On May 3, 2010, at 10:38 AM, Ista Zahn wrote:
Hi Serdal,
There is a lot of confusion here (how much is yours and how much is
mine remains to be seen). See specific comments in line.
Also inline comments.
On Mon, May 3, 2010 at 9:19 AM, serdal ozusaglam
<saint-fi...@hotmail.com> wrote:
Dear R users,
I think i have a simple question which i want to explain by an
example;
i have several 2-digit industry codes that i want to use for
conducting by-industry analysis but i think there is a problem with
the degrees of freedom!
for example, when i do my analysis without any 2-digit industry
code, i got the following summary (i have 146574 observations in
total):
abc<-lm(lnQ~lnC+lnM+lnL+lnE+eco+inno, data=ds)
summary(abc)
Call:
lm(formula = lnQ ~ lnC + lnM + lnL + lnE + eco + inno, data = ds)
Residuals:
Min 1Q Median 3Q Max
-11.01340 -0.17637 -0.02217 0.14974 7.79005
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8870369 0.0050646 175.144 <2e-16 ***
lnC 0.0658922 0.0006549 100.614 <2e-16 ***
lnM 0.8027478 0.0006549 1225.764 <2e-16 ***
lnL 0.0173622 0.0004025 43.138 <2e-16 ***
lnE 0.0657710 0.0006745 97.516 <2e-16 ***
ecoTRUE 0.0101649 0.0045892 2.215 0.0268 *
innoTRUE 0.0945100 0.0030317 31.174 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.294 on 146160 degrees of freedom
(407 observations deleted due to missingness)
Multiple R-squared: 0.9705, Adjusted R-squared: 0.9705
F-statistic: 8.027e+05 on 6 and 146160 DF, p-value: < 2.2e-16
as we can see from the last row there are 146160 DF (407 deleted)
this is ok!
Usually it is better to make a small example that demonstrates your
issue. I have no idea what these variable are which makes it harder to
diagnose your problem.
but when i want to use for example just one of the industry lets
say just the 11th industry
1st: i create the dummy for this industry such as;
ind1=(ind_2d==11)# so here the R supposed to consider just the
11th industry!!
This makes no sense to me. What are you trying to do here? What is
ind_2d? Are you trying to subset your data.frame? If so, see ?subset,
or ?"["
Serdal is just making a logical indicator variable.
abc<-lm(lnQ~lnC+lnM+lnL+lnE+eco+inno+ind, data=ds)
summary(abc)
Call:
lm(formula = lnQ ~ lnC + lnM + lnL + lnE + eco + inno + ind,
data = ds)
Residuals:
Min 1Q Median 3Q Max
-11.03392 -0.17647 -0.02301 0.14901 7.74957
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.8980397 0.0050451 178.001 < 2e-16 ***
lnC 0.0672255 0.0006523 103.065 < 2e-16 ***
lnM 0.7990819 0.0006579 1214.596 < 2e-16 ***
lnL 0.0171633 0.0004004 42.870 < 2e-16 ***
lnE 0.0670030 0.0006716 99.770 < 2e-16 ***
ecoTRUE 0.0162249 0.0045672 3.552 0.000382 ***
innoTRUE 0.0966967 0.0030160 32.062 < 2e-16 ***
indTRUE -0.1251466 0.0031509 -39.717 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.2924 on 146159 degrees of freedom
(407 observations deleted due to missingness)
Multiple R-squared: 0.9709, Adjusted R-squared: 0.9709
F-statistic: 6.957e+05 on 7 and 146159 DF, p-value: < 2.2e-16
but as we can see it again counted in all the industries! so the DF
is 146159!!!
So i just wonder, where do i made mistake, or there is no mistake
at all, and i just misunderstood the DF issue?
I think the misunderstanding runs deeper than that. Try creating a
minimal example, and clearly stating a) what you are trying to
accomplish, b) what you tried, and c) what doesn't work as you expect.
I, too, was puzzled by the OP's reaction. Serdal added a single
logical predictor variable to an existing model that already had two
such variables and as a result his degrees of freedom in the model
increased by one and the degrees of freedom in the residuals decreased
by one. Where is the problem? And why wasn't this question posed even
earlier at the point of addition of "eco" and "inno" variables? He
perhaps was expecting that the degrees of freedom in the model would
increase by the number of records that shared an indTRUE value of
TRUE, but that is not the way ordinary regression works. Perhaps he
should do some reading on mixed effects modeling? Or perhaps that is
what his professor or supervisor is hoping he will learn by assigning
this task? Or perhaps he needs to learn to use the anova() function?
Best,
Ista
--
David Winsemius, MD
West Hartford, CT
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