Description Usage Arguments Details Value Author(s) See Also Examples

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

1 2 3 4 5 6 |

`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 missing.

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`

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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | ```
### 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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