Charlie Brush wrote:
Frank E Harrell Jr wrote:
Charlie Brush wrote:
I am doing multiple imputation with Hmisc, and
can't figure out how to replace the NA values with
the imputed values.
Here's a general ourline of the process:
> set.seed(23)
> library("mice")
> library("Hmisc")
> library("Design")
> d <- read.table("DailyDataRaw_01.txt",header=T)
> length(d);length(d[,1])
[1] 43
[1] 2666
Do for this data set, there are 43 columns and 2666 rows
Here is a piece of data.frame d:
> d[1:20,4:6]
P01 P02 P03
1 0.1 0.16 0.16
2 NA 0.00 0.00
3 NA 0.60 0.04
4 NA 0.15 0.00
5 NA 0.00 0.00
6 0.7 0.00 0.75
7 NA 0.00 0.00
8 NA 0.00 0.00
9 0.0 0.00 0.00
10 0.0 0.00 0.00
11 0.0 0.00 0.00
12 0.0 0.00 0.00
13 0.0 0.00 0.00
14 0.0 0.00 0.00
15 0.0 0.00 0.03
16 NA 0.00 0.00
17 NA 0.01 0.00
18 0.0 0.00 0.00
19 0.0 0.00 0.00
20 0.0 0.00 0.00
These are daily precipitation values at NCDC stations, and
NA values at station P01 will be filled using multiple
imputation and data from highly correlated stations P02 and P08:
> f <- aregImpute(~ I(P01) + I(P02) + I(P08),
n.impute=10,match='closest',data=d)
Iteration 13
> fmi <- fit.mult.impute( P01 ~ P02 + P08 , ols, f, d)
Variance Inflation Factors Due to Imputation:
Intercept P02 P08
1.01 1.39 1.16
Rate of Missing Information:
Intercept P02 P08
0.01 0.28 0.14
d.f. for t-distribution for Tests of Single Coefficients:
Intercept P02 P08
242291.18 116.05 454.95
> r <- apply(f$imputed$P01,1,mean)
> r
2 3 4 5 7 8 16 17 249 250 251
0.002 0.430 0.044 0.002 0.002 0.002 0.002 0.123 0.002 0.002 0.002
252 253 254 255 256 257 258 259 260 261 262
1.033 0.529 1.264 0.611 0.002 0.513 0.085 0.002 0.705 0.840 0.719
263 264 265 266 267 268 269 270 271 272 273
1.489 0.532 0.150 0.134 0.002 0.002 0.002 0.002 0.002 0.055 0.135
274 275 276 277 278 279 280 281 282 283 284
0.009 0.002 0.002 0.002 0.008 0.454 1.676 1.462 0.071 0.002 1.029
285 286 287 288 289 418 419 420 421 422 700
0.055 0.384 0.947 0.002 0.002 0.008 0.759 0.066 0.009 0.002 0.002
------------------------------------------------------------------
So far, this is working great.
Now, make a copy of d:
> dnew <- d
And then fill in the NA values in P01 with the values in r
For example:
> for (i in 1:length(r)){
dnew$P01[r[i,1]] <- r[i,2]
}
This doesn't work, because each 'piece' of r is two numbers:
> r[1]
2
0.002
> r[1,1]
Error in r[1, 1] : incorrect number of dimensions
My question: how can I separate the the two items in (for example)
r[1] to use the first part as an index and the second as a value,
and then use them to replace the NA values with the imputed values?
Or is there a better way to replace the NA values with the imputed
values?
Thanks in advance for any help.
You didn't state your goal, and why fit.mult.impute does not do what
you want. But you can look inside fit.mult.impute to see how it
retrieves the imputed values. Also see the example in documentation
for transcan in which the command impute(xt, imputation=1) to retrieve
one of the multiple imputations.
Note that you can say library(Design) (omit the quotes) to access both
Design and Hmisc.
Frank
Thanks for your help.
My goal is to replace the NA values in the (copy of the) data frame with
the means of the imputed values (which are now in variable 'r').
fit.mult.impute works fine. I just can't figure out the last step,
taking the results of fit.mult.impute (which are in variable 'r') and
replacing the NA values in the (copy of the) data frame.
A simple for loop doesn't work because the items in 'r' don't look like
a normal vector, as for example r[1] returns
2
0.002
Is there a command to replace the NA values in the data frame with the
means of the imputed values?
Thanks,
Charlie
Don't do that, as this would no longer be multiple imputation. If you
want single conditional mean imputation use transcan.
Frank
--
Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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