Which Observations are Influential
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.
which.influence(fit, cutoff=.2) show.influence(object, dframe, report=NULL, sig=NULL, id=NULL)
fit |
fit object |
object |
the result of |
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 |
id |
a character vector that labels rows of |
show.influence returns a marked dataframe with the first column being
a count of influence values
Frank Harrell
Department of Biostatistics, Vanderbilt University
fh@fharrell.com
Jens Oehlschlaegel-Akiyoshi
Center for Psychotherapy Research
Christian-Belser-Strasse 79a
D-70597 Stuttgart Germany
oehl@psyres-stuttgart.de
#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 resultsPlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.