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howManyImputations implements “how_many_imputations” from von Hippel (2020). See When carrying out multiple imputation, the old advice of 5-10 imputations is sufficient for a point estimate (e.g. an estimated coefficient), but not for estimates of standard errors (and consequently, hypothesis tests or confidence intervals).

von Hippel (2020) provides a way to calculate the number of imputations needed to have consistent estimates of the standard error. To do so requires an estimate of the Fraction of Missing Information (FMI) which can only be obtained after running some number of imputations. Therefore, the following procedure is recommended:

  1. Carry out a limited number of imputations to enable estimation of the FMI. von Hippel (2020) recommends 20 imputations.
  2. Use the function how_many_imputations() to calculate how many total imputations you will need.
  3. If the number of total imputations you will need is larger than your initial batch of 20, run additional imputations.


The only function in howManyImputations is how_many_imputations(). This takes in the results of a model fit on multiply imputed data (primarily from mice but see below for working with other MI packages) and estimates how many total imputations are needed.

imputed_data <- mice(data_with_missing, ...)
mi_model_fit <- with(imputed_data, lm(y ~ x))
how_many_imputations(mi_model_fit, cv = .1, alpha = .01)

The optional cv and alpha arguments can be used to tweaked to control how conservative or anti-conversative the estimate is. See documentation for further details.

Here’s a worked example:

> library(howManyImputations)
> data(airquality)
> # Add some missingness
> airquality[4:10, 3] <- rep(NA, 7)
> airquality[1:5, 4] <- NA
> airquality <- airquality[-c(5, 6)]
> impdata1 <- mice(airquality, m = 5, maxit = 10, method = 'pmm', seed = 500)
> modelFit1 <- with(impdata1, lm(Temp ~ Ozone + Solar.R + Wind))
> how_many_imputations(modelFit1)
[1] 72
> how_many_imputations(modelFit1, cv = .01)
[1] 1767

If you’re using a different package to carry out the imputation, and said package produces a list of models as the output of its modeling step, how_many_imputations() tries to convert the object to a mira object via mice::as.mira() . Here’s the above example reworked using the jomo package for the imputation (using mitools to convert the output of jomo::jomo1() into a list via mitools::imputationList()).

> library(jomo)
> library(mitools) # for the `imputationList` function
> jomodata <- jomo1(airquality, nburn = 100, nbetween = 100, nimp = 5)
> impdata2 <- imputationList(split(jomodata, jomodata$Imputation))
> modelFit2 <- with(impdata2, lm(Temp ~ Ozone + Solar.R + Wind))
> how_many_imputations(modelFit2)
[1] 77

Here’s another example using Amelia, again converting the imputed data into something mice can understand.

> library(Amelia)
> data(freetrade)
> a.out <- amelia(freetrade, m = 20, ts = "year", cs = "country")
> modelFit3 <- with(imputationList(a.out$imputations),
                    lm(tariff ~ polity + pop + gdp.pc + year + country))
> how_many_imputations(modelFit3)
[1] 128


Von Hippel, Paul T. “How many imputations do you need? A two-stage calculation using a quadratic rule.” Sociological Methods & Research. 2020;49(3):699-718. doi:10.1177/0049124117747303