Shortcut Helper Functions
vtable includes four shortcut functions. These are
generally intended for use with the summ option in
vtable and sumtable because nested functions
don’t look very nice in a vtable, or in a
sumtable unless you explicitly set the
summ.names.
nuniq
nuniq(x) returns length(unique(x)), the
number of unique values in the vector.
countNA, propNA, and
notNA
These three functions are shortcuts for dealing with missing data.
You have probably written out the nested versions of these many
times!
countNA() |
sum(is.na()) |
propNA() |
mean(is.na()) |
notNA() |
sum(!is.na()) |
is.round
This function is a shortcut for
!any(!(x == round(x,digits))).
It takes two arguments: a vector x and a number of
digits (0 by default). It checks whether you can round to
digits digits without losing any information.
Other Helper Functions
pctile
pctile(x) is short for
quantile(x,1:100/100). So in one sense this is another
shortcut function. But this inherently lets you interact with
percentiles a bit differently.
While quantile() has you specify which percentile you
want in the function call, pctile() returns an object with
all integer percentiles, and you can pull out which ones you want
afterwards. pctile(x)[50] is the 50th percentile, etc..
This can be convenient in several applications, an obvious one being in
sumtable.
library(vtable)
#Some random normal data, and its percentiles
d <- rnorm(1000)
pc <- pctile(d)
#25th, 50th, 75th percentile
pc[c(25,50,75)]
## 25% 50% 75%
## -0.698256183 0.005340094 0.661151636
#Inverse normal CDF with 100 points of articulation
plot(pc)
weighted.sd
weighted.sd(x, w) is a function to calculate a weighted
standard deviation of x using w as weights,
much like the base weighted.mean() does for means. It is
mostly used as a helper function for sumtable() when
group.weights is specified. However, you can use it on its
own if you like. Unlike weighted.mean(), setting
na.rm = TRUE will account for missings both in
x and w.
The weighted standard deviation is calculated as
\[
\frac{\sum_i(w_i*(x_i-\bar{x}_w)^2)}{\frac{N_w-1}{N_w}\sum_iw_i}
\]
Where \(\bar{x}_w\) is the weighted
mean of \(x\), and \(N_w\) is the number of observations with a
nonzero weight.
x <- 1:100
w <- 1:100
weighted.mean(x, w)
## [1] 67
## [1] 29.01149
## [1] 23.80476
independence.test
independence.test is a helper function for
sumtable(group.test=TRUE) that tests for independence
between a categorical variable x and another variable
y that may be categorical or numerical.
Then, it outputs a formatted string as its output, with
significance stars, for printing.
The function takes the format
independence.test(x,y,w=NA,
factor.test = NA,
numeric.test = NA,
star.cutoffs = c(.01,.05,.1),
star.markers = c('***','**','*'),
digits = 3,
fixed.digits = FALSE,
format = '{name}={stat}{stars}',
opts = list())
factor.test and numeric.test
These are functions that actually perform the independence test.
numeric.test is used when y is numeric, and
factor.test is used in all other instances.
Specifically, these functions should take only x,
y, and w=NULL as arguments, and should return
a list with three elements: the name of the test statistic, the test
statistic itself, and the p-value of the test.
By default, these are the internal functions
vtable:::chisq.it for factor.test and
vtable:::groupf.it for numeric.test, so you
can take a look at those (just put vtable:::chisq.it in the
terminal and it will show you the function’s code) if you’d like to make
your own test functions.
star.cutoffs and star.markers
These are numeric and character vectors, respectively, used for
p-value cutoffs and to create significance markers.
star.cutoffs indicates the cutoffs, and
star.markers indicates the markers to be used with each
cutoff, in the same order. So with
star.cutoffs = c(.01,.05,.1) and
star.markers = c('***','**','*'), each p-value below .01
will get marked with '***', each from .01 to .05 will get
'**', and each from .05 to .1 will get *.
Defaults are set to “economics defaults” (.1, .05, .01). But these
are of course easy to change.
data(iris)
independence.test(iris$Species,
iris$Sepal.Length,
star.cutoffs = c(.05,.01,.001))
## [1] "F=119.265*"
digits and fixed.digits
digits indicates how many digits after the decimal place
from the test statistics and p-values should be displayed.
fixed.digits determines whether trailing zeros are
maintained.
independence.test(iris$Species,
iris$Sepal.Width,
digits=1)
## [1] "F=49.2***"
independence.test(iris$Species,
iris$Sepal.Width,
digits=4,
fixed.digits = TRUE)
## [1] "F=49.1600***"
opts
You can create a named list where the names are the above options and
the values are the settings for those options, and input it into
independence.test using opts=. This is an easy
way to set the same options for many
independence.tests.