Sum of Square Errors
Compute the sum of squared prediction errors (or residual sum of squares) when a linear model is applied to a dataset.
sse(b, dataset)
b |
vector or column-matrix of regression coefficients |
dataset |
a matrix or dataframe. The final column is the outcome variable. |
The function returns the sum of square errors.
## Using simulated data derived from the iris dataset mu <- c(rep(0, 4)) covmatr <- matrix(c(0.7, -0.04, 1.3, 0.5, -0.04, 0.2, -0.3, -0.1, 1.3, -0.3, 3.1, 1.3, 0.5, -0.1, 1.3, 0.6), ncol = 4) sim.dat <- randnor(n = 100, mu = mu, Cov = covmatr) sim.dat <- cbind(1, sim.dat) ## resample and fit an ordinary least squares model, and then ## calculate the sum of square errors of the model when applied ## to the original data sim.boot <- randboot(sim.dat, replace = TRUE) boot.betas <- ols.rgr(sim.boot) sse(b = boot.betas, dataset = sim.dat)
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