fbsm: Global threshold for missing data imputation
In FSMUMI: Imputation of Time Series Based on Fuzzy Logic
Description
Usage
Arguments
Details
Author(s)
Description
Computing the new similarity measure between two univariate signals Y and X based on fuzzy logic .
Usage
1
fbsm(Y, X)
Arguments
Y
input vector
X
input vector
#' @examples
data(dataFSMUMI)
X <- dataFSMUMI[, 1] ; Y <- dataFSMUMI[, 2]
fbsm(Y,X)
Details
This function returns the similarity measure of two vectors 0 <= fbsm <= 3.
fbsm is close to 3 the two vectors are more similar.
Both vectors Y and X must be of equal length, otherwise an error will be appreared.
The input vectors do not contain NA.
Author(s)
Thi-Thu-Hong Phan, Andre Bigand, Emilie Poisson-Caillault
FSMUMI documentation built on May 2, 2019, 12:40 p.m.
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Description Usage Arguments Details Author(s)
Description
Computing the new similarity measure between two univariate signals Y and X based on fuzzy logic .
Usage
1 | fbsm(Y, X)
|
Arguments
Y |
input vector |
X |
input vector #' @examples data(dataFSMUMI) X <- dataFSMUMI[, 1] ; Y <- dataFSMUMI[, 2] fbsm(Y,X) |
Details
This function returns the similarity measure of two vectors 0 <= fbsm <= 3. fbsm is close to 3 the two vectors are more similar. Both vectors Y and X must be of equal length, otherwise an error will be appreared. The input vectors do not contain NA.
Author(s)
Thi-Thu-Hong Phan, Andre Bigand, Emilie Poisson-Caillault
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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