multComp: Create Model Selection Tables based on Multiple Comparisons

Description

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.

Usage

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 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 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 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 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 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 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 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 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 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", …)

Arguments

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 TRUE, letters are used as labels to denote the grouping structure. If FALSE, numbers are used as group labels.

second.ord

logical. If TRUE, the function returns the second-order Akaike information criterion (i.e., AICc), otherwise returns Akaike's Information Criterion (AIC).

nobs

this argument allows to specify a numeric value other than total sample size to compute the AICc (i.e., nobs defaults to total number of observations). This is relevant only for certain types of models such as mixed models where sample size is not straightforward. In such cases, one might use total number of observations or number of independent clusters (e.g., sites) as the value of nobs.

sort

logical. If TRUE, the model selection table is ranked according to the (Q)AIC(c) values.

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 factor.id variable. All levels of the factor.id variables should be included in the newdata data frame to get model-averaged predictions for each level. If NULL, model-averaged predictions are computed for each level of the factor.id variable while the values of the other explanatory variables are taken from the first row of the original data set.

uncond.se

either, "old", or "revised", specifying the equation used to compute the unconditional standard error of a model-averaged estimate. With uncond.se = "old", computations are based on equation 4.9 of Burnham and Anderson (2002), which was the former way to compute unconditional standard errors. With uncond.se = "revised", equation 6.12 of Burnham and Anderson (2002) is used. Anderson (2008, p. 111) recommends use of the revised version for the computation of unconditional standard errors and it is now the default. Note that versions of package AICcmodavg < 1.04 used the old method to compute unconditional standard errors.

conf.level

the confidence level ( $1 - α$ ) requested for the computation of unconditional confidence intervals around predicted values for each level of factor.id.

correction

the type of correction applied to obtain confidence intervals for simultaneous inference (i.e., corrected for multiple comparisons). Current corrections include "none" for uncorrected unconditional confidence intervals, "bonferroni" for Bonferroni-adjusted confidence intervals (Dunn 1961), and "sidak" for Sidak-adjusted confidence intervals (Sidak 1967).

type

the scale of prediction requested, one of "response" or "link". The latter is only relevant for glm and mer classes. Note that the value "terms" is not defined for multComp.

c.hat

value of overdispersion parameter (i.e., variance inflation factor) such as that obtained from c_hat. Note that values of c.hat different from 1 are only appropriate for binomial GLM's with trials > 1 (i.e., success/trial or cbind(success, failure) syntax) or with Poisson GLM's. If c.hat > 1, multComp will return the quasi-likelihood analogue of the information criterion requested. This option is not supported for generalized linear mixed models of the mer class.

gamdisp

the value of the gamma dispersion parameter in a gamma GLM.

…

additional arguments passed to the function.

Value

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.

Details

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 $α_{c o r r} = \frac{α}{m}$ (Dunn 1961). When correction = "sidak", multComp reports Sidak-adjusted confidence intervals, i.e., $α_{c o r r} = 1 - (1 - α)^{\frac{1}{m}}$ .

References

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.

Examples

Run this code

# NOT RUN {
##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")
# }
# 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")
# }
# 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))
# }

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