Hello
I am trying to fit a linear model with lm. I want to decompose
spatial-temporal pm10 data into pure spatial component and pure temporal
component with an error term. My data consists of 36 points with 480
timesteps.
The code i am using is
lm.half <- lm(pm10 ~factor(s) +factor(t),data= half)
so i expect to get 36 s values and 480 t values. Instead for the first cell
and the first timestep the value is missing, the lm result is only 35 s
values and 479 t values ( + one intercept coefficient ). Is there a reason
behind this and some way to fix it?
Second question is how could i skip the intercept value in my computation.
is this simple
lm.half <- lm(pm10 ~factor(s) +factor(t) -1,data= half)
??
I have found it in the internet, but i fear that then a -1 is taken into
account always...

And also one last question, my R sqaured value is not too high, its only
about 0.51. Does this tell me the model makes no sense? I would very like to
use this model but i dont know if this number tells me not to use this ;).
Have also heard that linear models without intercept have a higher r quared
value, perhaps it will fix a little bit of this.

Regards
Jan

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