Re^3: fitting double lognormal distribution on a dataset

Thanks for the clarification. I find this documentation very confusing. If I see an example like '1/2 * m * v^2' I believe that the caret is the operator for exponentiation and I do not see any need to even continue reading. Obviously wrong here.

A fully working example would be useful here to test such presumptions...

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Re^4: fitting double lognormal distribution on a dataset by sdani (Novice) on May 19, 2013 at 03:48 UTC
On an made up dataset the program runs fine. So the proper way to raise to the power is this: `n ^ 2` not this: `n*2`; here is the sample code: #!/usr/bin/perl use strict; use warnings; use Algorithm::CurveFit; my @X_data = qw(-20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 - +6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14); my @Y_data = qw(1212 1095 984 879 780 687 600 519 444 375 312 255 204 +159 120 87 60 39 24 15 12 15 24 39 60 87 120 159 204 255 312 375 444 +519 600); my $variable = 'x'; my $equation = 'r + m x^2'; my @parameters = ( ['r', '100'], ['m', '1'], ); my $maxiter = '200'; my $square_residual = Algorithm::CurveFit->curve_fit( formula => $equation, # may be a Math::Symbolic tree in +stead params => \@parameters, variable => $variable, xdata => \@X_data, ydata => \@Y_data, maximum_iterations => $maxiter, ); print "$parameters[0][1] (should be 12)\t$parameters[1][1] (should be +3)\n"; [download] I'll try to do the more complex equation and I let you know how it went! Thanks.	[reply] [d/l] [select]

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Re^4: fitting double lognormal distribution on a dataset
by sdani (Novice) on May 19, 2013 at 03:48 UTC

n ^ 2

n**2

#!/usr/bin/perl

use strict;
use warnings;
use Algorithm::CurveFit;

my @X_data = qw(-20 -19 -18 -17 -16 -15 -14 -13 -12 -11 -10 -9 -8 -7 -
+6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14);
my @Y_data = qw(1212 1095 984 879 780 687 600 519 444 375 312 255 204 
+159 120 87 60 39 24 15 12 15 24 39 60 87 120 159 204 255 312 375 444 
+519 600);
my $variable = 'x';
my $equation = 'r + m * x^2';
my @parameters = (
    ['r', '100'],
    ['m', '1'],
);
my $maxiter = '200';
my $square_residual = Algorithm::CurveFit->curve_fit(
    formula            => $equation, # may be a Math::Symbolic tree in
+stead
    params             => \@parameters,
    variable           => $variable,
    xdata              => \@X_data,
    ydata              => \@Y_data,
    maximum_iterations => $maxiter,
);

print "$parameters[0][1] (should be 12)\t$parameters[1][1] (should be 
+3)\n";
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