[R] Interpreting Multiple Linear Regression Summary
djnordlund at frontier.com
Wed Nov 9 18:37:18 CET 2011
> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
> On Behalf Of Rich Shepard
> Sent: Wednesday, November 09, 2011 9:05 AM
> To: r-help at r-project.org
> Subject: [R] Interpreting Multiple Linear Regression Summary
> I would appreciate pointers on what I should read to understand this
> summary(lm(TDS ~ Cond + Ca + Cl + Mg + Na + SO4))
> lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4)
> ALL 1 residuals are 0: no residual degrees of freedom!
> Coefficients: (6 not defined because of singularities)
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) 125 NA NA NA
> Cond NA NA NA NA
> Ca NA NA NA NA
> Cl NA NA NA NA
> Mg NA NA NA NA
> Na NA NA NA NA
> SO4 NA NA NA NA
> Residual standard error: NaN on 0 degrees of freedom
> (63 observations deleted due to missingness)
> When I look at the summary for the data frame used for this model I do
> see an excessive number of missing values or indications why there are no
> residual degrees of freedom. The same model applied to 8 other data frames
> did not produce similar results.
I don't see a 'data=' parameter in your call to lm(). How does lm() know where to find the variables referenced in the model parameter?
If that is not the problem, then we need to see str() output for the data frame that you are analyzing.
Bothell, WA USA
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