| cmsc {GCSM} | R Documentation |
Composite similarity between vectors
Description
Compute composite measures, GCSM or CMSC, between two vectors.
Usage
cmsc(
x,
y,
rescale = FALSE,
xmin = NA_real_,
xmax = NA_real_,
ymin = NA_real_,
ymax = NA_real_,
comp = "si"
)
cmsc_e1(
x,
y,
rescale = FALSE,
xmin = NA_real_,
xmax = NA_real_,
ymin = NA_real_,
ymax = NA_real_,
comp = "si"
)
cmsc_e2(
x,
y,
rescale = FALSE,
xmin = NA_real_,
xmax = NA_real_,
ymin = NA_real_,
ymax = NA_real_,
comp = "si"
)
gcsm(
x,
y,
rescale = FALSE,
xmin = NA_real_,
xmax = NA_real_,
ymin = NA_real_,
ymax = NA_real_,
comp = "si"
)
Arguments
x |
A vector. |
y |
The other vector. |
rescale |
Rescale or not before computation. |
xmin, xmax, ymin, ymax |
Normalization parameters. If |
comp |
Variable to return. If |
Details
These functions compute composite measures between vectors. Missing values
are omitted. Normalization parameters are used to rescale x and y, and
determine the global minimum (min) and maximum (max). If rescale is
TRUE, x and y are rescaled to (x-xmin)/(xmax-xmin) and
(y-ymin)/(ymax-ymin); and set min=0, max=1. If FALSE,
min=min(xmin,ymin), max=max(xmax,ymax).
Value
A number.
Examples
x = runif(9)
gcsm(x, x)
cmsc(x, x)
# mean shift
gcsm(x, x - 0.2, xmin = 0, xmax = 1, ymin = 0, ymax = 1)
cmsc(x, x - 0.2, xmin = 0, xmax = 1, ymin = 0, ymax = 1)
gcsm(x, x + 0.2, xmin = 0, xmax = 1, ymin = 0, ymax = 1)
cmsc(x, x + 0.2, xmin = 0, xmax = 1, ymin = 0, ymax = 1)
## dissimilarity
y = 1 - x # y is the perfect antianalog of x
gcsm(y, x)
gcsm(y, x - 0.2, xmin = 0, xmax = 1, ymin = 0, ymax = 1)
gcsm(y, x + 0.2, xmin = 0, xmax = 1, ymin = 0, ymax = 1)
# random noise
noise = rnorm(9, mean = 0, sd = 0.2)
gcsm(x, x + noise, xmin = 0, xmax = 1, ymin = 0, ymax = 1)
cmsc(x, x + noise, xmin = 0, xmax = 1, ymin = 0, ymax = 1)
## dissimilarity
gcsm(y, x + noise, xmin = 0, xmax = 1, ymin = 0, ymax = 1)
[Package GCSM version 0.1.1 Index]