dotchartpl.RdThis function produces a plotly interactive graphic and accepts
a different format of data input than the other dotchart
functions. It was written to handle a hierarchical data structure
including strata that further subdivide the main classes. Strata,
indicated by the mult variable, are shown on the same
horizontal line, and if the variable big is FALSE will
appear slightly below the main line, using smaller symbols, and having
some transparency. This is intended to handle output such as that
from the summaryP function when there is a superpositioning
variable group and a stratification variable mult,
especially when the data have been run through the addMarginal
function to create mult categories labelled "All" for
which the user will specify big=TRUE to indicate non-stratified
estimates (stratified only on group) to emphasize.
When viewing graphics that used mult and big, the user
can click on the legends for the small points for groups to
vanish the finely stratified estimates.
When group is used by mult and big are not, and
when the group variable has exactly two distinct values, you
can specify refgroup to get the difference between two
proportions in addition to the individual proportions. The individual
proportions are plotted, but confidence intervals for the difference
are shown in hover text and half-width confidence intervals for the
difference, centered at the midpoint of the proportions, are shown.
These have the property of intersecting the two proportions if and
only if there is no significant difference at the 1 - conf.int
level.
Specify fun=exp and ifun=log if estimates and confidence
limits are on the log scale. Make sure that zeros were prevented in
the original calculations. For exponential hazard rates this can be
accomplished by replacing event counts of 0 with 0.5.
dotchartpl(x, major=NULL, minor=NULL, group=NULL, mult=NULL,
big=NULL, htext=NULL, num=NULL, denom=NULL,
numlabel='', denomlabel='',
fun=function(x) x, ifun=function(x) x, op='-',
lower=NULL, upper=NULL,
refgroup=NULL, sortdiff=TRUE, conf.int=0.95,
minkeep=NULL, xlim=NULL, xlab='Proportion',
tracename=NULL, limitstracename='Limits',
nonbigtracename='Stratified Estimates',
dec=3, width=800, height=NULL,
col=colorspace::rainbow_hcl)a numeric vector used for values on the x-axis
major vertical category, e.g., variable labels
minor vertical category, e.g. category levels within variables
superpositioning variable such as treatment
strata names for further subdivisions without
groups
omit if all levels of mult are equally important or
if mult is omitted. Otherwise denotes major (larger points,
right on horizontal lines) vs. minor (smaller, transparent points
slightly below the line).
additional hover text per point
if x represents proportions, optionally specifies
numerators to be used in fractions added to hover text. When
num is given, x is automatically added to hover text,
rounded to 3 digits after the decimal point.
like num but for denominators
character string to put to the right of the numerator in hover text
character string to put to the right of the denominator in hover text
a transformation to make when printing estimates. For
example, one may specify fun=exp to anti-log estimates and
confidence limites that were computed on a log basis
inverse transformation of fun
set to for example '/' when fun=exp and
effects are computed as ratios instead of differences. This is used
in hover text.
lower limits for optional error bars
upper limits for optional error bars
if group is specified and there are exactly two
groups, specify the character string for the reference group in
computing difference in proportions. For example if
refgroup='A' and the group levels are 'A','B',
you will get B - A.
minor categories are sorted by descending
values of the difference in proportions when refgroup is used,
unless you specify sortdiff=FALSE
confidence level for computing confidence intervals
for the difference in two proportions. Specify conf.int=FALSE
to suppress confidence intervals.
if refgroup and minkeep are both given,
observations that are at or above minkeep for at least one of
the groups are retained. The defaults to to keep all observations.
x-axis limits
x-axis label
plotly trace name if group is not used
plotly trace name for lower and
upper if group is not used
plotly trace name used for non-big
elements, which usually represent stratified versions of the "big"
observations
a function or vector of colors to assign to group.
If a function it will be evaluated with an argument equal to the
number of distinct groups.
number of places to the right of the decimal place for formatting numeric quantities in hover text
width of plot in pixels
height of plot in pixels; computed from number of strata by default
a plotly object. An attribute levelsRemoved is
added if minkeep is used and any categories were omitted from
the plot as a result. This is a character vector with categories
removed. If major is present, the strings are of the form
major:minor
if (FALSE) { # \dontrun{
set.seed(1)
d <- expand.grid(major=c('Alabama', 'Alaska', 'Arkansas'),
minor=c('East', 'West'),
group=c('Female', 'Male'),
city=0:2)
n <- nrow(d)
d$num <- round(100*runif(n))
d$denom <- d$num + round(100*runif(n))
d$x <- d$num / d$denom
d$lower <- d$x - runif(n)
d$upper <- d$x + runif(n)
with(d,
dotchartpl(x, major, minor, group, city, lower=lower, upper=upper,
big=city==0, num=num, denom=denom, xlab='x'))
# Show half-width confidence intervals for Female - Male differences
# after subsetting the data to have only one record per
# state/region/group
d <- subset(d, city == 0)
with(d,
dotchartpl(x, major, minor, group, num=num, denom=denom,
lower=lower, upper=upper, refgroup='Male')
)
n <- 500
set.seed(1)
d <- data.frame(
race = sample(c('Asian', 'Black/AA', 'White'), n, TRUE),
sex = sample(c('Female', 'Male'), n, TRUE),
treat = sample(c('A', 'B'), n, TRUE),
smoking = sample(c('Smoker', 'Non-smoker'), n, TRUE),
hypertension = sample(c('Hypertensive', 'Non-Hypertensive'), n, TRUE),
region = sample(c('North America','Europe','South America',
'Europe', 'Asia', 'Central America'), n, TRUE))
d <- upData(d, labels=c(race='Race', sex='Sex'))
dm <- addMarginal(d, region)
s <- summaryP(race + sex + smoking + hypertension ~
region + treat, data=dm)
s$region <- ifelse(s$region == 'All', 'All Regions', as.character(s$region))
with(s,
dotchartpl(freq / denom, major=var, minor=val, group=treat, mult=region,
big=region == 'All Regions', num=freq, denom=denom)
)
s2 <- s[- attr(s, 'rows.to.exclude1'), ]
with(s2,
dotchartpl(freq / denom, major=var, minor=val, group=treat, mult=region,
big=region == 'All Regions', num=freq, denom=denom)
)
# Note these plots can be created by plot.summaryP when options(grType='plotly')
# Plot hazard rates and ratios with confidence limits, on log scale
d <- data.frame(tx=c('a', 'a', 'b', 'b'),
event=c('MI', 'stroke', 'MI', 'stroke'),
count=c(10, 5, 5, 2),
exposure=c(1000, 1000, 900, 900))
# There were no zero event counts in this dataset. In general we
# want to handle that, hence the 0.5 below
d <- upData(d, hazard = pmax(0.5, count) / exposure,
selog = sqrt(1. / pmax(0.5, count)),
lower = log(hazard) - 1.96 * selog,
upper = log(hazard) + 1.96 * selog)
with(d,
dotchartpl(log(hazard), minor=event, group=tx, num=count, denom=exposure,
lower=lower, upper=upper,
fun=exp, ifun=log, op='/',
numlabel='events', denomlabel='years',
refgroup='a', xlab='Events Per Person-Year')
)
} # }