# epi.cpresids: Covariate pattern residuals from a logistic regression model In epiR: Tools for the Analysis of Epidemiological Data

 epi.cpresids R Documentation

## Covariate pattern residuals from a logistic regression model

### Description

Returns covariate pattern residuals and delta betas from a logistic regression model.

### Usage

``````epi.cpresids(obs, fit, covpattern)
``````

### Arguments

 `obs` a vector of observed values (i.e., counts of ‘successes’) for each covariate pattern). `fit` a vector defining the predicted (i.e., fitted) probability of success for each covariate pattern. `covpattern` a `epi.cp` object.

### Value

A data frame with 13 elements: `cpid` the covariate pattern identifier, `n` the number of subjects in this covariate pattern, `obs` the observed number of successes, `pred` the predicted number of successes, `raw` the raw residuals, `sraw` the standardised raw residuals, `pearson` the Pearson residuals, `spearson` the standardised Pearson residuals, `deviance` the deviance residuals, `leverage` leverage, `deltabeta` the delta-betas, `sdeltabeta` the standardised delta-betas, and `deltachi` delta chi statistics.

### References

Hosmer DW, Lemeshow S (1989). Applied Logistic Regression. John Wiley & Sons, New York, USA, pp. 137 - 138.

`epi.cp`

### Examples

``````## EXAMPLE 1:
dat.glm01 <- glm(case ~ spontaneous + induced, family = binomial(),
data = infert)

## Covariate patterns:
dat.mf01 <- model.frame(dat.glm01)
dat.cp01 <- epi.cp(dat.mf01[-1])

dat.obs01 <- as.vector(by(infert\$case, as.factor(dat.cp01\$id),
FUN = sum))
dat.fit01 <- as.vector(by(fitted(dat.glm01), as.factor(dat.cp01\$id),
FUN = min))
dat.cpr01 <- epi.cpresids(obs = dat.obs01, fit = dat.fit01,
covpattern = dat.cp01)