Chapter 8: Contingency tables

Paul Northrop

This vignette provides some R code that is related to some of the content of Chapter 8 of the STAT0002 notes, namely to Contingency tables. It also contains some technical information about classes of R objects and the way in which this affects what R does when we call functions to operate on an object. If this interests you then that’s great, but otherwise focus on what the code below does rather than exactly how it works.

Graduate Admissions at Berkeley

We return to data that we considered briefly in the Chapter 3: Probability article. The object berkeley is a 3-dimensional array that contains information about applicants to graduate school at UC Berkeley in 1973 for the six largest departments. Use ?UCBAdmissions for more information. The 3 dimensions of the array correspond to the gender of the applicant (dimension named Gender), whether or not they were admitted (named Admit) and a letter code for the department to which they applied (named Dept). A given entry in berkeley gives the total number of applicants in the corresponding (Admit, Gender, Dept) category. In Chapter 3, we viewed these data are relating to a population containing the 4526 people who applied to graduate school at Berkeley in 1973 and did not seek to generalise beyond this population. In other words, we treated the relative frequencies in the various categories as known probabilities. Now, we view these data as a sample of data that may help us to make inferences about the application process at Berkeley in general. We will explore associations between the categorical variables (Admit, Gender, Dept), or perhaps just two of these variables. Note that, following standard statistical terminology, R refers to categorical variables as factors and the possible values of these factors as levels.

> library(stat0002)

The data

The berkeley dataset is a $2 \times 2 \times 6$ contingency table. For each of the 6 departments involved there is a $2 \times 2$ table for variables Admit and Gender.

> # Find the dimensions of the data
> dim(berkeley)
[1] 2 2 6
> # What type of R object is berkeley?
> class(berkeley)
[1] "table"
> # Print the data
> berkeley
, , Dept = A

          Gender
Admit      Male Female
  Admitted  512     89
  Rejected  313     19

, , Dept = B

          Gender
Admit      Male Female
  Admitted  353     17
  Rejected  207      8

, , Dept = C

          Gender
Admit      Male Female
  Admitted  120    202
  Rejected  205    391

, , Dept = D

          Gender
Admit      Male Female
  Admitted  138    131
  Rejected  279    244

, , Dept = E

          Gender
Admit      Male Female
  Admitted   53     94
  Rejected  138    299

, , Dept = F

          Gender
Admit      Male Female
  Admitted   22     24
  Rejected  351    317

Classes of R objects

Many objects in R have a class attribute that that contains a (character) vector of names, perhaps just one name, that describes what type of object it is. This is useful because for some types of object have standard methods are provided, to perform common tasks like printing, summarising and plotting. In the code above, instead of typing berkeley we could have typed print(berkeley). When we do this R searches for an appropriate way to print to the R Console the object berkeley, which has class "table". R looks for, and finds, a function print.table to use to do this printing. We will come back to this later, for example when we consider producing plots of contingency table data.

2-way tables

We collapse the 3-way table to a 2-way table by ignoring the values of one of the 3 categorical variables. One way to do this is to use the xtabs function in the stats package, which comes as standard when you install R. To use xtabs we first modify the structure of the data from a table to a data frame using the function as.data.frame. Then the following call to xtabs creates a 2-way table in which frequency Freq is classified by Gender and Admit. We have aggregated (summed) Freq within each Gender-Admit category, over all the values of Dept. Similarly, we could ignore Gender or Admit to produce a 2-way table for the remaining two variables.

> berkdf <- as.data.frame(berkeley)
> berkdf
      Admit Gender Dept Freq
1  Admitted   Male    A  512
2  Rejected   Male    A  313
3  Admitted Female    A   89
4  Rejected Female    A   19
5  Admitted   Male    B  353
6  Rejected   Male    B  207
7  Admitted Female    B   17
8  Rejected Female    B    8
9  Admitted   Male    C  120
10 Rejected   Male    C  205
11 Admitted Female    C  202
12 Rejected Female    C  391
13 Admitted   Male    D  138
14 Rejected   Male    D  279
15 Admitted Female    D  131
16 Rejected Female    D  244
17 Admitted   Male    E   53
18 Rejected   Male    E  138
19 Admitted Female    E   94
20 Rejected Female    E  299
21 Admitted   Male    F   22
22 Rejected   Male    F  351
23 Admitted Female    F   24
24 Rejected Female    F  317
> ga <- xtabs(Freq ~ Gender + Admit, berkdf)
> ga
        Admit
Gender   Admitted Rejected
  Male       1198     1493
  Female      557     1278

If we think about how the variables in this 2-way table may be related we might imagine that Gender could affect the value of Admit, that is, the gender of the applicant could affect the probability that the applicant is admitted. That is, Gender could have a causal effect on Admit, which is the main variable of interest in this example. In cases like this, we call Admit the response variable and Gender the explanatory variable because Gender may explain variation in the response variable Admit. It is natural to consider conditional probabilities of the levels of Admit given the value of Gender and we may wish to create our plots with this in mind.

Functions are available for calculating the totals and proportions that appear in Section 8.1 of the notes and in the Berkeley example in Chapter 3 of the notes.

> # Total number of applicants
> marginSums(ga)
[1] 4526
> # Number of males and females
> marginSums(ga, "Gender")
Gender
  Male Female 
  2691   1835 
> # Number of admitted and rejected applicants
> marginSums(ga, "Admit")
Admit
Admitted Rejected 
    1755     2771 
> # Add the marginal totals to the table
> addmargins(ga)
        Admit
Gender   Admitted Rejected  Sum
  Male       1198     1493 2691
  Female      557     1278 1835
  Sum        1755     2771 4526
> # Calculate proportions (relative frequencies)
> proportions(ga)
        Admit
Gender    Admitted  Rejected
  Male   0.2646929 0.3298719
  Female 0.1230667 0.2823685
> # Row proportions (sum to 1 across the rows)
> proportions(ga, "Gender")
        Admit
Gender    Admitted  Rejected
  Male   0.4451877 0.5548123
  Female 0.3035422 0.6964578
> # Column proportions (sum to 1 down the columns)
> proportions(ga, "Admit")
        Admit
Gender    Admitted  Rejected
  Male   0.6826211 0.5387947
  Female 0.3173789 0.4612053

What class does ga have?

> class(ga)
[1] "xtabs" "table"

Plotting frequencies

The object ga has 2 things in its vector class names: "xtabs" and "table". This means that we have available to us any methods functions that have been created for use on objects of class "xtabs" or class "table". If, for example, we use the code plot(ga) then, because "xtabs" appears first in the vector of class names, R looks first for a function called plot.xtabs. If it does not find a function with this name then it looks for plot.table. If it finds neither then it uses the function plot.default. The function plot.default definitely exists, but because it has not been designed for a specific input object then it might not work. In this case, there is no function plot.xtabs but there is a function plot.table. If the table has at least 2 factors then plot.table produces a plot using the function mosaicplot in the graphics package, which also comes as standard when you install R. Let’s see what happens if we do this.

> plot(ga, main = "Observed frequencies", color = TRUE)

A mosaic plot is produced. First, the plot area is first split into two parts vertically, with the sizes of the parts reflecting the marginal distribution of the first variable (Gender here). That is, the widths of the rectangles are proportional to the numbers of males and females respectively. We can see that there are more males in the data than females. Then similar splits are made horizontally within each of the vertical parts, with the sizes of the parts determined by the conditional distribution of the second variable (Admit) conditional on the value of the first variable, that is, conditional on Gender = Male and Gender = Female.

Recall that we said earlier that it made sense to consider conditioning on Gender and this is what has been done in this plot. Therefore, this plot is pretty much as we would like it to be. We may prefer to display the conditional distribution of Admit given Gender horizontally in the plot, rather than vertically. The following code achieves this, using the argument dir, which determines whether we split first in the horizontal or vertical direction. The information in the plot is the same, but cosmetically it is slightly different.

> plot(ga, main = "Observed frequencies", color = TRUE, dir = c("h", "v"))

We can see that more applicants are rejected than admitted and that the proportion of males that are admitted is greater than the proportion of females that are admitted.

If we had placed the variables in the data frame in the other order then, unless we make an adjustment, the mosaic plot is produced by conditioning on Admit first, producing the following plot.

> ag <- xtabs(Freq ~ Admit + Gender, berkdf)
> ag
          Gender
Admit      Male Female
  Admitted 1198    557
  Rejected 1493   1278
> # Alternatively, we could have transposed ga
> t(ga)
          Gender
Admit      Male Female
  Admitted 1198    557
  Rejected 1493   1278
> plot(ag, main = "Observed frequencies", color = TRUE)

This is not wrong, but it concentrates on conditional probabilities of Gender given Admit, which is not what we want. We can use the argument sort to reverse the order in which the mosaic plots takes the variables and reproduce our preferred plot.

> plot(ag, main = "Observed frequencies", color = TRUE, sort = 2:1)

Calculating estimated expected frequencies

We estimate expected frequencies under the assumption that the variables Gender and Admit are independent. We could use R to calculate these for ourselves, using the outer function below. Look at ?outer to see what this does. Alternatively, can use the function chisq.test in the stats package. We will come back to this function later, but for the moment we only want the values of the estimated expected frequencies that it calculates. We also produce a mosaic plot of the estimated expected frequencies

> efreq <- outer(marginSums(ga, "Gender"), marginSums(ga, "Admit")) / marginSums(ga)
> efreq
        Admit
Gender    Admitted Rejected
  Male   1043.4611 1647.539
  Female  711.5389 1123.461
> # Check using chisq.test
> efreq <- chisq.test(ga)$expected
> efreq
        Admit
Gender    Admitted Rejected
  Male   1043.4611 1647.539
  Female  711.5389 1123.461
> # Trick R into using plot.table
> class(efreq) <- "table"
> # Plot estimated expected frequencies
> plot(efreq, main = "Estimated expected frequencies", color = TRUE, 
+      dir = c("h", "v"))

As we expect, the relative sizes of the estimated expected frequencies for Admitted and Rejected are the same for males and females. This provides us with a visual illustration of what mosaic plots of observed frequencies should look approximately like if the variables concerned are independent. The horizontal and vertical gaps between the rectangles should look approximately like a grid in which all the horizontal gaps and the vertical gaps are approximately lined up.

Plotting residuals

The assocplot function in the graphics package produces an association plot that summarises how the (Pearson) residuals vary between the combinations of the categories. The vertical extent of a rectangle is proportional to the corresponding Pearson residual and the width is proportional to the square root of the estimated expected frequency. Therefore, the area of a box is proportional to the corresponding (raw) residual, that is, this difference between the observed and estimated expected frequency. Look at the definitions of these residuals in Section 8.1.1 to see how this works.

> assocplot(ga, col = c("black", "grey"))

Comparing the rectangles with the horizontal dashed line, we see that more men are admitted than is expected if Gender and Admit are independent.

Testing association

The function chisq.test in the stats package can be used to perform the chi-squared outlined at the end of Section 8.1.1 of the notes. We specify correct = FALSE to produce the same result given in the notes, that is, we do not use the Yates’s correction for continuity.

> chisq.test(ga, correct = FALSE)

    Pearson's Chi-squared test

data:  ga
X-squared = 92.205, df = 1, p-value < 2.2e-16

The value of the test statistic $92.205$ is very much larger than expected under the hypothesis that Gender and Admit are independent. Therefore, we would reject hypothesis.

The vcd package

The vcd package (Meyer, Zeileis, and Hornik (2022)) provides various functions to summarise, visualise and make inferences using categorical data. Its functions mosaic and assoc produce plots that are equivalent to those produced by plot.table and assocplot above. It deals more easily with some aspects of tables of dimension greater than 2 than the functions in base R and offers some more features.

One extra feature of the functions mosaic and assoc is the ability to shade the rectangles in an association plot with colours that reflect the size of a residual. This can draw our attention to cells of the contingency table with large residuals and help us to spot patterns. By default, these functions base the shading on the values of the Pearson residuals. This reflects the contributions to the test statistic in the chi-squared test.

We might also like the option to shade based on the values of the standardised Pearson residuals. If the variables Gender and Admit are independent then these residuals should look approximately as if they have been sampled from a standard normal distribution. Therefore, values that are greater than 2 in magnitude are unusual - they have an approximate probability of $5$ occurring - and values that are greater than 4 in magnitude are very surprising. We do this in the code below by providing the values of the standardised Pearson residuals to the functions in the vcd package, using the argument residuals. For the mosaic plot this just effects the numbers on the colour key. For the association plots using the standardisation of the Pearson residuals changes the vertical extents of the rectangles in the plots, so now the area of a rectangle is not proportional its residual. In this $2 \times 2$ case where all standardised Pearson residuals have the same magnitude all these vertical extents are equal.

For a technical reason, to do with wanting to change the label on the legend of the plot using residuals_type, we call the vcd function strucplot, which is the plotting function underlying the function assoc. The function mosaic also allows us to colour the parts of a moasic plot based on the values of residuals.

> library(vcd)
> # Extract the standardised Pearson residuals
> x2test <- chisq.test(ga)
> # Raw residuals
> x2test$observed - x2test$expected
        Admit
Gender    Admitted  Rejected
  Male    154.5389 -154.5389
  Female -154.5389  154.5389
> # Pearson residuals
> x2test$residuals
        Admit
Gender    Admitted  Rejected
  Male    4.784093 -3.807325
  Female -5.793466  4.610614
> # Standardised Pearson residuals
> x2test$stdres
        Admit
Gender    Admitted  Rejected
  Male    9.602358 -9.602358
  Female -9.602358  9.602358
> # Association plot of residuals with Pearson residual shading
> assoc(ga, shade = TRUE, margins = c(2.25, 1, 1, 2.5))

> # Association plot of residuals with standardised Pearson residual shading
> strucplot(ga, shade = TRUE, residuals = x2test$stdres, 
+           margins = c(2.25, 1, 1, 2.5),
+           residuals_type = "Standardised\nPearson\nresiduals", core = struc_assoc,
+           keep_aspect_ratio = FALSE, legend_width = 6)

> # Mosaic plot with standardised Pearson residual shading
> mosaic(ga, shade = TRUE, residuals = x2test$stdres, 
+        residuals_type = "Standardised Pearson", margins = c(0, 0, 0, 0))

These plots indicate that the (effectively one) standardised Pearson residual has a magnitude ($9.6$) that is very much larger than we expect if Gender and Admit are independent. In larger contingency tables, with more cells (combinations of the factors) and/or more than 2 dimensions, we can use colouring like this to draw our attention to patterns in the data and departures from independence.

3-way tables

If we have 3 (or more) variables then there are many possible associations that we could examine. See Section 8.2 of the notes for details.

We produce a moasic plot based on all three variables in the berkeley dataset. To avoid cluttering the plot with text, we create a new object x in which the levels of Admit have been abbreviated.

> b <- berkeley
> dimnames(b)$Admit <- c("A", "R")
> dimnames(b)$Gender <- c("M", "F")
> plot(b, main = "Observed frequencies", sort = 3:1, color = c(1, 8))

This plot tells us quite a lot. If we look individually at the parts of the plot relating to departments B to F then we find that Gender and Admit look to be approximately independent within each of these departments. Only in department A does Admit seem to depend on Gender with a larger proportion of females who apply to this department being admitted than the males.

Mutual independence

As we noted in Section 8.2.1 there seems little point in asking whether the 3 variables Gender, Admit and Dept are independent when we have already concluded that Gender and Admit are not independent. However, we perform a chi-squared test in any case, this time using the generic function summary. For an object of class table this function calls chisq.test to perform the test and also includes a very basic summary of the table in the output.

> summary(berkeley, correction = FALSE)
Number of cases in table: 4526 
Number of factors: 3 
Test for independence of all factors:
    Chisq = 2000.3, df = 16, p-value = 0

Marginal independence

We examine the association between Dept and Gender and then between Admit and Dept. We create our own function assoc2 that takes the contingency table tab and standardised residuals residuals as arguments, so that we can shorten the code needed to create an association plot with rectangles shaded based on the standardised Pearson residuals.

> assoc2 <- function(tab, residuals, ...) {
+   strucplot(tab, shade = TRUE, residuals = residuals, 
+             residuals_type = "Standardised Pearson", core = struc_assoc, ...)
+ }

Gender and department

> gd <- xtabs(Freq ~ Gender + Dept, berkdf)
> x2test <- chisq.test(gd, correct = FALSE)
> assoc2(gd, residuals = x2test$stdres, margins = c(0, 0, 0, 0))

There are some strong differences in the preferences of females and males concerning the departments to which they apply. Males prefer departments A and B and females the other departments, particularly departments C and E.

Admittance and department

> ad <- xtabs(Freq ~ Admit + Dept, berkdf)
> x2test <- chisq.test(ad, correct = FALSE)
> assoc2(ad, residuals = x2test$stdres, margins = c(0, 0, 0, 0))

This plot suggests that the probability of admittance is much greater in departments A and B, which are the departments to which males like to apply. Departments E and F have a relatively low probability of acceptance and females are more likely than males to apply to these departments.

Conditional independence

The plots that we have seen provide a possible explanation for the fact that the overall probability of admittance is lower for females than males: the males were more likely to apply to the departments that had the higher probability of admittance.

Now we explore how successful females and males are at being admitted to Berkeley within each of the departments A to F, that is, we condition on the variable Dept. We use the vcd function cotabplot to to produce an association plot for each of the departments A to F. The shading of the rectangles is based on the Pearson residuals.

> cotabplot(~ Admit + Gender | Dept, data = berkeley, layout = 3:2, shade = TRUE,
+           panel = cotab_assoc)

We see that only in department A is there a substantial difference between the admittance probability of females and males, with the females doing better than the males. Finally, we focus on department A and change the shading so that it is based on the standardised Pearson residuals, mainly to see that their common magnitude is $4.15$.

> # A function to produce an association plot within a given department
> deptplot <- function(dept) {
+   temp <- xtabs(Freq ~ Admit + Gender, berkdf, 
+                 subset = berkdf[, "Dept"] == dept)
+   x2test <- chisq.test(temp, correct = FALSE)
+   assoc2(temp, residuals = x2test$stdres, main = paste("Dept ", dept))
+   return(x2test$stdres)
+ }

> deptplot("A")

          Gender
Admit           Male    Female
  Admitted -4.153073  4.153073
  Rejected  4.153073 -4.153073

References

Meyer, D., A. Zeileis, and K. Hornik. 2022. vcd: Visualizing Categorical Data. https://cran.r-project.org/package=vcd.