I have just started using R so forgive me if this question is very simple. I have a data set (in a data frame called dm) that looks like this
x (Cells) y(males) 1 0 2 2 3 7 4 12 5 12 6 19 7 22 8 23 9 25 10 23 11 23 12 11 13 8 14 3 15 0 16 0 I centered the dependent and predictor variables and then squared the predictor to make a quadratic variable. This left me with 3 variables: MalesC CellsC CellsC2 I then used: >quadraticModel <- lm(MalesC ~ CellsC + CellsC2, data = dm) This has given me R^2= 0.8821, F= 48.63, and p<0.001 I ran the exact same data in sigma plot and got identical results. My problem comes from the estimated coefficients I am getting in R when using >summary(quadraticModel). My coefficient estimates in sigma plot fit my data set well and agree with Microsoft excels estimates. The results from R appear to be the coefficients of a curve fitting the residuals. If I >plot(quadraticModel) it does not draw a curve fitting my data, but rather the residuals. What I would like to know is how I can get the coefficient estimates for the data rather than the residuals, and how to plot that in R. Thanks, Matthew McKinney ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
