Displaying 4 results from an estimated 4 matches for "hypoxia".
2007 Dec 14
1
flagging glm models with warnings
...d using glm.nb from the MASS package and the
negbin function from the AOD package, but both still cause some models to
experience errors and warnings.
Examples of my 2 largest models, run from the 2 different functions
negBin.glm1 <- glm.nb(Count ~ offset(log(Tow.Area)) + Basin + Bathy +
Hypoxia + Period + Depth + Basin*Depth + Bathy*Depth + Hypoxia*Depth +
Period*Depth + Basin*Period + Bathy*Period + Hypoxia*Period +
Hypoxia:Period:Depth + Bathy:Period:Depth +
Basin:Period:Depth, control=glm.control(maxit=1000),
method="glm.fit", start=coefficients(...
2007 Dec 12
1
Defining the "random" term in function "negbin" of AOD package
...negbin(formula, random, data, phi.ini = NULL, warnings = FALSE,
na.action = na.omit, fixpar = list(),
hessian = TRUE, control = list(maxit = 2000), ...)
My largest model using glm.nb looks like this:
negBin.glm1 <- glm.nb(Count ~ offset(log(Tow.Area)) + Basin + Bathy +
Hypoxia + Period + Depth + Basin*Depth + Bathy*Depth + Hypoxia*Depth +
Period*Depth + Basin*Period + Bathy*Period + Hypoxia*Period +
Hypoxia:Period:Depth + Bathy:Period:Depth +
Basin:Period:Depth, control=glm.control(maxit=1000),
method="glm.fit",
da...
2011 Jun 23
2
new to R need urgent help!
...er,data=drt,family=poisson(link =
"log"))
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept) as.factor(time)24
3.051177 -2.705675
condHypoxia as.factor(time)24:condHypoxia
-0.402259 1.429034
> pv=2*(1-pnorm(abs(summary(fit)$coef[,5])))
> data.frame(summary(fit)$coef,pv)
Estimate Naive.S.E. Naive.z Robust.S.E.
Robust.z
(Intercept) 3.051177 0...
2011 Jun 24
0
understand GEE output for wald test
...er,data=drt,family=poisson(link =
"log"))
Beginning Cgee S-function, @(#) geeformula.q 4.13 98/01/27
running glm to get initial regression estimate
(Intercept) as.factor(time)24
3.051177 -2.705675
condHypoxia as.factor(time)24:condHypoxia
-0.402259 1.429034
> pv=2*(1-pnorm(abs(summary(fit)$coef[,5])))
> data.frame(summary(fit)$coef,pv)
Estimate Naive.S.E. Naive.z Robust.S.E.
(Intercept) 3.051177 0.02221052...