Hi, everyone,
I met a problem with mixed effect model with lmer. Basically, I want to
treat region and mother ID as a nested random effect structure with mother
ID nested within a specific region. However, in my dataset, many subjects
had no mother ID (about 25% of them). I want to know, if I use
Thanks a lot for your help! I have tried Peter's code: it works perfectly.
Hopefully, this method will also be helpful to others with similar problems.
Best regards,
Jianghua
--
View this message in context:
http://n4.nabble.com/How-to-control-spaces-between-axis-tick-and-label-in-xyplot-or-xYpl
10)
> xyplot(y ~ x,
>scales = list(
> y = list(
> at = 1:10,
> lab = formatC(1:10,
> format = "f", digits = 1
>
> ?formatC
> ?xyplot
>
> -Peter Ehlers
>
>
> willow1980 wrote:
Dear R users,
I encounter a problem regarding number of significant digits on y-axis.
Below is my basic code:
myplotkid<-xyplot(expected_offspringnumber~afr|decade,groups=SES,data1,
auto.key=list(space="right"),layout=c(9,1),xlab="",ylab="Offspring number",
aspect="fill",scales=list(x=list(draw=F)
Dear R users,
I encounter a problem regarding space control in xyplot. Basically, I want
to control spaces between label, tick and axis. I remember there is a
function called mgp in general plot. Is there a similar function for xyplot
or xYplot?
Below is my basic code:
myplotkid<-xyplot(expected_o
Dear R users,
I have posted a similar message in the following link:
http://old.nabble.com/standard-error-for-the-estimated-value-(lmer-fitted-model)-td26414507.html
However, I did not get responses. I guess my question is not clear. Now, I
would like to clarify it and if someone is familiar with
Sorry for making a mistake! It should be,
(m...@deviance["wrss"]...@deviance["wrss"])/m...@deviance["wrss"]
willow1980 wrote:
>
> Hi, Fabio,
> I only have an idea on how to calculate deviance explained by the fixed
> effects. If you remove fixed effects
Dear R users,
I am frustrated by the residual sum of squares calculation. From Statistical
Medhods by Snedecor & Cochran, I know sum(resid(model)^2) should be one
method. However, there is a automatic code in R for this operation:
deviance(model). When I compare the results from these two methods,
Hi, Fabio,
I only have an idea on how to calculate deviance explained by the fixed
effects. If you remove fixed effects and introduce one null model such as
m2<-lmer(response-var ~ 1+(1|Site/Area/Transect),family="binomial"). Then,
(deviance(m2)-deviance(m1))/deviance(m2) will represent deviance e
Dear R users,
I want to draw standard error lines for the predicted regression line
estimated by logistic regression using lmer. I have two predictors: cafr and
its quadratic form I(cafr^2), where cafr is a variable centered around the
mean of original variable. Now, the estimated value from the f
Dear R users,
I have a problem with multicollinearity in mixed models and I am using lmer
in package lme4. From previous mailing list, I learn of a reply
"http://www.mail-archive.com/r-h...@stat.math.ethz.ch/msg38537.html"; which
states that if not for interpretation but just for prediction,
multi
lot. Hopefully,
this experience can do some help to other users with similar problems.
However, I am still trying to use the method suggested by Dr Sarkar, since I
think wireframe or levelplot may produce more beautiful pictures.
Thank you again!
Cheers!
willow1980 wrote:
>
> Dear R users,
>
Dear Professor Murdoch,
That is exactly the difficulty for me. I don't know how to make a prediction
with lmer using "expand.grid"; at the moment, I can use
“mo...@x%*%fixef(model)” to get predicted values for existing observational
data, but not data by "expand.grid". Actually, if I know this, I
Dear R users,
I have a problem in plotting 3 dimensional graph using mixed models.
My model is
sur_prop ~
afr_c+I(afr_c^2)+I(afr_c^3)+byear_c+I(byear_c^2)+I(byear_c^3)+I(byear_c^4)+(1|Studyparish)+afr_c:byear_c
+afr_c:I(byear_c^2)+afr_c:I(byear_c^3)+afr_c:I(byear_c^4)+I(afr_c^2):byear_c+I(afr_c^2
duct
> smooths (`te') may be preferable, as the are independent of the relative
> scaling of the variables. For plot interpretability, I'd drop the `main
> effect' smooths and just leave in the interaction.
>
> best,
> Simon
>
> On Tuesday 05 May 2009 16:53, willow1980
Sorry, I did not notice you were using GAM package. Most R users are using
Simon Wood's MGCV package. I recommend you to use it. I have never used GAM
package, so I cannot make further comments. Good luck!
楊 詩韻 wrote:
>
>
> dear all,
>
>
>
> i have a little question, but it make me torment
Strangely, summary.gam(m1) should give you significance results of parametric
terms such as ost, wst, park10, sch50, comm, build and suite. These results
should be located above the summary results for smooth terms.
Please using summary.gam(m1) to have a look if there is the information you
need.
I am a little bit confusing about the following help message on how to fit a
GAM model with interaction between factor and smooth terms from
http://rss.acs.unt.edu/Rdoc/library/mgcv/html/gam.models.html:
“Sometimes models of the form:
E(y)=b0+f(x)z
need to be estimated (where f is a smooth functi
ected automatically). Are you using gam:gam or mgcv:gam (and which
> version
> numbers)?
>
> best,
> Simon
>
> On Tuesday 28 April 2009 12:38, willow1980 wrote:
>> Hello, everybody,
>> There is the first time for me to post a question, because I really
>> canno
gam(sum_surv15~s(FLBS)+s(byear)+s(FLBS,byear),family=quasipoisson)
anova.gam(modelsurs_fer14,modelsurs_fer13,test="F")
#
Would you please make further suggestions? Thank you in advance!
Jianghua
Dieter Menne wrote:
>
>
Hello, everybody,
There is the first time for me to post a question, because I really cannot
find answer from books, websites or my colleagues. Thank you in advance for
your help!
I am running likelihood ratio test to find if the simpler model is not
significant from more complicated model. Howeve
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