>
> <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.weyla...@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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