fastglmPure {fastglm} | R Documentation |
fast generalized linear model fitting
Description
fast generalized linear model fitting
Usage
fastglmPure(
x,
y,
family = gaussian(),
weights = rep(1, NROW(y)),
offset = rep(0, NROW(y)),
start = NULL,
etastart = NULL,
mustart = NULL,
method = 0L,
tol = 1e-07,
maxit = 100L
)
Arguments
x |
input model matrix. Must be a matrix object |
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, 3 for the LDLT Cholesky, 4 for the full pivoted QR decomposition, 5 for the Bidiagonal Divide and Conquer SVD |
tol |
threshold tolerance for convergence. Should be a positive real number |
maxit |
maximum number of IRLS iterations. Should be an integer |
Value
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 |
Examples
set.seed(1)
x <- matrix(rnorm(1000 * 25), ncol = 25)
eta <- 0.1 + 0.25 * x[,1] - 0.25 * x[,3] + 0.75 * x[,5] -0.35 * x[,6] #0.25 * x[,1] - 0.25 * x[,3]
y <- 1 * (eta > rnorm(1000))
yp <- rpois(1000, eta ^ 2)
yg <- rgamma(1000, exp(eta) * 1.75, 1.75)
# binomial
system.time(gl1 <- glm.fit(x, y, family = binomial()))
system.time(gf1 <- fastglmPure(x, y, family = binomial(), tol = 1e-8))
system.time(gf2 <- fastglmPure(x, y, family = binomial(), method = 1, tol = 1e-8))
system.time(gf3 <- fastglmPure(x, y, family = binomial(), method = 2, tol = 1e-8))
system.time(gf4 <- fastglmPure(x, y, family = binomial(), method = 3, tol = 1e-8))
max(abs(coef(gl1) - gf1$coef))
max(abs(coef(gl1) - gf2$coef))
max(abs(coef(gl1) - gf3$coef))
max(abs(coef(gl1) - gf4$coef))
# poisson
system.time(gl1 <- glm.fit(x, yp, family = poisson(link = "log")))
system.time(gf1 <- fastglmPure(x, yp, family = poisson(link = "log"), tol = 1e-8))
system.time(gf2 <- fastglmPure(x, yp, family = poisson(link = "log"), method = 1, tol = 1e-8))
system.time(gf3 <- fastglmPure(x, yp, family = poisson(link = "log"), method = 2, tol = 1e-8))
system.time(gf4 <- fastglmPure(x, yp, family = poisson(link = "log"), method = 3, tol = 1e-8))
max(abs(coef(gl1) - gf1$coef))
max(abs(coef(gl1) - gf2$coef))
max(abs(coef(gl1) - gf3$coef))
max(abs(coef(gl1) - gf4$coef))
# gamma
system.time(gl1 <- glm.fit(x, yg, family = Gamma(link = "log")))
system.time(gf1 <- fastglmPure(x, yg, family = Gamma(link = "log"), tol = 1e-8))
system.time(gf2 <- fastglmPure(x, yg, family = Gamma(link = "log"), method = 1, tol = 1e-8))
system.time(gf3 <- fastglmPure(x, yg, family = Gamma(link = "log"), method = 2, tol = 1e-8))
system.time(gf4 <- fastglmPure(x, yg, family = Gamma(link = "log"), method = 3, tol = 1e-8))
max(abs(coef(gl1) - gf1$coef))
max(abs(coef(gl1) - gf2$coef))
max(abs(coef(gl1) - gf3$coef))
max(abs(coef(gl1) - gf4$coef))