25), rep(1,5)) x2 = rep(seq(0,1,by=.25),5) z = 4 * x1 - 2*x2 + x1 * x2 + rnorm(length(x1), sd = 0.001) ## look at the observed effects prior to fitting the GP ## plotObservedEffects(cbind(x1,x2), z) ## fit the Gaussian process ## fit = mlegp(cbind(x1,x2), z, param.names = c("x1", "x2")) ## look at the observed effects of the fitted GP (which are same as above) plotObservedEffects(fit) ```

mlegp documentation built on Oct. 23, 2020, 5:53 p.m.