R: Which Observations are Influential

which.influence {rms} | R Documentation

## Which Observations are Influential

### Description

Creates a list with a component for each factor in the model. The names of the components are the factor names. Each component contains the observation identifiers of all observations that are "overly influential" with respect to that factor, meaning that |dfbetas| > u for at least one beta i associated with that factor, for a given `cutoff`. The default `cutoff` is `.2`. The fit must come from a function that has `resid(fit, type="dfbetas")` defined.

`show.influence`, written by Jens Oehlschlaegel-Akiyoshi, applies the result of `which.influence` to a data frame, usually the one used to fit the model, to report the results.

### Usage

    which.influence(fit, cutoff=.2)

    show.influence(object, dframe, report=NULL, sig=NULL, id=NULL)

### Arguments

`fit` | fit object
`object` | the result of `which.influence`
`dframe` | data frame containing observations pertinent to the model fit
`cutoff` | cutoff value
`report` | other columns of the data frame to report besides those corresponding to predictors that are influential for some observations
`sig` | runs results through `signif` with `sig` digits if `sig` is given
`id` | a character vector that labels rows of `dframe` if `row.names` were not used

### Value

`show.influence` returns a marked dataframe with the first column being a count of influence values

### Author(s)

Frank Harrell
Department of Biostatistics, Vanderbilt University
f.harrell@vanderbilt.edu

Jens Oehlschlaegel-Akiyoshi
Center for Psychotherapy Research
Christian-Belser-Strasse 79a
D-70597 Stuttgart Germany
oehl@psyres-stuttgart.de

### See Also

`residuals.lrm`, `residuals.cph`, `residuals.ols`, `rms`, `lrm`, `ols`, `cph`

### Examples

    #print observations in data frame that are influential,
    #separately for each factor in the model
    x1 <- 1:20
    x2 <- abs(x1-10)
    x3 <- factor(rep(0:2,length.out=20))
    y  <- c(rep(0:1,8),1,1,1,1)
    f  <- lrm(y ~ rcs(x1,3) + x2 + x3, x=TRUE,y=TRUE)
    w <- which.influence(f, .55)
    nam <- names(w)
    d   <- data.frame(x1,x2,x3,y)
    for(i in 1:length(nam)) {
     print(paste("Influential observations for effect of ",nam[i]),quote=FALSE)
     print(d[w[[i]],])
    }

    show.influence(w, d)  # better way to show results

[Package rms version 5.1-1 Index]