or possibly even more appropriate is quant.stackexchange.com.
On Thu, Mar 5, 2020 at 4:38 AM Eric Berger wrote:
> Alternatively you might try posting to
> r-sig-fina...@r-project.org
>
>
>
> On Wed, Mar 4, 2020 at 9:38 PM Bert Gunter wrote:
>
> > Your question is way off topic here -- this lis
Alternatively you might try posting to
r-sig-fina...@r-project.org
On Wed, Mar 4, 2020 at 9:38 PM Bert Gunter wrote:
> Your question is way off topic here -- this list is for R programming
> questions, not statistical consulting. You might wish to try
> stats.stackexchange.com for the latter.
Your question is way off topic here -- this list is for R programming
questions, not statistical consulting. You might wish to try
stats.stackexchange.com for the latter.
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka
On 3/12/2016 12:39 PM, Axel wrote:
The main goal of my analysis is to
determine which are the fatty acids that characterize the origin of an oil. As
a secondary goal, I wolud like to insert the results of the chemical analysis
of an oil that I analyzed (I am a Chemistry student) in order to deter
Dear Axel
Since you are using princomp (among other things) you might find the
biplot function useful on the output of princomp.
I have not studies your code in detail but you do seem to be doing
several things in multiple ways using functions from different sources.
I wonder whether it mig
Hi Axel,
It seems to me that cluster analysis could be what you are seeking.
Identify the clusters of different combinations of fatty acids in the
oils. Do they correspond to location? If so, is there a method to
predict the cluster membership of a new set of measurements? Have a
look at the cluste
Inline.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Sat, Mar 12, 2016 at 9:39 AM, Axel wrote:
> Hi to all the members of the list!
>
> I am a
Hi Mike,
The book makes use of .csv files, which are provided, along with all R code and
.RData files.
You have an interesting thought about people pulling data from diverse sources
and making everyday use of R. For this, I would suggest using Excel or Google
Docs Spreadsheets to compile and o
Since todays ground water may be influenced by yesterdays rainfall, you may
want to look at the dynlm package and possibly lag.plot and the zoo package.
--
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.s...@imail.org
801.408.8111
> -Original Message
Rainfall data is widely accepted as Random walk process and hence it is
non-stationary. Therefore if correlation or regression coef. is measured on
raw data then you may land in the world of spurious measures. I would
suggest you to check whether unit root is there in your data or not first.
If it
Hi Chris,
If I understand your question correctly, what you want is both easy and hard.
Easy:
# making a reproducible example, as asked in the posting guide
# two vectors
water <- rnorm(1000)
rain <- rgamma(1000,.5)
# the following does everything you mention and more
summary(lm(water~rain))
cor(
Chris Li wrote:
Hi all,
I have got two datasets, one of them is rainfall data and the other one is
groundwater level data.
I would like to see whether there is a correlation between these two
datasets and if there is, to what extent they are correlated.
My stats background is limited, therefor
Chris Li wrote:
>
> Hi all,
>
> I have got two datasets, one of them is rainfall data and the other one is
> groundwater level data.
>
> I would like to see whether there is a correlation between these two
> datasets and if there is, to what extent they are correlated.
>
> My stats backgroun
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