catsim  R Documentation 
The categorical structural similarity index measure for 2D or 3D categorical or
binary images for multiple scales. The default is to compute over 5 scales.
This determines whether this is a 2D or 3D image and applies the appropriate
windowing, weighting, and scaling. Additional arguments can be passed.
This is a wrapper function for the 2D and 3D functions whose functionality
can be accessed through the ... arguments. This function is a wrapper for the
catmssim_2d()
, catmssim_3d_slice()
, and
catmssim_3d_cube()
functions.
catsim(
x,
y,
...,
cube = TRUE,
levels = NULL,
weights = NULL,
method = "Cohen",
window = NULL
)
x , y 
a binary or categorical image 
... 
additional arguments, such as window, can be passed as well as arguments for internal functions. 
cube 
for the 3D method, whether to use the true 3D method
(cube or 
levels 
how many levels of downsampling to use. By default, 5. If

weights 
a vector of weights for the different scales. By default,
equal to 
method 
whether to use Cohen's kappa ( 
window 
by default 11 for 2D and 5 for 3D images, but can be
specified as a vector if the window sizes differ by dimension.
The vector must have the same number of
dimensions as the inputted 
a value less than 1 indicating the similarity between the images.
set.seed(20181207)
dim < 16
x < array(sample(0:4, dim^3, replace = TRUE), dim = c(dim, dim, dim))
y < x
for (j in 1:dim) {
for (i in 1:dim) y[i, i, j] < 0
for (i in 1:(dim  1)) y[i, i + 1, j] < 0
}
catsim(x, y, weights = c(.75, .25))
# Now using a different similarity score
catsim(x, y, levels = 2, method = "accuracy")
# with the slice method:
catsim(x, y, weights = c(.75, .25), cube = FALSE, window = 8)
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