Hi
I made a simple spredsheet for PSD using Rosin Rammler equation and I am lazy
to transform it to R. However for single purpose you can use nls.
Reverse your cumulative values
PSD$cum<-cumsum(PSD$ret)
plot(PSD$size, PSD$cum)
fit<-nls(cum~ exp(-((size/r)^gama))*100, data=PSD, start=c(r=80, gama=2))
summary(fit)
Formula: cum ~ exp(-((size/r)^gama)) * 100
Parameters:
Estimate Std. Error t value Pr(>|t|)
r 88.9664 2.3360 38.09 2.35e-07 ***
gama 2.5435 0.2244 11.33 9.36e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.411 on 5 degrees of freedom
Number of iterations to convergence: 7
Achieved convergence tolerance: 1.612e-06
lines(PSD$size, predict(fit))
Regards
Petr
> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-bounces@r-
> project.org] On Behalf Of Zorig Davaanyam
> Sent: Friday, December 20, 2013 2:01 AM
> To: r-help@r-project.org
> Subject: [R] Fitting particle size analysis data
>
> Hi all,
>
> How do you fit a sieve analysis data to a statistical function?
> I have many sieve analysis data of crushed rocks and I'd like to find
> out which statistical distributions describe the particular particle
> size distributions (PSD) the best. So basically I need to find fitted
> parameters to statistical distributions (mostly weibull and truncated
> lognormal).
> Here is an example of particle size (in microns) versus percent weight
> retained.
> Sieve size Wt% Cumulative passing%
> +250 0.1 99.9
> -250+180 2.9 97
> -180+125 9.5 87.5
> -125+90 21.2 66.3
> -90+63 29.4 36.9
> -63+45 26 10.9
> -45 10.9
>
> PSD<-
> data.frame(size=c(250,180,125,90,63,45,0),retained=c(0.1,2.9,9.5,21.2,2
> 9.4,26,10.9),cumulative=c(99.9,97,87.5,66.3,36.9,10.9,0))
>
> The above example is truncated to 350micron and I can't have particles
> with minus dimension. Any help will be greatly appreciated.
>
> Thank you,
>
> Zorig
>
> [[alternative HTML version deleted]]
>
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