which.influence {Design} | R Documentation |
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 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
|
show.influence
returns a marked dataframe with the first column being
a count of influence values
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
residuals.lrm
, residuals.cph
, residuals.ols
, Design
, lrm
, ols
, cph
#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