| stats.default | R Documentation |
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.
stats.default(x, quantile.type = 7, ...)
x |
A vector or numeric, factor, character or logical values. |
quantile.type |
An integer from 1 to 9, passed as the |
... |
Further arguments (ignored). |
A list. For numeric x, the list contains the numeric elements:
N: the number of non-missing values
NMISS: the number of missing values
SUM: the sum of the non-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
GSD: the geometric standard deviation 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, including NA in the denominator
PCTnoNA: the percent relative frequency, excluding NA from the denominator
NMISS: the number of missing values
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))
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