logitreg {analogue}R Documentation

Logistic regression models for assessing analogues/non-analogues

Description

Fits logistic regression models to each level of group to model the probability of two samples being analogues conditional upon the dissimilarity between the two samples.

Usage

logitreg(object, groups, k = 1, ...)

## Default S3 method:
logitreg(object, groups, k = 1, ...)

## S3 method for class 'analog':
logitreg(object, groups, k = 1, ...)

## S3 method for class 'logitreg':
summary(object, p = 0.9, ...)

Arguments

object for logitreg; a full dissimilarity matrix. For summary.logitreg an object of class "logitreg", the result of a call to logitreg.
groups factor (or object that can be coerced to one) containing the group membership for each sample in object.
k numeric; the k closest analogues to use in the model fitting.
p probability at which to predict the dose needed.
... arguments passed to other methods.

Details

Fits logistic regression models to each level of group to model the probability of two samples being analogues (i.e. in the same group) conditional upon the dissimilarity between the two samples.

This function can be seen as a way of directly modelling the probability that two sites are analogues, conditional upon dissimilarity, that can also be done less directly using roc and bayesF.

Value

logitreg returns an object of class "logitreg"; a list whose components are objects returned by glm. See glm for further details on the returned objects.
The components of this list take their names from group.
For summary.logitreg an object of class "summary.logitreg", a data frame with summary statistics of the model fits. The components of this data frame are:

In, Out The number of analogue and non-analogue dissimilarities analysed in each group,
Est.(Dij), Std.Err Coefficient and its standard error for dissimilarity from the logit model,
Z-value, p-value Wald statistic and associated p-value for each logit model.
Dij(p=?), Std.Err(Dij) The dissimilarity at which the posterior probability of two samples being analogues is equal to p, and its standard error. These are computed using dose.p.

Note

The function may generate warnings from function glm.fit. These should be investigated and not simply ignored.

If the message is concerns fitted probabilities being numerically 0 or 1, then check the fitted values of each of the models. These may well be numerically 0 or 1. Heed the warning in glm and read the reference cited therein which may indicate problems with the fitted models, such as (quasi-)complete separation.

Author(s)

Gavin L. Simpson

See Also

roc, bayesF, glm.

Examples

## continue the example from ?roc
example(roc)

## fit the logit models to the analog object
swap.lrm <- logitreg(swap.ana, grps)
swap.lrm

## summary statistics
summary(swap.lrm)

## plot the fitted logit curves
plot(swap.lrm, conf.type = "polygon")

[Package analogue version 0.6-6 Index]