>
> <Sigh>  Please note that your "df" and "M" are undoubtedly different
> objects by now:
>
> Right.  Not my most coherent day.

thanks
W



> > M <- matrix(runif(5*20), nrow=20)
> > colnames(M) <- c('a', 'b', 'c', 'd', 'e')
> > l1 <- lm(e~., data=as.data.frame(M))
> > l1
>
> Call:
> lm(formula = e ~ ., data = as.data.frame(M))
>
> Coefficients:
> (Intercept)            a            b            c            d
>    0.40139     -0.15032     -0.06242      0.13139      0.23905
>
>
> > l3 <- lm(M[,5]~M[,1]+M[,2]+M[,3]+M[,**4])
> > l3
>
>
> Call:
> lm(formula = M[, 5] ~ M[, 1] + M[, 2] + M[, 3] + M[, 4])
>
> Coefficients:
> (Intercept)       M[, 1]       M[, 2]       M[, 3]       M[, 4]
>    0.40139     -0.15032     -0.06242      0.13139      0.23905
>
> As expected.
>
> --
> David.
>
>
>> On Sat, Dec 3, 2011 at 5:10 PM, R. Michael Weylandt <
>> michael.weylandt@gmail.com> wrote:
>>
>>  In your code by supplying a vector M[,"e"] you are regressing "e"
>>> against all the variables provided in the data argument, including "e"
>>> itself -- this gives the very strange regression coefficients you
>>> observe. R has no way to know that that's somehow related to the "e"
>>> it sees in the data argument.
>>>
>>>
>>  In the suggested way,
>>>
>>> lm(formula = e ~ ., data = as.data.frame(M))
>>>
>>> e is regressed against everything that is not e and sensible results are
>>> given.
>>>
>>>
>> But still 'l1 <- lm(e~., data=df)' is not the same as 'l3 <-
>> lm(M[,5]~M[,1]+M[,2]+M[,3]+M[,**4])'
>>
>>  M <- matrix(runif(5*20), nrow=20)
>>> colnames(M) <- c('a', 'b', 'c', 'd', 'e')
>>> l1 <- lm(e~., data=df)
>>> summary(l1)
>>>
>>
>> Call:
>> lm(formula = e ~ ., data = df)
>>
>> Residuals:
>>    Min       1Q   Median       3Q      Max
>> -0.38343 -0.21367  0.03067  0.13757  0.49080
>>
>> Coefficients:
>>           Estimate Std. Error t value Pr(>|t|)
>> (Intercept)  0.28521    0.29477   0.968    0.349
>> a            0.09283    0.30112   0.308    0.762
>> b            0.23921    0.22425   1.067    0.303
>> c           -0.16027    0.24154  -0.664    0.517
>> d            0.24025    0.20054   1.198    0.250
>>
>> Residual standard error: 0.2871 on 15 degrees of freedom
>> Multiple R-squared: 0.1602,    Adjusted R-squared: -0.06375
>> F-statistic: 0.7153 on 4 and 15 DF,  p-value: 0.5943
>>
>>  l3 <- lm(M[,5]~M[,1]+M[,2]+M[,3]+M[,**4])
>>> summary(l3)
>>>
>>
>> Call:
>> lm(formula = M[, 5] ~ M[, 1] + M[, 2] + M[, 3] + M[, 4])
>>
>> Residuals:
>>    Min       1Q   Median       3Q      Max
>> -0.36355 -0.22679 -0.01202  0.18462  0.37377
>>
>> Coefficients:
>>           Estimate Std. Error t value Pr(>|t|)
>> (Intercept)  0.76972    0.24501   3.142  0.00672 **
>> M[, 1]      -0.23830    0.24123  -0.988  0.33890
>> M[, 2]      -0.02046    0.21958  -0.093  0.92699
>> M[, 3]      -0.29518    0.22559  -1.308  0.21040
>> M[, 4]      -0.31545    0.24570  -1.284  0.21866
>> ---
>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>> Residual standard error: 0.2668 on 15 degrees of freedom
>> Multiple R-squared: 0.2762,    Adjusted R-squared: 0.08317
>> F-statistic: 1.431 on 4 and 15 DF,  p-value: 0.272
>>
>>
>>>
>>        [[alternative HTML version deleted]]
>>
>> ______________________________**________________
>> R-help@r-project.org mailing list
>> https://stat.ethz.ch/mailman/**listinfo/r-help<https://stat.ethz.ch/mailman/listinfo/r-help>
>> PLEASE do read the posting guide http://www.R-project.org/**
>> posting-guide.html <http://www.R-project.org/posting-guide.html>
>> and provide commented, minimal, self-contained, reproducible code.
>>
>
> David Winsemius, MD
> West Hartford, CT
>
>

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