But if the multicollinearity is so strong, then I am wondering why it worked in the data frame as opposed to 4 seprate vectors? It should not make any difference... Dimitri
On Tue, Apr 21, 2009 at 12:21 PM, Vemuri, Aparna <avem...@epri.com> wrote: > Thanks Dimitri! Following exactly what you did, I wrote all my individual > variable vectors to a data frame and used lm(formula,data) and this time it > works for me too. > > Marc, your theory is correct.NH4 variable shares a strong correlation with > one of the IV along with the DV. > SO4 NO3 NH4 PBW > SO4 1 -0.0867 0.999 0.999 > NO3 -0.0867 1 -0.0527 -0.0938 > NH4 0.999 -0.0527 1 0.999 > PBW 0.999 -0.0938 0.999 1 > > > Aparna > > -----Original Message----- > From: Dimitri Liakhovitski [mailto:ld7...@gmail.com] > Sent: Tuesday, April 21, 2009 9:02 AM > To: Vemuri, Aparna > Cc: r-help@r-project.org; David Winsemius > Subject: Re: [R] Fitting linear models > > I am not sure what the problem is. > I found no errors: > > data<-read.csv(file.choose()) # I had to change your file extension > to .csv first > dim(data) > names(data) > > lapply(data,function(x){sum(is.na(x))}) > lm.model.1<-lm(PBW~SO4+NO3+NH4,data) > lm.model.2<-lm(PBW~SO4+NH4+NO3,data) > print(lm.model.1) # Getting nice results > print(lm.model.2) # Getting same results > > # Another method (gets exactly the same results): > library(Design) > ols.model.1<-ols(PBW~SO4+NO3+NH4,data) > ols.model.2<-ols(PBW~SO4+NH4+NO3,data) > > Dimitri > On Tue, Apr 21, 2009 at 11:50 AM, Vemuri, Aparna <avem...@epri.com> wrote: >> Attached are the first hundred rows of my data in comma separated format. >> Forcing the regression line through the origin, still does not give a >> coefficient on the last independent variable. Also, I don't mind if there is >> a coefficient on the dependent axis. I just want all of the variables to >> have coefficients in the regression equation or a at least a consistent >> result, irrespective of the order of input information. >> >> -----Original Message----- >> From: David Winsemius [mailto:dwinsem...@comcast.net] >> Sent: Tuesday, April 21, 2009 8:38 AM >> To: Vemuri, Aparna >> Cc: r-help@r-project.org >> Subject: Re: [R] Fitting linear models >> >> >> On Apr 21, 2009, at 11:12 AM, Vemuri, Aparna wrote: >> >>> David, >>> Thanks for the suggestions. No, I did not label my dependent >>> variable "function". >> >> That was from my error in reading your call to lm. In my defense I am >> reasonably sure the proper assignment to arguments is lm(formula= ...) >> rather than lm(function= ...). >>> >>> >>> My dependent variable PBW and all the independent variables are >>> continuous variables. It is especially troubling since the order in >>> which I input independent variables determines whether or not it >>> gets a coefficient. Like I already mentioned, I checked the >>> correlation matrix and picked the variables with moderate to high >>> correlation with the independent variable. . So I guess it is not so >>> naïve to expect a regression coefficient on all of them. >>> >>> Dimitri >>> model1<-lm(PBW~SO4+NO3+NH4), gives me the same result as before. >> >> Did you get the expected results with; >> model1<-lm(formula=PBW~SO4+NO3+NH4+0) >> >> You could, of course, provide either the data or the results of str() >> applied to each of the variables and then we could all stop guessing. >> >>> >>> Aparna >>> >>>> >>>> >>>> I am using the lm() function in R to fit a dependent variable to a >>>> set >>>> of 3 to 5 independent variables. For this, I used the following >>>> commands: >>>> >>>>> model1<-lm(function=PBW~SO4+NO3+NH4) >>>> Coefficients: >>>> (Intercept) SO4 NO3 NH4 >>>> 0.01323 0.01968 0.01856 NA >>>> >>>> and >>>> >>>>> model2<-lm(function=PBW~SO4+NO3+NH4+Na+Cl) >>>> >>>> Coefficients: >>>> (Intercept) SO4 NO3 NH4 >>>> Na Cl >>>> -0.0006987 -0.0119750 -0.0295042 0.0842989 0.1344751 >>>> NA >>>> >>>> In both cases, the last independent variable has a coefficient of NA >>>> in >>>> the result. I say last variable because, when I change the order of >>>> the >>>> variables, the coefficient changes (see below). Can anyone point me >>>> to >>>> the reason R behaves this way? Is there anyway for me to force R to >>>> use >>>> all the variables? I checked the correlation matrices to makes sure >>>> there is no orthogonality between the variables. >>> >>> You really did not name your dependent variable "function" did you? >>> Please stop that. >>> >>> Just a guess, ... since you have not provided enough information to do >>> otherwise, ... Are all of those variables 1/0 dummy variables? If so >>> and if you want to have an output that satisfies your need for >>> labeling the coefficients as you naively anticipate, then put "0+" at >>> the beginning of the formula or "-1" at the end, so that the intercept >>> will disappear and then all variables will get labeled as you expect. >> -- >> David Winsemius, MD >> Heritage Laboratories >> West Hartford, CT >> >> > > > > -- > Dimitri Liakhovitski > MarketTools, Inc. > dimitri.liakhovit...@markettools.com > -- Dimitri Liakhovitski MarketTools, Inc. dimitri.liakhovit...@markettools.com ______________________________________________ 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.
