Missing value generator

Missing(object, formula, Rformula, missing.name, suffix = "0", ...)

Arguments

object

lvm-object.

formula

The right hand side specifies the name of a latent variable which is not always observed. The left hand side specifies the name of a new variable which is equal to the latent variable but has missing values. If given as a string then this is used as the name of the latent (full-data) name, and the observed data name is 'missing.data'

Rformula

Missing data mechanism with left hand side specifying the name of the observed data indicator (may also just be given as a character instead of a formula)

missing.name

Name of observed data variable (only used if 'formula' was given as a character specifying the name of the full-data variable)

suffix

If missing.name is missing, then the name of the oberved data variable will be the name of the full-data variable + the suffix

...

Passed to binomial.lvm.

Value

lvm object

Details

This function adds a binary variable to a given lvm model and also a variable which is equal to the original variable where the binary variable is equal to zero

Author

Thomas A. Gerds <tag@biostat.ku.dk>

Examples

library(lava) set.seed(17) m <- lvm(y0~x01+x02+x03) m <- Missing(m,formula=x1~x01,Rformula=R1~0.3*x02+-0.7*x01,p=0.4) sim(m,10)
#> y0 x01 x02 x03 R1 x1 #> 1 -0.3307614 1.18078924 0.6810276 -1.17756957 0 NA #> 2 -1.0786445 0.64319207 -0.6820334 -0.96016651 0 NA #> 3 -0.5398741 1.29532187 -0.7232567 -0.87895224 0 NA #> 4 -2.5119604 0.18791807 1.6735260 -3.55613648 1 0.1879181 #> 5 0.3507905 1.59120510 -0.5957556 -1.41674984 0 NA #> 6 0.4902836 -0.05517906 1.1598438 -0.44876927 0 NA #> 7 1.1528003 0.83847112 0.1174224 -0.77596771 1 0.8384711 #> 8 1.3032974 0.15937013 0.2592214 -0.83182805 0 NA #> 9 1.3153836 0.62595440 0.3823621 0.05183012 1 0.6259544 #> 10 -0.3278672 0.63358473 -0.7114817 -0.61655131 0 NA
m <- lvm(y~1) m <- Missing(m,"y","r") ## same as ## m <- Missing(m,y~1,r~1) sim(m,10)
#> y r y0 #> 1 0.07419352 1 0.07419352 #> 2 1.75169617 0 NA #> 3 -0.23148744 0 NA #> 4 0.54345248 0 NA #> 5 -0.98900140 0 NA #> 6 0.31553146 1 0.31553146 #> 7 2.44232746 1 2.44232746 #> 8 0.54969286 1 0.54969286 #> 9 -0.02924337 1 -0.02924337 #> 10 -0.83078338 0 NA
## same as m <- lvm(y~1) Missing(m,"y") <- r~x sim(m,10)
#> y r y0 x #> 1 0.03054575 1 0.03054575 0.5602348 #> 2 -0.78551741 0 NA -1.7924178 #> 3 0.32544056 0 NA -1.5654169 #> 4 -0.88084355 0 NA -3.3203189 #> 5 0.20932594 0 NA 0.1547216 #> 6 0.15103295 1 0.15103295 -0.3646267 #> 7 -0.34347879 0 NA -2.4336839 #> 8 0.90587760 0 NA 0.3364643 #> 9 0.91895485 0 NA -0.6404528 #> 10 -0.55598749 1 -0.55598749 1.8211204
m <- lvm(y~1) m <- Missing(m,"y","r",suffix=".") ## same as ## m <- Missing(m,"y","r",missing.name="y.") ## same as ## m <- Missing(m,y.~y,"r") sim(m,10)
#> y r y. #> 1 0.46345064 1 0.46345064 #> 2 0.88066627 1 0.88066627 #> 3 0.13942195 0 NA #> 4 -0.37505483 0 NA #> 5 0.04253903 0 NA #> 6 0.63388029 1 0.63388029 #> 7 -1.70748846 1 -1.70748846 #> 8 0.01968655 1 0.01968655 #> 9 -0.29879637 0 NA #> 10 -0.44176283 1 -0.44176283