BYOM function startvals.m

Syntax: [Y0,ECx,logc] = startvals(DATA,W)

This function calculates starting values for fitting a dose-response curve, and ranges for plotting model curves, from the data set entered.

As input, it gets:

Output is * Y0 initial value for control response * ECx initial value for ECx (uses the EC50) * logc range for plotting the model curve on log-scale

Copyright (c) 2012-2021, Tjalling Jager, all rights reserved.
This source code is licensed under the MIT-style license found in the
LICENSE.txt file in the root directory of BYOM.

Contents

Start

function [Y0,ECx,logc] = startvals(DATA,W)

if DATA(1,1) < 0 % then we have survival data
    mn_resp = mean(DATA(2:end,2:end)./W,2,'omitnan'); % calculate mean response over all replicates (ignore NaNs)
    % Note: recalculate the mean response to a survival probability
    Y0      = mn_resp(1); % initial value for control response is mean of control (as probability)
else
    mn_resp = mean(DATA(2:end,2:end),2,'omitnan'); % calculate mean response over all replicates (ignore NaNs)
    Y0      = mn_resp(1); % initial value for control response (before scaling)
    mn_resp = mn_resp / mn_resp(1); % scale the mean response to that in the control treatment
end

ind_1   = find(mn_resp > 0.5,1,'last'); % find highest concentration that has less than 50% effect
ind_2   = find(mn_resp < 0.5,1,'first'); % find lowest concentration that has more than 50% effect
c       = DATA(2:end,1); % take concentrations from data set (first column)

if c(ind_1) == 0 % catch cases where there is more than 50% effect in the first treatment
    % and the first concentration is zero
    ECx = c(ind_2); % just use the second one (first treatment with >50% effect)
else
    ECx = 10.^(mean(log10(c([ind_1 ind_2])))); % initial value for ECx around 50% effect (geometric mean)
end

logc    = [log10(c(2)) - 2.4*mean(diff(log10(c(c>0)))) log10(1.2*c(end))]; % useful range for plotting the model curve on log-scale

Published with MATLAB® R2021b