Can this code run faster?

I currently have a code, that I have to run a 100+ times, with 100+ different seeds. But only one round already takes 72 seconds, and my fellow students who run the code in R say their code runs a lot faster. particularly the fitlm part of part 5 of the code takes up some time. Does anyone know how to make the following code faster or more effective?

tic

seed = 2;

1 %%

rng('default');

rng(seed);

x = zeros(4000,1);

sz = size(x);

Income = lognrnd(3.60,0.5, sz);

v = normrnd(0,1,sz);

Beta = zeros(4000,1);

B0 = -2;

B1 = 1;

for i = 1:4000

Beta(i) = B0 + B1*(Income(i)/100000) + v(i);

end

2 %%

rng(seed);

HouseholdSize = zeros(4000,1);

sz = size(HouseholdSize);

for i = 1:800

x = randi([1 2]);

HouseholdSize(i) = x;

end

for i = 801:1600

x = randi([1 3]);

HouseholdSize(i) = x;

end

for i = 1601:2400

x = randi([1 4]);

HouseholdSize(i) = x;

end

for i = 2401:3200

x = randi([1 5]);

HouseholdSize(i) = x;

end

for i = 3201:4000

x = randi([1 6]);

HouseholdSize(i) = x;

end

HouseholdSize = HouseholdSize(randperm(length(HouseholdSize)));

u = normrnd(0,0.1,sz);

Rho = zeros(4000,1);

P0 = 0.7;

P1 = 0.015;

for i = 1:4000

Rho(i) = P0 + P1*HouseholdSize(i) + u(i);

end

3 %%

rng(seed)

X = 0.5 + (1).*rand(4000,24);

q = zeros(4000,1);

sizeq = size(q);

Alpha = normrnd(10,0.1,sizeq);

y = zeros(4000,24);

y(:,1) = 10;

sizey = size(y);

epsilon = normrnd(0,1,sizey);

for hh = 1:4000

for time = 2:24

y(hh,time) = Alpha(hh) + Beta(hh)*X(hh,time) + Rho(hh)*y(hh,time-1) + epsilon(hh,time);

end

end

4 %%

SamenTabel = zeros(92000,5);

for i = 1:4000

for j = 1:23

SamenTabel((i-1)*23 + j,1) = j + 1;

SamenTabel((i-1)*23 + j,2) = i;

SamenTabel((i-1)*23 + j,3) = y(i, j + 1);

SamenTabel((i-1)*23 + j,4) = X(i, j);

SamenTabel((i-1)*23 + j,5) = y(i, j);

end

end

SamenTabel = array2table(SamenTabel);

SamenTabel.Properties.VariableNames = {'Time' 'HH' 'Buys' 'Price' 'LagBuys'};

5 %%

tic

Amount1 = unique(SamenTabel(:,2));

Amount = height(Amount1);

Dataset1 = table2array(SamenTabel);

Dataset = cell(Amount,3);

SimuAlpha = zeros(4000,2);

SimuBeta = zeros(4000,2);

SimuRho = zeros(4000,2);

for i = 1:1:Amount

[Rows, ~] = find(Dataset1(:,2) == i);

DatasetUSE = SamenTabel(Rows,:);

Regression = fitlm(DatasetUSE,...

'Buys ~ 1 + Price + LagBuys');

Dataset(i,1) = {Regression};

T1 = table2array(Dataset{i, 1}.Coefficients(1,1));

T11 = table2array(Dataset{i, 1}.Coefficients(1,2));

T2 = table2array(Dataset{i, 1}.Coefficients(2,1));

T22 = table2array(Dataset{i, 1}.Coefficients(2,2));

T3 = table2array(Dataset{i, 1}.Coefficients(3,1));

T33 = table2array(Dataset{i, 1}.Coefficients(3,2));

SimuAlpha(i,1) = T1;

SimuAlpha(i,2) = T11;

SimuBeta(i,1) = T2;

SimuBeta(i,2) = T22;

SimuRho(i,1) = T3;

SimuRho(i,2) = T33;

end

toc

6 %%

Laag2 = zeros(4000,2);

Laag3 = zeros(4000,2);

for i = 1:4000

Laag2(i,1) = SimuBeta(i,1);

Laag2(i,2) = Income(i,1);

Laag3(i,1) = SimuRho(i,1);

Laag3(i,2) = HouseholdSize(i,1);

end

Laag2 = array2table(Laag2);

Laag3 = array2table(Laag3);

Laag2.Properties.VariableNames = {'Beta' 'Income'};

Laag3.Properties.VariableNames = {'Rho' 'Size'};

7 %%

BetaEstimate = fitlm(Laag2,...

'Beta ~ 1 + Income');

RhoEstimate = fitlm(Laag3,...

'Rho ~ 1 + Size');

%%

toc

Answer 1

[Michael W]

Use the MATLAB profiler to find out where your code is slow.

Answer 2

[Nam Vu]

I've run #2, I think you could review line by line to do the code-refactoring. For example:

for i = 1:800
    x = randi([1 2]);
    HouseholdSize(i) = x;
end

could be :

for i = 1:800
    HouseholdSize(i) = randi([1 2]);
end

less 1 assignment command => more faster.

For thousands of runs, I think we have another way to create the HouseholdSize:

HouseholdSize = [randi([1 2], [800,1]) randi([1 3], [800,1]) randi([1 4], [800,1]) randi([1 5], [800,1]) randi([1 6], [800,1])];
HouseholdSize = HouseholdSize(randperm(4000));

You could use the vector-operator to do the calculate Rho:

u = normrnd(0,0.1,[1,4000]); 
P0 = 0.7;
P1 = 0.015;
Rho = P0 + P1*HouseholdSize +u

Regards,