
mi.scatterplot( Yobs, Yimp, X = NULL, xlab = NULL, ylab = NULL,
main = "Imputed Variable Scatter Plot",
display.zero = TRUE, gray.scale = FALSE,
obs.col = rgb( 0, 0, 1 ),
imp.col = rgb( 1, 0, 0 ),
obs.pch = 20 , imp.pch = 20,
obs.cex = 0.3, imp.cex = 0.3,
obs.lty = 1 , imp.lty = 1,
obs.lwd = 2.5, imp.lwd = 2.5, ... )
marginal.scatterplot ( data, object, use.imputed.X = FALSE, ... )
mi
object.jitter
for more details.
Lowess line is fitted to both imputed and observed data.mi
,
plot
# true data
x<-rnorm(100,0,1) # N(0,1)
y<-rnorm(100,(1+2*x),1.2) # y ~ 1 + 2*x + N(0,1.2)
# create artificial missingness on y
y[seq(1,100,10)]<-NA
dat.xy <- data.frame(x,y)
# imputation
imp.cont<-mi.continuous(y~x, data = dat.xy)
mi.scatterplot(y,imputed(imp.cont,y))
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