R help - Feb 2010 - "1 observation deleted due to missingness" from summary() on the result of aov()

I have the R code at the end. The last command gives me "1 observation
deleted due to missingness". I don't understand what this error
message. Could somebody help me understand it and how to fix the
problem?
> summary(afit)
             Df  Sum Sq Mean Sq F value  Pr(>F)
A             2   0.328 0.16382  0.1899 0.82727
B             3   2.882 0.96057  1.1136 0.34644
C             4   1.676 0.41897  0.4857 0.74617
A:B           6   5.133 0.85554  0.9919 0.43404
A:C           8  14.193 1.77406  2.0568 0.04543 *
B:C          12   7.244 0.60366  0.6999 0.74896
A:B:C        24  19.849 0.82705  0.9589 0.52442
Residuals   119 102.642 0.86254
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
1 observation deleted due to missingness


#######################
a=3
b=4
c=5

A=1:a
B=1:b
C=1:c

n=3

X=matrix(nr=a*b*c*n,nc=n)
colnames(X)=LETTERS[1:n]

for(i_a in 1:a-1) {
  for(i_b in 1:b-1) {
    for(i_c in 1:c-1) {
      for(i_n in 1:n-1) {
        X[((i_a * b + i_b) * c + i_c) * n + i_n + 1, ] = c(i_a+1, i_b+1, i_c+1)
      }
    }
  }
}

set.seed(0)
Y=matrix(nr=a*b*c*n,nc=1)
for(i in 1:(a*b*c)) {
  for(i_n in 1:n-1) {
    fa=X[i,'A']
    fb=X[i,'B']
    fc=X[i,'C']

    #Y[(i-1)*n+i_n,1]= fa +fb +fc +fa*fb +fa*fc +fb*fc +fa*fb*fc + rnorm(1)
    Y[(i-1)*n+i_n,1]= rnorm(1)
  }
}

aframe = data.frame(
  A=as.factor(X[,'A'])
  , B=as.factor(X[,'B'])
  , C=as.factor(X[,'C'])
  , Y)

afit=aov(Y ~ A * B * C, aframe)

summary(afit)

On Feb 8, 2010, at 10:28 PM, bluesky315 at gmail.com wrote:
> I have the R code at the end. The last command gives me "1 observation
> deleted due to missingness". I don't understand what this error
> message. Could somebody help me understand it and how to fix the
> problem?
>
>> summary(afit)
>             Df  Sum Sq Mean Sq F value  Pr(>F)
> A             2   0.328 0.16382  0.1899 0.82727
> B             3   2.882 0.96057  1.1136 0.34644
> C             4   1.676 0.41897  0.4857 0.74617
> A:B           6   5.133 0.85554  0.9919 0.43404
> A:C           8  14.193 1.77406  2.0568 0.04543 *
> B:C          12   7.244 0.60366  0.6999 0.74896
> A:B:C        24  19.849 0.82705  0.9589 0.52442
> Residuals   119 102.642 0.86254
> ---
> Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
> 1 observation deleted due to missingness
>
>
> #######################
> a=3
> b=4
> c=5
>
> A=1:a
> B=1:b
> C=1:c
>
> n=3
>
> X=matrix(nr=a*b*c*n,nc=n)
> colnames(X)=LETTERS[1:n]
>
> for(i_a in 1:a-1) {
>  for(i_b in 1:b-1) {
>    for(i_c in 1:c-1) {
>      for(i_n in 1:n-1) {
>        X[((i_a * b + i_b) * c + i_c) * n + i_n + 1, ] = c(i_a+1, i_b 
> +1, i_c+1)
>      }
>    }
>  }
> }
which(is.na(aframe$Y)  )
[1] 180

>
> set.seed(0)
> Y=matrix(nr=a*b*c*n,nc=1)
> for(i in 1:(a*b*c)) {
>  for(i_n in 1:n-1) {
>    fa=X[i,'A']
>    fb=X[i,'B']
>    fc=X[i,'C']
>
>    #Y[(i-1)*n+i_n,1]= fa +fb +fc +fa*fb +fa*fc +fb*fc +fa*fb*fc +  
> rnorm(1)
>    Y[(i-1)*n+i_n,1]= rnorm(1)
>  }
> }
>
> aframe = data.frame(
>  A=as.factor(X[,'A'])
>  , B=as.factor(X[,'B'])
>  , C=as.factor(X[,'C'])
>  , Y)
>
> afit=aov(Y ~ A * B * C, aframe)
>
> summary(afit)
>
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David Winsemius, MD
Heritage Laboratories
West Hartford, CT