| descriptiveTable {rockchalk} | R Documentation |
Summary stats table-maker for regression users
Description
rockchalk::summarize does the numerical calculations
Usage
descriptiveTable(
object,
stats = c("mean", "sd", "min", "max"),
digits = 4,
probs = c(0, 0.5, 1),
varLabels,
...
)
Arguments
object |
A fitted regression or an R data.frame, or any other object type that does not fail in codemodel.frame(object). |
stats |
Default is a vector c("mean", "sd", "min", "max"). Other stats reported by rockchalk::summarize should work fine as well |
digits |
2 decimal points is default |
probs |
Probability cut points to be used in the calculation
of summaries of numeric variables. Default is c(0, 0.5, 1), meaning
|
varLabels |
A named vector of variables labels, as in outreg function. Format is c("oldname"="newlabel"). |
... |
Other arguments passed to rockchalk::summarizeNumerics and summarizeFactors. |
Details
This is, roughly speaking, doing the right thing, but not in a clever way. For the categorical variables, the only summary is proportions.
Value
a character matrix
Author(s)
Paul Johnson pauljohn@ku.edu
Examples
dat <- genCorrelatedData2(1000, means=c(10, 10, 10), sds = 3,
stde = 3, beta = c(1, 1, -1, 0.5))
dat$xcat1 <- factor(sample(c("a", "b", "c", "d"), 1000, replace=TRUE))
dat$xcat2 <- factor(sample(c("M", "F"), 1000, replace=TRUE), levels = c("M", "F"),
labels = c("Male", "Female"))
dat$y <- dat$y + contrasts(dat$xcat1)[dat$xcat1, ] %*% c(0.1, 0.2, 0.3)
m4 <- lm(y ~ x1 + x2 + x3 + xcat1 + xcat2, dat)
m4.desc <- descriptiveTable(m4)
m4.desc
## Following may cause scientific notation, want to avoid.
dat <- genCorrelatedData2(1000, means=c(10, 100, 400),
sds = c(3, 10, 20), stde = 3, beta = c(1, 1, -1, 0.5))
m5 <- lm(y ~ x1 + x2 + x3, dat)
m5.desc <- descriptiveTable(m5, digits = 4)
m5.desc