Description Usage Arguments Details Examples

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.

1 2 3 4 5 6 7 | ```
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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | ```
############# 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)
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.