This is kind of an elegant little solution

This code can be used to find the distance between x locations (stored in vect1) and y locations (stored in vect2). I modified this from something I found online. Works pretty good and I like the out put, also optimal in terms of speed.
Using this to construct similarity ranks! win!

% for finding the distance between the sets of points and printing them out
% by labels!

b = zeros(length(vect1),2);
for i = 1: length(vect1)
    b(i,1) = vect1(i);
    b(i,2) = vect2(i);
end

k = 0;
for i = 1:length(b)
    for j = 1:length(b)
        if j~=i
            k=k+1;
            index(k,:) = [i j];
        end;
    end;
end;

dist = ((b(index(:,2),1)-b(index(:,1),1)).^2 + (b(index(:,2),2)-b(index(:,2),2)).^2).^0.5;
m = [index(:,1),index(:,2),dist];

Soil depth

I expected this but it still seems funny that soils are deepest at low... and high... elevations. 

It's because on this landscape the ridges are relatively flat, and are located between precipices out of the extent of the study area that are eroding soils onto them. Still. Funny. 

best help manual I've ever seen

http://www.box.net/shared/mugpnnjy21

Ikea couldn't have done it better.

How to read text for matlab

fid = fopen('biomatlabels.dat');
C = textscan(fid,'%s %f32 %f32 %f32 %f32 %f32 %f32 %f32');
fclose(fid);

The first line opens the file. You must save it as a .dat file!

The second line scans the file. it is also important to name it fid.
%s means "string" (like a word)
%f32 means "number"(in the broadest sense-- there are options for decimals, too)

The third line closes the file. When you want to call up one of your labels, call it as
C{1} or C{2}-- it saves as a cell array so brackets are key!

a code for stepwise regression with twelve possible fitting parameters

% litter.m


file = importdata('biomass_workbook2.csv',',');


% names of information | bio in 1980 | percenthardwood in 1980 |
% hardwood bio 1980|...etc.
bio80 = file(:,1);
perhard80 = file(:,2);
hardbio07 = file(:,3);
perhard07 = file (:,4);
litterdat = file(:,5);
anpp1 = file(:,7);
basal07 = file(:,8);
anpp6 = file(:,9);
bio07 = file(:,11);
num_tree = file(:, 12);
stemden = file(:,13);
logherb = file(:,14);
hwbio80 = file (:,15);


% poly fit finds the parmaters (pi) and the norms of the residuals (si) for
% linear and poly combinations of variables


[p1,s1] = polyfit(bio80, litterdat, 1);
[p2,s2] = polyfit(perhard80, litterdat, 1);
[p3,s3] = polyfit(hardbio07, litterdat, 1);
[p4,s4] = polyfit(perhard07, litterdat, 1);
[p5,s5] = polyfit(anpp1, litterdat, 1);
[p6,s6] = polyfit(basal07, litterdat, 1);
[p7,s7] = polyfit(anpp6, litterdat, 1);
[p8,s8] = polyfit(bio07, litterdat, 1);
[p9,s9] = polyfit(num_tree, litterdat, 1);
[p10,s10] = polyfit(stemden, litterdat, 1);
[p11,s11] = polyfit(logherb, litterdat, 1);
[p12,s12] = polyfit(hwbio80, litterdat, 1);


% polyval finds the correlation matrix
output1 = polyval(p1, bio80);
corr1 = corrcoef(litterdat,output1);


output2 = polyval(p2, perhard80);
corr2 = corrcoef(litterdat,output2);


output3 = polyval(p3, hardbio07);
corr3 = corrcoef(litterdat,output3);


output4 = polyval(p4, perhard07);
corr4 = corrcoef(litterdat,output4);


output5 = polyval(p5, anpp1);
corr5 = corrcoef(litterdat,output5);


output6 = polyval(p6, basal07);
corr6 = corrcoef(litterdat,output6);


output7 = polyval(p7, anpp6);
corr7 = corrcoef(litterdat,output7);


output8 = polyval(p8, bio07);
corr8 = corrcoef(litterdat,output8);


output9 = polyval(p9, num_tree);
corr9 = corrcoef(litterdat,output9);


output10 = polyval(p10, stemden);
corr10 = corrcoef(litterdat,output10);


output11 = polyval(p11, logherb);
corr11 = corrcoef(litterdat,output11);


output12 = polyval(p2, hwbio80);
corr12 = corrcoef(litterdat,output12);


% M is a matrix to hold thePearson's correlation coefficient
m = zeros(12,1);
m(1) = corr1(1,2);
m(2) = corr2 (1,2);
m(3) = corr3 (1,2);
m(4) = corr4 (1,2);
m(5) = corr5 (1,2);
m(6) = corr6 (1,2);
m(7) = corr7 (1,2);
m(8) = corr8 (1,2);
m(9) = corr9 (1,2);
m(10) = corr10 (1,2);
m(11) = corr11 (1,2);
m(12) = corr12 (1,2);


% yh(1) holds the fit of the predicted 


yh1 = polyval(p1,bio80);
yh2 = polyval(p2, perhard80);
yh3 = polyval(p3, hardbio07);
yh4 = polyval(p4, perhard07);
yh5 = polyval(p5, anpp1);
yh6 = polyval(p6, basal07);
yh7 = polyval(p7, anpp6);
yh8 = polyval(p8, bio07);
yh9 = polyval(p9, num_tree);
yh10 = polyval(p10, stemden);
yh11 = polyval(p11, logherb);
yh12 = polyval(p12, hwbio80);


% resid is what is not predicted


resid1 = litterdat - yh1;
resid2 = litterdat - yh2;
resid3 = litterdat - yh3;
resid4 = litterdat - yh4;
resid5 = litterdat - yh5;
resid6 = litterdat - yh6;
resid7 = litterdat -yh7;
resid8 = litterdat - yh8;
resid9 = litterdat - yh9;
resid10 = litterdat - yh10;
resid11 = litterdat - yh11;
resid12 = litterdat - yh12;


rss1 = sum((yh1- mean(litterdat)).^2);
tss1 = sum((litterdat - mean(litterdat)).^2);


% Rsq asks how much better predictedis thanmean
Rsq1 = 1-sum(resid1.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq2 = 1-sum(resid2.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq3 = 1-sum(resid3.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq4 = 1-sum(resid4.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq5 = 1-sum(resid5.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq6 = 1-sum(resid6.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq7 = 1-sum(resid7.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq8 = 1-sum(resid8.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq9 = 1-sum(resid9.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq10 = 1-sum(resid10.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq11 = 1-sum(resid11.^2)/sum((litterdat-mean(litterdat)).^2);
Rsq12 = 1-sum(resid12.^2)/sum((litterdat-mean(litterdat)).^2);


% n is a matrix of R sq
n= zeros(12,1);
n(1) = Rsq1;
n(2) = Rsq2;
n(3) = Rsq3;
n(4) = Rsq4;
n(5) = Rsq5;
n(6) = Rsq6;
n(7) = Rsq7;
n(8) = Rsq8;
n(9) = Rsq9;
n(10) = Rsq10;
n(11) = Rsq11;
n(12) = Rsq12;


% mat is a matrix of predictors
mat = zeros(length(litterdat),12);
mat(:,1:4) = file(:,1:4);
mat(:,5:7) = file(:,7:9);
mat(:,8:12) = file(:,11:15);


% this will do a stepwise fit. b isparameters, se is residuals, pval is
% values.. in model shows the process of gettingthere, statsis stats,next
% step and history are more dtailed
[b, se, pval, inmodel, stats, nextstep, history] = stepwisefit(mat, litterdat);


% model_fit is parameters
model_fit = inmodel.*b';
model_fit = model_fit';


% model 2is the predicted
model2 = zeros(length(mat),1);
model2= 4.4443 + model_fit(4).*mat(:,4) + model_fit(7)*mat(:,7) + model_fit(8).*mat(:,8);


% calculateresiduals
resid_step = litterdat-model2;


% calculate r2
Rsq_step = 1-sum(resid_step.^2)/sum((litterdat-mean(litterdat)).^2);


% calculate a fitting "line" to plot
[p_step, s_step] = polyfit(model2, litterdat,1);
a = p_step(1);
b = p_step(2);
Vcalc = a.*model2 + b;


% plot
figure(1)
plot(model2,litterdat,'k.');
hold on
plot(model2, Vcalc,'r-');
xlabel('Stepwise litterfall model')
ylabel('Litterfall data')
title ('Fitness testing for litterfall model');

Processing Tutorials

 I'm working on my data visualization skills here a bit, and I thought I'd start trying to figure out how to work with Processing. Although ultimately I think VELMA will switch languages, Processing does have the advantage of NOT being in C and looking nice.
So to the right is the start of an example from
Data Visualization. Since I'm a total n00b I thought, I wonder what happens when we change the size of the image.

As you can see here, changing the first number cuts it off from the right to left, and the second number from the bottom to the top.

If we change the image numbers, we see that going from 0 to 100 shifts the image within the background.
This, I suppose, is like being in "data mode" in Arc.