## -----------------------------------------------------------------------------
set.seed(1234)
n <- 1000
x <- rnorm(n)
w <- x + rnorm(n)
y <- x + rnorm(n)
x[(n * 0.1):n] <- NA
simData <- data.frame(x, w, y)

## -----------------------------------------------------------------------------
library(smcfcs)
imps <- smcfcs(simData,
  smtype = "lm", smformula = "y~x",
  method = c("norm", "", ""), m = 5
)

## -----------------------------------------------------------------------------
predMat <- array(0, dim = c(3, 3))
predMat[1, 2] <- 1

## -----------------------------------------------------------------------------
imps <- smcfcs(simData,
  smtype = "lm", smformula = "y~x",
  method = c("norm", "", ""), m = 5,
  predictorMatrix = predMat
)

## -----------------------------------------------------------------------------
library(mitools)
impobj <- imputationList(imps$impDatasets)
models <- with(impobj, lm(y ~ x))
summary(MIcombine(models))

## -----------------------------------------------------------------------------
x <- rnorm(n)
w1 <- x + rnorm(n)
w2 <- x + rnorm(n)
w2[(n * 0.1):n] <- NA
y <- x + rnorm(n)
x <- rep(NA, n)
simData <- data.frame(x, w1, w2, y)

## -----------------------------------------------------------------------------
errMat <- array(0, dim = c(4, 4))
errMat[1, c(2, 3)] <- 1
imps <- smcfcs(simData,
  smtype = "lm", smformula = "y~x",
  method = c("latnorm", "", "", ""), m = 5,
  errorProneMatrix = errMat
)

## -----------------------------------------------------------------------------
impobj <- imputationList(imps$impDatasets)
models <- with(impobj, lm(y ~ x))
summary(MIcombine(models))

## -----------------------------------------------------------------------------
summary(imps$impDatasets[[1]])

## ----fig.width=6--------------------------------------------------------------
imps <- smcfcs(simData,
  smtype = "lm", smformula = "y~x",
  method = c("latnorm", "", "", ""), m = 1, numit = 100,
  errorProneMatrix = errMat
)
plot(imps)

## -----------------------------------------------------------------------------
x <- rnorm(n)
x1 <- x + rnorm(n)
x2 <- x + rnorm(n)
w2[(n * 0.1):n] <- NA
z <- x + rnorm(n)
z1 <- z + 0.1 * rnorm(n)
z2 <- z + 0.1 * rnorm(n)
y <- x - z + rnorm(n)
x <- rep(NA, n)
z <- rep(NA, n)
simData <- data.frame(x, x1, x2, z, z1, z2, y)

errMat <- array(0, dim = c(7, 7))
errMat[1, c(2, 3)] <- 1
errMat[4, c(5, 6)] <- 1
imps <- smcfcs(simData,
  smtype = "lm", smformula = "y~x+z",
  method = c("latnorm", "", "", "latnorm", "", "", ""), m = 5,
  errorProneMatrix = errMat
)

## -----------------------------------------------------------------------------
impobj <- imputationList(imps$impDatasets)
models <- with(impobj, lm(y ~ x + z))
summary(MIcombine(models))