[R] About the covariant

[David Winsemius]

On Jun 28, 2011, at 12:57 AM, Lao Meng wrote:

> Thanks David for your reply.
> You said "a single slope and intercept are estimated for each  
> variable".Actually I can only get one intercept no matter how many

Sorry. You are right. You get individual slopes (and differences for  
factors) reference to a single intercept (unless you use different  
formula specification)

>
> variables exist,but a slope for each variable.
>
> Since the regression is done via:lm(CD4 ~ time + gender + income)
> It seems that the explanatory variable(time) and the two  
> covariants(gender,income) are treated in the same way,but I think  
> explanatory variable and covariant should be treated differently

I do not understand what you are saying when you use the word  
'differently' and increasing the number of times you say it is not  
improving communication.

> although I don't know how to do it.
> Also,they are not both numeric,if gender are F(Female) and  
> M(Male),and income are L(Low),M(median),H(High).

Yes. discrete, unordered factors can have associated estimated  
effects, which will be differences from the intercept level. The  
intercept in you case would probably be Female/High, since the default  
ordering of factor levels is alphabetic. How are these multiple  
question arising? Are you in the middle of an introductory regression  
class?

-- 
David
>
>
>
> 2011/6/28 David Winsemius <dwinsemius at comcast.net>
>
> On Jun 27, 2011, at 10:02 PM, Lao Meng wrote:
>
> Hi all,I have some questions about the covariants of regression.
>
> My target: To explore the trend of CD4 level through a period of time.
>
> Response variable: CD4 count
> Explanatory variable:time
>
> Also, the demology information is available,such as  
> gender,occupation,income
> level...
>
> Q1,Are these variables of demology information called covariant?
> Q2,How can I correct the impact of "covariant" so that I can get the
> "corrected result" of CD4's change through the time period?
> Q3,How to treat the covariants in regression?I've looked up to many  
> papers
> of R on regression,which treat the covariant in the same
>
> way as the Explanatory variable,like following:
> lm(CD4 ~ time + gender + income)
>
> Yes that seems pretty standard practice. It does, of course, force  
> the relationships to a) be linear and b) means that a single slope  
> and intercept are estimated for each variable, neither of a} or b}  
> assumptions may be true.
>
>
> From above expression of regression,it's obvious that the response  
> variables
> and covariants are treated the same way,
>
> In what sense are you making that claim? True they are both numeric,  
> but what else are you saying?
>
> -- 
> David
>
> but acturally
>
> they are totally different.
>
>
>
> Thanks for your help.
>
> My best.
>
>        [[alternative HTML version deleted]]
>
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> David Winsemius, MD
> West Hartford, CT
>
>

David Winsemius, MD
West Hartford, CT