Missing {lava}

Missing value generator

Description

Missing value generator

Usage

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.

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

Value

lvm object

Author(s)

Thomas A. Gerds <[e-mail]>

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)


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)