Thanks alot for the quick responses.
I have some additional questions related to this topic. In fact, my
intention was to be able to answer questions like what percent of the
regressions have p_values less than a certain threshold, how do
residuals look like, how do the plots of y vs. x look like, etc.
I tried the following commands and found that the second line (and
similar ones) does not work for extracting certain statistics.
regress=lapply(split(egfr, as.factor(egfr$P_ID)), function(df)
{anova(lm(VALUE ~ LAB_DT, data=df)) })
regress[1]$residuals; regress[1]$fstatistic[1]
So, is it possible to record statistics of each regression such as
p_value, F-value, residuals, etc. as a vector?
Thanks,
On Dec 28, 6:23 pm, Entropi ntrp <entropy...@gmail.com> wrote:
> Hi,
> I have been examining large data and need to do simple linear regression
> with the data which is grouped based on the values of a particular
> attribute. For instance, consider three columns : ID, x, y, and I need to
> regress x on y for each distinct value of ID. Specifically, for the set of
> data corresponding to each of the 4 values of ID (76,111,121,168) in the
> below data, I should invoke linear regression 4 times. The challenge is
> that, the length of the ID vector is around 20000 and therefore linear
> regression must be done automatically for each distinct value of ID.
>
> ID x y
> 76 36476 15.8 76 36493 66.9 76 36579 65.6 111 35465 10.3 111 35756 4.8
> 121 38183 16 121 38184 15 121 38254 9.6 121 38255 7 168 37727 21.9 168
> 37739 29.7 168 37746 97.4
> I was wondering whether there is an easy way to group data based on the
> values of ID in R so that linear regression can be done easily for each
> group determined by each value of ID. Or, is the only way to construct
> loops with 'for' or 'while' in which a matrix is generated for each
> distinct value of ID that stores corresponding values of x and y by
> screening the entire ID vector?
>
> Thanks in advance,
>
> Yasin
>
> [[alternative HTML version deleted]]
>
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