calc_ed: Calculate encoding distance
In biogram: N-Gram Analysis of Biological Sequences

Description Usage Arguments Value See Also Examples

Computes the encoding distance between two encodings.

1	calc_ed(a, b, prop = NULL, measure)

`a`	encoding (see `validate_encoding` for more information about the required structure of encoding).
`b`	encoding to which `a` should be compared. Must have equal number of groups or less than `a`. Both `a` and b must have the the same number of elements.
`prop`	`matrix` of physicochemical properties to normalize the encoding distance. Each column should represent properties of the single amino acid/nucleotide. If `NULL`, encoding distance is not normalized.
`measure`	`character` vector of length one specifying the measure. Currently avaible measures are `"pi"` (partition index) and `"si"` (similarity index). If the parameter `prop` is supplied, the encoding distance is normalized by the factor equal to the sum of distances for each group in `a` and the closest group in `b`. The position of a group is defined as the mean value of properties of amino acids or nucleotides belonging the group. See the package vignette for more details.

an encoding distance.

calc_si: compute the similarity index of two encodings. encoding2df: converts an encoding to a data frame. validate_encoding: validate a structure of an encoding.

# calculate encoding distance between two encodings of amino acids
aa1 = list(`1` = c("g", "a", "p", "v", "m", "l", "i"), 
           `2` = c("k", "h"), 
           `3` = c("d", "e"), 
           `4` = c("f", "r", "w", "y", "s", "t", "c", "n", "q"))

aa2 = list(`1` = c("g", "a", "p", "v", "m", "l", "q"), 
           `2` = c("k", "h", "d", "e", "i"), 
           `3` = c("f", "r", "w", "y", "s", "t", "c", "n"))
calc_ed(aa1, aa2, measure = "pi") 
    
# the encoding distance between two identical encodings is 0
calc_ed(aa1, aa1, measure = "pi")