Thanks a lot, Martin and William. Looks like, we can't apply prediction on
lsfit and lm.fit objects. Because, i am trying to use lm object to predict
the values for new data frame.
Thanks & Regards
Sasi
On Tue, Nov 17, 2015 at 9:49 AM, Martin Maechler wrote:
> > William Dunlap
> >
That is what I meant about saving compute time and increasing programming time.
You can do prediction by do the matrix multiplication explicitly.
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Tue, Nov 17, 2015 at 9:01 PM, Sasikumar Kandhasamy wrote:
> Thanks a lot, Martin and William. Looks li
> William Dunlap
> on Mon, 16 Nov 2015 16:01:42 -0800 writes:
> If a quick running time is important and your models involve only
> numeric data with no missing values and you are willing to spend more
> programming time setting things up, the lsfit() function may work
Thanks a lot Bill & Bert.
Hi Bill,
Sorry i was wrong on number of records, actually, i am using two
dimensional data of 250K records each. And regarding CPU usage, it was the
elapsed time. Infact, i have pined one core to run R.
Thanks & Regards
Sasi
On Mon, Nov 16, 2015 at 2:04 PM, William Dun
If a quick running time is important and your models involve only
numeric data with no missing values and you are willing to spend more
programming time setting things up, the lsfit() function may work
better for you.
Bill Dunlap
TIBCO Software
wdunlap tibco.com
On Mon, Nov 16, 2015 at 3:25 PM, S
You cannot do a linear regression with one column of data - there must
be at least one response column and one predictor. By default, lm
throws in a constant term which gives you a second predictor. If your
predictor is categorical, you get a new column for all but the first
unique value in it.
Do your own homework.
Google on "memory usage in R." etc.
You should have no trouble finding what you need there.
Cheers,
Bert
Bert Gunter
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
-- Clifford Stoll
On Mon, Nov 16, 2015 at 12:25 PM, S
Hi All,
I have couple of clarifications on R run-time performance. I have R-3.2.2
package compiled for MIPS64 and am running it on my linux machine with
mips64 processor (core speed 1.5GHz) and observing the following behaviors,
1. Applying "linear regression model" (lm) on 1MB of data (contains
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