# Mode

0th

Percentile

##### Mode, Most Frequent Value(s)

Calculate the mode, the most frequent value, of a variable x. This makes mostly sense for qualitative data, at most for x being an integer vector.

Keywords
univar
##### Usage
Mode(x, na.rm = FALSE)
##### Arguments
x

a (non-empty) numeric vector of data values.

na.rm

logical. Should missing values be removed? Defaults to FALSE.

##### Value

Returns the most frequent value. If there are more than one, all of them will be returned in a vector.

##### Note

Consider using density(x)$x[which.max(density(x)$y)] for quantitative data or alternatively use hist(). Another interesting idea:

peak <- optimize(function(x, model) predict(model, data.frame(x = x)),
c(min(x), max(x)),
maximum = TRUE,
model = y.loess)points(peak$maximum, peak$objective, pch=FILLED.CIRCLE <- 19)


##### References

https://stackoverflow.com/questions/55212746/rcpp-fast-statistical-mode-function-with-vector-input-of-any-type/ https://stackoverflow.com/a/55213471/8416610

mean, median

• Mode
##### Examples
# NOT RUN {
# normal mode
Mode(c(0:5, 5))

Mode(5)
Mode(NA)
Mode(c(NA, NA))
Mode(c(NA, 0:5))
Mode(c(NA, 0:5), na.rm=TRUE)
Mode(c(NA, 0:5, 5), na.rm=TRUE)

# returns all encountered modes, if several exist
Mode(c(0:5, 4, 5, 6))

data(d.pizza)
Mode(d.pizza\$driver)

# use sapply for evaluating data.frames (resp. apply for matrices)
sapply(d.pizza[,c("driver","temperature","date")], Mode, na.rm=TRUE)
# }

Documentation reproduced from package DescTools, version 0.99.32, License: GPL (>= 2)

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