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summaryStats is a generic function used to produce summary statistics, confidence intervals,
and results of hypothesis tests. 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, standard deviation, median, min, and max. Optional additional summary statistics include
1st quartile, 3rd quartile, and stadard error.summaryStats(object, ...)
## S3 method for class 'formula':
summaryStats(object, data = NULL, subset,
na.action = na.pass, ...)
## S3 method for class 'default':
summaryStats(object, group = NULL,
drop.unused.levels = TRUE, se = FALSE, quartiles = FALSE,
digits = max(3, getOption("digits") - 3),
digit.type = "round", drop0trailing = TRUE,
show.na = TRUE, show.0.na = FALSE, p.value = FALSE,
p.value.digits = 2, p.value.digit.type = "signif",
test = "parametric", test.arg.list = NULL,
combine.groups = p.value, rm.group.na = TRUE,
group.p.value.type = NULL, alternative = "two.sided",
ci = NULL, ci.between = NULL, conf.level = 0.95,
stats.in.rows = FALSE,
data.name = deparse(substitute(object)), ...)
## S3 method for class 'factor':
summaryStats(object, group = NULL,
drop.unused.levels = TRUE,
digits = max(3, getOption("digits") - 3),
digit.type = "round", drop0trailing = TRUE,
show.na = TRUE, show.0.na = FALSE, p.value = FALSE,
p.value.digits = 2, p.value.digit.type = "signif",
test = "chisq", test.arg.list = NULL, combine.levels = TRUE,
combine.groups = FALSE, rm.group.na = TRUE,
ci = p.value & test != "chisq", conf.level = 0.95,
stats.in.rows = FALSE, ...)
## S3 method for class 'character':
summaryStats(object, ...)
## S3 method for class 'logical':
summaryStats(object, ...)
## S3 method for class 'data.frame':
summaryStats(object, ...)
## S3 method for class 'matrix':
summaryStats(object, ...)
## S3 method for class 'list':
summaryStats(object, ...)object can be a numeric vector, factor, character vector,
logical vector, data frame, matrix, or list.
When object is a charactobject 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 dataobject is a formula, subset specifies an optional vector specifying
a subset of observations to be used.object is a formula, na.action specifies a function which indicates
what should happen when the data contain NAs. The default is na.pass.object is a numeric vector or factor, 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 coludrop.unused.levels=TRUE, groups with no observations are dropped.se=FALSE.quartiles=FALSE.digit.type="signif", digits indicates the number of significant
digits. When digit.type="round", digits indicatesdigits argument refers to significant digits
(digit.type="signif"), or how many decimal places to round to
(digit.type="round", the default).print.summarshow.na=TRUE.show.0.na=FALSE.p.value=FALSE.
Numeric data: if there are no groups the p-value is associated with the t-test to test
whep.value.digit.type="signif", p.value.digits indicates the
number of significant digits. When p.value.digit.type="round", p.value.digitp.value.digits argument refers to
significant digits (p.value.digit.type="signif", the default), or how many
decimal places to round to (p.value.digit.type="round").test="parametric"; the default) or nonparametric
(test="nonparametric") tests when p.value=test="parametric", p.value=TRUE,
group.p.value.type="between" and there are two TRUE if p.value=TRUE, otherwise FALSE.
Factors: the default value is group argument.
If rm.group.na=FALSE and group contains missing values then an error is returned.
If rm.group.na=TRUE and group.p.value.type="between" (the default when
combine.groups=TRUE), the p-value is associated with the tw"two.sided" (the default),
"less", and "greater". This argument is
igFALSE unless
p.value=TRUE and there are no groups, or when
p.value=ci.between=TRUE when p.value=TRUE
and
conf.level=0.95.stats.in.rows=FALSE."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).
Summary statistics for numeric data include sample size, mean, standard deviation, median,
min, and max. Options include the standard error of the mean (when se=TRUE),
the estimated quartiles (when quartiles=TRUE), p-values (when p.value=TRUE),
and/or confidence intervals (when ci=TRUE and/or ci.between=TRUE).
Summary statistics for factors include the sample size for each level of the factor and the
percent of the total for that level. Options include a p-value (when p.value=TRUE).
Note that unlike the Rfunction summary and the summaryFull, by default the digits argument for the summaryStats refers to how many decimal places to round to, not how many
significant digits to use (see the explanation of the argument digit.type above).summary, summaryFull, t.test, anova.lm,
wilcox.test, kruskal.test,
chisq.test, fisher.test, binom.test.# The guidance document USEPA (1994b, pp. 6.22--6.25)
# contains measures of 1,2,3,4-Tetrachlorobenzene (TcCB)
# concentrations (in parts per billion) from soil samples
# at a Reference area and a Cleanup area. These data are strored
# in the data frame EPA.94b.tccb.df.
#----------
# First, create summary statistics by area based on the log-transformed data.
summaryStats(log10(TcCB) ~ Area, data = EPA.94b.tccb.df)
# N Mean SD Median Min Max
#Cleanup 77 -0.2377 0.5908 -0.3665 -1.0458 2.2270
#Reference 47 -0.2691 0.2032 -0.2676 -0.6576 0.1239
#----------
# Now create summary statistics by area based on the log-transformed data
# and use the t-test to compare the areas.
summaryStats(log10(TcCB) ~ Area, data = EPA.94b.tccb.df, p.value = TRUE)
summaryStats(log10(TcCB) ~ Area, data = EPA.94b.tccb.df,
p.value = TRUE, stats.in.rows = TRUE)
# Cleanup Reference Combined
#N 77 47 124
#Mean -0.2377 -0.2691 -0.2496
#SD 0.5908 0.2032 0.481
#Median -0.3665 -0.2676 -0.3143
#Min -1.0458 -0.6576 -1.0458
#Max 2.227 0.1239 2.227
#Diff -0.0313
#p.value.between 0.73
#95%.LCL.between -0.2082
#95%.UCL.between 0.1456
#====================================================================
# Page 9-3 of USEPA (2009) lists trichloroethene
# concentrations (TCE; mg/L) collected from groundwater at two wells.
# Here, the seven non-detects have been set to their detection limit.
#----------
# First, compute summary statistics for all TCE observations.
summaryStats(TCE.mg.per.L ~ 1, data = EPA.09.Table.9.1.TCE.df,
digits = 3, data.name = "TCE")
# N Mean SD Median Min Max NA's N.Total
#TCE 27 0.09 0.064 0.1 0.004 0.25 3 30
summaryStats(TCE.mg.per.L ~ 1, data = EPA.09.Table.9.1.TCE.df,
se = TRUE, quartiles = TRUE, digits = 3, data.name = "TCE")
# N Mean SD SE Median Min Max 1st Qu. 3rd Qu. NA's N.Total
#TCE 27 0.09 0.064 0.012 0.1 0.004 0.25 0.031 0.12 3 30
#----------
# Now compute summary statistics by well.
summaryStats(TCE.mg.per.L ~ Well, data = EPA.09.Table.9.1.TCE.df,
digits = 3)
# N Mean SD Median Min Max NA's N.Total
#Well.1 14 0.063 0.079 0.031 0.004 0.25 1 15
#Well.2 13 0.118 0.020 0.110 0.099 0.17 2 15
summaryStats(TCE.mg.per.L ~ Well, data = EPA.09.Table.9.1.TCE.df,
digits = 3, stats.in.rows = TRUE)
# Well.1 Well.2
#N 14 13
#Mean 0.063 0.118
#SD 0.079 0.02
#Median 0.031 0.11
#Min 0.004 0.099
#Max 0.25 0.17
#NA's 1 2
#N.Total 15 15
# If you want to keep trailing 0's, use the drop0trailing argument:
summaryStats(TCE.mg.per.L ~ Well, data = EPA.09.Table.9.1.TCE.df,
digits = 3, stats.in.rows = TRUE, drop0trailing = FALSE)
# Well.1 Well.2
#N 14.000 13.000
#Mean 0.063 0.118
#SD 0.079 0.020
#Median 0.031 0.110
#Min 0.004 0.099
#Max 0.250 0.170
#NA's 1.000 2.000
#N.Total 15.000 15.000
#====================================================================
# Page 13-3 of USEPA (2009) lists iron concentrations (ppm) in
# groundwater collected from 6 wells.
#----------
# First, compute summary statistics for each well.
summaryStats(Iron.ppm ~ Well, data = EPA.09.Ex.13.1.iron.df,
combine.groups = FALSE, digits = 2, stats.in.rows = TRUE)
# Well.1 Well.2 Well.3 Well.4 Well.5 Well.6
#N 4 4 4 4 4 4
#Mean 47.01 55.73 90.86 70.43 145.24 156.32
#SD 12.4 20.34 59.35 25.95 92.16 51.2
#Median 50.05 57.05 76.73 76.95 137.66 171.93
#Min 29.96 32.14 39.25 34.12 60.95 83.1
#Max 57.97 76.71 170.72 93.69 244.69 198.34
#----------
# Note the large differences in standard deviations between wells.
# Compute summary statistics for log(Iron), by Well.
summaryStats(log(Iron.ppm) ~ Well, data = EPA.09.Ex.13.1.iron.df,
combine.groups = FALSE, digits = 2, stats.in.rows = TRUE)
# Well.1 Well.2 Well.3 Well.4 Well.5 Well.6
#N 4 4 4 4 4 4
#Mean 3.82 3.97 4.35 4.19 4.8 5
#SD 0.3 0.4 0.66 0.45 0.7 0.4
#Median 3.91 4.02 4.29 4.34 4.8 5.14
#Min 3.4 3.47 3.67 3.53 4.11 4.42
#Max 4.06 4.34 5.14 4.54 5.5 5.29
#----------
# Include confidence intervals for the mean log(Fe) concentration
# at each well, and also the p-value from the one-way
# analysis of variance to test for a difference in well means.
summaryStats(log(Iron.ppm) ~ Well, data = EPA.09.Ex.13.1.iron.df,
digits = 1, ci = TRUE, p.value = TRUE, stats.in.rows = TRUE)
# Well.1 Well.2 Well.3 Well.4 Well.5 Well.6 Combined
#N 4 4 4 4 4 4 24
#Mean 3.8 4 4.3 4.2 4.8 5 4.4
#SD 0.3 0.4 0.7 0.5 0.7 0.4 0.6
#Median 3.9 4 4.3 4.3 4.8 5.1 4.3
#Min 3.4 3.5 3.7 3.5 4.1 4.4 3.4
#Max 4.1 4.3 5.1 4.5 5.5 5.3 5.5
#95%.LCL 3.3 3.3 3.3 3.5 3.7 4.4 4.1
#95%.UCL 4.3 4.6 5.4 4.9 5.9 5.6 4.6
#p.value.between 0.025
#====================================================================
# Using the built-in dataset HairEyeColor, summarize the frequencies
# of hair color and test whether there is a difference in proportions.
# NOTE: The data that was originally factor data has already been
# collapsed into frequency counts by catetory in the object
# HairEyeColor. In the examples in this section, we recreate
# the factor objects in order to show how summaryStats works
# for factor objects.
Hair <- apply(HairEyeColor, 1, sum)
Hair
#Black Brown Red Blond
# 108 286 71 127
Hair.color <- names(Hair)
Hair.fac <- factor(rep(Hair.color, times = Hair),
levels = Hair.color)
#----------
# Compute summary statistics and perform the chi-square test
# for equal proportions of hair color
summaryStats(Hair.fac, digits = 1, p.value = TRUE)
# N Pct ChiSq_p
#Black 108 18.2
#Brown 286 48.3
#Red 71 12.0
#Blond 127 21.5
#Combined 592 100.0 2.5e-39
#----------
# Now test the hypothesis that 10% of the population from which
# this sample was drawn has Red hair, and compute a 95% confidence
# interval for the percent of subjects with red hair.
Red.Hair.fac <- factor(Hair.fac == "Red", levels = c(TRUE, FALSE),
labels = c("Red", "Not Red"))
summaryStats(Red.Hair.fac, digits = 1, p.value = TRUE,
ci = TRUE, test = "binom", test.arg.list = list(p = 0.1))
# N Pct Exact_p 95%.LCL 95%.UCL
#Red 71 12 9.5 14.9
#Not Red 521 88
#Combined 592 100 0.11
#----------
# Now test whether the percent of people with Green eyes is the
# same for people with and without Red hair.
HairEye <- apply(HairEyeColor, 1:2, sum)
Hair.color <- rownames(HairEye)
Eye.color <- colnames(HairEye)
n11 <- HairEye[Hair.color == "Red", Eye.color == "Green"]
n12 <- sum(HairEye[Hair.color == "Red", Eye.color != "Green"])
n21 <- sum(HairEye[Hair.color != "Red", Eye.color == "Green"])
n22 <- sum(HairEye[Hair.color != "Red", Eye.color != "Green"])
Hair.fac <- factor(rep(c("Red", "Not Red"), c(n11+n12, n21+n22)),
levels = c("Red", "Not Red"))
Eye.fac <- factor(c(rep("Green", n11), rep("Not Green", n12),
rep("Green", n21), rep("Not Green", n22)),
levels = c("Green", "Not Green"))
#----------
# Here are the results using the chi-square test and computing
# confidence limits for the difference between the two percentages
summaryStats(Eye.fac, group = Hair.fac, digits = 1,
p.value = TRUE, ci = TRUE, test = "prop",
stats.in.rows = TRUE, test.arg.list = list(correct = FALSE))
# Green Not Green Combined
#Red(N) 14 57 71
#Red(Pct) 19.7 80.3 100
#Not Red(N) 50 471 521
#Not Red(Pct) 9.6 90.4 100
#ChiSq_p 0.01
#95%.LCL.between 0.5
#95%.UCL.between 19.7
#----------
# Here are the results using Fisher's exact test and computing
# confidence limits for the odds ratio
summaryStats(Eye.fac, group = Hair.fac, digits = 1,
p.value = TRUE, ci = TRUE, test = "fisher",
stats.in.rows = TRUE)
# Green Not Green Combined
#Red(N) 14 57 71
#Red(Pct) 19.7 80.3 100
#Not Red(N) 50 471 521
#Not Red(Pct) 9.6 90.4 100
#Fisher_p 0.015
#95%.LCL.OR 1.1
#95%.UCL.OR 4.6
rm(Hair, Hair.color, Hair.fac, Red.Hair.fac, HairEye, Eye.color,
n11, n12, n21, n22, Eye.fac)
#====================================================================
# The data set EPA.89b.cadmium.df contains information on
# cadmium concentrations in groundwater collected from a
# background and compliance well. Compare detection frequencies
# between the well types and test for a difference using
# Fisher's exact test.
summaryStats(factor(Censored) ~ Well.type, data = EPA.89b.cadmium.df,
digits = 1, p.value = TRUE, test = "fisher")
summaryStats(factor(Censored) ~ Well.type, data = EPA.89b.cadmium.df,
digits = 1, p.value = TRUE, test = "fisher", stats.in.rows = TRUE)
# FALSE TRUE Combined
#Background(N) 8 16 24
#Background(Pct) 33.3 66.7 100
#Compliance(N) 24 40 64
#Compliance(Pct) 37.5 62.5 100
#Fisher_p 0.81
#95%.LCL.OR 0.3
#95%.UCL.OR 2.5Run the code above in your browser using DataLab