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get_parameter_variation.m
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get_parameter_variation.m
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%% Vary the input parameter ("parameter") in a specified range ("v_vec")
function [out, grain2mean] = get_parameter_variation(Stot, c, g, tend, nsamples, change, change_rate, v_vec, parameter, doAR, varargin)
assert(isequal(size(Stot),[1,1]), 'First input argument must be double defining total richness')
assert(ismember(change, {'none', 'N', 'S', 'evenness'}), 'Undefined change type.')
% if specified, mean of lognormal SAD distribution
mu = 3; % warning: might not be up to date with default
if any(strcmp(varargin, 'mu'))
ind = find(strcmp(varargin, 'mu'));
mu = varargin{ind+1};
end
% if specified, variance of lognormal SAD distribution
var = 1.5; % warning: might not be up to date with default
if any(strcmp(varargin, 'var'))
ind = find(strcmp(varargin, 'var'));
var = varargin{ind+1};
end
% timelag (only every "timelag"st point is used for final timeseries)
timelag = 0;
if any(strcmp(varargin, 'timelag'))
ind = find(strcmp(varargin, 'timelag'));
timelag = varargin{ind+1};
end
% spatial distribution of start coordinates
start_coordinates = 'random';
if any(strcmp(varargin, 'aggregated'))
start_coordinates = 'aggregated';
end
% get spatial heterogeneity? (difference of cell to mean in landscape)
get_heterogeneity = 'off';
if any(strcmp(varargin, 'get_heterogeneity'))
get_heterogeneity = 'on';
end
% Default: no truncating. Alternatively: truncate pt first time points of timeseries
truncate = 'off';
pt = 0;
v_num = length(v_vec);
out.var = v_vec;
%% Start variation
tend_input = tend;
for i = 1:v_num
%% Vary the parameter
fprintf('\nvary %s, v = %d/%d', parameter, i, v_num)
if strcmp(parameter, 'g')
g = v_vec(i);
elseif strcmp(parameter, 'c')
c = v_vec(i);
elseif strcmp(parameter, 'cg_factor')
c = round(c*v_vec(i));
g = round(g*v_vec(i));
elseif strcmp(parameter, 'Stot')
Stot = v_vec(i);
elseif strcmp(parameter, 'mu')
mu = v_vec(i);
elseif strcmp(parameter, 'var')
var = v_vec(i);
elseif strcmp(parameter, 'tend')
tend = v_vec(i);
elseif strcmp(parameter, 'timelag')
timelag = v_vec(i);
elseif strcmp(parameter, 'pct_trunc')
pct_trunc = v_vec(i);
pt = round((pct_trunc/100)*tend_input);
tend = tend_input + pt; % make timeseries longer, such that after truncating
% the intitially provided number of points remain
elseif strcmp(parameter, 'change_rate')
change_rate = v_vec(i);
else
error('Parameter not yet defined for variation.')
end
%% Get results for the different biases
[timeseries_random, grain2mean_random] = get_diversity_timeseries(Stot, c, g, tend,...
nsamples, change, change_rate, 'timelag', timelag,...
'mu', mu, 'var', var, start_coordinates, 'truncate', pt);
if doAR == 1 % if auto-regression is on, include it in errors of regression
[trends_random] = get_trends_AR(timeseries_random);
else
[trends_random] = get_trends(timeseries_random);
end
samples = trends_random;
fields = fieldnames(samples);
for f = 1 : length(fields)
field = eval(sprintf('samples.%s', fields{f}));
if isequal(size(field), [nsamples 1])
eval( sprintf('out.%s_random(i,:) = samples.%s;', fields{f}, fields{f}) )
elseif isequal(size(field), [1 1])
eval( sprintf('out.%s_random(i) = samples.%s;', fields{f}, fields{f}) )
end
end
[timeseries_comm_biased, grain2mean_comm_biased] = get_diversity_timeseries(Stot, c, g, tend,...
nsamples, change, change_rate, 'sampling', 'comm-biased',...
change, change_rate, 'timelag', timelag,...
'mu', mu, 'var', var, start_coordinates, 'truncate', pt);
if doAR == 1
[trends_comm_biased] = get_trends_AR(timeseries_comm_biased);
else
[trends_comm_biased] = get_trends(timeseries_comm_biased);
end
samples = trends_comm_biased;
fields = fieldnames(samples);
for f = 1 : length(fields)
field = eval(sprintf('samples.%s', fields{f}));
if isequal(size(field), [nsamples 1])
eval( sprintf('out.%s_comm_biased(i,:) = samples.%s;', fields{f}, fields{f}) )
elseif isequal(size(field), [1 1])
eval( sprintf('out.%s_comm_biased(i) = samples.%s;', fields{f}, fields{f}) )
end
end
[timeseries_rich_biased, grain2mean_rich_biased] = get_diversity_timeseries(Stot, c, g, tend,...
nsamples, change, change_rate, 'sampling', 'rich-biased',...
change, change_rate, 'timelag', timelag,...
'mu', mu, 'var', var, start_coordinates, 'truncate', pt);
if doAR == 1
[trends_rich_biased] = get_trends_AR(timeseries_rich_biased);
else
[trends_rich_biased] = get_trends(timeseries_rich_biased);
end
samples = trends_rich_biased;
fields = fieldnames(samples);
for f = 1 : length(fields)
field = eval(sprintf('samples.%s', fields{f}));
if isequal(size(field), [nsamples 1])
eval( sprintf('out.%s_rich_biased(i,:) = samples.%s;', fields{f}, fields{f}) )
elseif isequal(size(field), [1 1])
eval( sprintf('out.%s_rich_biased(i) = samples.%s;', fields{f}, fields{f}) )
end
end
%% Get difference of selected grain to mean grain in landscape
grain2mean.N_random(i,:) = grain2mean_random.N;
grain2mean.N_st_random(i,:) = grain2mean_random.N_st;
grain2mean.S_random(i,:) = grain2mean_random.S;
grain2mean.S_st_random(i,:) = grain2mean_random.S_st;
grain2mean.N_comm_biased(i,:) = grain2mean_comm_biased.N;
grain2mean.N_st_comm_biased(i,:) = grain2mean_comm_biased.N_st;
grain2mean.S_comm_biased(i,:) = grain2mean_comm_biased.S;
grain2mean.S_st_comm_biased(i,:) = grain2mean_comm_biased.S_st;
grain2mean.N_rich_biased(i,:) = grain2mean_rich_biased.N;
grain2mean.N_st_rich_biased(i,:) = grain2mean_rich_biased.N_st;
grain2mean.S_rich_biased(i,:) = grain2mean_rich_biased.S;
grain2mean.S_st_rich_biased(i,:) = grain2mean_rich_biased.S_st;
end
end