Dear Something,
Ah, you need accessor functions. Set your lm() call to a variable name. If my data frame or object is called dat, for example, I usually use dat.lm. summary(dat.lm) resid(dat.lm) ?summary.lm gives a host of information, including summary(dat.lm)$sigma and $df for resolving SSE. Alternatively, sum(c(resid(dat.lm)^2)). See anova() as well. Also, vif() [car] for the variance inflation factor (e.g. tolerance=1/vif) Hope this helps. Sincerely, KeithC. From: Something Something [mailto:mailinglist...@gmail.com] Sent: Sunday, February 14, 2010 11:49 PM To: kMan Subject: Re: [R] lm function in R Thank you so much, kMan. That makes sense. Only one question, how can I see the value of 'error'? Here's what I see: Call: lm(formula = Y ~ X1 * X2 * X3, na.action = na.exclude) Coefficients: (Intercept) X1 X2 X3 X1:X2 X1:X3 X2:X3 X1:X2:X3 177.9245 0.2005 2.4482 3.1216 0.8127 -26.6166 -3.0398 29.6049 I don't see 'error' here. Please explain. Thanks again for your help. On Sun, Feb 14, 2010 at 3:26 PM, kMan <kchambe...@gmail.com> wrote: Dear Something, Tricky to differentiate subscripts vs. different variables in plain text, so I'll just use 2, X and Z. Y~X+Z yields the model Y=b0 + b1*X + b2*Z + error Y~X*Z yields the model Y=b0 + b1*X + b2*Z + b3*X*Z + error |b3 is the interaction term. Y~X+Z+I(X*Z) is the same thing but with the interaction term spelled out. See ?I XZint<-X*Z Y~X+Z+XZint same thing but avoids the call to I() in the model. Note that: Y~a*a or Y~a^2 does not evaluate an interaction, hence the use of I(). Sincerely, KeithC. -----Original Message----- From: Something Something [mailto:mailinglist...@gmail.com] Sent: Saturday, February 13, 2010 2:24 PM To: Daniel Nordlund Cc: r-help@r-project.org Subject: Re: [R] lm function in R Thanks Dan. Yes that was very helpful. I didn't see the change from '*' to '+'. Seems like when I put * it means - interaction & when I put + it's not an interaction. Is it correct to assume then that... When I put + R evaluates the following equation: Y-Hat = b0 + b1X1 + b2X2 + . . . bkXk + . . . + bkXk But when I put * R evaluates the following equation; Y-Hat = b0 + b1X1 + b2x2 + ... + bkXk + b12 X12+ b13 X13 +........ + c Is this correct? If it is then can someone point me to any sources that will explain how the coefficients (such as b0... bk, b12.. , b123..) are calculated. I guess, one source is the R source code :) but is there any other documentation anywhere? Please let me know. Thanks. On Fri, Feb 12, 2010 at 5:54 PM, Daniel Nordlund <djnordl...@verizon.net>wrote: > > -----Original Message----- > > From: r-help-boun...@r-project.org > > [mailto:r-help-boun...@r-project.org] > > On Behalf Of Something Something > > Sent: Friday, February 12, 2010 5:28 PM > > To: Phil Spector; r-help@r-project.org > > Subject: Re: [R] lm function in R > > > > Thanks for the replies everyone. Greatly appreciate it. Some > > progress, but now I am getting the following values when I don't use > > "as.factor" > > > > 13.14167 25.11667 28.34167 49.14167 40.39167 66.86667 > > > > Is that what you guys get? > > > If you look at Phil's response below, no, that is not what he got. > The difference is that you are specifying an interaction, whereas Phil > did not (because the equation you initially specified did not include > an interaction. Use Y ~ X1 + X2 instead of Y ~ X1*X2 for your formula. > > > > > > > On Fri, Feb 12, 2010 at 5:00 PM, Phil Spector > > <spec...@stat.berkeley.edu>wrote: > > > > > By converting the two variables to factors, you are fitting an > > > entirely different model. Leave out the as.factor stuff and it > > > will work exactly as you want it to. > > > > > > dat > > >> > > > V1 V2 V3 V4 > > > 1 s1 14 4 1 > > > 2 s2 23 4 2 > > > 3 s3 30 7 2 > > > 4 s4 50 7 4 > > > 5 s5 39 10 3 > > > 6 s6 67 10 6 > > > > > >> names(dat) = c('id','y','x1','x2') z = lm(y~x1+x2,dat) > > >> predict(z) > > >> > > > 1 2 3 4 5 6 15.16667 24.66667 > > > 27.66667 46.66667 40.16667 68.66667 > > > > > > > > > - Phil Spector > > > Statistical Computing Facility > > > Department of Statistics > > > UC Berkeley > > > spec...@stat.berkeley.edu > > > > > > > > > > > > On Fri, 12 Feb 2010, Something Something wrote: > > > > > > Hello, > > >> > > >> I am trying to learn how to perform Multiple Regression Analysis in R. > > I > > >> decided to take a simple example given in this PDF: > > >> http://www.utdallas.edu/~herve/abdi-prc-pretty.pdf > > >> > > >> I created a small CSV called, students.csv that contains the > > >> following > > >> data: > > >> > > >> s1 14 4 1 > > >> s2 23 4 2 > > >> s3 30 7 2 > > >> s4 50 7 4 > > >> s5 39 10 3 > > >> s6 67 10 6 > > >> > > >> Col headers: Student id, Memory span(Y), age(X1), speech > > >> rate(X2) > > >> > > >> Now the expected results are: > > >> > > >> yHat[0]:15.166666666666668 > > >> yHat[1]:24.666666666666668 > > >> yHat[2]:27.666666666666664 > > >> yHat[3]:46.666666666666664 > > >> yHat[4]:40.166666666666664 > > >> yHat[5]:68.66666666666667 > > >> > > >> This is based on the following equation (given in the PDF): Y = > > >> 1.67 > + > > X1 > > >> + > > >> 9.50 X2 > > >> > > >> I ran the following commands in R: > > >> > > >> data = read.table("students.csv", head=F, as.is=T, na.string=".", > > >> row.nam=NULL) > > >> X1 = as.factor(data[[3]]) > > >> X2 = as.factor(data[[4]]) > > >> Y = data[[2]] > > >> mod = lm(Y ~ X1*X2, na.action = na.exclude) Y.hat = fitted(mod) > > >> Y.hat > > >> > > >> This gives me the following output: > > >> > > >> Y.hat > > >>> > > >> 1 2 3 4 5 6 > > >> 14 23 30 50 39 67 > > >> > > >> Obviously I am doing something wrong. Please help. Thanks. > > >> > > Hope this is helpful, > > Dan > > Daniel Nordlund > Bothell, WA USA > > > ______________________________________________ > 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. > [[alternative HTML version deleted]] [[alternative HTML version deleted]] ______________________________________________ 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.
