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EnvStats (version 2.1.0)

summaryFull: 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, ...)

## S3 method for class 'default':
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 object must be a numeric vector, a data frame, a matrix, or a list. When object is a data frame, all columns must be numeric.
data
when object is a formula, data specifies an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data
subset
when object is a formula, subset specifies an optional vector specifying a subset of observations to be used.
na.action
when object is a formula, na.action specifies a function which indicates what should happen when the data contain NAs. The default is na.pass.
group
when object is a numeric vector, group is a factor or character vector indicating which group each observation belongs to. When object is a matrix or data frame this argument is ignored and the columns define
combine.groups
logical scalar indicating whether to show summary statistics for all groups combined. The default value is FALSE.
drop.unused.levels
when drop.unused.levels=TRUE, groups with no observations are dropped.
rm.group.na
logical scalar indicating whether to remove missing values from the group argument. By default rm.group.na=TRUE.
stats
character vector indicating which statistics to compute. Possible elements of the character vector include: "all" (indicating to include all summary statistics), "for.non.pos" (only compute statistics that are meaningfu
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 trim=0.1. If trim=0.5, this yields the median.
sd.method
character string specifying what method to use to compute the sample standard deviation. The possible values are "sqrt.ubiased" (the square root of the unbiased estimate of variance; the default), or "moments" (the metho
geo.sd.method
character string specifying what method to use to compute the sample standard deviation of the log-transformed observations prior to exponentiating this quantity. The possible values are "sqrt.ubiased" (the square root of the unbiase
skew.list
list of arguments to supply to the skewness function. See the help file for skewness for more information. The default value is skew.list=list(
kurtosis.list
list of arguments to supply to the kurtosis function. See the help file for kurtosis for more information. The default value is kurtosis.list=
cv.list
list of arguments to supply to the cv function. See the help file for cv for more information. The default value is cv.list=list(), which results in
digits
integer indicating the number of digits to use for the summary statistics. When digit.type="signif", digits indicates the number of significant digits. When digit.type="round", digits indicates
digit.type
character string indicating whether the digits argument refers to significant digits (digit.type="signif", the default), or how many decimal places to round to (digit.type="round").
stats.in.rows
logical scalar indicating whether to show the summary statistics in the rows or columns of the output. The default is stats.in.rows=TRUE.
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 print.summar
data.name
character string indicating the name of the data used for the summary statistics.
...
additional arguments affecting the summary statistics produced.

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).

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 built-in R function summary specifically for non-factor 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. ll{ 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 }

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.102-103. Millard, S.P., and N.K. Neerchal. (2001). Environmental Statistics with S-PLUS. 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. Prentice-Hall, Upper Saddle River, NJ.

See Also

summary, summaryStats.

Examples

Run this code
# 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 non-positive.

  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

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