Interactive analysis of covariance

`aoctool(x,y,group)`

aoctool(x,y,group,alpha)

aoctool(x,y,group,alpha,xname,yname,gname)

aoctool(x,y,group,alpha,xname,yname,gname,* displayopt*)

aoctool(x,y,group,alpha,xname,yname,gname,

`displayopt`

`model`

h = aoctool(...)

[h,atab,ctab] = aoctool(...)

[h,atab,ctab,stats] = aoctool(...)

`aoctool(x,y,group)`

fits a separate line to the column
vectors, `x`

and `y`

, for each group defined by the values
in the array `group`

. `group`

may be a categorical variable,
numeric vector, character array, string array, or cell array of character vectors. These types
of models are known as one-way analysis of covariance (ANOCOVA) models. The output consists of
three figures:

An interactive graph of the data and prediction curves

An ANOVA table

A table of parameter estimates

You can use the figures to change models and to test different
parts of the model. More information about interactive use of the `aoctool`

function
appears in Analysis of Covariance Tool.

`aoctool(x,y,group,alpha) `

determines the confidence levels of the prediction intervals. The
confidence level is `100(1-alpha)`

%. The default
value of `alpha`

is 0.05.

`aoctool(x,y,group,alpha,xname,yname,gname)`

specifies
the name to use for the `x`

, `y`

, and `g`

variables in the graph and tables. If you enter simple variable names for the```
x
```

, `y`

, and `g`

arguments, the
`aoctool`

function uses those names. If you enter an expression for one
of these arguments, you can specify a name to use in place of that expression by supplying
these arguments. For example, if you enter `m(:,2)`

as the
`x`

argument, you might choose to enter` 'Col 2'`

as
the `xname`

argument.

`aoctool(x,y,group,alpha,xname,yname,gname,`

enables
the graph and table displays when * displayopt*)

`displayopt`

`'on'`

(default)
and suppresses those displays when `displayopt`

`'off'`

.`aoctool(x,y,group,alpha,xname,yname,gname,`

specifies
the initial model to fit. The value of * displayopt*,

`model`

`model`

`'same mean'`

— Fit a single mean, ignoring grouping`'separate means'`

— Fit a separate mean to each group`'same line'`

— Fit a single line, ignoring grouping`'parallel lines'`

— Fit a separate line to each group, but constrain the lines to be parallel`'separate lines'`

— Fit a separate line to each group, with no constraints

`h = aoctool(...)`

returns
a vector of handles to the line objects in the plot.

`[h,atab,ctab] = aoctool(...)`

returns
cell arrays containing the entries in ANOVA table (`atab`

)
and the table of coefficient estimates (`ctab`

).
(You can copy a text version of either table to the clipboard by using
the `Copy Text`

item on the **Edit** menu.)

`[h,atab,ctab,stats] = aoctool(...)`

returns
a `stats`

structure that you can use to perform a
follow-up multiple comparison test. The ANOVA table output includes
tests of the hypotheses that the slopes or intercepts are all the
same, against a general alternative that they are not all the same.
Sometimes it is preferable to perform a test to determine which pairs
of values are significantly different, and which are not. You can
use the `multcompare`

function
to perform such tests by supplying the `stats`

structure
as input. You can test either the slopes, the intercepts, or population
marginal means (the heights of the curves at the mean `x`

value).

This example illustrates how to fit different models non-interactively. After loading the smaller car data set and fitting a separate-slopes model, you can examine the coefficient estimates.

load carsmall [h,a,c,s] = aoctool(Weight,MPG,Model_Year,0.05,... '','','','off','separate lines'); c(:,1:2) ans = 'Term' 'Estimate' 'Intercept' [45.97983716833132] ' 70' [-8.58050531454973] ' 76' [-3.89017396094922] ' 82' [12.47067927549897] 'Slope' [-0.00780212907455] ' 70' [ 0.00195840368824] ' 76' [ 0.00113831038418] ' 82' [-0.00309671407243]

Roughly speaking, the lines relating `MPG`

to `Weight`

have
an intercept close to 45.98 and a slope close to -0.0078. Each group's
coefficients are offset from these values somewhat. For instance,
the intercept for the cars made in 1970 is 45.98-8.58 = 37.40.

Next, try a fit using parallel lines. (The ANOVA table shows that the parallel-lines fit is significantly worse than the separate-lines fit.)

[h,a,c,s] = aoctool(Weight,MPG,Model_Year,0.05,... '','','','off','parallel lines'); c(:,1:2) ans = 'Term' 'Estimate' 'Intercept' [43.38984085130596] ' 70' [-3.27948192983761] ' 76' [-1.35036234809006] ' 82' [ 4.62984427792768] 'Slope' [-0.00664751826198]

Again, there are different intercepts for each group, but this time the slopes are constrained to be the same.