summaryFull {EnvStats}  R Documentation 
Full Complement of Summary Statistics
Description
summaryFull
is a generic function used to produce a full complement of summary statistics.
The function invokes particular methods
which depend on the class
of
the first argument. The summary statistics include: sample size, number of missing values,
mean, median, trimmed mean, geometric mean, skew, kurtosis, min, max, range, 1st quartile, 3rd quartile,
standard deviation, geometric standard deviation, interquartile range, median absolute deviation, and
coefficient of variation.
Usage
summaryFull(object, ...)
## S3 method for class 'formula'
summaryFull(object, data = NULL, subset,
na.action = na.pass, ...)
## Default S3 method:
summaryFull(object, group = NULL,
combine.groups = FALSE, drop.unused.levels = TRUE,
rm.group.na = TRUE, stats = NULL, trim = 0.1,
sd.method = "sqrt.unbiased", geo.sd.method = "sqrt.unbiased",
skew.list = list(), kurtosis.list = list(),
cv.list = list(), digits = max(3, getOption("digits")  3),
digit.type = "signif", stats.in.rows = TRUE,
drop0trailing = TRUE, data.name = deparse(substitute(object)),
...)
## S3 method for class 'data.frame'
summaryFull(object, ...)
## S3 method for class 'matrix'
summaryFull(object, ...)
## S3 method for class 'list'
summaryFull(object, ...)
Arguments
object 
an object for which summary statistics are desired. In the default method,
the argument 
data 
when 
subset 
when 
na.action 
when 
group 
when 
combine.groups 
logical scalar indicating whether to show summary statistics for all groups combined.
The default value is 
drop.unused.levels 
when 
rm.group.na 
logical scalar indicating whether to remove missing values from the 
stats 
character vector indicating which statistics to compute. Possible elements of the character
vector include: 
trim 
fraction (between 0 and 0.5 inclusive) of values to be trimmed from each end of the ordered data
to compute the trimmed mean. The default value is 
sd.method 
character string specifying what method to use to compute the sample standard deviation.
The possible values are 
geo.sd.method 
character string specifying what method to use to compute the sample standard deviation of the
logtransformed observations prior to exponentiating this quantity. The possible values are

skew.list 
list of arguments to supply to the 
kurtosis.list 
list of arguments to supply to the 
cv.list 
list of arguments to supply to the 
digits 
integer indicating the number of digits to use for the summary statistics.
When 
digit.type 
character string indicating whether the 
stats.in.rows 
logical scalar indicating whether to show the summary statistics in the rows or columns of the
output. The default is 
drop0trailing 
logical scalar indicating whether to drop trailing 0's when printing the summary statistics.
The value of this argument is added as an attribute to the returned list and is used by the

data.name 
character string indicating the name of the data used for the summary statistics. 
... 
additional arguments affecting the summary statistics produced. 
Details
The function summaryFull
returns summary statistics that are useful to describe various
characteristics of one or more variables. It is an extended version of the builtin R function
summary
specifically for nonfactor numeric data. The table below shows what
statistics are computed and what functions are called by summaryFull
to compute these statistics.
The object returned by summaryFull
is useful for printing or report purposes. You may also
use the functions that summaryFull
calls (see table below) to compute summary statistics to
be used by other functions.
See the help files for the functions listed in the table below for more information on these summary statistics.
Summary Statistic  Function Used 
Mean  mean 
Median  median 
Trimmed Mean  mean with trim argument 
Geometric Mean  geoMean 
Skew  skewness 
Kurtosis  kurtosis 
Min  min 
Max  max 
Range  range and diff 
1st Quartile  quantile 
3rd Quartile  quantile 
Standard Deviation  sd 
Geometric Standard Deviation  geoSD 
Interquartile Range  iqr 
Median Absolute Deviation  mad 
Coefficient of Variation  cv 
Value
an object of class "summaryStats"
(see summaryStats.object
.
Objects of class "summaryStats"
are numeric matrices that contain the
summary statisics produced by a call to summaryStats
or summaryFull
.
These objects have a special printing method that by default removes
trailing zeros for sample size entries and prints blanks for statistics that are
normally displayed as NA
(see print.summaryStats
).
Author(s)
Steven P. Millard (EnvStats@ProbStatInfo.com)
References
Berthouex, P.M., and L.C. Brown. (2002). Statistics for Environmental Engineers, Second Edition. Lewis Publishers, Boca Raton, FL.
Gilbert, R.O. (1987). Statistical Methods for Environmental Pollution Monitoring. Van Nostrand Reinhold, NY.
Helsel, D.R., and R.M. Hirsch. (1992). Statistical Methods in Water Resources Research. Elsevier, New York, NY.
Leidel, N.A., K.A. Busch, and J.R. Lynch. (1977). Occupational Exposure Sampling Strategy Manual. U.S. Department of Health, Education, and Welfare, Public Health Service, Center for Disease Control, National Institute for Occupational Safety and Health, Cincinnati, Ohio 45226, January, 1977, pp.102103.
Millard, S.P., and N.K. Neerchal. (2001). Environmental Statistics with SPLUS. CRC Press, Boca Raton, FL.
Ott, W.R. (1995). Environmental Statistics and Data Analysis. Lewis Publishers, Boca Raton, FL.
Zar, J.H. (2010). Biostatistical Analysis, Fifth Edition. PrenticeHall, Upper Saddle River, NJ.
See Also
Examples
# Generate 20 observations from a lognormal distribution with
# parameters mean=10 and cv=1, and compute the summary statistics.
# (Note: the call to set.seed simply allows you to reproduce this
# example.)
set.seed(250)
dat < rlnormAlt(20, mean=10, cv=1)
summary(dat)
# Min. 1st Qu. Median Mean 3rd Qu. Max.
#2.608 4.995 6.235 7.490 9.295 15.440
summaryFull(dat)
# dat
#N 20
#Mean 7.49
#Median 6.235
#10% Trimmed Mean 7.125
#Geometric Mean 6.674
#Skew 0.9877
#Kurtosis 0.03539
#Min 2.608
#Max 15.44
#Range 12.83
#1st Quartile 4.995
#3rd Quartile 9.295
#Standard Deviation 3.803
#Geometric Standard Deviation 1.634
#Interquartile Range 4.3
#Median Absolute Deviation 2.607
#Coefficient of Variation 0.5078
#
# Compare summary statistics for normal and lognormal data:
log.dat < log(dat)
summaryFull(list(dat = dat, log.dat = log.dat))
# dat log.dat
#N 20 20
#Mean 7.49 1.898
#Median 6.235 1.83
#10% Trimmed Mean 7.125 1.902
#Geometric Mean 6.674 1.835
#Skew 0.9877 0.1319
#Kurtosis 0.03539 0.4288
#Min 2.608 0.9587
#Max 15.44 2.737
#Range 12.83 1.778
#1st Quartile 4.995 1.607
#3rd Quartile 9.295 2.227
#Standard Deviation 3.803 0.4913
#Geometric Standard Deviation 1.634 1.315
#Interquartile Range 4.3 0.62
#Median Absolute Deviation 2.607 0.4915
#Coefficient of Variation 0.5078 0.2588
# Clean up
rm(dat, log.dat)
#
# Compute summary statistics for 10 observations from a normal
# distribution with parameters mean=0 and sd=1. Note that the
# geometric mean and geometric standard deviation are not computed
# since some of the observations are nonpositive.
set.seed(287)
dat < rnorm(10)
summaryFull(dat)
# dat
#N 10
#Mean 0.07406
#Median 0.1095
#10% Trimmed Mean 0.1051
#Skew 0.1646
#Kurtosis 0.7135
#Min 1.549
#Max 1.449
#Range 2.998
#1st Quartile 0.5834
#3rd Quartile 0.6966
#Standard Deviation 0.9412
#Interquartile Range 1.28
#Median Absolute Deviation 1.05
# Clean up
rm(dat)
#
# Compute summary statistics for the TcCB data given in USEPA (1994b)
# (the data are stored in EPA.94b.tccb.df). Arbitrarily set the one
# censored observation to the censoring level. Group by the variable
# Area.
summaryFull(TcCB ~ Area, data = EPA.94b.tccb.df)
# Cleanup Reference
#N 77 47
#Mean 3.915 0.5985
#Median 0.43 0.54
#10% Trimmed Mean 0.6846 0.5728
#Geometric Mean 0.5784 0.5382
#Skew 7.717 0.9019
#Kurtosis 62.67 0.132
#Min 0.09 0.22
#Max 168.6 1.33
#Range 168.5 1.11
#1st Quartile 0.23 0.39
#3rd Quartile 1.1 0.75
#Standard Deviation 20.02 0.2836
#Geometric Standard Deviation 3.898 1.597
#Interquartile Range 0.87 0.36
#Median Absolute Deviation 0.3558 0.2669
#Coefficient of Variation 5.112 0.4739