[R] Correct statistical inference for linear regression models without intercept in R

2010-07-20 Thread StatWM

Dear R community,

is there a way to get correct t- and p-values and R squared for linear
regression models specified without an intercept?

example model:
summary(lm(y ~ 0 + x))

This gives too low p-values and too high R squared. Is there a way to
correct it? Or should I specify with intercept to get the correct values?

Thank you in advance!

Wojtek Musial
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Re: [R] Correct statistical inference for linear regression models without intercept in R

2010-07-20 Thread StatWM

Let's assume x and y as stationary. It's not a spurious regression problem
here. I think the function lm() has to have an intercept to give correct
values of t- and p- and R squared. I wonder if you can correct the values in
R though?
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Re: [R] Correct statistical inference for linear regression models without intercept in R

2010-07-20 Thread StatWM

Thank you very much for your effort!

But is there a measure, which can compare the goodness of fit of regression
models with and without the intercept? Can I only compare them in terms of
sum of squares residual?
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[R] simple ts() object question

2010-09-04 Thread StatWM

Dear Community,

say, I have an annual ts() object sampled from 1960 to 1969 like:

ta<-ts(1:10, start=1960, frequency=1)

How can I extract the value from the year 1965?

I mean, not by:

ta[6]

but by something like:
 
ta[1965]

where I'm directly referring to the year of the observation?

Thank you in advance!

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Re: [R] simple ts() object question

2010-09-05 Thread StatWM

Thanks for the fast answers!
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