Quantiles, histograms and one-way frequency tables

tda_quant computes quantiles, tda_atab aggregates a variable into classes, which is how TDA makes a histogram, and tda_freq1 gives a one-way frequency table. All three describe the distribution of one variable at a time and return a table in the shape ... came in, one row per variable or class – unlike tda_freq2 (a cross-tabulation of two variables, a matrix rather than a table) or tda_loglin (a many-way contingency table for model fitting), which need their pages.

Usage

tda_quant(..., options = list(), dir = tempfile("tda"))

tda_atab(
  ...,
  breaks,
  open = c("right", "left"),
  nonempty_only = FALSE,
  options = list(),
  dir = tempfile("tda")
)

Arguments

...

a data frame, a matrix, or vectors.

options

a named list of further TDA options, passed through.

dir

working directory.

breaks

for tda_atab, the class boundaries.

open

for tda_atab, which side of each class is left open: "right" (default) or "left" – atab's s= (0/1).

nonempty_only

for tda_atab, print only classes that contain at least one case (atab's r=1).

Value

An object carrying a table: for tda_quant, one row per variable with the eleven quantiles p10..p90; for tda_atab, one row per class with its boundaries, count and percentage; for tda_freq1, one row per distinct value with count, percent and their cumulated versions.

Details

tda_quant reports a fixed set of orders – 0.1 through 0.9 in steps of 0.1, plus the quartiles 0.25 and 0.75 – interpolated on the sorted values at position \(p(n+1)\) (the manual, section 6.2.3, gives the exact rule). That is the same definition as R's quantile(x, type = 6), and the two agree to the last digit; stats::quantile's default (type = 7) is a different interpolation and will not match.

See also

Other descriptive statistics: tda_brr(), tda_cov(), tda_dstat(), tda_freq(), tda_independence(), tda_ineq(), tda_loglin(), tda_rcorr(), tda_segr(), tda_subm()

Examples

set.seed(1)
d <- data.frame(x = sample(1:5, 60, replace = TRUE,
                           prob = c(0.1, 0.2, 0.4, 0.2, 0.1)))
tda_quant(d)
#> Call: tda_quant(d)
#> 
#> Cases: 60 
#> 
#>   p10 p20 p25 p30 p40 p50 p60 p70 p75 p80 p90
#> x   2   2   2   3   3   3   3   4   4   4   4
tda_atab(d, breaks = seq(0, 5, 1))
#> Call: tda_atab(d, breaks = seq(0, 5, 1))
#> 
#> Cases: 60 
#> 
#>  lower upper count weighted mean_x
#>      1     2     4        4      1
#>      2     3    13       13      2
#>      3     4    22       22      3
#>      4     5    16       16      4
#>      5    NA     5        5      5
tda_freq1(d)
#> Call: tda_freq1(d)
#> 
#> Cases: 60 
#> 
#>  index value count   percent cum.count cum.percent
#>      1     1     4  6.666667         4    6.666667
#>      2     2    13 21.666667        17   28.333333
#>      3     3    22 36.666667        39   65.000000
#>      4     4    16 26.666667        55   91.666667
#>      5     5     5  8.333333        60  100.000000