glm.cons: Fitting Generalized Linear Models with constraint on the...

In zetadiv: Functions to Compute Compositional Turnover Using Zeta Diversity

Fitting Generalized Linear Models with constraint on the coefficients signs

Description

glm.cons is an adaptation of function glm2 from package {glm2} in which the least squares estimation is replaced by a regression with signs constraint on the coefficients using function nnnpls from package {nnls}.

Usage

glm.cons(
  formula,
  family = stats::gaussian(),
  data,
  weights,
  subset,
  na.action,
  start = NULL,
  etastart,
  mustart,
  offset,
  control = list(...),
  model = TRUE,
  method = "glm.fit.cons",
  cons = -1,
  cons.inter = 1,
  x = FALSE,
  y = TRUE,
  contrasts = NULL,
  ...
)

Arguments

formula	as for glm
family	as for glm
data	as for glm
weights	as for glm
subset	as for glm
na.action	as for glm
start	as for glm
etastart	as for glm
mustart	as for glm
offset	as for glm
control	as for glm
model	as for glm
method	the method used in fitting the model. The default method "glm.fit.cons" uses function nnnpls from package nnls instead of lm.fit to impose the sign of the coefficients. As in glm, the alternative method "model.frame" returns the model frame and does no fitting.
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.
x	as for glm
y	as for glm
contrasts	as for glm
...	as for glm

Value

The value returned by glm.cons has exactly the same structure as the value returned by glm and glm.2.

References

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

See Also

glm, glm2

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.