The function mianalyze.relimp takes a list of imputed data frames (or matrices), calculates relative importance metrics for each of these and combines these metrics into overall estimates with estimated variances according to the method by Rubin (1987). The output object can be summarized, printed and plotted.
mianalyze.relimp(implist, level = 0.95, sort = FALSE, ..., b = 50, type = "lmg",
diff = TRUE, no.CI = FALSE, rela = FALSE, always = NULL, groups = NULL,
groupnames = NULL, deslist = NULL, bootlist.out = FALSE, formula = NULL,
weights = NULL, strata=NULL, ids=NULL)The value returned by function mianalyze.relimp is an object of class relimplmbootMI
(if no.CI = FALSE, default) or an object of class relimplm (if no.CI=TRUE).
It can be printed, plotted and summarized using special methods.
For extracting its items, the @ or $ extractors can be used.
In addition to the items described for function calc.relimp, which are also available here,
the following items from class relimplmbootMI may be of interest for further calculations:
matrix of lower confidence bounds for “metric”: one row for each confidence level,
one column for each element of “metric”. “metric” can be any of lmg, lmg.rank,
lmg.diff, ...
(replace lmg with other available relative importance metrics, cf. calc.relimp)
matrix of upper confidence bounds for “metric”: one row for each confidence level, one column for each element of “metric”
number of bootstrap runs underlying the evaluations
confidence level
object of class MIresult that can be processed by the function summary.MIresult
from package survey
only available if bootlist.out=TRUE has been chosen;
list of objects of class boot.relimp; each list element can be input to function
booteval.relimp
list of data frames or matrices containing multiply-imputed datasets,
or object of class imputationList
If no formula is given, the first column of each data frame/matrix is assumed to be the response variable, the other columns are regressors.
If a list of designs is also given, the variables component of each design must
consist of the necessary columns from the respective entry in implist;
if no formula is given, the variables component of each design must coincide
(except for the order of columns) with the respective entry in implist.
is a single confidence level (between 0.5 and 1)
is a logical requesting output sorted by size of relative contribution
(sort=TRUE) or by variable position in list (sort=FALSE, default).
Further arguments, currently none available
is the number of bootstrap runs requested on boot.relimp (default: b=50).
Make sure to set this to a higher number, if you want to subsequently use the bootlist slot
for calculating further confidence intervals with function booteval.relimp.
cf. calc.relimp.
is a logical requesting bootstrapping of pairwise differences in relative importance (diff=TRUE,
default) for each metric in type
if set to TRUE, suppresses calculation of confidence intervals and only averages estimated metrics
from all imputed data sets in implist. Currently, no.CI = TRUE is the only setting for which
mianalyze.relimp works when using models with factors, groups or interactions.
cf. calc.relimp.
cf. calc.relimp.
cf. calc.relimp.
cf. calc.relimp.
is a list of design object of class survey.design (cf. package survey).
You can EITHER specify a deslist OR weights and/or strata and/or ids.
Note that the design list must contain the same data objects (in the “variables” element)
that are listed in implist, so that a lot of storage space is needed in case of large datasets.
If deslist is not given, the function creates a list of designs using weights,
strata, and ids information. Whenever the required designs are simple enough to be covered by
specifying weights, strata, and ids, this is by far preferrable in terms of
storage.
If TRUE, the individual bootstrap results for each multiply imputed data set are stored in the bootlist slot of the output object (may be storage-intensive).
cf. boot.relimp;
NOTE: If no.CI = FALSE, i.e. confidence intervals are not suppressed,
formula has to follow the same restrictions as mentioned under item design for
boot.relimp (no calculated variables, no interaction terms, no factors),
since confidence interval calculations in mianalyze.relimp are design-based,
even if no deslist-option is given.
is a vector of case weights for the observations in the data frame (or matrix).
You can EITHER specify weights OR a deslist.
If weights is NULL, equal weights are assumed, unless otherwise specified in deslist.
For the different types of weights and their appropriate treatment for obtaining confidence intervals,
cf. the “Details” section of boot.relimp.
is a strata request that will be handed to function svydesign for defining the
strata in a survey design (to be given to mianalyze without the ~).
You can EITHER specify strata OR a deslist.
If strata is NULL, one stratum is assumed, unless otherwise specified in deslist.
is an id-request that will be handed to function svydesign for defining the clusters
in a survey design (to be given to mianalyze without the ~).
You can EITHER specify ids OR a deslist.
If ids is NULL, it is assumed that there are no clusters,
unless otherwise specified in deslist.
Ulrike Groemping, BHT Berlin
The confidence intervals produced here should be used for exploratory purposes only.
They can be somewhat liberal and are likely to be too symmetric particularly for small data sets.
The confidence intervals produced by function mianalyze.relimp need further research into their behaviour
and are currently considered experimental.
Be aware that the methods themselves (lmg and even more pmvd) need some computing time in case of
many regressors. Hence, bootstrapping of multiple data sets should be used with awareness of computing time issues.
Multiple imputation is a contemporary method for handling data with a substantial missing value problem. It produces a number of completed data sets (e.g. 10) the inference from which is subsequently combined. The most frequently used way of combination is the one by Rubin: estimates from the different completed data sets are averaged, and the variance is estimated by combining the average over the estimated variances (within imputation variance) with an appropriately-scaled variance between estimates, and confidence intervals are obtained by using a t-distribution with appropriately chosen degrees of freedom.
The variance-covariance matrix of the vector of estimates for each individual completed data set is obtained
from function withReplicates in package survey based on survey's bootstrap replication weights.
On request (bootlist.out=TRUE), the underlying bootstrap resamples are also stored in the
bootlist-slot of the output object. In this case, list elements of the bootlist-slot
are objects of class relimplmboot and can be processed by function booteval.relimp.
This can help in getting an impression whether the overall aggregated confidence intervals are heavily distorted
towards symmetry. If such sanity-checking is intended, the default value for b should be substantially
increased.
Function mianalyze.relimp needs a list of multiply-imputed data sets or an object of class
imputationList for input.
Multiply imputed data sets can - within R - be obtained from various packages. Hints for creating lists
of the form needed for mianalyze.relimp are given below for users of functions aregImpute,
mice, and amelia. Users of packages norm, cat, mix, or pan
(who have managed to operate these extremely uncomfortable packages) can of course also produce lists of
imputed data sets (only less comfortably).
For an object imp of class mids obtained from function mice in package mice,
the code
lapply(as.list(1:imp$m),function(obj) complete(imp,action=obj))
produces a list of multiply-imputed data sets as needed for function mianalyze.relimp.
For an object f of class aregImpute produced by function aregImpute in package Hmisc,
lapply(as.list(1:f$m),function(obj) impute(imp,imputation=obj))
produces the required list of multiply-imputed data sets.
For an object output produced by function amelia in package Amelia, the code
output[1:output$amelia.args$m]
produces the list of multiply-imputed data sets as needed for function mianalyze.relimp.
For multiple imputation, practice is in many cases ahead of theory; this is no different with function
mianalyze.relimp. Users should note that the validity of confidence intervals has only been proven for
likelihood-based analyses. Since the metrics calculated in relaimpo are not strictly likelihood-based,
the confidence intervals from function mianalyze.relimp must be considered approximate and experimental.
Chevan, A. and Sutherland, M. (1991) Hierarchical Partitioning. The American Statistician 45, 90--96.
Darlington, R.B. (1968) Multiple regression in psychological research and practice. Psychological Bulletin 69, 161--182.
Feldman, B. (2005) Relative Importance and Value. Manuscript (Version 1.1, March 19 2005), downloadable at https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2255827
Genizi, A. (1993) Decomposition of R2 in multiple regression with correlated regressors. Statistica Sinica 3, 407--420. Downloadable at https://www3.stat.sinica.edu.tw/statistica/password.asp?vol=3&num=2&art=10
Groemping, U. (2006) Relative Importance for Linear Regression in R: The Package relaimpo Journal of Statistical Software 17, Issue 1. Downloadable at https://www.jstatsoft.org/v17/i01
Lindeman, R.H., Merenda, P.F. and Gold, R.Z. (1980) Introduction to Bivariate and Multivariate Analysis, Glenview IL: Scott, Foresman.
Little, R.J.A. and Rubin, D.B. (2002) Statistical Analysis with Missing Data, Wiley, New York.
Zuber, V. and Strimmer, K. (2010) Variable importance and model selection by decorrelation. Preprint, downloadable at https://arxiv.org/abs/1007.5516
Go to https://prof.bht-berlin.de/groemping/ for further information and references.
relaimpo, calc.relimp, booteval.relimp,
classesmethods.relaimpo
## smi contains a list of 5 imputed datasets (class imputationList) from package mitools
## (first element of smi is list of data frames)
## it is not a well-suited example for relative importance but easily available for demonstrating
## multiple imputation-related functionality
data(smi)
## obtain averaged estimates only, without confidence intervals
## works with factors and interactions
mianalyze.relimp(smi[[1]], formula = cistot ~ drkfre+sex+wave, no.CI = TRUE)
## for obtaining all individual estimates, use lapply:
smi.cr.list <- lapply(smi[[1]], function(obj) calc.relimp(cistot ~ drkfre+sex+wave, data=obj))
## display result for first individual imputed data set
smi.cr.list[[1]]
## obtain confidence intervals,
## currently only usable for models without calculated variables, factors, groups, interactions
## call without using weights, strata, clusters or a design list
mianalyze.relimp(smi[[1]], formula = cistot ~ mdrkfre+sex+wave)
## call using the id column (identical in all smi data sets) for cluster structure
ident <- smi[[1]][[1]]$id
mitest <- mianalyze.relimp(smi[[1]], formula = cistot ~ mdrkfre+sex+wave, ids=ident)
mitest
## postprocess: look at intervals with different confidence level
summary(mitest,lev=0.8)
## call with design list
deslist <- lapply(smi[[1]], function(obj) svydesign(~id,strata=~sex,weights=~cistot,data=obj))
mitest <- mianalyze.relimp(smi[[1]], formula = cistot ~ mdrkfre+sex+wave, deslist=deslist,
level=c(0.8))
mitest
Run the code above in your browser using DataLab