knitr::opts_chunk$set( collapse = TRUE, warning = FALSE, fig.height = 6, fig.width = 7, fig.path = "fig/tut02-", dev = "png", comment = "##" ) # save some typing knitr::set_alias(w = "fig.width", h = "fig.height", cap = "fig.cap") # Old Sweave options # \SweaveOpts{engine=R,eps=TRUE,height=6,width=7,results=hide,fig=FALSE,echo=TRUE} # \SweaveOpts{engine=R,height=6,width=7,results=hide,fig=FALSE,echo=TRUE} # \SweaveOpts{prefix.string=fig/vcd-tut,eps=FALSE} # \SweaveOpts{keep.source=TRUE} # preload datasets ??? set.seed(1071) library(vcd) library(vcdExtra) library(ggplot2) data(HairEyeColor) data(PreSex) data(Arthritis, package="vcd") art <- xtabs(~Treatment + Improved, data = Arthritis) if(!file.exists("fig")) dir.create("fig")

OK, now we're ready to do some analyses. This vignette focuses on relatively simple non-parametric tests and measures of association.

For tabular displays,
the `CrossTable()`

function in the `gmodels`

package produces cross-tabulations
modeled after `PROC FREQ`

in SAS or `CROSSTABS`

in SPSS.
It has a wealth of options for the quantities that can be shown in each cell.

Recall the GSS data used earlier.

# Agresti (2002), table 3.11, p. 106 GSS <- data.frame( expand.grid(sex = c("female", "male"), party = c("dem", "indep", "rep")), count = c(279,165,73,47,225,191)) (GSStab <- xtabs(count ~ sex + party, data=GSS))

Generate a crosstable showing cell frequency and the cell contribution to $\chi^2$.

# 2-Way Cross Tabulation library(gmodels) CrossTable(GSStab, prop.t=FALSE, prop.r=FALSE, prop.c=FALSE)

There are options to report percentages (row, column, cell), specify decimal
places, produce Chi-square, Fisher, and McNemar tests of independence, report
expected and residual values (pearson, standardized, adjusted standardized),
include missing values as valid, annotate with row and column titles, and format
as SAS or SPSS style output! See `help(CrossTable)`

for details.

For 2-way tables you can use `chisq.test()`

to test independence of the row
and column variable. By default, the $p$-value is calculated from the asymptotic
chi-squared distribution of the test statistic. Optionally, the $p$-value can be
derived via Monte Carlo simulation.

(HairEye <- margin.table(HairEyeColor, c(1, 2))) chisq.test(HairEye) chisq.test(HairEye, simulate.p.value = TRUE)

`fisher.test(X)`

provides an **exact test** of independence. `X`

must be a two-way
contingency table in table form. Another form,
`fisher.test(X, Y)`

takes two
categorical vectors of the same length.

For tables larger than $2 \times 2$ the method can be computationally intensive (or can fail) if
the frequencies are not small.

```
fisher.test(GSStab)
```

Fisher's test is meant for tables with small total sample size.
It generates an error for the `HairEye`

data with $n$=592 total frequency.

```
fisher.test(HairEye)
```

Use the `mantelhaen.test(X)`

function to perform a Cochran-Mantel-Haenszel
$\chi^2$ chi
test of the null hypothesis that two nominal variables are
*conditionally independent*, $A \perp B \; | \; C$, in each stratum, assuming that there is no three-way
interaction. `X`

is a 3 dimensional contingency table, where the last dimension
refers to the strata.

The `UCBAdmissions`

serves as an example of a $2 \times 2 \times 6$ table,
with `Dept`

as the stratifying variable.

# UC Berkeley Student Admissions mantelhaen.test(UCBAdmissions)

The results show no evidence for association between admission and gender
when adjusted for department. However, we can easily see that the assumption
of equal association across the strata (no 3-way association) is probably
violated. For $2 \times 2 \times k$ tables, this can be examined
from the odds ratios for each $2 \times 2$ table (`oddsratio()`

), and
tested by using `woolf_test()`

in `vcd`

.

oddsratio(UCBAdmissions, log=FALSE) lor <- oddsratio(UCBAdmissions) # capture log odds ratios summary(lor) woolf_test(UCBAdmissions)

We can visualize the odds ratios of Admission for
each department with fourfold displays using `fourfold()`

. The cell
frequencies $n_{ij}$ of each $2 \times 2$ table are shown as a quarter circle whose
radius is proportional to $\sqrt{n_{ij}}$, so that its area is proportional to the
cell frequency.

UCB <- aperm(UCBAdmissions, c(2, 1, 3)) dimnames(UCB)[[2]] <- c("Yes", "No") names(dimnames(UCB)) <- c("Sex", "Admit?", "Department")

Confidence rings for the odds ratio allow a visual test of the null of no association;
the rings for adjacent quadrants overlap *iff* the observed counts are consistent
with the null hypothesis. In the extended version (the default), brighter colors
are used where the odds ratio is significantly different from 1.
The following lines produce \figref{fig:fourfold1}.

#| fourfold1, #| h=5, w=7.5, #| cap = "Fourfold display for the `UCBAdmissions` data. Where the odds ratio differs #| significantly from 1.0, the confidence bands do not overlap, and the circle quadrants are #| shaded more intensely." col <- c("#99CCFF", "#6699CC", "#F9AFAF", "#6666A0", "#FF0000", "#000080") fourfold(UCB, mfrow=c(2,3), color=col)

Another `vcd`

function, `cotabplot()`

, provides a more general approach
to visualizing conditional associations in contingency tables,
similar to trellis-like plots produced by `coplot()`

and lattice graphics.
The `panel`

argument supplies a function used to render each conditional
subtable. The following gives a display (not shown) similar to \figref{fig:fourfold1}.

cotabplot(UCB, panel = cotab_fourfold)

When we want to view the conditional
probabilities of a response variable (e.g., `Admit`

)
in relation to several factors,
an alternative visualization is a `doubledecker()`

plot.
This plot is a specialized version of a mosaic plot, which
highlights the levels of a response variable (plotted vertically)
in relation to the factors (shown horizontally). The following
call produces \figref{fig:doubledecker}, where we use indexing
on the first factor (`Admit`

) to make `Admitted`

the highlighted level.

In this plot, the
association between `Admit`

and `Gender`

is shown
where the heights of the highlighted conditional probabilities
do not align. The excess of females admitted in Dept A stands out here.

#| doubledecker, #| h=5, w=8, #| out.width = "75%", #| cap = "Doubledecker display for the `UCBAdmissions` data. The heights #| of the highlighted bars show the conditional probabilities of `Admit`, #| given `Dept` and `Gender`." doubledecker(Admit ~ Dept + Gender, data=UCBAdmissions[2:1,,])

Finally, the there is a `plot()`

method for `oddsratio`

objects.
By default, it shows the 95% confidence interval for the log odds ratio.
\figref{fig:oddsratio} is produced by:

#| oddsratio, #| h=6, w=6, #| out.width = "60%", #| cap = "Log odds ratio plot for the `UCBAdmissions` data." plot(lor, xlab="Department", ylab="Log Odds Ratio (Admit | Gender)")

{#fig:oddsratio}

The standard $\chi^2$ tests for association in a two-way table treat both table factors as nominal (unordered) categories. When one or both factors of a two-way table are quantitative or ordinal, more powerful tests of association may be obtaianed by taking ordinality into account, using row and or column scores to test for linear trends or differences in row or column means.

More general versions of the CMH tests (Landis etal., 1978)
[@Landis-etal:1978] are provided by assigning
numeric scores to the row and/or column variables.
For example, with two ordinal factors (assumed to be equally spaced), assigning
integer scores, `1:R`

and `1:C`

tests the linear $\times$ linear component
of association. This is statistically equivalent to the Pearson correlation between the
integer-scored table variables, with $\chi^2 = (n-1) r^2$, with only 1 $df$
rather than $(R-1)\times(C-1)$ for the test of general association.

When only one table variable is ordinal, these general CMH tests are analogous to an ANOVA, testing whether the row mean scores or column mean scores are equal, again consuming fewer $df$ than the test of general association.

The `CMHtest()`

function in `vcdExtra`

calculates these various
CMH tests for two possibly ordered factors, optionally stratified other factor(s).

** Example**:

## A 4 x 4 table Agresti (2002, Table 2.8, p. 57) Job Satisfaction JobSat <- matrix(c(1,2,1,0, 3,3,6,1, 10,10,14,9, 6,7,12,11), 4, 4) dimnames(JobSat) = list(income=c("< 15k", "15-25k", "25-40k", "> 40k"), satisfaction=c("VeryD", "LittleD", "ModerateS", "VeryS")) JobSat <- as.table(JobSat)

Recall the $4 \times 4$ table, `JobSat`

introduced in \@(sec:creating),

JobSat

Treating the `satisfaction`

levels as equally spaced, but using
midpoints of the `income`

categories as row scores gives the following results:

CMHtest(JobSat, rscores=c(7.5,20,32.5,60))

Note that with the relatively small cell frequencies, the test for general
give no evidence for association. However, the the `cor`

test for linear x linear
association on 1 df is nearly significant. The `coin`

package contains the
functions `cmh_test()`

and `lbl_test()`

for CMH tests of general association and linear x linear association respectively.

There are a variety of statistical measures of *strength* of association for
contingency tables--- similar in spirit to $r$ or $r^2$ for continuous variables.
With a large sample size, even a small degree of association can show a
significant $\chi^2$, as in the example below for the `GSS`

data.

The `assocstats()`

function in `vcd`

calculates the $\phi$
contingency coefficient, and Cramer's V for an $r \times c$ table.
The input must be in table form, a two-way $r \times c$ table.
It won't work with `GSS`

in frequency form, but by now you should know how
to convert.

```
assocstats(GSStab)
```

For tables with ordinal variables, like `JobSat`

, some people prefer the
Goodman-Kruskal $\gamma$ statistic
[@vcd:Agresti:2002, \S 2.4.3]
based on a comparison of concordant
and discordant pairs of observations in the case-form equivalent of a two-way table.

```
GKgamma(JobSat)
```

A web article by Richard Darlington, [http://node101.psych.cornell.edu/Darlington/crosstab/TABLE0.HTM] gives further description of these and other measures of association.

The
`Kappa()`

function in the `vcd`

package calculates Cohen's $\kappa$ and weighted
$\kappa$ for a square two-way table with the same row and column categories [@Cohen:60].
\footnote{
Don't confuse this with `kappa()`

in base R that computes something
entirely different (the condition number of a matrix).
}
Normal-theory $z$-tests are obtained by dividing $\kappa$ by its asymptotic standard
error (ASE). A `confint()`

method for `Kappa`

objects provides confidence intervals.

data(SexualFun, package = "vcd") (K <- Kappa(SexualFun)) confint(K)

A visualization of agreement [@Bangdiwala:87], both unweighted and weighted for degree of departure
from exact agreement is provided by the `agreementplot()`

function.
\figref{fig:agreesex} shows the agreementplot for the `SexualFun`

data,
produced as shown below.

The Bangdiwala measures (returned by the function) represent the proportion of the shaded areas of the diagonal rectangles, using weights $w_1$ for exact agreement, and $w_2$ for partial agreement one step from the main diagonal.

#| agreesex, #| h=6, w=7, #| out.width = "70%", #| cap = "Agreement plot for the `SexualFun` data." agree <- agreementplot(SexualFun, main="Is sex fun?") unlist(agree)

In other examples, the agreement plot can help to show *sources*
of disagreement. For example, when the shaded boxes are above or below the diagonal
(red) line, a lack of exact agreement can be attributed in part to
different frequency of use of categories by the two raters-- lack of
*marginal homogeneity*.

Correspondence analysis is a technique for visually exploring relationships
between rows and columns in contingency tables. The `ca`

package gives one implmentation.
For an $r \times c$ table,
the method provides a breakdown of the Pearson $\chi^2$ for association in up to $M = \min(r-1, c-1)$
dimensions, and finds scores for the row ($x_{im}$) and column ($y_{jm}$) categories
such that the observations have the maximum possible correlations.%
^[Related methods are the non-parametric CMH tests using assumed row/column scores (\secref{sec:CMH}),
the analogous `glm()`

model-based methods (\secref{sec:CMH}), and the more general RC models which can be fit using `gnm()`

. Correspondence analysis differs in that it is a primarily descriptive/exploratory method (no significance tests), but is directly tied to informative graphic displays of the row/column categories.]

Here, we carry out a simple correspondence analysis of the `HairEye`

data.
The printed results show that nearly 99% of the association between hair color and eye color
can be accounted for in 2 dimensions, of which the first dimension accounts for 90%.

library(ca) ca(HairEye)

The resulting `ca`

object can be plotted just by running the `plot()`

method on the `ca`

object, giving the result in
\figref{fig:ca-haireye}. `plot.ca()`

does not allow labels for dimensions;
these can be added with `title()`

.
It can be seen that most of the association is accounted for by the ordering
of both hair color and eye color along Dimension 1, a dark to light dimension.

plot(ca(HairEye), main="Hair Color and Eye Color")

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