| MDA {qualvar} | R Documentation |
Mean Difference Analog (MDA or MNDIF)
Description
Computes the mean difference analog (MDA or MNDIF) for a vector of frequencies of categories.
Usage
MDA(x, na.rm = TRUE)
Arguments
x |
a vector of frequencies |
na.rm |
if TRUE, missing values are removed. If FALSE, NA is returned if there is any NA value. |
Details
According to Wilcox (1973, p. 328), the MDA is 'an analog of the mean difference, a measure of variation that is discussed and used much less frequently than the average deviation or the standard deviation. It is defined as "the average of the differences of all the possible pairs of variate-values, taken regardless of sign"'. The formula for the MDA is:
1 - \frac{\sum_{i=1}^{k-1} \sum_{j=i+1}^k |f_i - f_j|}{N(K-1)}
Value
The value of the MDA statistics, which is normalised (varies between 0 and 1).
References
Wilcox, Allen R. 'Indices of Qualitative Variation and Political Measurement.' The Western Political Quarterly 26, no. 2 (1 June 1973): 325-43. doi:10.2307/446831.
Examples
x <- rmultinom(1, 100, rep_len(0.25, 4))
x <- as.vector(t(x))
MDA(x)
df <- rmultinom(10, 100, rep_len(0.25, 4))
df <- as.data.frame(t(df))
apply(df, 1, MDA)