Convert R code to MATLAB [closed] - r

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r code for an algorithm with fictitious data:
I am working to translate this to MATLAB but struggling with the calculation that's running inside the loop. Any help will be appreciated.
data <- c(-0.39, 0.12, 0.94, 1.67, 1.76, 2.44, 3.72,
4.28, 4.92, 5.53, 0.06, 0.48, 1.01, 1.68, 1.80,
3.25, 4.12, 4.60, 5.28, 6.22)
pi <- 0.546
sigmas1 <- 0.87
sigmas2 <- 0.77
mu1 <- numeric(0)
mu2 <- numeric(0)
r <- numeric(0)
R1 <- matrix (0 ,20 ,100)
mu1[1] <- 4.62
mu2[1] <- 1.06
for(j in 1:100){
for ( i in 1:20){
r [i] <- pi * dnorm (data[i] , mu2[j], sigmas2^(1/2))/((1- pi)*dnorm(data[i],
mu1[j], sigmas1^(1/2))+ pi*dnorm(data[i], mu2[j], sigmas2^(1/2)))
R1[i, j] <- r[i]
}
r
mu1[j+1] <- sum((1-r)*data)/sum(1-r)
mu2[j+1] <- sum(r*data)/sum(r)
Muu1 <- mu1[j+1]
Muu2 <- mu2[j+1]
}
Muu1
Muu2
x11()
layout(matrix(c(1, 2)))
plot(mu1, type="l", main="", xlab="EM Iteration for the Fictitious Data")
plot(mu2, type="l", main="", xlab='EM Iteration for the Fictitious Data')

Answer 1

The MATLAB equivalent of the dnorm function of R is normpdf. The arguments are the same as in R:
normpdf(X,mu,sigma)
With that the for loop can easily be adapted. As the normpdf function allows vectors as inputs, you can dump the inner for loop and use a vectorized approach instead. Always keep in mind, that * and / are the matrix multiplication and division in MATLAB. To get element-wise operators, use .* and ./ instead.
Note that in MATLAB it is better to preallocate all variables. As mu1 and mu2 go from 1 to 100, but in each step you set the value mu[j+1], it will have size 1x101. For rand R1 the size is clear i think.
All together, this would give the following code:
data = [-0.39, 0.12, 0.94, 1.67, 1.76, 2.44, 3.72,...
4.28, 4.92, 5.53, 0.06, 0.48, 1.01, 1.68, 1.80,...
3.25, 4.12, 4.60, 5.28, 6.22];
pi=0.546;
sigmas1 = 0.87;
sigmas2 = 0.77;
mu1 = zeros(1,101);
mu2 = zeros(1,101);
r = zeros(1,20);
R1 = zeros(20,100);
mu1(1) = 4.62;
mu2(1) = 1.06;
for j=1:100
r= pi*normpdf(data,mu2(j),sigmas2^(1/2)) ./ ...
((1-pi)*normpdf(data,mu1(j),sigmas1^(1/2)) + ...
pi*normpdf(data,mu2(j),sigmas2^(1/2)));
R1(:,j) = r;
mu1(j+1) = sum((1-r).*data)/sum(1-r);
mu2(j+1) = sum(r.*data)/sum(r);
end
figure;
subplot(1,2,1);
plot(mu1);
subplot(1,2,2);
plot(mu2);
If this doesn't work correctly for you, or you have any questions on the code, feel free to comment.