ipf_step | R Documentation |
C++ routines to invoke a single iteration of the Iterative proportional updating (IPU) scheme. Targets and classes
are assumed to be one dimensional in the ipf_step
functions. combine_factors
aggregates several vectors of
type factor into a single one to allow multidimensional ipu-steps. See examples.
ipf_step_ref(w, classes, targets) ipf_step(w, classes, targets) ipf_step_f(w, classes, targets) combine_factors(dat, targets)
w |
a numeric vector of weights. All entries should be positive. |
classes |
a factor variable. Must have the same length as |
targets |
key figure to target with the ipu scheme. A numeric verctor of the same length as |
dat |
a |
ipf_step
returns the adjusted weights. ipf_step_ref
does the same, but updates w
by reference rather than
returning. ipf_step_f
returns a multiplicator: adjusted weights divided by unadjusted weights. combine_factors
is
designed to make ipf_step
work with contingency tables produced by xtabs.
############# one-dimensional ipu ############## ## create random data nobs <- 10 classLabels <- letters[1:3] dat = data.frame( weight = exp(rnorm(nobs)), household = factor(sample(classLabels, nobs, replace = TRUE)) ) dat ## create targets (same lenght as classLabels!) targets <- 3:5 ## calculate weights new_weight <- ipf_step(dat$weight, dat$household, targets) cbind(dat, new_weight) ## check solution xtabs(new_weight ~ dat$household) ## calculate weights "by reference" ipf_step_ref(dat$weight, dat$household, targets) dat ############# multidimensional ipu ############## ## load data factors <- c("time", "sex", "smoker", "day") tips <- data.frame(sex=c("Female","Male","Male"), day=c("Sun","Mon","Tue"), time=c("Dinner","Lunch","Lunch"), smoker=c("No","Yes","No")) tips <- tips[factors] ## combine factors con <- xtabs(~., tips) cf <- combine_factors(tips, con) cbind(tips, cf)[sample(nrow(tips), 10, replace = TRUE),] ## adjust weights weight <- rnorm(nrow(tips)) + 5 adjusted_weight <- ipf_step(weight, cf, con) ## check outputs con2 <- xtabs(adjusted_weight ~ ., data = tips) sum((con - con2)^2)
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