Fitting a Triangular Distribution to Bivariate Data

Hello,
I have some xy data which clearly shows a non-monotonic, peaked
triangular trend. You can get an idea of what it looks like with:

x<-1:20
y<-c(2*x[1:10]+1,-2*x[11:20]+42)

I've tried fitting a quadratic, but it just doesn't the data-structure
with the break point adequately. Is there anyway to fit a triangular
or 'tent' function to my data in R?

Some sample code would be appreciated; I'm not new to R, but I
sometimes have difficulty understanding the model-fitting functions
(finally figured out how to extrapolate with predict() today!)

Thanks!
-Dave Bapst, UChicago

Hi,

you may try the following code:

 > library(triangle)
 > library(fitdistrplus)
 > summary(fitdist(y, "triangle", start = list(a = 1.9, b= 21.1, c = 11.5)))
Fitting of the distribution ' triangle ' by maximum likelihood
Parameters :
    estimate Std. Error
a -1.400007  2.3523724
b 23.627448  1.9804026
c 13.000000  0.1107073
Loglikelihood:  -62.41994   AIC:  130.8399   BIC:  133.8271
Correlation matrix:
             a           b           c
a  1.00000000 -0.14537297 -0.01203898
b -0.14537297  1.00000000 -0.01439500
c -0.01203898 -0.01439500  1.00000000

HTH ...

Jinsong Zhao