meammd | R Documentation |
Computes maximum mean discrepancy statistics with Laplacian or Gaussian kernel. Suitable for multivariate data. Naive approach, quadratic in number of observations.
meammd(
X,
Y,
beta = -0.1,
pval = TRUE,
type = c("proj", "dist"),
numproj = 20,
nmethod = c(2, 1),
distpval = c("Hommel", "Fisher"),
numperm = 200,
seednum = 0,
alternative = c("greater", "two.sided"),
allowzeropval = FALSE,
faster = TRUE
)
X |
Matrix (or vector) of observations in first sample. |
Y |
Matrix (or vector) of observations in second sample. |
beta |
kernel parameter. Must be positive; if not, computes
median heuristic in quadratic time for each projection.
Default value
is |
pval |
Boolean for whether to compute p-value or not. |
type |
The type of projection used. Either |
numproj |
Number of projections (only used if |
nmethod |
Norm used for interpoint distances, if |
distpval |
The p-value combination procedure if |
numperm |
Number of permutations. Default is |
seednum |
Seed number for generating permutations. Default is |
alternative |
A character string specifying the alternative hypothesis,
which must be either |
allowzeropval |
A boolean, specifying whether we will allow zero
p-values or not. Default is |
faster |
A boolean, specifying if to use faster algorithm
when computing p-value. Default is |
A list with the following elements:
pval
The p-value of the test, if it is
computed (pval=TRUE
). Otherwise,
it is set to NA
.
stat
The statistic of the test, which
is only returned when type="proj"
,
otherwise it is set to NA
.
Bodenham, D. A., and Kawahara, Y. (2023) "euMMD: efficiently computing the MMD two-sample test statistic for univariate data." Statistics and Computing 33.5 (2023): 110.
X <- matrix(c(1:12), ncol=2, byrow=TRUE)
Y <- matrix(c(13:20), ncol=2, byrow=TRUE)
# using the random projections method
mmdList <- meammd(X=X, Y=Y, pval=TRUE, type="proj", numproj=50)
# using the method were distances are computed to the various points
mmdList <- meammd(X=X, Y=Y, pval=TRUE, type="dist")
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