If the family is Poisson or binomial and the dispersion= argument to summary
is omitted then sigma is 1. See ?summary.glm
On Tue, Dec 24, 2024 at 8:45 AM Christofer Bogaso
wrote:
>
> Hi,
>
> I have below GLM fit
>
> clotting <- data.frame(
> u = c(5,10,15,20,30,40,60,80,100),
> lot1 = c(
Às 23:29 de 24/12/2024, Bert Gunter escreveu:
... but do note:
glm(lot1 ~ log(u), data = clotting, family = gaussian)
is a plain old *linear model*, which is of course a specific type of
glm, but not one that requires the machinery of glm() to fit. That
is, the above is exactly the same as:
lm
... but do note:
glm(lot1 ~ log(u), data = clotting, family = gaussian)
is a plain old *linear model*, which is of course a specific type of
glm, but not one that requires the machinery of glm() to fit. That
is, the above is exactly the same as:
lm(lot1 ~ log(u), data = clotting)
and gives exac
?sigma
On 12/24/24 10:13, Bert Gunter wrote:
?deviance ?anova
Bert
On Tue, Dec 24, 2024 at 6:22 AM Christofer Bogaso
wrote:
I think vcov() gives estimates of VCV for coefficients.
I want estimate of SD for residuals
On Tue, Dec 24, 2024 at 7:24 PM Ben Bolker wrote:
vcov(). ?
On Tu
?deviance ?anova
Bert
On Tue, Dec 24, 2024 at 6:22 AM Christofer Bogaso
wrote:
>
> I think vcov() gives estimates of VCV for coefficients.
>
> I want estimate of SD for residuals
>
> On Tue, Dec 24, 2024 at 7:24 PM Ben Bolker wrote:
> >
> > vcov(). ?
> >
> >
> > On Tue, Dec 24, 2024, 8:45 AM C
I think vcov() gives estimates of VCV for coefficients.
I want estimate of SD for residuals
On Tue, Dec 24, 2024 at 7:24 PM Ben Bolker wrote:
>
> vcov(). ?
>
>
> On Tue, Dec 24, 2024, 8:45 AM Christofer Bogaso
> wrote:
>>
>> Hi,
>>
>> I have below GLM fit
>>
>> clotting <- data.frame(
>> u
vcov(). ?
On Tue, Dec 24, 2024, 8:45 AM Christofer Bogaso
wrote:
> Hi,
>
> I have below GLM fit
>
> clotting <- data.frame(
> u = c(5,10,15,20,30,40,60,80,100),
> lot1 = c(118,58,42,35,27,25,21,19,18),
> lot2 = c(69,35,26,21,18,16,13,12,12))
> summary(glm(lot1 ~ log(u), data = clotti
Hi,
I have below GLM fit
clotting <- data.frame(
u = c(5,10,15,20,30,40,60,80,100),
lot1 = c(118,58,42,35,27,25,21,19,18),
lot2 = c(69,35,26,21,18,16,13,12,12))
summary(glm(lot1 ~ log(u), data = clotting, family = gaussian))
Is there any direct function to extract estimate of Error s
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