confint.gnm | R Documentation |

Computes confidence intervals for one or more parameters in a generalized nonlinear model, based on the profiled deviance.

## S3 method for class 'gnm' confint(object, parm = ofInterest(object), level = 0.95, trace = FALSE, ...) ## S3 method for class 'profile.gnm' confint(object, parm = names(object), level = 0.95, ...)

`object` |
an object of class |

`parm` |
(optional) either a numeric vector of indices or a
character vector of names, specifying the parameters for which
confidence intervals are to be estimated. If |

`level` |
the confidence level required. |

`trace` |
a logical value indicating whether profiling should be traced. |

`...` |
arguments passed to or from other methods |

These are methods for the generic function `confint`

in the
`base`

package.

For `"gnm"`

objects, `profile.gnm`

is first called to
profile the deviance over each parameter specified by `parm`

, or
over all parameters in the model if `parm`

is `NULL`

.

The method for `"profile.gnm"`

objects is then called, which
interpolates the deviance profiles to estimate the limits of the
confidence interval for each parameter, see `profile.gnm`

for more details.

If a `"profile.gnm"`

object is passed directly to `confint`

,
parameters specified by `parm`

must be a subset of the profiled
parameters.

For unidentified parameters a confidence interval cannot be calculated
and the limits will be returned as `NA`

. If the deviance curve
has an asymptote and a limit of the confidence interval cannot be
reached, the limit will be returned as `-Inf`

or `Inf`

appropriately. If the range of the profile does not extend far enough
to estimate a limit of the confidence interval, the limit will be
returned as `NA`

. In such cases, it may be desirable create a
profile object directly, see `profile.gnm`

for more
details.

A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2 in % (by default 2.5% and 97.5%).

Modification of `MASS:::confint.glm`

by W. N. Venables and
B. D. Ripley. Adapted for `"gnm"`

objects by Heather Turner.

`profile.gnm`

, `gnm`

,
`confint.glm`

, `profile.glm`

### Example in which profiling doesn't take too long count <- with(voting, percentage/100 * total) yvar <- cbind(count, voting$total - count) classMobility <- gnm(yvar ~ -1 + Dref(origin, destination), constrain = "delta1", family = binomial, data = voting) ## profile diagonal effects confint(classMobility, parm = 3:7, trace = TRUE) ## Not run: ### Profiling takes much longer here, but example more interesting! unidiff <- gnm(Freq ~ educ*orig + educ*dest + Mult(Exp(educ), orig:dest), ofInterest = "[.]educ", constrain = "[.]educ1", family = poisson, data = yaish, subset = (dest != 7)) ## Letting 'confint' compute profile confint(unidiff, trace = TRUE) ## 2.5 % 97.5 % ## Mult(Exp(.), orig:dest).educ1 NA NA ## Mult(Exp(.), orig:dest).educ2 -0.5978901 0.1022447 ## Mult(Exp(.), orig:dest).educ3 -1.4836854 -0.2362378 ## Mult(Exp(.), orig:dest).educ4 -2.5792398 -0.2953420 ## Mult(Exp(.), orig:dest).educ5 -Inf -0.7007616 ## Creating profile object first with user-specified stepsize prof <- profile(unidiff, trace = TRUE, stepsize = 0.1) confint(prof, ofInterest(unidiff)[2:5]) ## 2.5 % 97.5 % ## Mult(Exp(.), orig:dest).educ2 -0.5978324 0.1022441 ## Mult(Exp(.), orig:dest).educ3 -1.4834753 -0.2362138 ## Mult(Exp(.), orig:dest).educ4 NA -0.2950790 ## Mult(Exp(.), orig:dest).educ5 NA NA ## For 95% confidence interval, need to estimate parameters for which ## z = +/- 1.96. Profile has not gone far enough for last two parameters range(prof[[4]]$z) ## -1.566601 2.408650 range(prof[[5]]$z) ## -0.5751376 1.1989487 ## End(Not run)

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