>
> Use `lm` the way it is designed to be used, with a data argument:
>
> > l2 <- lm(e~. , data=as.data.frame(M))
> > summary(l2)
>
> Call:
> lm(formula = e ~ ., data = as.data.frame(M))
>
>
And what is the regression being done in this case? How are the
independent variables used?
It looks like M[,5]~M[,1]+M[,2]+M[,3]+M[,4] as those are the
coefficients. But the results are different when I do that explicitly:
> M <- matrix(runif(5*20), nrow=20)
> colnames(M) <- c('a', 'b', 'c', 'd', 'e')
> l1 <- lm(df[,'e']~., data=df)
> summary(l1)
Call:
lm(formula = df[, "e"] ~ ., data = df)
Residuals:
Min 1Q Median 3Q Max
-9.580e-17 -3.360e-17 -8.596e-18 9.114e-18 2.032e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.505e-17 7.158e-17 -1.048e+00 0.312
a -1.653e-17 7.117e-17 -2.320e-01 0.820
b -5.042e-17 5.480e-17 -9.200e-01 0.373
c 4.236e-17 5.774e-17 7.340e-01 0.475
d -3.878e-17 4.946e-17 -7.840e-01 0.446
e 1.000e+00 6.083e-17 1.644e+16 <2e-16 ***
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 6.763e-17 on 14 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 6.435e+31 on 5 and 14 DF, p-value: < 2.2e-16
> 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
-0.49398 -0.14203 0.01588 0.14157 0.31335
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.6681 0.1859 3.594 0.00266 **
M[, 1] -0.1767 0.2419 -0.730 0.47644
M[, 2] -0.3874 0.2135 -1.814 0.08970 .
M[, 3] 0.3695 0.2180 1.695 0.11078
M[, 4] 0.1361 0.2366 0.575 0.57360
---
Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1
Residual standard error: 0.2449 on 15 degrees of freedom
Multiple R-squared: 0.2988, Adjusted R-squared: 0.1119
F-statistic: 1.598 on 4 and 15 DF, p-value: 0.2261
cheers
Worik
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