# gad: General ANOVA Design In GAD: GAD: Analysis of variance from general principles

## Description

Fits a general ANOVA design with any combination of orthogonal/nested and fixed/random factors through function `estimates`

## Usage

 `1` ```gad(object) ```

## Arguments

 `object` an object of class lm, containing the specified design with random and/or fixed factors

## Details

Function gad returns an analysis of variance table using the `estimates` function to identify the appropriate F-ratios and consequently p-values for any complex model of orthogonal or nested, fixed or random factors as described by Underwood(1997).

## Value

An object of class "anova" inheriting from class "data.frame"

## Author(s)

Leonardo Sandrini-Neto (leonardosandrini@gmail.com)

## References

Underwood, A.J. 1997. Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance. Cambridge University Press, Cambridge.

## See Also

`estimates`

## Examples

 ``` 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``` ```#Example 1 library(GAD) data(rohlf95) CG <- as.fixed(rohlf95\$cages) MQ <- as.random(rohlf95\$mosquito) model <- lm(wing ~ CG + CG%in%MQ, data = rohlf95) gad(model) ## ## #Example 2 data(rats) names(rats) TR <- as.fixed(rats\$treat) RA <- as.random(rats\$rat) LI <- as.random(rats\$liver) model <- lm(glycog ~ TR + RA%in%TR + LI%in%RA%in%TR, data=rats) gad(model) ## ## #Example 3 data(snails) O <- as.random(snails\$origin) S <- as.random(snails\$shore) B <- as.random(snails\$boulder) C <- as.random(snails\$cage) model <- lm(growth ~ O + S + O*S + B%in%S + O*(B%in%S) + C%in%(O*(B%in%S)), data = snails) gad(model) ```

### Example output

```Loading required package: matrixStats
Loading required package: R.methodsS3
R.methodsS3 v1.7.1 (2016-02-15) successfully loaded. See ?R.methodsS3 for help.
Analysis of Variance Table

Response: wing
Df  Sum Sq Mean Sq  F value    Pr(>F)
CG        2  665.68  332.84   1.7409    0.2295
CG:MQ     9 1720.68  191.19 146.8781 6.981e-11 ***
Residual 12   15.62    1.30
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
[1] "glycog" "treat"  "rat"    "liver"
Analysis of Variance Table

Response: glycog
Df  Sum Sq Mean Sq F value  Pr(>F)
TR        2 1557.56  778.78  2.9290 0.19710
TR:RA     3  797.67  265.89  5.3715 0.01411 *
TR:RA:LI 12  594.00   49.50  2.3386 0.05029 .
Residual 18  381.00   21.17
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Variance Table

Response: growth
Df  Sum Sq Mean Sq  F value   Pr(>F)
O          1 118.582 118.582 109.1874 0.001871 **
S          3  36.360  12.120
O:S        3   3.258   1.086   6.5359 0.015201 *
S:B        8   1.187   0.148   0.8927 0.561821
O:S:B      8   1.329   0.166   1.4177 0.239629
O:S:B:C   24   2.813   0.117   1.0430 0.413970
Residual 192  21.576   0.112
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
```

GAD documentation built on May 2, 2019, 3:01 a.m.