Description Usage Arguments Value See Also Examples
Show prediction plots when varying over only one dimension. Most useful when setting all values to 0.5 because it will have the most points.
1 2 3 4 5 6 7 8 9 | CGGPplotslice(
CGGP,
proj = 0.5,
np = 300,
color = "pink",
outdims,
scales = "free_y",
facet = "grid"
)
|
CGGP |
CGGP object |
proj |
Point to project onto |
np |
Number of points to use along each dimension |
color |
Color to make error region |
outdims |
If multiple outputs, which of them should be plotted? |
scales |
Parameter passed to ggplot2::facet_grid() |
facet |
If "grid", will use ggplot2::facet_grid(), if "wrap" will use ggplot2::facet_wrap(). Only applicable for a single output dimension. |
ggplot2 object
Other CGGP plot functions:
CGGPplotblocks()
,
CGGPplotcorr()
,
CGGPplotheat()
,
CGGPplothist()
,
CGGPplotsamplesneglogpost()
,
CGGPplottheta()
,
CGGPplotvariogram()
,
CGGPvalplot()
1 2 3 4 5 6 7 8 9 | d <- 5
f1 <- function(x){x[1]+x[2]^2 + cos(x[3]^2*2*pi*4) - 3.3}
s1 <- CGGPcreate(d, 200)
s1 <- CGGPfit(s1, apply(s1$design, 1, f1))
#s1 <- CGGPappend(s1, 200)
#s1 <- CGGPfit(s1, apply(s1$design, 1, f1))
CGGPplotslice(s1)
CGGPplotslice(s1, 0.)
CGGPplotslice(s1, s1$design[nrow(s1$design),])
|
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