# Anova

##### Anova Tables for Various Statistical Models

Calculates type-II or type-III analysis-of-variance tables for
model objects produced by `lm`

, `glm`

, `multinom`

(in the `polr`

(in the `coxph`

(in the `lmer`

in the `lme`

in the `vcov`

and `coef`

functions. For linear
models, F-tests are calculated; for generalized linear models,
likelihood-ratio chisquare, Wald chisquare, or F-tests are calculated;
for multinomial logit and proportional-odds logit models, likelihood-ratio
tests are calculated. Various test statistics are provided for multivariate
linear models produced by `lm`

or `manova`

. Partial-likelihood-ratio tests
or Wald tests are provided for Cox models. Wald chi-square tests are provided for fixed effects in
linear and generalized linear mixed-effects models. Wald chi-square or F tests are provided
in the default case.

- Keywords
- models, regression, htest

##### Usage

```
Anova(mod, ...)
Manova(mod, ...)
## S3 method for class 'lm':
Anova(mod, error, type=c("II","III", 2, 3),
white.adjust=c(FALSE, TRUE, "hc3", "hc0", "hc1", "hc2", "hc4"),
singular.ok, ...)
## S3 method for class 'aov':
Anova(mod, ...)
## S3 method for class 'glm':
Anova(mod, type=c("II","III", 2, 3),
test.statistic=c("LR", "Wald", "F"),
error, error.estimate=c("pearson", "dispersion", "deviance"),
singular.ok, ...)
## S3 method for class 'multinom':
Anova(mod, type = c("II","III", 2, 3), ...)
## S3 method for class 'polr':
Anova(mod, type = c("II","III", 2, 3), ...)
## S3 method for class 'mlm':
Anova(mod, type=c("II","III", 2, 3), SSPE, error.df,
idata, idesign, icontrasts=c("contr.sum", "contr.poly"), imatrix,
test.statistic=c("Pillai", "Wilks", "Hotelling-Lawley", "Roy"),...)
## S3 method for class 'manova':
Anova(mod, ...)
## S3 method for class 'mlm':
Manova(mod, ...)
## S3 method for class 'Anova.mlm':
print(x, ...)
## S3 method for class 'Anova.mlm':
summary(object, test.statistic, multivariate=TRUE,
univariate=TRUE, digits=getOption("digits"), ...)
## S3 method for class 'coxph':
Anova(mod, type=c("II","III", 2, 3),
test.statistic=c("LR", "Wald"), ...)
## S3 method for class 'lme':
Anova(mod, type=c("II","III", 2, 3),
vcov.=vcov(mod), singular.ok, ...)
## S3 method for class 'mer':
Anova(mod, type=c("II","III", 2, 3),
test.statistic=c("chisq", "F"), vcov.=vcov(mod), singular.ok, ...)
## S3 method for class 'svyglm':
Anova(mod, ...)
## S3 method for class 'default':
Anova(mod, type=c("II","III", 2, 3),
test.statistic=c("Chisq", "F"), vcov.=vcov(mod),
singular.ok, ...)
```

##### Arguments

- mod
`lm`

,`aov`

,`glm`

,`multinom`

,`polr`

`mlm`

,`coxph`

,`lme`

,`mer`

,`svyglm`

or other suitable model object.- error
- for a linear model, an
`lm`

model object from which the error sum of squares and degrees of freedom are to be calculated. For F-tests for a generalized linear model, a`glm`

object from which the dispersion is to be e - type
- type of test,
`"II"`

,`"III"`

,`2`

, or`3`

. - singular.ok
- defaults to
`TRUE`

for type-II tests, and`FALSE`

for type-III tests (where the tests for models with aliased coefficients will not be straightforwardly interpretable); if`FALSE`

, a model with aliased coef - test.statistic
- for a generalized linear model, whether to calculate
`"LR"`

(likelihood-ratio),`"Wald"`

, or`"F"`

tests; for a Cox model, whether to calculate`"LR"`

(partial-likelihood ratio) or`"Wald"`

- error.estimate
- for F-tests for a generalized linear model, base the
dispersion estimate on the Pearson residuals (
`"pearson"`

, the default); use the dispersion estimate in the model object (`"dispersion"`

), which, e.g., is fixed to 1 - white.adjust
- if not
`FALSE`

, the default, tests use a heteroscedasticity-corrected coefficient covariance matrix; the various values of the argument specify different corrections. See the documentation for`h`

- SSPE
- The error sum-of-squares-and-products matrix; if missing, will be computed from the residuals of the model.
- error.df
- The degrees of freedom for error; if missing, will be taken from the model.
- idata
- an optional data frame giving a factor or factors defining the
intra-subject model for multivariate repeated-measures data. See
*Details*for an explanation of the intra-subject design and for further explanation of the other argume - idesign
- a one-sided model formula using the ``data'' in
`idata`

and specifying the intra-subject design. - icontrasts
- names of contrast-generating functions to be applied by default to factors and ordered factors, respectively, in the within-subject ``data''; the contrasts must produce an intra-subject model matrix in which different terms are orthogonal.
- imatrix
- as an alternative to specifying
`idata`

,`idesign`

, and (optionally)`icontrasts`

, the model matrix for the within-subject design can be given directly in the form of list of named elements. Each element gives - x, object
- object of class
`"Anova.mlm"`

to print or summarize. - multivariate, univariate
- print multivariate and univariate tests for a repeated-measures
ANOVA; the default is
`TRUE`

for both. - digits
- minimum number of significant digits to print.
- vcov.
- an optional coefficient-covariance matrix, computed by default by applying the
generic
`vcov`

function to the model object. - ...
- do not use.

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##### Details

The designations "type-II" and "type-III" are borrowed from SAS, but the
definitions used here do not correspond precisely to those employed by SAS.
Type-II tests are calculated according to the principle of marginality,
testing each term after all others, except ignoring the term's higher-order relatives;
so-called type-III tests violate marginality, testing
each term in the model after all of the others. This definition of Type-II tests
corresponds to the tests produced by SAS for analysis-of-variance models, where all of the predictors
are factors, but not more generally (i.e., when there are quantitative predictors).
Be very careful in formulating the model for type-III tests, or the hypotheses tested
will not make sense.
As implemented here, type-II Wald tests are a generalization of the linear hypotheses used to generate
these tests in linear models.
For tests for linear models, multivariate linear models, and Wald tests for generalized linear models,
Cox models, mixed-effects models, generalized linear models fit to survey data, and in the default case,
`Anova`

finds the test statistics without refitting the model. The `svyglm`

method simply
calls the `default`

method and therefore can take the same arguments.
The standard R `anova`

function calculates sequential ("type-I") tests.
These rarely test interesting hypotheses in unbalanced designs.
A MANOVA for a multivariate linear model (i.e., an object of
class `"mlm"`

or `"manova"`

) can optionally include an
intra-subject repeated-measures design.
If the intra-subject design is absent (the default), the multivariate
tests concern all of the response variables.
To specify a repeated-measures design, a data frame is provided defining the repeated-measures factor or
factors
via `idata`

, with default contrasts given by the `icontrasts`

argument. An intra-subject model-matrix is generated from the formula
specified by the `idesign`

argument; columns of the model matrix
corresponding to different terms in the intra-subject model must be orthogonal
(as is insured by the default contrasts). Note that the contrasts given in
`icontrasts`

can be overridden by assigning specific contrasts to the
factors in `idata`

. As an alternative, the within-subjects model matrix
can be specified directly via the `imatrix`

argument.
`Manova`

is essentially a synonym for `Anova`

for multivariate linear models.

##### Value

- An object of class
`"anova"`

, or `"Anova.mlm"`

, which usually is printed.
For objects of class `"Anova.mlm"`

, there is also a `summary`

method,
which provides much more detail than the `print`

method about the MANOVA, including
traditional mixed-model univariate F-tests with Greenhouse-Geisser and Huynh-Feldt
corrections.

##### Warning

Be careful of type-III tests.

##### References

Fox, J. (2008)
*Applied Regression Analysis and Generalized Linear Models*,
Second Edition. Sage.
Fox, J. and Weisberg, S. (2011)
*An R Companion to Applied Regression*, Second Edition, Sage.
Hand, D. J., and Taylor, C. C. (1987)
*Multivariate Analysis of Variance and Repeated Measures: A Practical
Approach for Behavioural Scientists.* Chapman and Hall.
O'Brien, R. G., and Kaiser, M. K. (1985)
MANOVA method for analyzing repeated measures designs: An extensive primer.
*Psychological Bulletin* **97**, 316--333.

##### See Also

`linearHypothesis`

, `anova`

`anova.lm`

, `anova.glm`

,
`anova.mlm`

, `anova.coxph`

, `link[survey]{svyglm}`

.

##### Examples

```
## Two-Way Anova
mod <- lm(conformity ~ fcategory*partner.status, data=Moore,
contrasts=list(fcategory=contr.sum, partner.status=contr.sum))
Anova(mod)
## One-Way MANOVA
## See ?Pottery for a description of the data set used in this example.
summary(Anova(lm(cbind(Al, Fe, Mg, Ca, Na) ~ Site, data=Pottery)))
## MANOVA for a randomized block design (example courtesy of Michael Friendly:
## See ?Soils for description of the data set)
soils.mod <- lm(cbind(pH,N,Dens,P,Ca,Mg,K,Na,Conduc) ~ Block + Contour*Depth,
data=Soils)
Manova(soils.mod)
## a multivariate linear model for repeated-measures data
## See ?OBrienKaiser for a description of the data set used in this example.
phase <- factor(rep(c("pretest", "posttest", "followup"), c(5, 5, 5)),
levels=c("pretest", "posttest", "followup"))
hour <- ordered(rep(1:5, 3))
idata <- data.frame(phase, hour)
idata
mod.ok <- lm(cbind(pre.1, pre.2, pre.3, pre.4, pre.5,
post.1, post.2, post.3, post.4, post.5,
fup.1, fup.2, fup.3, fup.4, fup.5) ~ treatment*gender,
data=OBrienKaiser)
(av.ok <- Anova(mod.ok, idata=idata, idesign=~phase*hour))
summary(av.ok, multivariate=FALSE)
## A "doubly multivariate" design with two distinct repeated-measures variables
## (example courtesy of Michael Friendly)
## See ?WeightLoss for a description of the dataset.
imatrix <- matrix(c(
1,0,-1, 1, 0, 0,
1,0, 0,-2, 0, 0,
1,0, 1, 1, 0, 0,
0,1, 0, 0,-1, 1,
0,1, 0, 0, 0,-2,
0,1, 0, 0, 1, 1), 6, 6, byrow=TRUE)
colnames(imatrix) <- c("WL", "SE", "WL.L", "WL.Q", "SE.L", "SE.Q")
rownames(imatrix) <- colnames(WeightLoss)[-1]
(imatrix <- list(measure=imatrix[,1:2], month=imatrix[,3:6]))
contrasts(WeightLoss$group) <- matrix(c(-2,1,1, 0,-1,1), ncol=2)
(wl.mod<-lm(cbind(wl1, wl2, wl3, se1, se2, se3)~group, data=WeightLoss))
Anova(wl.mod, imatrix=imatrix, test="Roy")
## mixed-effects models examples:
library(nlme)
example(lme)
Anova(fm2)
library(lme4)
example(lmer)
Anova(gm1)
```

* Documentation reproduced from package car, version 2.0-13,
License: GPL (>= 2)
*
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