On Aug 7, 2015, at 2:41 PM, kira taylor wrote:
> Hi!
>
> I am trying to use predict to apply my model to data from one time period to
> see what might be the values for another time period. I did this
> successfully for one dataset, and then tried on another with identical code
> and got the following error:
>
> Error in eval(predvars, data, env) :
> numeric 'envir' arg not of length one
>
> The only difference between the two datasets was that my predictor model for
> the first dataset had two predictor variables and my model for the second
> dataset had only one. Why would this make a difference?
>
> My dougfir.csv contains just two columns with thirty numbers in each,
> labeled height and dryshoot.
>
> my lm is: fitdougfir <- lm(dryshoot~height,data=dougfir)
>
> It gets a little complicated (and messy, sorry! I am new to R) because I
> then made a second .csv - the one I used to make my model contained values
> from just June. My new .csv (called alldatadougfir.csv) includes values
> from October as well, and also contains a "date" column that labels the
> values either "june" or "october."
>
> I did the following to separate the height data by date:
>
> alldatadougfir[alldatadougfir$date=="june",c("height")]->junedatadougfir
> alldatadougfir[alldatadougfir$date=="october",c("height")]->
> octoberdatadougfir
Those are no longer lists or dataframe, which are the proper classes of object
to pass to predict.
--
David.
>
> I then want to use my June model to predict my October dryshoots using
> height as my variable and I did the following:
>
> predict(fitdougfir, newdata=junedatadougfir)
> predict(fitdougfir, newdata=octoberdatadougfir)
>
> Again, I did this with an identical dataset successfully - the only
> difference was that my model in the successful dataset had two predictor
> variables instead of the one variable (height) I have in this dataset.
>
> Sorry again for my messy code!
>
> Thank you very much,
>
> Kira
>
> [[alternative HTML version deleted]]
>
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David Winsemius
Alameda, CA, USA
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