fastglm (version 0.0.3)

fastglm: fast generalized linear model fitting

Description

fast generalized linear model fitting

bigLm default

Usage

fastglm(x, ...)
# S3 method for default
fastglm(
  x,
  y,
  family = gaussian(),
  weights = NULL,
  offset = NULL,
  start = NULL,
  etastart = NULL,
  mustart = NULL,
  method = 0L,
  tol = 1e-08,
  maxit = 100L,
  ...
)

Arguments

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 fastglm this can be a character string naming a family function, a family function or the result of a call to a family function. For fastglmPure only the third option is supported. (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, 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

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

# NOT RUN {
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))


# }
# NOT RUN {
nrows <- 50000
ncols <- 50
bkFile <- "bigmat2.bk"
descFile <- "bigmatk2.desc"
bigmat <- filebacked.big.matrix(nrow=nrows, ncol=ncols, type="double",
                                backingfile=bkFile, backingpath=".",
                                descriptorfile=descFile,
                                dimnames=c(NULL,NULL))
for (i in 1:ncols) bigmat[,i] = rnorm(nrows)*i
y <- 1*(rnorm(nrows) + bigmat[,1] > 0)

system.time(gfb1 <- fastglm(bigmat, y, family = binomial(), method = 3))
# }
# NOT RUN {
# }