This function is an alternative to traditional multiple comparison
tests in designed experiments. It creates a model selection table based
on different grouping patterns of a factor and computes model-averaged
predictions for each of the factor levels. The current version works
with objects of `aov`

, `glm`

, `gls`

, `lm`

,
`lme`

, `mer`

, `merMod`

, and `rlm`

, `survreg`

classes.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ```
multComp(mod, factor.id, letter.labels = TRUE, second.ord = TRUE, nobs =
NULL, sort = TRUE, newdata = NULL, uncond.se = "revised",
conf.level = 0.95, correction = "none", ...)
## S3 method for class 'aov'
multComp(mod, factor.id, letter.labels = TRUE, second.ord =
TRUE, nobs = NULL, sort = TRUE, newdata = NULL, uncond.se =
"revised", conf.level = 0.95, correction = "none", ...)
## S3 method for class 'lm'
multComp(mod, factor.id, letter.labels = TRUE, second.ord =
TRUE, nobs = NULL, sort = TRUE, newdata = NULL, uncond.se =
"revised", conf.level = 0.95, correction = "none", ...)
## S3 method for class 'gls'
multComp(mod, factor.id, letter.labels = TRUE, second.ord
= TRUE, nobs = NULL, sort = TRUE, newdata = NULL, uncond.se =
"revised", conf.level = 0.95, correction = "none", ...)
## S3 method for class 'glm'
multComp(mod, factor.id, letter.labels = TRUE, second.ord
= TRUE, nobs = NULL, sort = TRUE, newdata = NULL, uncond.se =
"revised", conf.level = 0.95, correction = "none", type =
"response", c.hat = 1, gamdisp = NULL, ...)
## S3 method for class 'lme'
multComp(mod, factor.id, letter.labels = TRUE, second.ord
= TRUE, nobs = NULL, sort = TRUE, newdata = NULL, uncond.se =
"revised", conf.level = 0.95, correction = "none", ...)
## S3 method for class 'rlm'
multComp(mod, factor.id, letter.labels = TRUE, second.ord
= TRUE, nobs = NULL, sort = TRUE, newdata = NULL, uncond.se =
"revised", conf.level = 0.95, correction = "none", ...)
## S3 method for class 'survreg'
multComp(mod, factor.id, letter.labels = TRUE, second.ord
= TRUE, nobs = NULL, sort = TRUE, newdata = NULL, uncond.se =
"revised", conf.level = 0.95, correction = "none", type =
"response", ...)
## S3 method for class 'mer'
multComp(mod, factor.id, letter.labels = TRUE, second.ord
= TRUE, nobs = NULL, sort = TRUE, newdata = NULL, uncond.se =
"revised", conf.level = 0.95, correction = "none", type =
"response", ...)
## S3 method for class 'merMod'
multComp(mod, factor.id, letter.labels = TRUE,
second.ord = TRUE, nobs = NULL, sort = TRUE, newdata = NULL,
uncond.se = "revised", conf.level = 0.95, correction =
"none", type = "response", ...)
``` |

`mod` |
a model of one of the above-mentioned classes that includes at least one factor as an explanatory variable. |

`factor.id` |
the factor of interest, on which the groupings (multiple comparisons) are based. The user must supply the name of the categorical variable between quotes as it appears in the model formula. |

`letter.labels` |
logical. If |

`second.ord` |
logical. If |

`nobs` |
this argument allows to specify a numeric value other than total sample
size to compute the AICc (i.e., |

`sort` |
logical. If |

`newdata` |
a data frame with the same structure as that of the original data
frame for which we want to make predictions. This data frame should
hold all variables constant other than the |

`uncond.se` |
either, |

`conf.level` |
the confidence level ( |

`correction` |
the type of correction applied to obtain confidence
intervals for simultaneous inference (i.e., corrected for multiple
comparisons). Current corrections include |

`type` |
the scale of prediction requested, one of |

`c.hat` |
value of overdispersion parameter (i.e., variance inflation factor)
such as that obtained from |

`gamdisp` |
the value of the gamma dispersion parameter in a gamma GLM. |

`...` |
additional arguments passed to the function. |

A number of pairwise comparison tests are available for traditional experimental designs, some controlling for the experiment-wise error and others for comparison-wise errors (Day and Quinn 1991). With the advent of information-theoretic approaches, there has been a need for methods analogous to multiple comparison tests in a model selection framework. Dayton (1998) and Burnham et al. (2011) suggested using different parameterizations or grouping patterns of a factor to perform multiple comparisons with model selection. As such, it is possible to assess the support in favor of certain grouping patterns based on a factor.

For example, a factor with three levels has four possible grouping
patterns: abc (all groups are different), abb (the first group
differs from the other two), aab (the first two groups differ from the
third), and aaa (all groups are equal). `multComp`

implements
such an approach by pooling groups of the factor variable in a model and
updating the model, for each grouping pattern possible. The models are
ranked according to one of four information criteria (AIC, AICc, QAIC,
and QAICc), and the labels in the table correspond to the grouping
pattern. Note that the factor levels are sorted according to their means
for the response variable before being assigned to a group. The
function also returns model-averaged predictions and unconditional
standard errors for each level of the `factor.id`

variable based on
the support in favor of each model (i.e., grouping pattern).

The number of grouping patterns increases substantially with the number
of factor levels, as *2^{k - 1}*, where *k* is the number of
factor levels. `multComp`

supports factors with a maximum of 6
levels. Also note that `multComp`

does not handle models where
the `factor.id`

variable is involved in an interaction. In such
cases, one should create the interaction variable manually before
fitting the model (see Examples).

`multComp`

currently implements three methods of computing
confidence intervals. The default unconditional confidence intervals
do not account for multiple comparisons (`correction = "none"`

).
With a large number *m* of potential pairwise comparisons among
levels of `factor.id`

, there is an increased risk of type I
error. For *m* pairwise comparisons and a given *α*
level, `correction = "bonferroni"`

computes the unconditional
confidence intervals based on *α_{corr} = \frac{α}{m}*
(Dunn 1961). When `correction = "sidak"`

, `multComp`

reports Sidak-adjusted confidence intervals, i.e., *α_{corr}
= 1 - (1 - α)^{\frac{1}{m}}*.

`multComp`

creates a list of class `multComp`

with the
following components:

`factor.id` |
the factor for which grouping patterns are investigated. |

`models` |
a list with the output of each model representing a different grouping pattern for the factor of interest. |

`model.names` |
a vector of model names denoting the grouping pattern for each level of the factor. |

`model.table` |
the model selection table for the models corresponding to each grouping pattern for the factor of interest. |

`ordered.levels` |
the levels of the factor ordered according to the mean of the response variable. The grouping patterns (and model names) in the model selection table are based on the same order. |

`model.avg.est` |
a matrix with the model-averaged prediction, unconditional standard error, and confidence intervals for each level of the factor. |

`conf.level` |
the confidence level used for the confidence intervals. |

`correction` |
the type of correction applied to the confidence intervals to account for potential pairwise comparisons. |

Marc J. Mazerolle

Burnham, K. P., Anderson, D. R., Huyvaert, K. P. (2011) AIC model
selection and multimodel inference in behaviorial ecology: some
background, observations and comparisons. *Behavioral Ecology and
Sociobiology* **65**, 23–25.

Day, R. W., Quinn, G. P. (1989) Comparisons of treatments after an
analysis of variance in ecology. *Ecological Monographs*
**59**, 433–463.

Dayton, C. M. (1998) Information criteria for the paired-comparisons
problem. *American Statistician*, **52** 144–151.

Dunn, O. J. (1961) Multiple comparisons among means. *Journal of the
American Statistical Association* **56**, 52–64.

Sidak, Z. (1967) Rectangular confidence regions for the means of
multivariate normal distributions. *Journal of the American
Statistical Association* **62**, 626–633.

`aictab`

, `confset`

, `c_hat`

,
`evidence`

, `glht`

, `fit.contrast`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | ```
##one-way ANOVA example
data(turkey)
##convert diet to factor
turkey$Diet <- as.factor(turkey$Diet)
##run one-way ANOVA
m.aov <- lm(Weight.gain ~ Diet, data = turkey)
##compute models with different grouping patterns
##and also compute model-averaged group means
out <- multComp(m.aov, factor.id = "Diet", correction = "none")
##look at results
out
##look at grouping structure of a given model
##and compare with original variable
cbind(model.frame(out$models[[2]]), turkey$Diet)
##evidence ratio
evidence(out$model.table)
##compute Bonferroni-adjusted confidence intervals
multComp(m.aov, factor.id = "Diet", correction = "bonferroni")
##two-way ANOVA with interaction
## Not run:
data(calcium)
m.aov2 <- lm(Calcium ~ Hormone + Sex + Hormone:Sex, data = calcium)
##multiple comparisons
multComp(m.aov2, factor.id = "Hormone")
##returns an error because 'Hormone' factor is
##involved in an interaction
##create interaction variable
calcium$inter <- interaction(calcium$Hormone, calcium$Sex)
##run model with interaction
m.aov.inter <- lm(Calcium ~ inter, data = calcium)
##compare both
logLik(m.aov2)
logLik(m.aov.inter)
##both are identical
##multiple comparisons
multComp(m.aov.inter, factor.id = "inter")
## End(Not run)
##Poisson regression
## Not run:
##example from ?glm
##Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
glm.D93 <- glm(counts ~ outcome + treatment, data = d.AD, family = poisson)
multComp(mod = glm.D93, factor.id = "outcome")
## End(Not run)
##example specifying 'newdata'
## Not run:
data(dry.frog)
m1 <- lm(log_Mass_lost ~ Shade + Substrate +
cent_Initial_mass + Initial_mass2,
data = dry.frog)
multComp(m1, factor.id = "Substrate",
newdata = data.frame(
Substrate = c("PEAT", "SOIL", "SPHAGNUM"),
Shade = 0, cent_Initial_mass = 0,
Initial_mass2 = 0))
## End(Not run)
``` |

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