Missing value generator
Missing value generator
Missing(object, formula, Rformula, missing.name, suffix = "0", ...)
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. |
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
lvm object
Thomas A. Gerds <tag@biostat.ku.dk>
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) m <- lvm(y~1) m <- Missing(m,"y","r") ## same as ## m <- Missing(m,y~1,r~1) sim(m,10) ## same as m <- lvm(y~1) Missing(m,"y") <- r~x sim(m,10) 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)
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