granovagg.ds: Elemental Graphic for Display of Dependent Sample Data

In ComputationalModeling/intro-to-pyplot-briandk: Graphical Analysis of Variance Using ggplot2

Description

Plots dependent sample data beginning from a scatterplot for the X,Y pairs; proceeds to display difference scores as point projections; also X and Y means, as well as the mean of the difference scores.

Usage

granovagg.ds(data = NULL, revc = FALSE, main = "default_granova_title",
  xlab = NULL, ylab = NULL, conf.level = 0.95,
  plot.theme = "theme_granova_ds", northeast.padding = 0,
  southwest.padding = 0, ...)

Arguments

data	is an n X 2 dataframe or matrix. First column defines X (intially for horzontal axis), the second defines Y.
revc	reverses X,Y specifications
main	optional main title (as character); can be supplied by user. The default value is "default_granova_title", which will print a generic title for the graphic.
xlab	optional label (as character) for horizontal axis. If not defined, axis labels are taken from colnames of data.
ylab	optional label (as character) for vertical axis. If not defined, axis labels are taken from colnames of data.
conf.level	The confidence level at which to perform a dependent sample t-test. Defaults to 0.95 (95% Confidence)
plot.theme	argument indicating a ggplot2 theme to apply to the graphic; defaults to a customized theme created for the dependent sample graphic
northeast.padding	(numeric) extends axes toward lower left, effectively moving data points to the southwest. Defaults to zero padding.
southwest.padding	(numeric) extends axes toward upper right, effectively moving data points to the southwest. Defaults to zero padding. Making both southwest and northeast padding smaller moves points farther apart, while making both larger moves data points closer together.
...	Optional arguments to/from other functions

Details

Paired X and Y values are plotted as scatterplot. The identity reference line (for Y = X) is drawn. Parallel projections of data points to (a lower-left) line segment show how each point relates to its X-Y = D difference; semitransparent "shadow" points are used to display the distribution of difference scores, with thin grey lines leading from each raw datapoint to its shadow projection on the difference distribution. The range of that difference score distribution is drawn as a blue line beneath the shadow points and the mean difference is displayed as a heavy dashed purple line, parallel to the identity reference line. Means for X and Y are also plotted (as thin dashed vertical and horizontal lines), and rug plots are shown for the distributions of X (at the top of graphic) and Y (on the right side). The 95% confidence interval for the population mean difference is also shown graphically as a green band, perpendicular to the mean treatment effect line. Because all data points are plotted relative to the identity line, and summary results are shown graphically, clusters, data trends, outliers, and possible uses of transformations are readily seen, possibly to be accommodated.

In summary, the graphic shows all initial data points relative to the identity line, adds projections (to the 'north' and 'east') showing the marginal distributions of X and Y, as well as projections to the 'southwest' where the difference scores for each point are drawn. Means for all three distributions are shown using straight lines; the confidence interval for the population mean difference score is also shown. Summary statistics are printed as side effects of running the function for the dependent sample analysis.

Value

Returns a plot object of class ggplot.

Author(s)

Brian A. Danielak [e-mail]
Robert M. Pruzek [e-mail]

with contributions by:
William E. J. Doane [e-mail]
James E. Helmreich [e-mail]
Jason Bryer [e-mail]

References

Pruzek, R. M., & Helmreich, J. E. (2009). Enhancing Dependent Sample Analyses with Graphics. Journal of Statistics Education, 17(1), 21.

Wickham, H. (2009). Ggplot2: Elegant Graphics for Data Analysis. New York: Springer.

Wilkinson, L. (1999). The Grammar of Graphics. Statistics and computing. New York: Springer.

See Also

granovagg.1w, granovagg.ds, granovaGG

Examples

### Using granovagg.ds to examine trends or effects for repeated measures data.

# This example corresponds to case 1b in Pruzek and Helmreich (2009). In this
# graphic we're looking for the effect of treatment on patients with anorexia.

data(anorexia.sub)
granovagg.ds(anorexia.sub,
             revc = TRUE,
             main = "Assessment Plot for weights to assess\
                     Family Therapy treatment for Anorexia Patients",
             xlab = "Weight after therapy (lbs.)",
             ylab = "Weight before therapy (lbs.)"
)

### Using granovagg.ds to compare two experimental treatments (with blocking)

# This example corresponds to case 2a in Pruzek and Helmreich (2009). For this
# data, we're comparing the effects of two different virus preparations on the
# number of lesions produced on a tobacco leaf.

data(tobacco)
granovagg.ds(tobacco[, c("prep1", "prep2")],
             main = "Local Lesions on Tobacco Leaves",
             xlab = "Virus Preparation 1",
             ylab = "Virus Preparation 2"
)

### Using granovagg.ds to compare two experimental treatments (with blocking)

# This example corresponds to case 2a in Pruzek and Helmreich (2009). For this
# data, we're comparing the wear resistance of two different shoe sole
# materials, each randomly assigned to the feet of 10 boys.

library(MASS) # Contains the shoes dataset
shoes <- as.data.frame(shoes)
granovagg.ds(shoes,
             revc = TRUE,
             main = "Shoe Wear",
             xlab = "Sole Material B",
             ylab = "Sole Material A",
)

### Using granovagg.ds to compare matched individuals for two treatments

# This example corresponds to case 2b in Pruzek and Helmreich (2009). For this
# data, we're examining the level of lead (in mg/dl) present in the blood of
# children. Children of parents who had worked in a factory where lead was used
# in making batteries were matched by age, exposure to traffic, and neighborhood
# with children whose parents did not work in lead-related industries.

data(blood_lead)
granovagg.ds(blood_lead,
             sw = .1,
             main = "Dependent Sample Assessment Plot
             Blood Lead Levels of Matched Pairs of Children",
             xlab = "Exposed (mg/dl)",
             ylab = "Control (mg/dl)"
)

ComputationalModeling/intro-to-pyplot-briandk documentation built on May 6, 2019, 12:49 p.m.