Produces a spike plot of a numeric vector.
spikeplot(x, freq = FALSE, as.table = FALSE, col = par("col"),
lty = par("lty"), lwd = par("lwd"), lend = par("lend"),
type = "h", xlab = deparse1(substitute(x)), ylab = NULL,
capped = FALSE, cex = sqrt(lwd) / 2, pch = 19, pcol = col, scol = NULL,
slty = NULL, slwd = NULL, new.plot = TRUE, offset.x = 0, ymux = 1, ...)
Numeric, passed into table
.
Logical. If TRUE
then the y-axis measures
the frequencies, else the sample proportions.
Intended to be as hist
.
See par
.
See par
.
Similar to col
, lty
and lwd
but
apply to some selected values.
The input may be a named list such as
scol = list("green" = c(2, 4, 6), "blue" = 5)
,
slty = list("dashed" = c(2, 4, 6), "dotted" = 5)
,
slwd = list("2" = c(2, 4, 6), "3" = 5)
,
else a named vector such as
scol = c("green" = 2, "green" = 4, "green" = 6, "blue" = 5)
,
slty = c("dashed" = 2, "dashed" = 4, "dashed" = 6, "dotted" = 5)
,
slwd = c("2" = 2, "2" = 4, "2" = 6, "3" = 5)
.
The three arguments are ignored if as.table = TRUE
.
Logical and numeric.
Add to an existing plot? If so, set new.plot = FALSE
and it is useful for
the spikes to be shifted by some amount offset.x
.
Numeric, y-multiplier. The response is multiplied by ymux
.
This can be useful when plotting subsets side-by-side so that
the constituent proportions add up to the overall proportion.
Additional graphical arguments passed into an ordinary
plot
, for example,
xlim
, las
, main
.
Returns invisibly table(x)
.
Heaping is a very commonly occurring phenomenon in
retrospective self-reported survey data.
Also known as digit preference data,
it is often characterized by an excess of multiples of 10 or 5
upon rounding.
For this type of data
this simple function is meant to be convenient for
plotting the frequencies or sample proportions of
a vector x
representing a discrete random variable.
This type of plot
is known as a spike plot in STATA circles.
If table(x)
works then this function should hopefully
work.
The default for type
means that any heaping and
seeping should easily be seen.
If such features exist then GAITD regression is
potentially useful---see gaitdpoisson
etc.
Currently missing values are ignored totally because
table(x)
is used without further arguments;
this might change in the future.
table
,
plot
,
par
,
deparse1
,
dgaitdplot
,
plotdgaitd
,
gaitdpoisson
.
# NOT RUN {
spikeplot(with(marital.nz, age), col = "pink2", lwd = 2)
# }
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