averageMI {mirt}

R Documentation

Collapse values from multiple imputation draws

Description

This function computes updated parameter and standard error estimates using multiple imputation methodology. Given a set of parameter estimates and their associated standard errors the function returns the weighted average of the overall between and within variability due to the multiple imputations according to Rubin's (1987) methodology.

Usage

averageMI(par, SEpar, as.data.frame = TRUE)

Arguments

par	a list containing parameter estimates which were computed the imputed datasets
SEpar	a list containing standard errors associated with par
as.data.frame	logical; return a data.frame instead of a list? Default is TRUE

Value

returns a list or data.frame containing the updated averaged parameter estimates, standard errors, and t-values with the associated degrees of freedom and two tailed p-values

Author(s)

Phil Chalmers rphilip.chalmers@gmail.com

References

Chalmers, R., P. (2012). mirt: A Multidimensional Item Response Theory Package for the R Environment. Journal of Statistical Software, 48(6), 1-29. doi:10.18637/jss.v048.i06

Rubin, D.B. (1987) Multiple Imputation for Nonresponse in Surveys. Wiley & Sons, New York.

Examples

## No test: 

# simulate data
set.seed(1234)
N <- 1000

# covariates
X1 <- rnorm(N); X2 <- rnorm(N)
covdata <- data.frame(X1, X2)
Theta <- matrix(0.5 * X1 + -1 * X2 + rnorm(N, sd = 0.5))

# items and response data
a <- matrix(1, 20); d <- matrix(rnorm(20))
dat <- simdata(a, d, 1000, itemtype = '2PL', Theta=Theta)

mod1 <- mirt(dat, 1, 'Rasch', covdata=covdata, formula = ~ X1 + X2)
coef(mod1, simplify=TRUE)

## $items
##         a1      d g u
## Item_1   1 -0.409 0 1
## Item_2   1  0.491 0 1
## Item_3   1  0.313 0 1
## Item_4   1  1.965 0 1
## Item_5   1  1.753 0 1
## Item_6   1 -0.246 0 1
## Item_7   1 -1.077 0 1
## Item_8   1  0.533 0 1
## Item_9   1 -1.232 0 1
## Item_10  1  0.603 0 1
## Item_11  1 -0.404 0 1
## Item_12  1  1.238 0 1
## Item_13  1  1.033 0 1
## Item_14  1  1.524 0 1
## Item_15  1 -0.548 0 1
## Item_16  1  2.075 0 1
## Item_17  1 -0.695 0 1
## Item_18  1 -1.200 0 1
## Item_19  1  0.121 0 1
## Item_20  1  0.523 0 1
## 
## $means
## F1 
##  0 
## 
## $cov
##       F1
## F1 0.215
## 
## $lr.betas
##                 F1
## (Intercept)  0.000
## X1           0.527
## X2          -1.036

# draw plausible values for secondary analyses
pv <- fscores(mod1, plausible.draws = 10)
pvmods <- lapply(pv, function(x, covdata) lm(x ~ covdata$X1 + covdata$X2),
                 covdata=covdata)

# compute Rubin's multiple imputation average
so <- lapply(pvmods, summary)
par <- lapply(so, function(x) x$coefficients[, 'Estimate'])
SEpar <- lapply(so, function(x) x$coefficients[, 'Std. Error'])
averageMI(par, SEpar)

##                par SEpar       t      df     p
## (Intercept)  0.001 0.017   0.057 126.225 0.239
## covdata$X1   0.525 0.021  25.078  36.818     0
## covdata$X2  -1.034 0.019 -53.308  58.854     0

## End(No test)

---