This function computes a pairwise numerical distance between two numerical data sets.
distNumeric(x, y, method = "mrw", xyequal = TRUE)Function returns a distance matrix with the number of rows equal to
the number of objects in the x matrix (\(n_x\)) and the number of
columns equals to the number of objects in the y matrix (\(n_y\)).
A first data matrix (see Details).
A second data matrix (see Details).
A method to calculate the pairwise numerical distance (see Details).
A logical if x is equal to y (see
Details).
Weksi Budiaji
Contact: budiaji@untirta.ac.id
The x and y arguments have to be matrices with
the same number of columns where the row indicates the object and the
column is the variable. This function calculate all pairwise distance
between rows in the x and y matrices. Although it calculates
a pairwise distance between two data sets, the default function computes all
distances in the x matrix. If the x matrix is not equal to
the y matrix, the xyequal has to be set FALSE.
The method available are mrw (Manhattan weighted by range),
sev (squared Euclidean weighted by variance), ser
(squared Euclidean weighted by range), ser.2 (squared Euclidean
weighted by squared range) and se (squared Euclidean).
Their formulas are:
$$mrw_{ij} = \sum_{r=1}^{p_n} \frac{|x_{ir} - x_{jr}|}{R_r}$$
$$sev_{ij} = \sum_{r=1}^{p_n} \frac{(x_{ir} - x_{jr})^2}{s_r^2}$$
$$ser_{ij} = \sum_{r=1}^{p_n} \frac{(x_{ir} - x_{jr})^2}{ R_r }$$
$$ser.2_{ij} = \sum_{r=1}^{p_n} \frac{(x_{ir} - x_{jr})^2}{ R_r^2 }$$
$$se_{ij} = \sum_{r=1}^{p_n} (x_{ir} - x_{jr})^2$$
where \(p_n\) is the number of numerical variables, \(R_r\) is the range
of the r-th variables, \(s_r^2\) is the variance of the r-th
variables.
num <- as.matrix(iris[,1:4])
mrwdist <- distNumeric(num, num, method = "mrw")
mrwdist[1:6,1:6]
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