Displaying 5 results from an estimated 5 matches for "parameterestimates".
2005 Sep 06
2
(no subject)
my problem actually arised with fitting the data to the weibulldistribution,
where it is hard to see, if the proposed parameterestimates make sense.
data1:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491;
?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334
how am I supposed to know what starting values i have to take?
i get different parameterestimates depending on the starting values i...
2005 Sep 06
2
fitting distributions with R
...all
I've got the dataset
data:2743;4678;21427;6194;10286;1505;12811;2161;6853;2625;14542;694;11491;
?? ?? ?? ?? ?? 14924;28640;17097;2136;5308;3477;91301;11488;3860;64114;14334
I know from other testing that it should be possible to fit the data with the
exponentialdistribution. I tried to get parameterestimates for the
exponentialdistribution with R, but as the values
of the parameter are very close to 0 i get into troubles. Do you know, what i
could do in order to get estimates?How do you choose the starting values? in
my opinion it should be around 1/mean(data).
#Parameterestimation ??with mle()...
2005 Jul 28
1
conversion from SAS
...ear;
model logchla=year cos1 sin1 cos2 sin2 cos3 sin3
cos4 sin4 /solution;
by station;
where bloom=0;
output out=chla_res predicted=pred student=studres
cookd=cookd rstudent=rstudent u95=u95;
lsmeans year / at (cos1 sin1 cos2 sin2 cos3 sin3
cos4 sin4)=(0 0 0 0 0 0 0 0);
ODS output ParameterEstimates=parmest
LSmeans=lsmeans;
run;*/
proc glm data=sort_dataset;
class year month;
model chla=/solution;
by station;
weight w_chla;
where bloom=0;
output out=chla_res predicted=pred student=studres
cookd=cookd
daynumber<-data$date-mdy(1,1,y)+1
rstudent=rstudent ucl=ucl lcl=lcl / a...
2012 Nov 12
1
R lmer & SAS glimmix
...a model with lmer in R and proc glimmix in SAS. I have
simplified my code but I am surprised to see I get different results from
the two softwares.
My R code is :
lmer(y~age_cat + (1|cat),data=fic,family=binomial(link = "logit"), NaGQ=1)
My SAS code is :
ods output Glimmix.Glimmix.ParameterEstimates=t_estimates;
proc glimmix data=tab_psi method=laplace;
class age_cat cat;
model psi (event='1') = age_cat / solution dist=B link=logit ;
random intercept / subject=cat;
run;
>From R, I get the following fixed effects
(Intercept) age_cat2. 76-85 ans age_cat3. 66-75 ans age_cat4. 41-65...
2007 Apr 23
4
Estimates at each iteration of optim()?
I am trying to maximise a complicated loglikelihood function with the "optim" command. Is there some way to get to know the estiamtes at each iteration? When I put "control=list(trace=TRUE)" as an option in "optim", I just got the initial and final values of the loglikelihood, number of iterations and whether the routine has converged or not. I need to know the