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Fitting particle size analysis data
2 messages · Zorig Davaanyam, PIKAL Petr
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-bounces at r-project.org [mailto:r-help-bounces at r- project.org] On Behalf Of Zorig Davaanyam Sent: Friday, December 20, 2013 2:01 AM To: r-help at 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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