From: Yuelin Li <liy12_at_mskcc.org>

Date: Thu, 31 Mar 2011 15:56:27 -0400

> library(Hmisc)

Loading required package: survival

Loading required package: splines

> data(kyphosis, package = "rpart")

> kp <- lapply(kyphosis, function(x)

+ { is.na(x) <- sample(1:length(x), size = 10); x })

*> kp <- data.frame(kp)
*

*> kp$kyp <- kp$Kyphosis == "present"
*

> set.seed(7)

> imp <- aregImpute( ~ kyp + Age + Start + Number, dat = kp, n.impute = 10,

> f <- fit.mult.impute(kyp ~ Age + Start + Number, fitter=glm, xtrans=imp,

> -0.1979/0.0652068

[1] -3.0350

> summary(f)

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Thu 31 Mar 2011 - 19:59:15 GMT

Date: Thu, 31 Mar 2011 15:56:27 -0400

I tried multiple imputation with aregImpute() and
fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1.

The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored.

"Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use vcov(fit) to get the correct covariance matrix, sqrt(diag(vcov(fit))) to get s.e."

But the standard errors in summary(f) agree with the values from sqrt(diag(vcov(f))) to the 4th decimal point. It would seem that summary(f) actually adjusts for multiple imputation?

Does summary(f) in Hmisc 3.8-3 actually adjust for MI?

If it does not adjust for MI, then how do I get the MI-adjusted coefficients and standard errors?

I can't seem to find answers in the documentations, including rereading section 8.10 of the Harrell (2001) book Googling located a thread in R-help back in 2003, which seemed dated. Many thanks in advance for the help,

Yuelin.

http://idecide.mskcc.org

> library(Hmisc)

Loading required package: survival

Loading required package: splines

> data(kyphosis, package = "rpart")

> kp <- lapply(kyphosis, function(x)

+ { is.na(x) <- sample(1:length(x), size = 10); x })

> set.seed(7)

> imp <- aregImpute( ~ kyp + Age + Start + Number, dat = kp, n.impute = 10,

+ type = "pmm", match = "closest")Iteration 13

> f <- fit.mult.impute(kyp ~ Age + Start + Number, fitter=glm, xtrans=imp,

+ family = "binomial", data = kp)

Variance Inflation Factors Due to Imputation:

(Intercept) Age Start Number 1.06 1.28 1.17 1.12

Rate of Missing Information:

(Intercept) Age Start Number 0.06 0.22 0.14 0.10

d.f. for t-distribution for Tests of Single Coefficients:

(Intercept) Age Start Number 2533.47 193.45 435.79 830.08

The following fit components were averaged over the 10 model fits:

fitted.values linear.predictors

Warning message:

In fit.mult.impute(kyp ~ Age + Start + Number, fitter = glm, xtrans = imp, :
Not using a Design fitting function; summary(fit) will use
standard errors, t, P from last imputation only. Use vcov(fit) to get the
correct covariance matrix, sqrt(diag(vcov(fit))) to get s.e.

*> f
*

Call: fitter(formula = formula, family = "binomial", data = completed.data)

Coefficients:

(Intercept) Age Start Number -3.6971 0.0118 -0.1979 0.6937 Degrees of Freedom: 80 Total (i.e. Null); 77 Residual Null Deviance: 80.5 Residual Deviance: 58 AIC: 661.5444782 0.0063984 0.0652068 0.2454408

> sqrt(diag(vcov(f)))

(Intercept) Age Start Number

> -0.1979/0.0652068

[1] -3.0350

> summary(f)

Call:

fitter(formula = formula, family = "binomial", data = completed.data)

Deviance Residuals:

Min 1Q Median 3Q Max -1.240 -0.618 -0.288 -0.109 2.409

Coefficients:

Estimate Std. Error z value Pr(>|z|) (Intercept) -3.6971 1.5445 -2.39 0.0167 Age 0.0118 0.0064 1.85 0.0649 Start -0.1979 0.0652 -3.03 0.0024 Number 0.6937 0.2454 2.83 0.0047

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 80.508 on 80 degrees of freedom
Residual deviance: 57.965 on 77 degrees of freedom
**AIC: 65.97
**
Number of Fisher Scoring iterations: 5

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Thu 31 Mar 2011 - 19:59:15 GMT

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