Function to draw a multivari chart
A multivari chart of one quantitative response variable depending on two to four categorical variables can be drawn.
multivari(var, fac1, fac2, fac3 = NULL, fac4 = NULL, data, sort = FALSE,
fun = mean, pch = list(15, 16, 17), col = list("black", "blue", "red"),
col0 = "black", cex = 1, fac.cex = 2, xlab.depth = 3, legend = FALSE,
main = paste("multivari chart of", var), add = FALSE, ...)var |
variable name (character string) or column index of response variable, required |
fac1 |
variable name (character string) or column index of first
level factor, required; precedes |
fac2 |
variable name (character string) or column index of second
level factor, required; follows |
fac3 |
variable name (character string) or column index of third
level factor, optional; if present, |
fac4 |
variable name (character string) or column index of fourth level factor, optional; can only be specified if there is also a third level factor; if present, this factor is the first in the hierarchy (see Details), and separate multivari charts for the first three factors are drawn for each level of this factor |
data |
a data frame, required |
sort |
logical, specifying whether or not levels are sorted, when converting
character vectors to factors (a single choice for all factors is needed); default: |
fun |
a function to be used in aggregation; default: |
pch |
a list of length 2 or 3, depending on whether or not |
col |
a list of length 2 or 3, depending on whether or not |
col0 |
the color for the first line to be drawn |
cex |
the size of axis annotation text (annotation of the fourth level header is 1.5 times this size) |
fac.cex |
a multiplier for |
xlab.depth |
labels for the horizontal axis are printed down to this level of the hierarchy (default: 3); if the depth is reduced, different plot symbols should be used, and a legend should be drawn |
legend |
logical determining whether or not a legend should be drawn (default: FALSE); the function determines wether top right or bottom right yields a better position (it is not guaranteed that there is no overlab); if this does not work well, one can manually draw a legend in the outer margin |
main |
title, as usual; a default is provided |
add |
logical; add to an existing plot (which of course has to have suitable axis limits)?; default: FALSE;
note that horizontal axis labeling will always be printed by function multivari, while vertical axis labeling will be omitted for |
... |
further arguments to functions |
The function is inspired by Minitabs behavior for multivari charts (see also Bruno Scibilia's blog which is linked in the references). It does not attempt to visualize individual observations.
A multivari chart mainly serves exploratory purposes.
It works particularly well with balanced data, but can also be used for messy data.
multivari can visualize the dependence of a single quantitative variable on up to four factors (i.e., interactions of order up to four can be visualized). The display is hierarchical: for factors later in the hierarchy, conditional means given level combinations of factors earlier in the hierarchy are displayed. Therefore, the order of the
factors can make a big difference in the display. If there is no natural
order, it may be worthwhile to inspect several orders.
For interactions with two factors only, it is often preferrable to use
function interaction.plot or raw.means.plot.
a list of (lists of) data frames with summary statistics to be plotted
Ulrike Groemping
Scibilia, Bruno (2013). Using Multi-Vari Charts to Analyze Families of Variations. https://blog.minitab.com/blog/using-variability-charts-to-analyze-call-center-wait-times.
See also interaction.plot, raw.means.plot
##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
## Not run:
require(car)
multivari("cycles", "len", "load", "amp", data=Wool,
col=list("black","red",c("grey70","grey45","grey20")),
pch=list(15,17,8), legend=TRUE, xlab.depth = 2, lwd=2)
multivari("cycles", "load", "len", "amp", data=Wool,
col=list("black",c("red","blue","darkgreen"),
c("grey70","grey45","grey20")),
pch=list(15,17,8), legend=TRUE, xlab.depth = 2, lwd=2)
## create a fake fourth factor
fakedat <- rbind(cbind(newfac="blabla",Wool),cbind(newfac="albalb",Wool))
## make it character for demonstrating the effect of sort option
fakedat$newfac <- as.character(fakedat$newfac)
## default: sort order in the data is respected (order of unique is used)
multivari("cycles", "load", "len", "amp", "newfac", data=fakedat,
col=list("black",c("red","blue","darkgreen"),
c("grey70","grey45","grey20")),
pch=list(15,17,8), legend=TRUE, xlab.depth = 2, lwd=2, cex=0.8)
## sort=TRUE: levels are sorted (order of sort(unique))
multivari("cycles", "load", "len", "amp", "newfac", data=fakedat,
col=list("black",c("red","blue","darkgreen"),
c("grey70","grey45","grey20")),
pch=list(15,17,8), legend=TRUE, xlab.depth = 2, lwd=2, cex=0.8,
sort=TRUE)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.