synthpop (version 1.9-0)

syn.passive: Passive synthesis

Description

Derives a new variable according to a specified function of synthesised data.

Usage

syn.passive(data, func)

Value

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").

Arguments

data

a data frame with synthesised data.

func

a formula specifying transformations on data. It is specified as a string starting with ~.

Author

Gillian Raab, 2021 based on Stef van Buuren, Karin Groothuis-Oudshoorn, 2000

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.

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")

See Also

syn

Examples

### 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")

if (FALSE) {
### 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)  
}

### 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