Assume that you have stored the lm object as 'fit' and the summary as fit.summ as such
x <- rnorm(100) y <- rnorm(100) fit <- lm( y ~ x ) fit.summ <- summary( fit ) fit.summ$coefficients and fit.summ$adj.r.squared gives you the coefficients and adjusted R-square. names( fit.summ ) or str( fit.summ ) will give further clue as how fit.summ looks like. Regards, Adai On Thu, 2005-02-17 at 15:12 -0700, Heather Maughan wrote: > Hello: > > I want to use values from the output of linear models done using permuted > data to construct a random distribution. The problem I am having is the > extraction of a value, say the p-value or the regression coefficient, from > the summary of a linear model. When summarizing a linear model I get this: > > Call: > lm(formula = fitness ~ mm) > > Residuals: > Min 1Q Median 3Q Max > -0.57369 -0.17551 -0.01602 0.15723 0.68844 > > Coefficients: > Estimate Std. Error t value Pr(>|t|) > (Intercept) 1.783440 0.074052 24.084 < 2e-16 *** > mm -0.004272 0.001456 -2.933 0.00662 ** > --- > Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1 > > Residual standard error: 0.3261 on 28 degrees of freedom > Multiple R-Squared: 0.2351, Adjusted R-squared: 0.2077 > F-statistic: 8.604 on 1 and 28 DF, p-value: 0.006621 > > How do I pick out the p-value, or the R-squared using R code? > > Thanks, > Heather ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
