# Passive synthesis Derives a new variable according to a specified function of synthesised data. ```r syn.passive(data, func) ``` ## Arguments - `data`: a data frame with synthesised data. - `func`: a `formula` specifying transformations on data. It is specified as a string starting with `~`. ## Details Any function of the synthesised data can be specified. Note that several operators such as `+`, `-`, `*` and `^` have different meanings in `formula` syntax. Use the identity function `I()` if they should be interpreted as arithmetic operators, e.g. `"~I(age^2)"`. Function `syn()` checks whether the passive assignment is correct in the original data and fails with a warning if this is not true. The variables synthesised passively can be used to predict later variables in the synthesis except when they are numeric variables with missing data. A warning is produced in this last case. ## Returns A list with two components: - **res**: a vector of length `k` including the result of applying the `formula`. - **fit**: a name of the method used for synthesis (`"passive"`). ## References Van Buuren, S. and Groothuis-Oudshoorn, K. (2011). `mice`: Multivariate Imputation by Chained Equations in `R`. **Journal of Statistical Software**, 45 (3), 1-67. tools:::Rd_expr_doi("10.18637/jss.v045.i03") ## Author(s) Gillian Raab, 2021 based on Stef van Buuren, Karin Groothuis-Oudshoorn, 2000 ## See Also `syn` ## Examples ```r ### the examples shows how inconsistencies in the SD2011 data are picked up ### by syn.passive() ods <- SD2011[, c("height", "weight", "bmi", "age", "agegr")] ods$hsq <- ods$height^2 ods$sex <- SD2011$sex meth <- c("cart", "cart", "~I(weight / height^2 * 10000)", "cart", "~I(cut(age, c(15, 24, 34, 44, 59, 64, 120)))", "~I(height^2)", "logreg") ## Not run: ### fails for bmi s1 <- syn(ods, method = meth, seed = 6756, models = TRUE) ### fails for agegr ods$bmi <- ods$weight / ods$height^2 * 10000 s2 <- syn(ods, method = meth, seed = 6756, models = TRUE) ### fails because of wrong order ods$agegr <- cut(ods$age, c(15, 24, 34, 44, 59, 64, 120)) s3 <- syn(ods, method = meth, visit.sequence = 7:1, seed = 6756, models = TRUE) ## End(Not run) ### runs without errors ods$bmi <- ods$weight / ods$height^2 * 10000 ods$agegr <- cut(ods$age, c(15, 24, 34, 44, 59, 64, 120)) s4 <- syn(ods, method = meth, seed = 6756, models = TRUE) ### bmi and hsq do not predict sex because of missing values s4$models$sex ### hsq with no missing values used to predict sex ods2 <- ods[!is.na(ods$height),] s5 <- syn(ods2, method = meth, seed = 6756, models = TRUE) s5$models$sex ### agegr with missing values used to predict sex because not numeric ods3 <- ods ods3$age[1:4] <- NA ods3$agegr <- cut(ods3$age, c(15, 24, 34, 44, 59, 64, 120)) s6 <- syn(ods3, method = meth, seed = 6756, models = TRUE) s6$models$sex ```

syn.passive function