| freqdist {lazy.tools} | R Documentation |
Frequency distribution of multivariate numeric data with case weight
freqdist(x, weights = 1, npoints = NULL, min = NULL, max = NULL, prob = 0, wid = 6, dig = 1, maxf = NULL, rev = 0, class = 0, omitmiss = 0, midpoints = NULL, title = NULL, digits = 3, print = 0, plot = 0)
x |
numeric vector or column vector |
weights |
case weight |
npoints |
# of mid-points |
min |
minimum value of mid-points |
max |
maximum value of mid-points |
prob |
= 1 to return relative frequency in stead of frequency |
wid |
Format of mid-point value (wid.dig) |
dig |
Format of mid-point value (wid.dig) |
maxf |
max value of frequency for plot |
rev |
= 1 to change the last element of x first. |
class |
= 1 to output classification |
omitmiss |
= 1 to omit the missing grids |
midpoints |
vector of predefied mid-point values |
title |
tile to be used in plot |
digits |
Format for mid-points |
print |
= 1 to print result |
plot |
= 1 to plot historgam |
(npoints, min, max) defines the mid-points.
If min or max is NULL, min(x) and max(x) will be used.
midpoints has priority over (npoints, min, max).
The out of range observations, that is, those x satisfying
x < midpoints[1] or midpoints[npoints] < x
will be be included in midpoints[1] and midpoints[npoints], resp.
Try this to see how the obs on the border are classified.
xx=c(1,2,3,4,5,6,7)
brks=c(0,2,4,6,8)
cc=cut(xx,brks)
A list consisting of
midpoints= list of midpoints
midpoints1= list of midpoints with lost values
midpindex cartesian product of midpoints number
grid= cartesian product of midpoints
grid[,j]=midpoints[[j]][midpindex[,j]], j=1,2,...,nvar
freq= column vector of frequency at midpoints
breaks= breaks associated with the midpoints
class= classification of each obs into the midpoints if class=1.
nobs= sum of weights
set.seed(1701) n <- 1000 X <- matrix(rnorm(2*n),n,2) res <- freqdist( X, npoints=7,min=-3,max=3, print=1, weights=1, plot=1) X <- expand.grid(seq(-3,3),seq(-3,3)) weight <- round(n*exp(-0.5*rowSums(X^2))) res1 <- freqdist( X, weight, npoints=c(7,5),min=-3,max=3, print=1, plot=1) rm(X)