glm.fit.cons: Generalized Linear Models fitting method with negative...

In zetadiv: Functions to Compute Compositional Turnover Using Zeta Diversity

Generalized Linear Models fitting method with negative coefficients constraint

Description

glm.fit.cons is an adaptation of function glm.fit2 from package {glm2} in which the least squares estimation is replaced by a non-positive regression using function nnnpls from package {nnls}.

Usage

glm.fit.cons(
  x,
  y,
  weights = rep(1, nobs),
  cons = -1,
  cons.inter = 1,
  start = NULL,
  etastart = NULL,
  mustart = NULL,
  offset = rep(0, nobs),
  family = stats::gaussian(),
  control = list(),
  intercept = TRUE
)

Arguments

x	as for glm.fit
y	as for glm.fit
weights	as for glm.fit
cons	type of constraint. Default is -1 for negative coefficients on the predictors. The other option is 1 for positive coefficients on the predictors.
cons.inter	type of constraint for the intercept. Default is 1 for positive intercept, suitable for Gaussian family. The other option is -1 for negative intercept, suitable for binomial family.
start	as for glm.fit
etastart	as for glm.fit
mustart	as for glm.fit
offset	as for glm.fit
family	as for glm.fit
control	as for glm.fit
intercept	as for glm.fit

Value

The value returned by glm.fit.cons has exactly the same structure as the value returned by glm.fit and glm.fit2.

References

Marschner, I.C. (2011) glm2: Fitting generalized linear models with convergence problems. The R Journal, 3(2), 12-15.

See Also

glm.fit, glm.fit2

Examples

## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
glm.D93 <- glm.cons(counts ~ outcome + treatment, family = poisson())
glm.D93.ngl <- glm.cons(counts ~ outcome + treatment, family = poisson(),
   method="glm.fit.cons")
summary(glm.D93)
summary(glm.D93.ngl)

zetadiv documentation built on June 11, 2022, 1:12 a.m.