I just added S3 `print`

, `residuals`

, and
`coef`

methods for the phylogenetic reduced major axis regression
function, `phyl.RMA`

. The updates can be seen
here.

The following is a quick demo using simulated data:

```
library(phytools)
packageVersion("phytools")
```

```
## [1] '0.5.68'
```

```
tree
```

```
##
## Phylogenetic tree with 26 tips and 25 internal nodes.
##
## Tip labels:
## A, B, C, D, E, F, ...
##
## Rooted; includes branch lengths.
```

```
x
```

```
## A B C D E F
## 5.02625173 3.84069323 1.40907359 1.62584177 -0.38820852 -0.03660051
## G H I J K L
## -1.15405529 -1.05555766 -1.10500369 -1.82982215 -0.58220778 -0.62621299
## M N O P Q R
## 2.58984857 1.65864863 1.38732527 1.10492094 0.89527230 -1.40154533
## S T U V W X
## 1.23500803 -0.24029572 -0.26507837 -0.77611134 -0.88118355 -2.59262521
## Y Z
## -0.11687224 -1.68772212
```

```
y
```

```
## A B C D E F
## 5.03364484 4.45525372 2.33034028 1.70260435 1.86376515 0.63005315
## G H I J K L
## -1.27847189 -0.51986100 0.42627759 -0.59603022 -1.02732412 -1.06377260
## M N O P Q R
## 0.11285994 0.08066473 -0.02928941 -0.14296226 -0.38034287 -2.11329071
## S T U V W X
## -0.12823996 1.88409506 2.23634682 -0.82779628 -0.49460318 -2.37103424
## Y Z
## -2.07654579 -0.58623675
```

```
phylomorphospace(tree,cbind(x,y),node.size=c(0,0))
points(cbind(x,y),cex=1.2,pch=21,bg="grey")
```

Now let's run the RMA regression:

```
obj<-phyl.RMA(x,y,tree)
## print
obj
```

```
##
## Coefficients:
## (Intercept) x
## -0.0424625 1.0647447
##
## VCV matrix:
## x y
## x 1.504905 1.119709
## y 1.119709 1.706083
##
## Model for the covariance structure of the error is "BM"
##
## Estimates (or set values):
## lambda log(L)
## 1.00000 -69.49404
##
## Hypothesis test based on Clarke (1980; Biometrika):
## r2 T df P
## 0.488316 0.429650 21.290154 0.671767
##
## Note that the null hypothesis test is h0 = 1
```

```
coef(obj)
```

```
## (Intercept) x
## -0.0424625 1.0647447
```

```
residuals(obj)
```

```
## A B C D E
## -0.275567308 0.408358649 0.872499213 0.013960519 2.319570602
## F G H I J
## 0.711485847 -0.007235186 0.646500875 1.645286860 1.394725642
## K L M N O
## -0.364958989 -0.354553159 -2.602204971 -1.642910026 -1.463974066
## P Q R S T
## -1.276958414 -1.291116753 -0.578540317 -1.400745657 2.182411146
## U V W X Y
## 2.561050105 0.041026621 0.486094798 0.431912090 -1.909644200
## Z
## 1.253218854
```

I also wrote a simple `plot`

method that throws the RMA line
on top of a plotted phylomorphospace. Details can be seen
here.

This is what it looks like:

```
plot(obj)
```

We can also flip `x`

or `y`

just to see what it looks
like to plot a negative slope. Note that we can only test a null
hypothesis that has the same sign as our fitted RMA line. Here (for fun),
I'll test the null hypothesis h_{0} = -2/3.

```
neg.x<--x
obj<-phyl.RMA(neg.x,y,tree,h0=-2/3)
obj
```

```
##
## Coefficients:
## (Intercept) x
## -0.0424625 -1.0647447
##
## VCV matrix:
## x y
## x 1.504905 -1.119709
## y -1.119709 1.706083
##
## Model for the covariance structure of the error is "BM"
##
## Estimates (or set values):
## lambda log(L)
## 1.00000 -69.49404
##
## Hypothesis test based on Clarke (1980; Biometrika):
## r2 T df P
## 0.488316 3.206537 21.290154 0.004187
##
## Note that the null hypothesis test is h0 = -0.666666666666667
```

```
plot(obj)
```

That's the idea anyway.

Data for this demo were simulated as follows:

```
library(phytools)
tree<-pbtree(n=26,tip.label=LETTERS)
xy<-fastBM(tree)
x<-xy+fastBM(tree,sig2=0.4)
y<-xy+fastBM(tree,sig2=0.4)
```

nice

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