Descriptive statistics

These take data rather than a formula, following cor and summary: there is no response and no model here. A data frame, a matrix, or loose vectors are all accepted, and non-numeric columns are dropped.

Usage

tda_dstat(..., by = NULL, options = list(), dir = tempfile("tda"))

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

Arguments

...

a data frame, a matrix, or vectors.

by

optional grouping, as a vector or a one-sided formula, in the manner of aggregate. TDA prints one table per group and they are returned stacked with a Group column.

options

a named list of further TDA options, passed through.

dir

working directory.

Value

An object carrying a table, or for tda_corr a matrix.

Details

tda_dstat reports each variable's minimum, maximum, mean, standard deviation (denominator \(n - 1\), or the weight total minus one under case weights) and sum. tda_corr and tda_cov are the ordinary product-moment correlation and the \(n - 1\)-denominator covariance – cor() and cov() – extended to case weights when a cwt command is active (manual, section 6.2.6). All of them compute over every case given, including any -5 standing in for an NA – see ?tdaR on missing values.

See also

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

Examples

set.seed(1)
d <- data.frame(x = round(rnorm(40, 10, 3), 1))
d$y <- round(2 + 0.8 * d$x + rnorm(40, sd = 2), 1)
tda_dstat(d)
#> Call: tda_dstat(d)
#> 
#> Cases: 40 
#> 
#>  Variable Minimum Maximum    Mean Std.Dev.   Sum
#>         x     3.4    14.8 10.2825 2.661394 411.3
#>         y     2.5    17.0 10.4700 3.123295 418.8
tda_corr(d$x, d$y)
#> Call: tda_corr(d$x, d$y)
#> 
#> Cases: 40 
#> 
#>           d$x       d$y
#> d$x 1.0000000 0.8181522
#> d$y 0.8181522 1.0000000