table1 (version 1.1)

stats.default: Compute some basic descriptive statistics.

Description

Values of type factor, character and logical are treated as categorical. For logicals, the two categories are given the labels `Yes` for TRUE, and `No` for FALSE. Factor levels with zero counts are retained.

Usage

stats.default(x, useNA = NULL, quantile.type = 7)

Arguments

x

A vector or numeric, factor, character or logical values.

useNA

For categorical x, should missing values be treated as a category?

quantile.type

An integer from 1 to 9, passed as the type argument to function quantile.

Value

A list. For numeric x, the list contains the numeric elements:

• N: the number of non-missing values

• NMISS: the number of missing values

• MEAN: the mean of the non-missing values

• SD: the standard deviation of the non-missing values

• MIN: the minimum of the non-missing values

• MEDIAN: the median of the non-missing values

• CV: the percent coefficient of variation of the non-missing values

• GMEAN: the geometric mean of the non-missing values if non-negative, or NA

• GCV: the percent geometric coefficient of variation of the non-missing values if non-negative, or NA

• qXX: various quantiles (percentiles) of the non-missing values (q01: 1%, q02.5: 2.5%, q05: 5%, q10: 10%, q25: 25% (first quartile), q33.3: 33.33333% (first tertile), q50: 50% (median, or second quartile), q66.7: 66.66667% (second tertile), q75: 75% (third quartile), q90: 90%, q95: 95%, q97.5: 97.5%, q99: 99%)

• Q1: the first quartile of the non-missing values (alias q25)

• Q2: the second quartile of the non-missing values (alias q50 or Median)

• Q3: the third quartile of the non-missing values (alias q75)

• IQR: the inter-quartile range of the non-missing values (i.e., Q3 - Q1)

• T1: the first tertile of the non-missing values (alias q33.3)

• T2: the second tertile of the non-missing values (alias q66.7)

If x is categorical (i.e. factor, character or logical), the list contains a sublist for each category, where each sublist contains the numeric elements:

• FREQ: the frequency count

• PCT: the percent relative frequency

Examples

# NOT RUN {
x <- exp(rnorm(100, 1, 1))
stats.default(x)

y <- factor(sample(0:1, 99, replace=TRUE), labels=c("Female", "Male"))
y[1:10] <- NA
stats.default(y)
stats.default(is.na(y))

# }