# stats0: Descriptive Statistics for a Vector or a Data Frame In alexanderrobitzsch/miceadds: Some Additional Multiple Imputation Functions, Especially for 'mice'

## Description

Applies descriptive statistics to a vector or a data frame. The function `stats0` is a general function. This function is used for extending the basic descriptive statistics functions from the base and stats package. The function `prop_miss` computes the proportion of missing data for each variable.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```stats0(x, FUN, na.rm=TRUE,...) max0(x, na.rm=TRUE) mean0(x, na.rm=TRUE) min0(x, na.rm=TRUE) quantile0(x, probs=seq(0, 1, 0.25), na.rm=TRUE) sd0(x, na.rm=TRUE) var0(x, na.rm=TRUE) prop_miss(x) ```

## Arguments

 `x` Vector or a data frame `FUN` Function which is applied to `x` `na.rm` Logical indicating whether missing data should be removed `probs` Probabilities `...` Further arguments to be passed

## Value

A vector or a matrix

`base::max`, `base::mean`, `base::min`, `stats::quantile`, `stats::sd`, `stats::var`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35``` ```############################################################################# # EXAMPLE 1: Descriptive statistics toy datasets ############################################################################# #--- simulate vector y and data frame dat set.seed(765) N <- 25 # number of observations y <- stats::rnorm(N) V <- 4 # number of variables dat <- matrix( stats::rnorm( N*V ), ncol=V ) colnames(dat) <- paste0("V",1:V) #-- standard deviation apply( dat, 2, stats::sd ) sd0( dat ) #-- mean apply( dat, 2, base::mean ) mean0( dat ) #-- quantile apply( dat, 2, stats::quantile ) quantile0( dat ) #-- minimum and maximum min0(dat) max0(dat) #*** apply functions to missing data dat1 <- dat dat1[ cbind( c(2,5),2) ] <- NA #-- proportion of missing data prop_miss( dat1 ) #-- MAD statistic stats0( dat, FUN=stats::mad ) #-- SD sd0(y) ```