Convenient helper function, which creates an initial sample - either based on random (uniform) sampling or using latin hypercube sampling.
createInitialSample(n.obs, dim, control)
Arguments
n.obs: [integer(1)]
Number of observations.
dim: [integer(1)]
Number of dimensions.
control: [list]
Control argument. For further information refer to the details.
Returns
[matrix].
A matrix, consisting of n.obs rows of dim-dimensional observations.
Details
Per default, this function will produce n.obs observations of size dim in the range from 0 to 1. If you want to create a more specific initial sample, the following control arguments might be helpful:
init_sample.type: Should the initial sample be created based on random uniform sampling ("random") or on a latin hypercube sample ("lhs")? The default is "random".
init_sample.lower: The lower bounds of the initial sample. Either a vector of size dim or a scalar (if all lower bounds are identical). The default is 0.
init_sample.upper: The upper bounds of the initial sample. Either a vector of size dim or a scalar (if all upper bounds are identical). The default is 1.
Examples
# (1) create a simple initial sample:X = createInitialSample(300,5)summary(X)# (2) create a more specific initial sample:ctrl = list(init_sample.type ="lhs", init_sample.lower = c(-5,2,0), init_sample.upper =10)X = createInitialSample(200,3, control = ctrl)summary(X)