mfv: Most frequent value (statistical mode) of frequency...

In fdth: Frequency Distribution Tables, Histograms and Polygons

Most frequent value (statistical mode) of frequency distribution table (numerical and categorical variable)

Description

S3 methods for the most frequent value (statistical mode) of a fdt.
Useful to estimate the most frequent value (statistical mode). It may also be used with a previous fdt when the original data vector is not known.

Usage

## S3 generic
mfv(x, ...)

## S3 methods: numerical and categorical 
## Default S3 method:
mfv(x, ...)

## S3 method for class 'fdt'
mfv(x, ...)

## S3 method for class 'fdt.multiple'
mfv(x, ...)

## S3 method for class 'fdt_cat'
mfv(x, ...)

## S3 method for class 'fdt_cat.multiple'
mfv(x, ...)

Arguments

x	for mfv.default, a numeric or categorical vector; for the other methods, a fdt or fdt_cat (simple or multiple) object.
...	further arguments (required by the generic).

Details

mfv.fdt and mfv.fdt_cat calculate the most frequent value (mfv) based on a known formula. mfv.fdt.multiple and mfv.fdt_cat.multiple call mfv.fdt or mfv.fdt_cat, respectively, for each variable, that is, each column of the data.frame.

Value

mfv.default returns a vector containing the mfv value(s) of x. In multimodal cases, this vector has length greater than one. mfv.fdt returns a numeric vector containing the mfv value(s) of the fdt. In multimodal cases, this vector has length greater than one. mfv.fdt.multiple returns a list, where each element is a numeric vector containing the mfv value(s) of the fdt for each variable. mfv.fdt_cat returns a character vector containing the mfv value(s) of the fdt_cat. mfv.fdt_cat.multiple returns a list, where each element is a character vector containing the mfv value(s) of the fdt_cat for each variable.

Author(s)

Faria, J. C.
Allaman, I. B
Jelihovschi, E. G.

See Also

mean.fdt, median.fdt, quantile.fdt.

Examples

library(fdth)

## Numerical (multimodal examples)
vx <- c(rep(3,
            5),
        rep(5,
            5),
        sample(6:10,
               5))
vx
mfv(vx)      # Two modes: 3 and 5

tb <- fdt(vx)
tb
mfv(tb)      # Mode estimated from grouped data (two modal classes)

vy <- c(rep(3,
            5),
        sample(6:9,
               10,
               rep=TRUE),
        rep(10,
            5))
vy

tb2 <- fdt(vy,
           start=3,
           end=11,
           h=1)
tb2
mfv(tb2)     # Two modes in non-adjacent classes

vz <- c(rep(1.2,
            6),
        rep(6.4,
            6),
        rep(3.3,
            2),
        rep(4.7,
            2))
vz

tb3 <- fdt(vz,
           start=0,
           end=8,
           h=1)
tb3
mfv(tb3)     # Two modes in non-adjacent classes (deterministic example)


## Categorical
mdf <- data.frame(c1=sample(letters[1:5],
                            1e3,
                            rep=TRUE),
                  c2=sample(letters[6:10],
                            1e3,
                            rep=TRUE),
                  c3=sample(letters[11:21],
                            1e3,
                            rep=TRUE),
                  stringsAsFactors=TRUE)
head(mdf)

mfv(mdf$c1)   # From vector c1
mfv(mdf$c2)   # From vector c2
mfv(mdf$c3)   # From vector c3

(ft <- fdt_cat(mdf))

mfv(ft)       # From grouped data in a fdt object

fdth documentation built on May 12, 2026, 1:08 a.m.