fast generalized linear model fitting
fast generalized linear model fitting
bigLm default
fastglm(x, ...) ## Default S3 method: fastglm(x, y, family = gaussian(), weights = NULL, offset = NULL, start = NULL, etastart = NULL, mustart = NULL, method = 0L, tol = 1e-08, maxit = 100L, ...)
x |
input model matrix. Must be a matrix object |
... |
not used |
y |
numeric response vector of length nobs. |
family |
a description of the error distribution and link function to be used in the model.
For |
weights |
an optional vector of 'prior weights' to be used in the fitting process. Should be a numeric vector. |
offset |
this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be a numeric vector of length equal to the number of cases |
start |
starting values for the parameters in the linear predictor. |
etastart |
starting values for the linear predictor. |
mustart |
values for the vector of means. |
method |
an integer scalar with value 0 for the column-pivoted QR decomposition, 1 for the unpivoted QR decomposition, 2 for the LLT Cholesky, or 3 for the LDLT Cholesky |
tol |
threshold tolerance for convergence. Should be a positive real number |
maxit |
maximum number of IRLS iterations. Should be an integer |
A list with the elements
coefficients |
a vector of coefficients |
se |
a vector of the standard errors of the coefficient estimates |
rank |
a scalar denoting the computed rank of the model matrix |
df.residual |
a scalar denoting the degrees of freedom in the model |
residuals |
the vector of residuals |
s |
a numeric scalar - the root mean square for residuals |
fitted.values |
the vector of fitted values |
x <- matrix(rnorm(10000 * 100), ncol = 100) y <- 1 * (0.25 * x[,1] - 0.25 * x[,3] > rnorm(10000)) system.time(gl1 <- glm.fit(x, y, family = binomial())) system.time(gf1 <- fastglm(x, y, family = binomial())) system.time(gf2 <- fastglm(x, y, family = binomial(), method = 1)) system.time(gf3 <- fastglm(x, y, family = binomial(), method = 2)) system.time(gf4 <- fastglm(x, y, family = binomial(), method = 3)) max(abs(coef(gl1) - gf1$coef)) max(abs(coef(gl1) - gf2$coef)) max(abs(coef(gl1) - gf3$coef)) max(abs(coef(gl1) - gf4$coef))
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