computes permutation importance
computes the change in prediction error from permuting variables.
permutationImportance(data, vars, y, model, nperm = 100L, predict.fun = function(object, newdata) predict(object, newdata = newdata), loss.fun = function(x, y) defaultLoss(x, y), contrast.fun = function(x, y) x - y)
data |
a |
vars |
a character vector specifying columns of |
y |
a character vector giving the name of the target/outcome variable. |
model |
an object with a predict method which returns a vector or matrix. presumably this object represents a model fit. |
nperm |
positive integer giving the number of times to permute the indicated variables (default is 100). |
predict.fun |
what function to generate predictions using |
loss.fun |
what loss function to use to measure prediction errors. default is mean squared-error for ordered predictions and mean misclassification error for unordered prediction errors. this function must take two arguments, “x” and “y”, which operate on the output of |
contrast.fun |
what function to use to contrast the permuted and unpermuted predictions. default is the difference. this function takes two arguments “x” and “y”, which are the output of the |
a numeric vector or matrix, depending on contrast.fun and loss.fun, giving the change in prediction error from nperm permutations of vars.
X = replicate(3, rnorm(100)) y = X %*% runif(3) data = data.frame(X, y) fit = lm(y ~ -1 + X1 + X2 + X3, data) permutationImportance(data, "X1", "y", fit)
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