1-D Scatter Plots with Confidence Intervals
stripChart
is a modification of the R function stripchart
.
It is a generic function used to produce one dimensional scatter
plots (or dot plots) of the given data, along with text indicating sample size and
estimates of location (mean or median) and scale (standard deviation
or interquartile range), as well as confidence intervals for the population
location parameter.
One dimensional scatterplots are a good alternative to boxplots
when sample sizes are small or moderate. The function invokes particular
methods
which depend on the class
of the first argument.
stripChart(x, ...) ## S3 method for class 'formula' stripChart(x, data = NULL, dlab = NULL, subset, na.action = NULL, ...) ## Default S3 method: stripChart(x, method = ifelse(paired && paired.lines, "overplot", "stack"), seed = 47, jitter = 0.1 * cex, offset = 1/2, vertical = TRUE, group.names, group.names.cex = cex, drop.unused.levels = TRUE, add = FALSE, at = NULL, xlim = NULL, ylim = NULL, ylab = NULL, xlab = NULL, dlab = "", glab = "", log = "", pch = 1, col = par("fg"), cex = par("cex"), points.cex = cex, axes = TRUE, frame.plot = axes, show.ci = TRUE, location.pch = 16, location.cex = cex, conf.level = 0.95, min.n.for.ci = 2, ci.offset = 3/ifelse(n > 2, (n-1)^(1/3), 1), ci.bar.lwd = cex, ci.bar.ends = TRUE, ci.bar.ends.size = 0.5 * cex, ci.bar.gap = FALSE, n.text = "bottom", n.text.line = ifelse(n.text == "bottom", 2, 0), n.text.cex = cex, location.scale.text = "top", location.scale.digits = 1, nsmall = location.scale.digits, location.scale.text.line = ifelse(location.scale.text == "top", 0, 3.5), location.scale.text.cex = cex * 0.8 * ifelse(n > 6, max(0.4, 1 - (n-6) * 0.06), 1), p.value = FALSE, p.value.digits = 3, p.value.line = 2, p.value.cex = cex, group.difference.ci = p.value, group.difference.conf.level = 0.95, group.difference.digits = location.scale.digits, ci.and.test = "parametric", ci.arg.list = NULL, test.arg.list = NULL, alternative = "two.sided", plot.diff = FALSE, diff.col = col[1], diff.method = "stack", diff.pch = pch[1], paired = FALSE, paired.lines = paired, paired.lty = 1:6, paired.lwd = 1, paired.pch = 1:14, paired.col = NULL, diff.name = NULL, diff.name.cex = group.names.cex, sep.line = TRUE, sep.lty = 2, sep.lwd = cex, sep.col = "gray", diff.lim = NULL, diff.at = NULL, diff.axis.label = NULL, plot.diff.mar = c(5, 4, 4, 4) + 0.1, ...)
x |
the data from which the plots are to be produced. In the default method the data can be
specified as a list or data frame where each component is numeric, a numeric matrix,
or a numeric vector. In the formula method, a symbolic specification of the form
|
data |
for the formula method, a data.frame (or list) from which the variables in |
subset |
for the formula method, an optional vector specifying a subset of observations to be used for plotting. |
na.action |
for the formula method, a function which indicates what should happen when the data
contain |
... |
additional parameters passed to the default method, or by it to |
method |
the method to be used to separate coincident points. When |
seed |
when |
jitter |
when |
offset |
when stacking is used, points are stacked this many line-heights (symbol widths) apart. |
vertical |
when |
group.names |
group labels which will be printed alongside (or underneath) each plot. |
group.names.cex |
numeric scalar indicating the amount by which the group labels should be scaled
relative to the default (see the help file for |
drop.unused.levels |
when |
add |
logical, if true add the chart to the current plot. |
at |
numeric vector giving the locations where the charts should be drawn,
particularly when |
xlim, ylim |
plot limits: see |
ylab, xlab |
labels: see |
dlab, glab |
alternate way to specify axis labels. The |
log |
on which axes to use a log scale: see |
pch, col, cex |
Graphical parameters: see |
points.cex |
Sets the |
axes, frame.plot |
Axis control: see |
show.ci |
logical scalar indicating whether to plot the confidence interval. The default is
|
location.pch |
integer indicating which plotting character to use to indicate the estimate of location
(mean or median) for each group (see the help file for |
location.cex |
numeric scalar giving the amount by which the plotting characters indicating the
estimate of location for each group should be scaled relative to the default
(see the help file for |
conf.level |
numeric scalar between 0 and 1 indicating the confidence level associated with the
confidence interval for the group location (population mean or median).
The default value is |
min.n.for.ci |
integer indicating the minimum sample size required in order to plot a confidence interval
for the group location. The default value is |
ci.offset |
numeric scalar or vector of length equal to the number of groups ( |
ci.bar.lwd |
numeric scalar indicating the line width for the confidence interval bars.
The default is the current value of the graphics parameter |
ci.bar.ends |
logical scalar indicating whether to add flat ends to the confidence interval bars.
The default value is |
ci.bar.ends.size |
numeric scalar in units of |
ci.bar.gap |
logical scalar indicating with to add a gap between the estimate of group location and the
confidence interval bar. The default value is |
n.text |
character string indicating whether and where to indicate the sample size for each group.
Possible values are |
n.text.line |
integer indicating on which plot margin line to show the sample sizes for each group. The
default value is |
n.text.cex |
numeric scalar giving the amount by which the text indicating the sample size for
each group should be scaled relative to the default (see the help file for |
location.scale.text |
character string indicating whether and where to indicate the estimates of location
(mean or median) and scale (standard deviation or interquartile range) for each group.
Possible values are |
location.scale.digits |
integer indicating the number of digits to round the estimates of location and scale. The
default value is |
nsmall |
integer passed to the function |
location.scale.text.line |
integer indicating on which plot margin line to show the estimates of location and scale
for each group. The default value is |
location.scale.text.cex |
numeric scalar giving the amount by which the text indicating the estimates of
location and scale for each group should be scaled relative to the default
(see the help file for |
p.value |
logical scalar indicating whether to show the p-value associated with testing whether all groups
have the same population location. The default value is |
p.value.digits |
integer indicating the number of digits to round to when displaying the p-value associated with
the test of equal group locations. The default value is |
p.value.line |
integer indicating on which plot margin line to show the p-value associated with the test of
equal group locations. The default value is |
p.value.cex |
numeric scalar giving the amount by which the text indicating the p-value associated
with the test of equal group locations should be scaled relative to the default
(see the help file for |
group.difference.ci |
for the case when there are just 2 groups, a logical scalar indicating whether to display
the confidence interval for the difference between group locations. The default is
the value of the |
group.difference.conf.level |
for the case when there are just 2 groups, a numeric scalar between 0 and 1
indicating the confidence level associated with the confidence interval for the
difference between group locations. The default is |
group.difference.digits |
for the case when there are just 2 groups, an integer indicating the number of digits to
round to when displaying the confidence interval for the difference between group locations.
The default value is |
ci.and.test |
character string indicating whether confidence intervals and tests should be based on parametric
or nonparametric ( |
ci.arg.list |
an optional list of arguments to pass to the function used to compute confidence intervals.
The default value is |
test.arg.list |
an optional list of arguments to pass to the function used to test for group differences in location.
The default value is |
alternative |
character string describing the alternative hypothesis for the test of group differences in the
case when there are two groups. Possible values are |
plot.diff |
applicable only to the case when there are two groups: When When |
diff.col |
applicable only to the case when there are two groups and |
diff.method |
applicable only to the case when there are two groups, |
diff.pch |
applicable only to the case when there are two groups, |
paired |
applicable only to the case when there are two groups: |
paired.lines |
applicable only to the case when there are two groups and |
paired.lty |
applicable only to the case when there are two groups, |
paired.lwd |
applicable only to the case when there are two groups, |
paired.pch |
applicable only to the case when there are two groups, |
paired.col |
applicable only to the case when there are two groups, |
diff.name |
applicable only to the case when there are two groups and |
diff.name.cex |
applicable only to the case when there are two groups and |
sep.line |
applicable only to the case when there are two groups and |
sep.lty |
applicable only to the case when there are two groups, |
sep.lwd |
applicable only to the case when there are two groups, |
sep.col |
applicable only to the case when there are two groups, |
diff.lim |
applicable only to the case when there are two groups and |
diff.at |
applicable only to the case when there are two groups and |
diff.axis.label |
applicable only to the case when there are two groups and |
plot.diff.mar |
applicable only to the case when there are two groups, |
stripChart
invisibly returns a list with the following components:
group.centers |
numeric vector of values on the group axis (the x-axis unless
|
group.stats |
a matrix with the number of rows equal to the number of groups and six columns indicating the sample size of the group (N), the estimate of the group location parameter (Mean or Median), the estimate of the group scale (SD or IQR), the lower confidence limit for the group location parameter (LCL), the upper confidence limit for the group location parameter (UCL), and the confidence level associated with the confidence interval (Conf.Level) |
In addition, if the argument p.value=TRUE
and/or 1) there are two groups and 2) plot.diff=TRUE
,
the list also includes these components:
group.difference.p.value |
numeric scalar indicating the p-value associated with the test of equal group locations. |
group.difference.conf.int |
numeric vector of two elements indicating the confidence interval for the difference between the group locations. Only present when there are two groups. |
Steven P. Millard (EnvStats@ProbStatInfo.com)
Hollander, M., and D.A. Wolfe. (1999). Nonparametric Statistical Methods. Second Edition. John Wiley and Sons, New York.
Millard, S.P., and N.K. Neerchal. (2001). Environmental Statistics with S-PLUS. CRC Press, Boca Raton, FL.
Zar, J.H. (2010). Biostatistical Analysis. Fifth Edition. Prentice-Hall, Upper Saddle River, NJ.
#------------------------ # Two Independent Samples #------------------------ # 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 one-dimensional scatterplots to compare the # TcCB concentrations between the areas and use a nonparametric # test to test for a difference between areas. dev.new() stripChart(TcCB ~ Area, data = EPA.94b.tccb.df, col = c("red", "blue"), p.value = TRUE, ci.and.test = "nonparametric", ylab = "TcCB (ppb)") #---------- # Now log-transform the TcCB data and use a parametric test # to compare the areas. dev.new() stripChart(log10(TcCB) ~ Area, data = EPA.94b.tccb.df, col = c("red", "blue"), p.value = TRUE, ylab = "log10 [ TcCB (ppb) ]") #---------- # Repeat the above procedure, but also plot the confidence interval # for the difference between the means. dev.new() stripChart(log10(TcCB) ~ Area, data = EPA.94b.tccb.df, col = c("red", "blue"), p.value = TRUE, plot.diff = TRUE, diff.col = "black", ylab = "log10 [ TcCB (ppb) ]") #---------- # Repeat the above procedure, but allow the variances to differ. dev.new() stripChart(log10(TcCB) ~ Area, data = EPA.94b.tccb.df, col = c("red", "blue"), p.value = TRUE, plot.diff = TRUE, diff.col = "black", ylab = "log10 [ TcCB (ppb) ]", test.arg.list = list(var.equal = FALSE)) #---------- # Repeat the above procedure, but jitter the points instead of # stacking them. dev.new() stripChart(log10(TcCB) ~ Area, data = EPA.94b.tccb.df, col = c("red", "blue"), p.value = TRUE, plot.diff = TRUE, diff.col = "black", ylab = "log10 [ TcCB (ppb) ]", test.arg.list = list(var.equal = FALSE), method = "jitter", ci.offset = 4) #---------- # Clean up #--------- graphics.off() #==================== #-------------------- # Paired Observations #-------------------- # The data frame ACE.13.TCE.df contians paired observations of # trichloroethylene (TCE; mg/L) at 10 groundwater monitoring wells # before and after remediation. # # Create one-dimensional scatterplots to compare TCE concentrations # before and after remediation and use a paired t-test to # test for a difference between periods. ACE.13.TCE.df # TCE.mg.per.L Well Period #1 20.900 1 Before #2 9.170 2 Before #3 5.960 3 Before #... ...... .. ...... #18 0.520 8 After #19 3.060 9 After #20 1.900 10 After dev.new() stripChart(TCE.mg.per.L ~ Period, data = ACE.13.TCE.df, col = c("brown", "green"), p.value = TRUE, paired = TRUE, ylab = "TCE (mg/L)") #---------- # Repeat the above procedure, but also plot the confidence interval # for the mean of the paired differences. dev.new() stripChart(TCE.mg.per.L ~ Period, data = ACE.13.TCE.df, col = c("brown", "green"), p.value = TRUE, paired = TRUE, ylab = "TCE (mg/L)", plot.diff = TRUE, diff.col = "blue") #========== # Repeat the last two examples, but use a one-sided alternative since # remediation should decrease TCE concentration. dev.new() stripChart(TCE.mg.per.L ~ Period, data = ACE.13.TCE.df, col = c("brown", "green"), p.value = TRUE, paired = TRUE, ylab = "TCE (mg/L)", alternative = "less", group.difference.digits = 2) #---------- # Repeat the above procedure, but also plot the confidence interval # for the mean of the paired differences. # # NOTE: Although stripChart can *report* one-sided confidence intervals # for the difference between two groups (see above example), # when *plotting* the confidence interval for the difference, # only two-sided CIs are allowed. # Here, we will set the confidence level of the confidence # interval for the mean of the paired differences to 90%, # so that the upper bound of the CI corresponds to the upper # bound of a 95% one-sided CI. dev.new() stripChart(TCE.mg.per.L ~ Period, data = ACE.13.TCE.df, col = c("brown", "green"), p.value = TRUE, paired = TRUE, ylab = "TCE (mg/L)", group.difference.digits = 2, plot.diff = TRUE, diff.col = "blue", group.difference.conf.level = 0.9) #---------- # Clean up #--------- graphics.off() #========== # The data frame Helsel.Hirsch.02.Mayfly.df contains paired counts # of mayfly nymphs above and below industrial outfalls in 12 streams. # # Create one-dimensional scatterplots to compare the # counts between locations and use a nonparametric test # to compare counts above and below the outfalls. Helsel.Hirsch.02.Mayfly.df # Mayfly.Count Stream Location #1 12 1 Above #2 15 2 Above #3 11 3 Above #... ... .. ..... #22 60 10 Below #23 53 11 Below #24 124 12 Below dev.new() stripChart(Mayfly.Count ~ Location, data = Helsel.Hirsch.02.Mayfly.df, col = c("green", "brown"), p.value = TRUE, paired = TRUE, ci.and.test = "nonparametric", ylab = "Number of Mayfly Nymphs") #---------- # Repeat the above procedure, but also plot the confidence interval # for the pseudomedian of the paired differences. dev.new() stripChart(Mayfly.Count ~ Location, data = Helsel.Hirsch.02.Mayfly.df, col = c("green", "brown"), p.value = TRUE, paired = TRUE, ci.and.test = "nonparametric", ylab = "Number of Mayfly Nymphs", plot.diff = TRUE, diff.col = "blue") #---------- # Clean up #--------- graphics.off()
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