interp.median {psych} | R Documentation

## Find the interpolated sample median, quartiles, or specific quantiles for a vector, matrix, or data frame

### Description

For data with a limited number of response categories (e.g., attitude items), it is useful treat each response category as range with width, w and linearly interpolate the median, quartiles, or any quantile value within the median response.

### Usage

    interp.median(x, w = 1,na.rm=TRUE)
    interp.quantiles(x, q = .5, w = 1,na.rm=TRUE)
    interp.quartiles(x,w=1,na.rm=TRUE)
    interp.boxplot(x,w=1,na.rm=TRUE)
    interp.values(x,w=1,na.rm=TRUE)
    interp.qplot.by(y,x,w=1,na.rm=TRUE,xlab="group",ylab="dependent",
                   ylim=NULL,arrow.len=.05,typ="b",add=FALSE,...)

### Arguments

`x` | input vector
`q` | quantile to estimate ( 0 < q < 1
`w` | category width
`y` | input vector for interp.qplot.by
`na.rm` | should missing values be removed
`xlab` | x label
`ylab` | Y label
`ylim` | limits for the y axis
`arrow.len` | length of arrow in interp.qplot.by
`typ` | plot type in interp.qplot.by
`add` | add the plot or not
`...` | additional parameters to plotting function

### Details

If the total number of responses is N, with median, M, and the number of responses at the median value, Nm >1, and Nb= the number of responses less than the median, then with the assumption that the responses are distributed uniformly within the category, the interpolated median is M - .5w + w*(N/2 - Nb)/Nm.

The generalization to 1st, 2nd and 3rd quartiles as well as the general quantiles is straightforward.

A somewhat different generalization allows for graphic presentation of the difference between interpolated and non-interpolated points. This uses the interp.values function.

If the input is a matrix or data frame, quantiles are reported for each variable.

### Value

`im` | interpolated median(quantile)
`v` | interpolated values for all data points

### See Also

`median`

### Examples

    interp.median(c(1,2,3,3,3))  # compare with median = 3
    interp.median(c(1,2,2,5))
    interp.quantiles(c(1,2,2,5),.25)
    x <- sample(10,100,TRUE)
    interp.quartiles(x)
    #
    x <-  c(1,1,2,2,2,3,3,3,3,4,5,1,1,1,2,2,3,3,3,3,4,5,1,1,1,2,2,3,3,3,3,4,2)
    y <-  c(1,2,3,3,3,3,4,4,4,4,4,1,2,3,3,3,3,4,4,4,4,5,1,5,3,3,3,3,4,4,4,4,4)
    x <-  x[order(x)]   #sort the data by ascending order to make it clearer
    y <- y[order(y)]
    xv <- interp.values(x)
    yv <- interp.values(y)
    barplot(x,space=0,xlab="ordinal position",ylab="value")
    lines(1:length(x)-.5,xv)
    points(c(length(x)/4,length(x)/2,3*length(x)/4),interp.quartiles(x))
    barplot(y,space=0,xlab="ordinal position",ylab="value")
    lines(1:length(y)-.5,yv)
    points(c(length(y)/4,length(y)/2,3*length(y)/4),interp.quartiles(y))
    data(galton)
    interp.median(galton)
    interp.qplot.by(galton$child,galton$parent,ylab="child height"
    ,xlab="Mid parent height")

[Package psych version 1.6.9 Index]