fastglmPure: fast generalized linear model fitting

In fastglm: Fast and Stable Fitting of Generalized Linear Models using 'RcppEigen'

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 fastglmPure this can only be the result of a call to a family function. (See family for details of family functions.)
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))

fastglm documentation built on May 23, 2022, 5:06 p.m.