1 Introduction and Motivation

A central goal of most data analyses is to determine how one or more response variables relate to a set of covariates. In many such studies, the covariates take on only a few distinct values. In others, “shingling” continuous covarates (Cleveland 1993, in which it is referred to as slicing) yields a similar effect. Factorial designed experiments in which the design factors may occur at only 2 or 3 levels apiece in order to keep the experimental size manageable, are a common example of this kind of setup.

Effective visualization is an essential part of the data analysis process. Searching for and interpreting both expected and anomalous patterns, checking for unusual data that may signal possible problems, and clearly communicating the results are all facilitated by good graphics. Conditional plots (Cleveland 1993), coplots, an example of what Tufte called “small multiples” (Tufte 1983), are widely used in such efforts. Trellis Graphics was developed as an implementation of these ideas by William Cleveland, Richard Becker, and colleagues at AT&T Bell Labs and became part the S language system. Subsequently, Deepayan Sarkar developed the lattice package (Sarkar 2008) for R (R Core Team 2015), as an open source implementation. Other R packages, e.g. ggplot2, provide similar capabilities that differ somewhat in the details of how the plots are arranged and annotated. Stripless adds some additional features to lattice, but the same features could very likely be added to others.

I illustrate the concepts with the famous barley data that Cleveland extensively studied in his book and which is available as one of R’s stock data sets in the datasets package. The barley data frame has 4 columns, named “yield”,“variety”, “year”, and “site” giving barley yields from 10 varieties of barley grown at 6 sites in Minnesota in 1931 and 32 – 120 yields (bushels/acre) in all. The variety, site, and year variables were coded as factors. However, rather than using the default alphanumeric order for the factor levels, they were reordered by their median yields. Thus, for example, since the median of all sites and varieties in 1931 exceeded that of 1932, 1932 is the first level of “year” and 1931 is the second. The following R code does this reordering:

barley[,-1] <- lapply(barley[,-1],function(x)reorder(x,barley$yield,median))

As will be clear in a moment for readers not already familiar with Cleveland’s book, arranging plots in such informative orderings can reveal important relationships that would otherwise be practically impossible to see. Using this ordering scheme, here is lattice’s dotplot of the results (but using “as.table = TRUE” so that median yields increase from top to bottom; Cleveland’s display used the default bottom to top ordering).

dotplot(variety~yield|year*site,data=barley,
        col = "darkblue",
        scales = list(alter = 1),
        as.table = TRUE)

Cleveland’s concern was “the Morris anomaly,” which reverses the patterns of 1931 yields being higher at all locations and interrupts the parallel increasing trends of location effects for each year. Cleveland makes a convincing case through graphics and further analysis that the two years’ results were mistakenly reversed at Morris.

However, my point here is to focus on the architecture of the display, not its content. In particular, replicated strips for location and year labels take up a lot of room, resulting in a squashed data area. While this could be alleviated by fiddling with various parameters of the display like font size, strip height, and so forth – or just enlarging the overall display – the fact is that the labels really aren’t necessary: the regularity of the layout permits a separate legend to convey the panel identities without the labeling excess. This is what the strucplot() function of stripless does. Here is the strucplot version:

strucplot(variety~ yield|year*site,data=barley, horizontal=TRUE,
        panel=panel.dotplot, 
        col = "darkblue",
        scales = list(alternat = 1),
        spacings = list(x=0, y=.5))

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## year:  1932  1931
## 
## _Vertical_
## site:  Grand Rapids  Duluth  University Farm  Morris  Crookston  Waseca

Note that a legend giving the plot structure is printed on the console. Additionally, there is a print.stuctured() method that can be explicitly invoked to add it to a side of the plot or on a separate plot page in addition to the console printout. The “Horizontal” list gives the horizontal factors (only one here) and their levels in order from left to right. For more than one factor, the first varies fastest, the second next fastest, and so forth, as will be described more fully in what follows. The “Vertical” list does the same for the vertical factors (again, only one here) from top to bottom. I think most would agree that strip identifying labels aren’t really necessary with this legend.

I think sometimes a bar chart version of the dotplot makes it easier to discern patterns, even though Tufte (1983) considers the bars “chartjunk” because only the ends of the bars convey the information. The stripless panel function panel.bars(), which is just a thin wrapper for the lattice panel.barchart() function with a few added defaults, provides a quick way to do this.

strucplot(variety~yield|year*site,data=barley, horizontal = TRUE,
          panel=panel.bars,
          scales = list(alter = 1),
          spacings = list(x=0, y = .5))

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## year:  1932  1931
## 
## _Vertical_
## site:  Grand Rapids  Duluth  University Farm  Morris  Crookston  Waseca

By removing the strips and putting their information in the legend, the displays are more compact and, I think, somewhat clearer. Of course, this can only be done when the displays form visual table, i.e. a regular array where the marginals of the array comprise the levels of each factor. But when this is the case, the identifying information in the strips becomes redundant and somewhat ‘chart-junky,’ especially when there are more than just one or two factors.

With only three conditioning factors, one of which provides the categories for the dotplot panels, the simple 6 x 2 layout works with or without the panel strips. Here is an example illustrating the difficulties with more factors using the hsb data in the faraway package, which you should first install from your favorite CRAN package repository (there are many interesting data sets in this package). It consists of observations of classification variables (gender, race, public or private school, etc.) and scores on a battery of 5 standardized pre-high school tests for 200 high school students. There was interest in determining which factors influenced the high school program – academic, vocational, or general – that students chose. Rather then do a fine-grained analysis, here, I merely sum the 5 scores for each student and use that total instead of the separate scores. As before, reordering the levels of the categorical factors in some sensible way rather than using the default lexicographic ordering is advisable. I use the median total score for all the factors except ses, socioeconomic-economic status, which has a natural ordering of “low”<“middle”<“high”.

hsb$score <- rowSums(hsb[,7:11])
hsb[,c(2,3,5,6)] <-lapply(hsb[,c(2,3,5,6)],reorder,hsb$score,median)
hsb$ses <- factor(hsb$ses,levels = c("low","middle","high"))

I want to display how the total scores vary with chosen program using the other 4 factors, gender, race, ses, and schooltype as conditioning variables. This means that there will be 2x4x3x2 = 48 panels in all. I use an 8 row x 6 column layout, with gender and ses varying horizontally and schtyp and race varying vertically:

hsbprog <- strucplot(prog~score|gender*race*ses*schtyp,data=hsb,
          main = "High School Program Choice",
          panel= panel.dotplot,
          horizontal = TRUE, cex=.5,
          col = "darkblue", 
          scales = list(alternat = 1),
          xyLay= list(x=c(1,3), y = c(4,2)))
plot(hsbprog)

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## gender:  female  male
## ses:  low  middle  high
## 
## _Vertical_
## schtyp:  public  private
## race:  hispanic  african-amer  asian  white

Here’s how the the legend and spacings of the panels identifies the factors and their levels. The first horizontal factor is gender, so the first column of juxtaposed paired panels (= 0 spaces between) is for “female” and the second is for “male”, with overall median “male” scores greater than overall median “female” scores (which might of course be reversed within any particular pair or row of pairs). The second horizontal factor is ses, so the first pair of columns of juxtaposed panels is for “low” ses, the second is for “middle”, and the third is for “high”. The same is true vertically: the juxtaposed pairs of rows are for “public” above and “private” below; the four pairs or rows are the four different levels of race, from “hispanic” on top to “white” on bottom. Median overall scores for each factor increase down the array.

It is thus immediately clear from this plot that:

• Essentially all data are from “public” schools, so including schtype as a covariate is unlikely to be useful

• Almost all the data are for race = “white”, so including race as a covariate is unlikely to be useful

• There appears to be a clear association between choice of program and total score on the exams in the way one would expect (highest scores tend to choose “academic”; lowest choose “vocation”; middle choose “general”)

• It appears that those in the “high” ses group tend to choose “academic”, again as one might expect

One should certainly now consider how to improve this display, perhaps using statistical methods to help refine or modify it. However, I think it stands on its own as a reasonably informative first look at the data.

So the question is: how well would this work with strip labeling included? Here’s a default version with the same overall size.

dotplot(prog~score|gender*ses*schtyp*race,data=hsb,
        main = "High School Program Choice",
        layout = c(6,8),
        col = "darkblue",
        scales = list(alter = 1),
        as.table = TRUE)

Clearly, this is useless. Of course it could be made to work by, for example, reducing the font size and vertical width in the labels, enlarging the display size, perhaps encoding gender by symbol to reduce the number of factors, and so forth. But having to work so hard to get something useful makes the point: the strip labels have become chartjunk that hide rather than reveal what the data have to say. Removing and replacing them by a simple legend, which is made possible by the regularity of the layout, is a better solution.

2 Features and Syntax

strucplot() is a S3 generic that is a wrapper for the lattice package’s xyplot() function. The default formula method in the previous examples has basically just 2 new arguments, spacings and xyLayout(there is also a 3rd, center used for 2 level experimental designs that will be discussed later). A few xyplot arguments, such as ‘layout’, ‘between’, ‘skip’,‘strip’, ‘perm.cond’, ‘index.cond’, and ‘drop.unused.levels’, are set by strucplot and are therefore ignored in a strucplot call; but most other xyplot arguments, especially the ‘panel’ function, ‘scales’, ‘groups’, and those like ‘cex’, ‘pch’, and ‘col’ that control the plot characteristics, are passed to xyplot and so work as expected.

The formula argument is as in xyplot() (whose Help page should be consulted as needed), except for an added convenience shortcut: a “.” on the righthand side after the “|” conditioning symbol means “all other variables in the data.frame data argument in their column order.” Consequently, it is an error to omit the data argument when “.” is used. When the conditioning variables are explicitly given, they will be looked up in the usual way.

print (hsbprog,legendLoc = "t", 
       cex.font = .7,
       head = c("Left-Right","Up-Down"),
       abbrev = list(race=c(0,4), schtyp = c(0,4)))

## 
## PLOT STRUCTURE
## 
## _Left-Right_
## gender:  female  male
## ses:  low  middle  high
## 
## _Up-Down_
## schtyp:  pblc  prvt
## race:  hspn  afr-  asin  whit

3 Additional Methods

barley <- within(barley,{changed_year <- year
    changed_year[site=="Morris" & year == 1932] <- 1931
    changed_year[site=="Morris" & year == 1931] <- 1932}
)
barleyFit <- lm(yield ~ variety + site + changed_year, data=barley)

strucplot(barleyFit, pch=16, col = "darkblue",
         panel= function(...){panel.grid(h=3,v=0); panel.xyplot(...)} )

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## variety:  Svansota  No. 462  Manchuria  No. 475  Velvet  Peatland  Glabron  No. 457  Wisconsin No. 38  Trebi
## site:  Grand Rapids  Duluth  University Farm  Morris  Crookston  Waseca
## 
## _Vertical_
## changed_year:  1932  1931

strucplot(barleyFit, pch=16, col = "darkblue",
          xyLayout = list(y = 2),
         panel= panel.bars )

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## variety:  Svansota  No. 462  Manchuria  No. 475  Velvet  Peatland  Glabron  No. 457  Wisconsin No. 38  Trebi
## changed_year:  1932  1931
## 
## _Vertical_
## site:  Grand Rapids  Duluth  University Farm  Morris  Crookston  Waseca

strucplot(variety~ fitted(barleyFit)|changed_year*site,data=barley, horizontal=TRUE,
        panel=panel.dotplot,
        col = "darkblue",
        scales = list(alternat = 1),
        spacings = list(x=0, y=.5))

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## changed_year:  1932  1931
## 
## _Vertical_
## site:  Grand Rapids  Duluth  University Farm  Morris  Crookston  Waseca

## A 7 factor 12 run PB design matrix (using first 7 columns of PB 12 matrix)
pb12 <- matrix(1, nrow=12,ncol=11,dimnames = list(NULL,LETTERS[1:11]))
pb12[1,]<- c(-1,1,-1,rep(1,3),rep(-1,3),1,-1)
for(i in 1:10)pb12[i+1,] <- c(pb12[i,11],pb12[i,-11])
pbex <- data.frame(pb12[,1:7])

## A 7 factor 16 run ff of Resolution IV
ffex<- expand.grid(rep(list(c(-1,1)),4))
names(ffex) <- LETTERS[1:4]
ffex <- within(ffex,{G <- A*B*C; F <- B*C*D; E <- A*B*D})

strucplot(pbex)

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## A:  -1  1
## B:  -1  1
## C:  -1  1
## D:  -1  1
## 
## _Vertical_
## E:  -1  1
## F:  -1  1
## G:  -1  1

strucplot(ffex)

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## A:  -1  1
## B:  -1  1
## C:  -1  1
## D:  -1  1
## 
## _Vertical_
## E:  -1  1
## F:  -1  1
## G:  -1  1

  pbex <- rbind(pbex,c(0, rep(c(-.5,.5),3)))
  strucplot(pbex, center = TRUE)

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## A:  -1  0  1
## B:  -1  -0.5  1
## C:  -1  0.5  1
## D:  -1  -0.5  1
## 
## _Vertical_
## E:  -1  0.5  1
## F:  -1  -0.5  1
## G:  -1  0.5  1

4 Examples

Let’s now use these plots to analyze some real data. The weldstrength data set in the farwaway package is a 9 factor, 16 run ff (a 2^9-5) exploring the effect of 9 factors on the strengths of welds. The low and high levels of the factors are coded as 0 and 1. A barplot of the response with all 9 factors using the default xyLayout of 5 factors across the rows and 4 factors down the columns would be produced by the following code, but the display is not shown: there would be 2^9 = 512 separate panels, only a few of which contain data.

  strucplot(~Strength|., data = weldstrength, panel = panel.bars,
  main = "Full Weldstrength Design")

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## Rods:  0  1
## Drying:  0  1
## Material:  0  1
## Thickness:  0  1
## Angle:  0  1
## 
## _Vertical_
## Opening:  0  1
## Current:  0  1
## Method:  0  1
## Preheating:  0  1

A reasonable alternative is to first run a regression to see if the number of factors to display can be reduced to those few that may reveal interesting structure.

  round(coef(lm(Strength~., data = weldstrength)),2)

## (Intercept)        Rods      Drying    Material   Thickness       Angle 
##       43.54        0.40        2.15       -3.10        0.13        0.05 
##     Opening     Current      Method  Preheating 
##       -0.40        0.15       -0.15       -0.37

We see that the Drying and Material factors have by far the largest magnitude (standardized) coefficients, that Rods, Opening, and Preheating have marginal and about equal in magnitude effects, and that the remaining 4 factors seem likely to be nothing more than experimental noise. It seems reasonable to view a projection onto the 5 largest factors. Because the Drying and Material effects should be easy to see, I display these vertically and the remaining 3 smaller effects horizontally, so that the results can be more easily compared across the rows.

  strucplot(~Strength|Rods*Drying*Material*Opening*Preheating, xyLay = c(1,4,5),
  data = weldstrength, panel= panel.bars,
  main = "Weldstrength Results for the 5 Largest Effects")

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## Rods:  0  1
## Opening:  0  1
## Preheating:  0  1
## 
## _Vertical_
## Drying:  0  1
## Material:  0  1

The positive Drying effect is evident in the consistent increase in strength from the first to second row and third to fourth. Similary for the negative Material effect in the reduction from the top 2 rows (Material = 0) to the bottom 2 (Material =1).

The remaining 3 factors seem to have negligible effect except for the top right corner of the design. This is where the Material is “0” and Preheating is “1”. At this condition, there are (at least) 2 equivalent ways of describing what is seen in the display: either weld strength decreases by about 2 units (about 5%) when Opening increases and/or when Rods decreases. The inability to say which – or even to say whether it could just be experimental noise – is due to the design’s “parsimony” and the resulting inherent “confounding” of various effects. See (Box, Hunter, and Hunter 2005) for a fuller discussion of such matters.

However, as that discussion emphasizes, screening designs such as this are not meant nor expected to be conclusive. Rather, they enlist the aid of Mother Nature in whittling down a large list of possible sources of variability in a phenomenon under study to those few with greatest impact. These then would need to be further studied in more focused designs. The interpretation here seems reasonable and consistent with such a program. What is relevant for our purposes is that it was simply derived without recourse to complex statistical arguments from a minimal statistical analysis and effective data visualization. In many applications of experimental design especially in industry, such simplicity is a virtue.

strucplot(sono[,-c(1,8)],
        groups=sono$Flask, 
        col=c("red","blue"),
        main = "sono design, colored by Flask (red = down)"
  )

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## Molarity:  down  up
## Solute:  down  up
## pH:  down  up
## 
## _Vertical_
## Gas:  down  up
## Water:  down  up
## Horn:  down  up

strucplot(Intensity~Molarity|Solute*pH*Gas*Water*Horn, groups =Flask, 
          data = sono,
          panel=panel.superpose,
          par.settings = list(superpose.polygon = list(col=c("red","blue"))),
          fill = c("red","blue"),
          auto.key = list(points=FALSE,rectangles=TRUE, 
              title = "Flask",lines.title =1.5, border =TRUE,cex.title = 1.5),
          panel.groups = function(x,y,fill,...) panel.bars(x=x,y=y,col=fill))

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## Solute:  down  up
## pH:  down  up
## Gas:  down  up
## 
## _Vertical_
## Water:  down  up
## Horn:  down  up

  strucplot(~Intensity|Molarity*Solute*pH*Flask, data = sono,
            panel=panel.bars
  )

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## Molarity:  down  up
## Solute:  down  up
## 
## _Vertical_
## pH:  down  up
## Flask:  down  up

strucplot(~Intensity|Molarity*Solute*pH, data = sono, panel=panel.bars)

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## Molarity:  down  up
## Solute:  down  up
## 
## _Vertical_
## pH:  down  up

## Create a data frame with 4 factors, 2 at 3 levels each and 2 and 2 levels each, 
## with 200 random beta(4,4) responses for each of the 36 settings

##Set a random seed for reproducibility and generate the data
set.seed(2222)
d <- data.frame(x=rbeta(7200,4,4),
    expand.grid(a= 0:1, b= 1:3, c=letters[1:3], d = c("A","B"))[rep(1:36,e=200),])
                                        
## Construct the display
strucplot(x~x|.,data = d, ylim = c(0,3.2), ylab = "Density",
      col= "lightblue", col.line = "red",lwd=2,
      panel = function(x,y,col, col.line, ...){
      panel.histogram(x, col=col,breaks=NULL, ...)
      panel.densityplot(x, plot.points= FALSE,col= col.line,... )
       }
)

## 
## PLOT STRUCTURE
## 
## _Horizontal_
## a:  0  1
## b:  1  2  3
## 
## _Vertical_
## c:  a  b  c
## d:  A  B

5 Summary

Using regular layouts and simple legends instead of layers of strip labels to encode factor settings in trellis graphics often helps to better reveal and apprehend informative patterns in the data. The strucplot methods described here are a simple extension of the lattice package’s xyplot framework that allows this to be easily done. The examples show how this may be useful, especially for the factorial designs widely used in industrial applications. But multipanel conditioning – aka small multiples – is a broadly applicable (and underutilized) data display paradigm; strucplot is meant to make stripless versions available for most situations in which such displays are contemplated. I hope that this will not only be useful in its own right, but that it also encourages greater application of trellis-type multivariate visualization for the many data analysis tasks where they can be of value.