function [H,slope]=plot_speed_vs_errors(data, minerr, color, linefit)
% similar to plot_growth_vs_errors(), but with data as loaded by load_xgrow_data()
% each Gmc in the data set gets its mean values computed, so don't combine
%   data from different types of run. (e.g. eps-variation and Gmc-variation)

if nargin<4
  linefit=0;
end

xgrow_i

sz  = data(:,xi_sz);
t   = data(:,xi_t);
mm  = data(:,xi_mm);
Gmc = data(:,xi_Gmc);

rate  = sqrt(2*sz)./t;
err   = max(minerr,mm./sz);

uGmc  = unique(Gmc);
for i=1:length(uGmc)
  ii = find(Gmc==uGmc(i));
  mrate(i) = mean(sqrt(2*sz(ii))./t(ii));   
  merr(i)  = max(minerr,mean(mm(ii)./sz(ii)));    
  bars(i)  = 2*sqrt(merr(i))./sqrt(sum(sz(ii)));
  mr(i)    = mean(sqrt(2*sz(ii))./t(ii));   
  me(i)    = mean(mm(ii)./sz(ii));   
end
I =find(me>0);  P =polyfit(log(mr(I)),log(me(I)),1);

if color(2)==' '
  H=loglog(mrate,merr,[color(1) '*-']);
else
  H=loglog(rate,err,color); loglog(mrate,merr,[color(1) '*-']);
end

%% note: for NxN proofreading tiles, since errors tend to be co-locallized,
%% the probability of an error per tile [i.e. fraction of tiles in error]
%%  = [number of tile errors / number of tiles]
%%  = N * (prob of an error per block [which will be N tile errors]) /
%%        (N^2 tiles/block)
%%
%% and we treat as if (#tiles / N^2) iid events, each with
%%  probability   eblock = etile*N
%%  var(#err blocks) = (#tiles / N^2) var(eblock)
%%                   = (#tiles)       var(etile)
%%                   = var(#err tiles if iid)

h=line([ mrate      .9*mrate   .9*mrate;   ...
         mrate      mrate/.9   mrate/.9],  ...
       [ merr+bars merr+bars max(minerr,merr-bars); ...
         max(minerr,merr-bars) merr+bars max(minerr,merr-bars)]);
set(h,'Color',color(1));


if linefit
 loglog(mr(I),exp(polyval(P,log(mr(I)))),'k--');
end

slope=P(1);