[R] Fitting linear models

[Dimitri Liakhovitski]

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 <avemuri at 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:ld7631 at gmail.com]
> Sent: Tuesday, April 21, 2009 9:02 AM
> To: Vemuri, Aparna
> Cc: r-help at 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 <avemuri at 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:dwinsemius at comcast.net]
>> Sent: Tuesday, April 21, 2009 8:38 AM
>> To: Vemuri, Aparna
>> Cc: r-help at 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.Liakhovitski at markettools.com
>



-- 
Dimitri Liakhovitski
MarketTools, Inc.
Dimitri.Liakhovitski at markettools.com