profLikCI: Find confidence intervals for parameters in an NBDA
In whoppitt/NBDA: A Package For Implementing Network-Based Diffusion Analysis

Description Usage Arguments Details Value Warning Getting confidence intervals for a difference between two parameters See Also

View source: R/profLikCI.R

Find confidence intervals for a specific parameter, or a difference between two parameters using the profile likelihood method.

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profLikCI(which, model, upperRange = NULL, lowerRange = NULL,
  constraintsVect = NULL, iterations = 150, inflation = 1,
  conf = 0.95, retainInt = NULL)

`which`	numeric giving the parameter for which the confidence interval is to be calculated. The appropriate number can be identified from the fitted model, by entering `<modelName>@varNames` to extract the variable names from the model. Each variable name is preceded by its number.
`model`	object of class `oadaFit` or `tadaFit`
`upperRange`	numeric vector of length two, providing a range within which the upper endpoint of the confidence interval is known to be located. This range can be indentified using `plotProfLik`. `upperRange` can be omitted if the confidence interval only has a lower endpoint- i.e. if the profile likelihood levels out below the dotted line.
`lowerRange`	numeric vector of length two, providing a range within which the lower endpoint of the confidence interval is known to be located. This range can be indentified using `plotProfLik`. `lowerRange` can be omitted if the confidence interval only has a upper endpoint- e.g. if the profile likelihood is beneath the dotted line for s=0.
`constraintsVect`	optional numeric vector. This only needs to be used if the confidence interval for the difference between two parameters is required (see specific section below).
`iterations`	optional numerical giving the maximum number of iterations to be used by the optimization alogorithms.
`inflation`	numerical to be used if the confidence intervals are to be inflated by a specified amount, as suggested by Burnham & Anderson (2000) to allow for model selection uncertainty.
`conf`	numerical giving the level of confidence required, defaulting to the traditional 0.95.
`retainInt`	logical, can be used to force the model to retain int_ilvs in an asocial model. This is used internally by other functions when there is an offset on the s parameters, but can be safely ignored by the user.

The profile likelihood method for finding (100-X)% confidence intervals works by finding the set of values for a parameter that would not be rejected in a likelihood ratio test at the X% level of significance. This is equivalent to finding the set of values for which the profile likelihood (-log likelihood optimized over all other parameters in the model) is within C units of the -log-likelihood for the model, where C is the critical value for rejection at the X% level of significance (1.92 for 95% confidence intervals). The plotProfLik function can be used to plot the profile likelihood for a parameter and find the approximate location of the endpoints of the confidence interval after which profLikCI can be used to locate the exact endpoints.

A list of the form ("Lower CI","Upper CI")

This function does not work when trueTies are present in an OADA. Instead use profLikCITrueTies for the confidence intervals on a parameter, or profLikCIDiffTrueTies for the difference between two parameters.

This can be achieved using the constraintsVect argument. e.g. if we wish to find the confidence interval for parameter 1 - parameter 2, we specify which=1 and constraintsVect=c(1,1,2,3,etc.). This constrains parameter 1 and 2 to be the same, but adds an offset to parameter 1 using the constrainedNBDAdata function. The resulting confidence interval is for parameter 1 - parameter 2. This can only be done for parameters of the same type i.e. differences must be within the s parameters, asoc_ilv, int_ilv or multi_ilv categories. If the user wishes to find confidence intervals for the difference between two s parameters which is thought to span zero, we advise doing this as a two step process. e.g. find the upper limit for s1-s2, setting range >0, then find the upper limit for s2-s1 setting range >0. This prevents values of s1 or s2<0 being condsidered in the optimization process, which may trigger errors.

plotProfLik

whoppitt/NBDA documentation built on April 25, 2021, 7:55 a.m.