mantelhaen.test {stats} | R Documentation |

## Cochran-Mantel-Haenszel Chi-Squared Test for Count Data

### Description

Performs a Cochran-Mantel-Haenszel chi-squared test of the null that two nominal variables are conditionally independent in each stratum, assuming that there is no three-way interaction.

### Usage

```
mantelhaen.test(x, y = NULL, z = NULL,
alternative = c("two.sided", "less", "greater"),
correct = TRUE, exact = FALSE, conf.level = 0.95)
```

### Arguments

`x` |
either a 3-dimensional contingency table in array form where each dimension is at least 2 and the last dimension corresponds to the strata, or a factor object with at least 2 levels. |

`y` |
a factor object with at least 2 levels; ignored if |

`z` |
a factor object with at least 2 levels identifying to which
stratum the corresponding elements in |

`alternative` |
indicates the alternative hypothesis and must be
one of |

`correct` |
a logical indicating whether to apply continuity
correction when computing the test statistic.
Only used in the 2 by 2 by |

`exact` |
a logical indicating whether the Mantel-Haenszel test or
the exact conditional test (given the strata margins) should be
computed.
Only used in the 2 by 2 by |

`conf.level` |
confidence level for the returned confidence
interval.
Only used in the 2 by 2 by |

### Details

If `x`

is an array, each dimension must be at least 2, and
the entries should be nonnegative integers. `NA`

's are not
allowed. Otherwise, `x`

, `y`

and `z`

must have the
same length. Triples containing `NA`

's are removed. All
variables must take at least two different values.

### Value

A list with class `"htest"`

containing the following components:

`statistic` |
Only present if no exact test is performed. In the
classical case of a 2 by 2 by |

`parameter` |
the degrees of freedom of the approximate chi-squared
distribution of the test statistic ( |

`p.value` |
the p-value of the test. |

`conf.int` |
a confidence interval for the common odds ratio.
Only present in the 2 by 2 by |

`estimate` |
an estimate of the common odds ratio. If an exact
test is performed, the conditional Maximum Likelihood Estimate is
given; otherwise, the Mantel-Haenszel estimate.
Only present in the 2 by 2 by |

`null.value` |
the common odds ratio under the null of
independence, |

`alternative` |
a character string describing the alternative
hypothesis.
Only present in the 2 by 2 by |

`method` |
a character string indicating the method employed, and whether or not continuity correction was used. |

`data.name` |
a character string giving the names of the data. |

### Note

The asymptotic distribution is only valid if there is no three-way
interaction. In the classical 2 by 2 by `K`

case, this is
equivalent to the conditional odds ratios in each stratum being
identical. Currently, no inference on homogeneity of the odds ratios
is performed.

See also the example below.

### References

Alan Agresti (1990).
*Categorical data analysis*.
New York: Wiley.
Pages 230–235.

Alan Agresti (2002).
*Categorical data analysis* (second edition).
New York: Wiley.

### Examples

```
## Agresti (1990), pages 231--237, Penicillin and Rabbits
## Investigation of the effectiveness of immediately injected or 1.5
## hours delayed penicillin in protecting rabbits against a lethal
## injection with beta-hemolytic streptococci.
Rabbits <-
array(c(0, 0, 6, 5,
3, 0, 3, 6,
6, 2, 0, 4,
5, 6, 1, 0,
2, 5, 0, 0),
dim = c(2, 2, 5),
dimnames = list(
Delay = c("None", "1.5h"),
Response = c("Cured", "Died"),
Penicillin.Level = c("1/8", "1/4", "1/2", "1", "4")))
Rabbits
## Classical Mantel-Haenszel test
mantelhaen.test(Rabbits)
## => p = 0.047, some evidence for higher cure rate of immediate
## injection
## Exact conditional test
mantelhaen.test(Rabbits, exact = TRUE)
## => p - 0.040
## Exact conditional test for one-sided alternative of a higher
## cure rate for immediate injection
mantelhaen.test(Rabbits, exact = TRUE, alternative = "greater")
## => p = 0.020
## UC Berkeley Student Admissions
mantelhaen.test(UCBAdmissions)
## No evidence for association between admission and gender
## when adjusted for department. However,
apply(UCBAdmissions, 3, function(x) (x[1,1]*x[2,2])/(x[1,2]*x[2,1]))
## This suggests that the assumption of homogeneous (conditional)
## odds ratios may be violated. The traditional approach would be
## using the Woolf test for interaction:
woolf <- function(x) {
x <- x + 1 / 2
k <- dim(x)[3]
or <- apply(x, 3, function(x) (x[1,1]*x[2,2])/(x[1,2]*x[2,1]))
w <- apply(x, 3, function(x) 1 / sum(1 / x))
1 - pchisq(sum(w * (log(or) - weighted.mean(log(or), w)) ^ 2), k - 1)
}
woolf(UCBAdmissions)
## => p = 0.003, indicating that there is significant heterogeneity.
## (And hence the Mantel-Haenszel test cannot be used.)
## Agresti (2002), p. 287f and p. 297.
## Job Satisfaction example.
Satisfaction <-
as.table(array(c(1, 2, 0, 0, 3, 3, 1, 2,
11, 17, 8, 4, 2, 3, 5, 2,
1, 0, 0, 0, 1, 3, 0, 1,
2, 5, 7, 9, 1, 1, 3, 6),
dim = c(4, 4, 2),
dimnames =
list(Income =
c("<5000", "5000-15000",
"15000-25000", ">25000"),
"Job Satisfaction" =
c("V_D", "L_S", "M_S", "V_S"),
Gender = c("Female", "Male"))))
## (Satisfaction categories abbreviated for convenience.)
ftable(. ~ Gender + Income, Satisfaction)
## Table 7.8 in Agresti (2002), p. 288.
mantelhaen.test(Satisfaction)
## See Table 7.12 in Agresti (2002), p. 297.
```

*stats*version 4.4.1 Index]