[R] fit.mult.impute() in Hmisc

[Frank Harrell]

For your approach how do you know that either summary or vcov used multiple
imputation?  You are using a non-rms fitting function so be careful. 
Compare with using the lrm fitting function.  Also repace Design with the
rms package.

Please omit confidentiality notices from your e-mails.

Frank


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 })
> kp <- data.frame(kp)
> kp$kyp <- kp$Kyphosis == "present"
> 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: 66 
> sqrt(diag(vcov(f)))
(Intercept)         Age       Start      Number 
  1.5444782   0.0063984   0.0652068   0.2454408 
> -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

 

-----
Frank Harrell
Department of Biostatistics, Vanderbilt University
--
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