# seg.control

##### Auxiliary for controlling segmented model fitting

Auxiliary function as user interface for 'segmented' fitting. Typically
only used when calling any 'segmented' method (`segmented.lm`

, `segmented.glm`

, `segmented.Arima`

or `segmented.default`

).

- Keywords
- regression

##### Usage

```
seg.control(n.boot=10, display = FALSE, tol = 1e-05, it.max = 30, fix.npsi=TRUE,
K = 10, quant = TRUE, maxit.glm = 25, h = 1, size.boot=NULL, jt=FALSE, nonParam=TRUE,
random=TRUE, seed=NULL, fn.obj=NULL, digits=NULL, conv.psi=FALSE,
alpha=.02, min.step=.0001,
powers=c(1,1), last = TRUE, stop.if.error = NULL, gap=FALSE)
```

##### Arguments

- n.boot
number of bootstrap samples used in the bootstrap restarting algorithm. If 0 the standard algorithm, i.e. without bootstrap restart, is used. Default to 10 that appears to be sufficient in most of problems. However when multiple breakpoints have to be estimated it is suggested to increase

`n.boot`

, e.g.`n.boot=50`

.- display
logical indicating if the value of the objective function should be printed (along with current breakpoint estimates) at each iteration or at each bootstrap resample. If bootstrap restarting is employed, the values of objective and breakpoint estimates should not change at the last runs.

- tol
positive convergence tolerance.

- it.max
integer giving the maximal number of iterations.

- fix.npsi
logical (it replaces previous argument

`stop.if.error`

) If`TRUE`

(default) the*number*(and not location) of breakpoints is held fixed throughout iterations. Otherwise a sort of `automatic' breakpoint selection is carried out, provided that several starting values are supplied for the breakpoints, see argument`psi`

in`segmented.lm`

or`segmented.glm`

. The idea, relying on removing the `non-admissible' breakpoint estimates at each iteration, is discussed in Muggeo and Adelfio (2011) and it is not compatible with the bootstrap restart algorithm.`fix.npsi=FALSE`

, indeed, should be considered as a preliminary and tentative approach to deal with an unknown number of breakpoints.- K
the number of quantiles (or equally-spaced values) to supply as starting values for the breakpoints when the

`psi`

argument of`segmented`

is set to`NA`

.`K`

is ignored when`psi`

is different from`NA`

.- quant
logical, indicating how the starting values should be selected. If

`FALSE`

equally-spaced values are used, otherwise the quantiles. Ignored when`psi`

is different from`NA`

.- maxit.glm
integer giving the maximum number of inner IWLS iterations (see details).

- h
positive factor modifying the increments in breakpoint updates during the estimation process (see details).

- size.boot
the size of the bootstrap samples. If

`NULL`

, it is taken equal to the actual sample size.- jt
logical. If

`TRUE`

the values of the segmented variable(s) are jittered before fitting the model to the bootstrap resamples.- nonParam
if

`TRUE`

nonparametric bootstrap (i.e. case-resampling) is used, otherwise residual-based. Currently working only for LM fits. It is not clear what residuals should be used for GLMs.- random
if

`TRUE`

, when the algorithm fails to obtain a solution, random values are employed to obtain candidate values.- seed
The seed to be passed on to

`set.seed()`

when`n.boot>0`

. Setting the seed can be useful to replicate the results when the bootstrap restart algorithm is employed. In fact a segmented fit includes`seed`

representing the integer vector saved just before the bootstrap resampling. Re-use it if you want to replicate the bootstrap restarting algorithm with the*same*samples.- fn.obj
A

*character string*to be used (optionally) only when`segmented.default`

is used. It represents the function (with argument`'x'`

) to be applied to the fit object to extract the objective function to be*minimized*. Thus for`"lm"`

fits (although unnecessary) it should be`fn.obj="sum(x$residuals^2)"`

, for`"coxph"`

fits it should be`fn.obj="-x$loglik[2]"`

. If`NULL`

the `minus log likelihood' extracted from the object, namely`"-logLik(x)"`

, is used. See`segmented.default`

.- digits
optional. If specified it means the desidered number of decimal points of the breakpoint to be used during the iterative algorithm.

- conv.psi
optional. Should convergence of iterative procedure to be assessed on changes of breakpoint estimates or changes in the objective? Default to FALSE.

- alpha
optional numerical value. The breakpoint is estimated within the quantiles

`alpha`

and`1-alpha`

of the relevant covariate.- min.step
optional. The minimum step size to break the iterative algorithm. Default to 0.0001.

- powers
The powers of the pseudo covariates employed by the algorithm. These can be altered during the iterative process to stabilize the estimation procedure. Usually of no interest for the user.

*This argument will be removed in next releases*.- last
logical indicating if output should include only the last fitted model.

*This argument will be removed in next releases*- stop.if.error
same than

`fix.npsi`

.*This argument will be removed in next releases*, and replaced by`fix.npsi`

. If provided, and different from`NULL`

, it overwrites`fix.npsi`

- gap
logical, if

`FALSE`

the gap coefficients are*always*constrained to zero at the convergence.*This argument will be removed in next releases*.

##### Details

Fitting a `segmented' GLM model is attained via fitting iteratively standard GLMs. The number of (outer)
iterations is governed by `it.max`

, while the (maximum) number of (inner) iterations to fit the GLM at
each fixed value of psi is fixed via `maxit.glm`

. Usually three-four inner iterations may be sufficient.

When the starting value for the breakpoints is set to `NA`

for any segmented variable specified
in `seg.Z`

, `K`

values (quantiles or equally-spaced) are selected as starting values for the breakpoints.
In this case, it may be useful to set also `fix.npsi=FALSE`

to automate the procedure, see Muggeo and Adelfio (2011).
The maximum number of iterations (`it.max`

) should be also increased when the `automatic' procedure is used.

If `last=TRUE`

, the object resulting from `segmented.lm`

(or `segmented.glm`

) is a
list of fitted GLM; the i-th model is the segmented model with the values of the breakpoints at the i-th iteration.

Since version 0.2-9.0 `segmented`

implements the bootstrap restarting algorithm described in Wood (2001).
The bootstrap restarting is expected to escape the local optima of the objective function when the
segmented relationship is flat. Notice bootstrap restart runs `n.boot`

iterations regardless of
`tol`

that only affects convergence within the inner loop.

##### Value

A list with the arguments as components.

##### References

Muggeo, V.M.R., Adelfio, G. (2011) Efficient change point detection in genomic sequences of
continuous measurements. *Bioinformatics* **27**, 161--166.

Wood, S. N. (2001) Minimizing model fitting objectives that contain spurious local minima
by bootstrap restarting. *Biometrics* **57**, 240--244.

##### Examples

```
# NOT RUN {
#decrease the maximum number inner iterations and display the
#evolution of the (outer) iterations
seg.control(display = TRUE, maxit.glm=4)
# }
```

*Documentation reproduced from package segmented, version 1.0-0, License: GPL*