[R] Do YOU know an equation for splines (ns)?

[Bert Gunter]

Ah ...
Iirc believe the problem is that you need to explicitly generate the
spline basis and then the predicted values via predict.ns and feed
that to predict.lme; i.e.

splineBas <- with(rcn10,ns(day,3))

newvals <- data.frame( predict(splineBas, num))

## then once you've fitted your model:
lines(num, predict(fit10, newvals))


I have NOT checked this though, so please post back to me and the list
whether this works.

-- Bert




On Wed, Jun 6, 2012 at 10:38 AM, Ranae <ranae.dietzel at gmail.com> wrote:
> I have not been able to get "predict" (or most functions) to run well with
> grouped data in nlme.  I may not have it coded right, but this is what it
> looks like:
>
> http://r.789695.n4.nabble.com/file/n4632567/spline.txt spline.txt
>
> library(nlme)
> library(splines)
>
> rootCN<-read.table("spline.txt", header=TRUE)
> rootCN$plotF<-as.factor(rootCN$plot)
>
> rcn10G<-groupedData(N ~ day | plotF, data=rcn10)
>
> fit10 <- lme( N~ns(day, 3), data = rcn10G)
>
> plot(augPred(fit10))
>
> num<- seq(88,300, len=200)
> lines(num, predict(fit10, data.frame(day=num)))
>
> -Ranae
>
>
> Does
>  ?predict.ns
>  not do what you want without having to explicitly manipulate the spline
> basis?
>
> -- Bert
>
> On Tue, Jun 5, 2012 at 1:56 PM, Ranae <[hidden email]> wrote:
>
>> Hi,
>>
>> I am looking at the change in N concentration in plant roots over 4 time
>> points and I have fit a spline to the data using ns and lme:
>>
>> fit10 <- lme( N~ns(day, 3), data = rcn10G)
>>
>> I may want to adjust the model a little bit, but for now, let's assume
>> it's
>> good.  I get output for the fixed effects:
>>
>> Fixed: N ~ ns(day, 3)
>> (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3
>>  1.15676524  0.14509171  0.04459627  0.09334428
>>
>> and coefficients for each experimental unit in my experiment:
>>
>>   (Intercept) ns(day, 3)1 ns(day, 3)2 ns(day, 3)3
>> 24    1.050360 -0.42666159 -0.56290877 -0.10714407
>> 13    1.104464 -0.30825350 -0.53311653 -0.05558150
>> 31    1.147878 -0.14548512 -0.78673906 -0.07231781
>> 46    1.177781 -0.22278380 -0.80278177 -0.02321460
>> 15    1.144215 -0.04484519 -0.06084798  0.07633663
>> 32    1.213007  0.00741061  0.03896933  0.15325849
>> 23    1.274615  0.16477514  0.00872224  0.23128320
>> 41    1.215626  0.57050767  0.11415467  0.10608867
>> 43    1.134203  0.48070741  0.72112899  0.18108193
>> 12    1.091422  0.39563632  1.01521528  0.22597459
>> 21    1.100631  0.44589314  0.98526322  0.23535739
>> 35    1.226980  0.82419937  0.39809568  0.16900841
>>
>> NOW, I want to write a spline function where I can incorporate these
>> coefficients to get the predicted N concentration value for each day.
>> However, I am having trouble finding the right spline equation, since
>> there
>> are many forms on the internets.
>>
>> I know it won't be a simple one, but can some one direct me to the
>> equation
>> that would be best to use for ns?
>>
>> Thanks a lot,
>>
>> Ranae
>
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-- 

Bert Gunter
Genentech Nonclinical Biostatistics

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