Description Usage Arguments Details Value References Examples
Run and control charts for multivariate data i trellis (grid) layout.
1 2 3 4 5 6 7  tcc(n, d, x, g1, g2, breaks, notes, data, chart = c("run", "i", "mr", "xbar",
"s", "t", "p", "c", "u", "g"), multiply = 1, freeze = NULL, exclude,
target = NA, n.sum = FALSE, y.neg = TRUE, y.percent = FALSE,
y.expand = NULL, x.pad = 1, x.date.format = NULL, cl.lab = TRUE,
cl.decimals = NULL, main, xlab = "Subgroup", ylab = "Value",
subtitle = NULL, caption = NULL, cex = 1, pex = 1, prime = TRUE,
flip = FALSE, dots.only = FALSE, print.summary = FALSE, ...)

n 
Numerator, numeric vector of counts or measures to plot. Mandatory. 
d 
Denominator, numeric vector of subgroup sizes. Mandatory for P and U charts. 
x 
Subgrouping vector used for aggregating data by subgroup and making xlabels. Mandatory for Xbar and S charts. 
g1 
Grouping vector 1 used for trellis layout (facets). 
g2 
Grouping vector 2 used for trellis layout (facets). 
breaks 
Numeric vector of break points. Useful for splitting graph in two or more sections with separate center line and control limits. 
notes 
Character vector of notes to be added to individual. data points. 
data 
Data frame containing variables. 
chart 
Type of control chart. Possible types are:

multiply 
Integer indicating a number to multiply y axis by, e.g. 100
for percents rather than proportions. See also 
freeze 
Number identifying the last data point to include in
calculations of center and limits (ignored if 
exclude 
Numeric vector of data points to exclude from runs analysis and calculations of center and control lines (same for each facet). 
target 
Numeric value indicating a target value to be plotted as a horizontal line (same for each facet). 
n.sum 
Logical value indicating whether the mean (default) or sum of numerator (n argument) per subgroup should be plotted. Only relevant for run, C, and I charts with multiple counts per subgroup. 
y.neg 
Logical value. If TRUE (default), the y axis is allowed to be negative (only relevant for I and Xbar charts). 
y.percent 
Logical. If TRUE, formats y axis labels as percent. 
y.expand 
Numeric value to include in y axis. Useful e.g. for beginning y axis at zero. 
x.pad 
Number indicating expansion of x axis to make room for center line labels. 
x.date.format 
Date format of x axis labels. See 
cl.lab 
Logical value. If TRUE (default), plots center line labels. 
cl.decimals 
Number of decimals on center line labels. 
main 
Character string specifying the title of the plot. 
xlab 
Character string specifying the x axis label. 
ylab 
Character string specifying the y axis label. 
subtitle 
Character string specifying the subtitle. 
caption 
Character string specifying the caption. 
cex 
Number indicating the amount by which text should be magnified. 
pex 
Number indicating the amount by which plotting symbols should be magnified. 
prime 
Logical value, If TRUE (default unless 
flip 
Logical. If TRUE rotates the plot 90 degrees. 
dots.only 
Logical value. If TRUE, data points are not connected by lines, prime is forced to be FALSE, and runs analysis is not performed. Useful for comparison and funnel plots. 
print.summary 
Logical. If TRUE, prints summary of tcc object. 
... 
Further arguments to ggplot function. 
tcc()
is a wrapper function for ggplot2()
that makes
multivariate run and control charts. It takes up to two grouping variables
for multidimensional trellis plots.
Note that, in contrast to the qic() function, the prime argument defaults to TRUE, which means that control limits of P and U charts by default incorporate betweensubgroup variation as proposed by Laney (2002).
An object of class ggplot.
Runs analysis:
Jacob Anhoej, Anne Vingaard Olesen (2014). Run Charts Revisited: A Simulation Study of Run Chart Rules for Detection of NonRandom Variation in Health Care Processes. PLoS ONE 9(11): e113825. doi: 10.1371/journal.pone.0113825 .
Jacob Anhoej (2015). Diagnostic Value of Run Chart Analysis: Using Likelihood Ratios to Compare Run Chart Rules on Simulated Data Series. PLoS ONE 10(3): e0121349. doi: 10.1371/journal.pone.0121349
Mark F. Schilling (2012). The Surprising Predictability of Long Runs. Math. Mag. 85, 141149.
Zhenmin Chen (2010). A note on the runs test. Model Assisted Statistics and Applications 5, 7377.
Calculation of control limits:
Douglas C. Montgomery (2009). Introduction to Statistical Process Control, Sixth Edition, John Wiley & Sons.
James C. Benneyan (2001). NumberBetween gType Statistical Quality Control Charts for Monitoring Adverse Events. Health Care Management Science 4, 305318.
Lloyd P. Provost, Sandra K. Murray (2011). The Health Care Data Guide: Learning from Data for Improvement. San Francisco: John Wiley & Sons Inc.
David B. Laney (2002). Improved control charts for attributes. Quality Engineering, 14(4), 531537.
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  # Run chart of 24 random normal variables
tcc(rnorm(24))
# Build data frame for examples
d < data.frame(x = rep(1:24, 4),
mo = (rep(seq(as.Date('201411'),
length.out = 24,
by = 'month'),
4)),
n = rbinom(4 * 24, 100, 0.5),
d = round(runif(4 * 24, 90, 110)),
g1 = rep(c('a', 'b'), each = 48),
g2 = rep(c('A', 'B'), each = 24))
# Run chart with two grouping variables
tcc(n, d, mo, g1 = g1, g2 = g2, data = d)
# P chart
tcc(n, d, mo, g1 = g1, g2 = g2, data = d, chart = 'p')
# P chart with baseline fixed to the first 12 data points
tcc(n, d, mo, g1 = g1, g2 = g2, data = d, chart = 'p', freeze = 12)
# P chart with two breaks and summary output
tcc(n, d, mo, g1 = g1, g2 = g2, data = d, chart = 'p',
breaks = c(12, 18), print.summary = TRUE)

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