Regression Method

struct RegressionGSA <: GSAMethod
    rank::Bool = false

RegressionGSA has the following keyword arguments:

  • rank: flag which determines whether to calculate the rank coefficients. Defaults to false.

It returns a RegressionGSAResult, which contains the pearson, standard_regression, and partial_correlation coefficients, described below. If rank is true, then it also contains the ranked versions of these coefficients. Note that the ranked version of the pearson coefficient is also known as the Spearman coefficient, which is returned here as the pearson_rank coefficient.

For multi-variable models, the coefficient for the $X_i$ input variable relating to the $Y_j$ output variable is given as the [i, j] entry in the corresponding returned matrix.

Regression Details

It is possible to fit a linear model explaining the behavior of Y given the values of X, provided that the sample size n is sufficiently large (at least n > d).

The measures provided for this analysis by us in GlobalSensitivity.jl are

a) Pearson Correlation Coefficient:

\[r = \frac{\sum_{i=1}^{n} (x_i - \overline{x})(y_i - \overline{y})}{\sqrt{\sum_{i=1}^{n} (x_i - \overline{x})^2(y_i - \overline{y})^2}}\]

b) Standard Regression Coefficient (SRC):

\[SRC_j = \beta_{j} \sqrt{\frac{Var(X_j)}{Var(Y)}}\]

where $\beta_j$ is the linear regression coefficient associated to $X_j$. This is also known as a sigma-normalized derivative.

c) Partial Correlation Coefficient (PCC):

\[PCC_j = \rho(X_j - \hat{X_{-j}},Y_j - \hat{Y_{-j}})\]

where $\hat{X_{-j}}$ is the prediction of the linear model, expressing $X_{j}$ with respect to the other inputs and $\hat{Y_{-j}}$ is the prediction of the linear model where $X_j$ is absent. PCC measures the sensitivity of $Y$ to $X_j$ when the effects of the other inputs have been canceled.

If rank is set to true, then the rank coefficients are also calculated.


function gsa(f, method::RegressionGSA, p_range::AbstractVector; samples::Int = 1000, batch::Bool = false, kwargs...)


using GlobalSensitivity

function linear_batch(X)
    A= 7
    B= 0.1
    @. A*X[1,:]+B*X[2,:]
function linear(X)
    A= 7
    B= 0.1

p_range = [[-1, 1], [-1, 1]]
reg = gsa(linear_batch, RegressionGSA(), p_range; batch = true)

reg = gsa(linear, RegressionGSA(), p_range; batch = false)
reg = gsa(linear, RegressionGSA(true), p_range; batch = false) #with rank coefficients