Hello. Consider the response-variable of data.frame df is constant, so analytically perfect fit of a linear model is expected. Fitting a regression line using lm result in residuals, slope and std.errors not exactly zero, which is acceptable in some way, but errorneous. But if you use summary.lm it shows inacceptable error propagation in the calculation of the t value and the corresponding p-value for the slope, as well R-Square just consider the adj R-Square of 0.6788! This result is independent of which mode used for the input vectors. Is there any way to get the perfect fitted regression curve using lm and prevent this error propagation? I consider rounding all values of the lm-object afterwards to somewhat precision as a bad idea. Unfortunately there is no option in lm for calculation precision.
> df<-data.frame(x=1:10,y=1) > myl<-lm(y~x,data=df) > myl Call: lm(formula = y ~ x, data = df) Coefficients: (Intercept) x 1.000e+00 9.463e-18 > summary(myl) Call: lm(formula = y ~ x, data = df) Residuals: Min 1Q Median 3Q Max -1.136e-16 -1.341e-17 7.886e-18 2.918e-17 5.047e-17 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.000e+00 3.390e-17 2.950e+16 <2e-16 *** x 9.463e-18 5.463e-18 1.732e+00 0.122 --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 4.962e-17 on 8 degrees of freedom Multiple R-squared: 0.7145, Adjusted R-squared: 0.6788 F-statistic: 20.02 on 1 and 8 DF, p-value: 0.002071 [[alternative HTML version deleted]]
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