confint.gnm: Compute Confidence Intervals of Parameters in a Generalized...

confint.gnmR Documentation

Compute Confidence Intervals of Parameters in a Generalized Nonlinear Model


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, ...)



an object of class "gnm" or "profile.gnm"


(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 parm is NULL, confidence intervals are found for all parameters.


the confidence level required.


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.

See Also

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
## -1.566601  2.408650
## -0.5751376  1.1989487

## End(Not run)

gnm documentation built on April 29, 2022, 5:06 p.m.