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.
vector of numerics to be compared to group2
vector of numerics to be compared to group1
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.
if 1 then difference group1 minus group2 is used, if 2 then group2 minus group1 is used. Defaults to 1.
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
CI.lines vector of confidence intervalls for the values of
lines (based on the assumption of normal distribution of differences
two the argument 'two'
critical.diff critical difference, i. e. 'two' times standard
deviation of differences, equals half the difference of lower.limit and
Bernhard Lehnert <firstname.lastname@example.org>
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# 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
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