[R] Predict

[David Winsemius]

> On Jun 30, 2017, at 9:13 AM, Sarah Goslee <sarah.goslee at gmail.com> wrote:
> 
> Once again, you are over-writing your variable. This time, you are overwriting
> the entirety of Stand_Height with the timeseries of height.
> 
> Perhaps you should spend some time with one of the good introductory R
> resources out there, and think a bit more about your procedure.
> 
> Sarah
> 
> On Fri, Jun 30, 2017 at 11:23 AM, Ahmed Attia <ahmedatia80 at gmail.com> wrote:
>> Sorry for the confusion, here is the edited question.
>> 
>> The data= Stand_Height (attached) is recorded from 12/1/2009 to

Also.  Nothing attached made it back to the list readership, although it probably did make it to Sarah Goslee. Attachments should have the file extension `.txt` so that your mail client can give it the proper MIME-type. The only MIME-type that the server accepts for data is 'plain text".

So, .... You should also spend more time reading the Mailing list Info page and the Posting Guide.


-- 
David.

>> 12/31/2015 (25 observations) and the other dataset (leafbiom) is
>> recorded from 10/7/2009 to 12/29/2016 (daily observations).
>> 
>> I want to use the 25 observations of stand height to predict the daily
>> stand height from 10/7/2009 to 12/29/2016. The daily stand height will
>> be multiplied by leaf biomass to produce a new variable.
>> 
>> I agree that a loop is not needed, would the forecast library help or
>> should I use predict library.
>> 
>> Stand_Height=ts(Stand_Height$height,start=2009,end = 2016,
>>                frequency =365)
>> 
>> plot(forecast(ets(Stand_Height),10))
>> a=seq(as.Date("2009-12-01"),by="weeks",length=11)
>> axis(1, at = a, labels = format(a, "%Y %b %d"), cex.axis=0.6)
>> 
>> 
>> #Error :$ operator is invalid for atomic vectors
>> 
>> Thanks
>> 
>> 
>> Ahmed Attia, Ph.D.
>> Agronomist & Soil Scientist
>> 
>> 
>> 
>> 
>> 
>> 
>> On Fri, Jun 30, 2017 at 10:37 AM, Sarah Goslee <sarah.goslee at gmail.com> wrote:
>>> There are a bunch of things wrong here, although without a
>>> reproducible example I can't really fix most of them.
>>> 
>>> - You're overwriting SH within the loop.
>>> - You're running the regression 2641 times, even though the result
>>> never changes.
>>> - You're never predicting from your linear model using the other data
>>> not in the regression.
>>> - Leaf biomass data is never used for anything. I would have thought
>>> that you would use leaf biomass as the predictor variable, not Date.
>>> - I'm not sure why you want the cumulative sum of stand height; that
>>> doesn't make sense to me.
>>> 
>>> I'm guessing you want:
>>> 
>>> height.model <- lm(height ~ leafbiomass, data = Stand_Height)
>>> pred.height <- predict(height.model, leafbiom)
>>> 
>>> # not sure about the reasoning behind this
>>> SH <- cumsum(pred.height)
>>> 
>>> You don't need a loop. Overwriting SH is the biggest R problem; the
>>> rest of my questions have to do with what your objective actually is,
>>> like what you are modeling and what you are doing with the
>>> predictions. But my sample code might be enough to get you headed in
>>> the right direction regardless.
>>> 
>>> Sarah
>>> 
>>> On Fri, Jun 30, 2017 at 9:27 AM, Ahmed Attia <ahmedatia80 at gmail.com> wrote:
>>>> Hi folks,
>>>> 
>>>> I have 25 stand height observations over 7 years period and daily
>>>> leafbiomass data during this period. I want to use the 25 plant height
>>>> observations as inputs and predict the daily stand height during the 7
>>>> years.
>>>> 
>>>> 
>>>> SH=matrix(data=NA , nrow = 2641, ncol = 1)
>>>> for (i in 1:2641) {
>>>>  SH<- predict(lm(height~Date, data=Stand_Height));
>>>> 
>>>>  dl=leafbiom$Date[i-1];
>>>>  de=leafbiom$Date[i];
>>>> SH[i]=sum(SH[leafbiom$Date==de&leafbiom$Date==dl])
>>>> 
>>>> 
>>>> }
>>>> SH
>>>> 
>>>> 
>>>> The SH output is the prediction of Stand height in 25 observations
>>>> only and provides NA for the remaining 2616 iterations.
>>>> 
>>>> Thanks for your help.
> 
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David Winsemius
Alameda, CA, USA