Description Usage Arguments Value Examples
View source: R/sampleUnivSNMoE.R
Draw a sample from a skewnormal mixture of linear experts model.
1  sampleUnivSNMoE(alphak, betak, sigmak, lambdak, x)

alphak 
The parameters of the gating network. 
betak 
Matrix of size (p + 1, K) representing the regression coefficients of the experts network. 
sigmak 
Vector of length K giving the standard deviations of the experts network. 
lambdak 
Vector of length K giving the skewness parameter of each experts. 
x 
A vector og length n representing the inputs (predictors). 
A list with the output variable y
and statistics.
y
Vector of length n giving the output variable.
zi
A vector of size n giving the hidden label of the
expert component generating the ith observation. Its elements are
zi[i] = k, if the ith observation has been generated by the
kth expert.
z
A matrix of size (n, K) giving the values of the binary
latent component indicators Zik such that
Zik = 1 iff Zi = k.
stats
A list whose elements are:
Ey_k
Matrix of size (n, K) giving the conditional
expectation of Yi the output variable given the value of the
hidden label of the expert component generating the ith observation
zi = k, and the value of predictor X = xi.
Ey
Vector of length n giving the conditional expectation
of Yi given the value of predictor X = xi.
Vary_k
Vector of length k representing the conditional
variance of Yi given zi = k, and X = xi.
Vary
Vector of length n giving the conditional expectation
of Yi given X = xi.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  n < 500 # Size of the sample
alphak < matrix(c(0, 8), ncol = 1) # Parameters of the gating network
betak < matrix(c(0, 2.5, 0, 2.5), ncol = 2) # Regression coefficients of the experts
lambdak < c(3, 5) # Skewness parameters of the experts
sigmak < c(1, 1) # Standard deviations of the experts
x < seq.int(from = 1, to = 1, length.out = n) # Inputs (predictors)
# Generate sample of size n
sample < sampleUnivSNMoE(alphak = alphak, betak = betak, sigmak = sigmak,
lambdak = lambdak, x = x)
# Plot points and estimated means
plot(x, sample$y, pch = 4)
lines(x, sample$stats$Ey_k[, 1], col = "blue", lty = "dotted", lwd = 1.5)
lines(x, sample$stats$Ey_k[, 2], col = "blue", lty = "dotted", lwd = 1.5)
lines(x, sample$stats$Ey, col = "red", lwd = 1.5)

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