bland.altman.stats {BlandAltmanLeh} R Documentation

## Calculate statistics for Bland-Altman-Plot

### Description

Does the computation for Bland Altman plots. This will usually be called from graphic functions like `bland.altman.plot` but will be usefull for customized plot (see examples for color coded BA plot). Offers symmetric confidence intervalls for bias and upper and lower limits.

### Usage

```bland.altman.stats(group1, group2, two = 1.96, mode = 1, conf.int = 0.95)
```

### Arguments

 `group1` vector of numerics to be compared to group2 `group2` vector of numerics to be compared to group1 `two` numeric defines how many standard deviations from mean are to be computed, defaults to 1.96 as this gives proper 95 percent CI. However, in the original publication a factor of 2 is used. `mode` if 1 then difference group1 minus group2 is used, if 2 then group2 minus group1 is used. Defaults to 1. `conf.int` usefull

### Value

`means` vector of means, i. e. data for the x axis

`diffs` vector of differences, i. e. data for the y axis

`groups` data.frame containing pairwise complete cases of group1 and group2. NAs are removed.

`based.on` count of pairwise complete cases in groups

`lower.limit` lower limit for BA plot

`mean.diffs` mean of differences, also called 'bias'

`upper.limit` upper limit for BA plot

`lines` vector containing y values where to draw horizontal lines, i. e. mean of differences minus "two" standard deviations, mean of differences and mean of differences plus "two" standard deviations (i. e. `c(lower.limit, mean.diffs, upper.limit`). This is convenient for printing.

`CI.lines` vector of confidence intervalls for the values of lines (based on the assumption of normal distribution of differences `diffs`).

`two` the argument 'two'

`critical.diff` critical difference, i. e. 'two' times standard deviation of differences, equals half the difference of lower.limit and upper.limit

### Author(s)

Bernhard Lehnert <bernhard.lehnert@uni-greifswald.de>

`bland.altman.plot`

### Examples

```# simple calculation of stats:
a <- rnorm(20)
b <- jitter(a)
print(bland.altman.stats(a, b))
print(bland.altman.stats(a, b)\$critical.diff)

# drawing Bland-Altman-Plot with color coding sex:
example.data <- data.frame(sex = gl(2,6,labels=c("f","m")),
m1 = c(16,10,14,18,16,15,18,19,14,11,11,17),
m2 = c(18, 9,15,19,19,13,19,20,14,11,13,17))
ba <- bland.altman.stats(example.data\$m1, example.data\$m2)
plot(ba\$means, ba\$diffs, col=example.data\$sex, ylim=c(-4,4))
abline(h=ba\$lines, lty=2)

# compute 95%-CIs for the bias and upper and lower limits of PEFR data as
# in Bland&Altman 1986
bland.altman.stats(bland.altman.PEFR[,1],bland.altman.PEFR[,3])\$CI.lines
# apparently wrong results? CAVE: Bland&Altman are using two=2, thus
bland.altman.stats(bland.altman.PEFR[,1],bland.altman.PEFR[,3], two=2)\$CI.lines
```

[Package BlandAltmanLeh version 0.3.1 Index]