[R] fitting gamm with interaction term

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error messag

[R] fitting gamm with interaction term

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error messag

[R] Confidence intervals for non-lm curve

Hi all! I would like to estimate confidence intervals for a non lm model. For example, I use a mixed model of the form: md=lme(y~x1+I(x1^2)+x2 ...) Parameters x1+I(x1^2) are fixed effects and I would like to plot the predicted (partial) curve corresponding to these ones, along with 90% CI

[R] Confidence intervals for non-lm curve

Hi all! I would like to estimate confidence intervals for a non lm model. For example, I use a mixed model of the form: md=lme(y~x1+I(x1^2)+x2 ...) Parameters x1+I(x1^2) are fixed effects and I would like to plot the predicted (partial) curve corresponding to these ones, along with 90%

[R] points size in plots

Dear list, I would like to produce a plot of variables where the size of the points will be indicative of their standard errors. How is that possible? Thank you! [[alternative HTML version deleted]] __ R-help@r-project.org mailing li

[R] partial residuals

Dear all, I am trying to estimate partial residuals for the multiple regression lm model: a.lm=lm(y~x1+x2) I use the function residuals(a.lm, type="partial") However, the results are much different when I use the "manual" method to get partial residuals for x2 (or for x1):

[R] acf function

Dear all, I have an annual time-series of population numbers and I would like to estimate the auto-correlation. Can I use acf() function and judge whether auto-correlation is significant by the plots? The acf array, eg: Autocorrelations of series 'x$log.s.r', by lag 0 1

[R] acf function

Dear all, I have an annual time-series of population numbers and I would like to estimate the auto-correlation. Can I use acf() function and judge whether auto-correlation is significant by the plots? The acf array produced by this functions gives the auto-correlation at lags 1, 2 Is that

[R] intercept in multiple regression

Hi all! Is it possible to model a multiple regression in which the response becomes zero when one of the two covariates is zero? lm(y~ x1+x2) and y=0 if x1=0. However, when x1=0, y=x2+1(intercept). Does this mean I cannot have a second covariate and intercept or should I eliminate only the

[R] interaction of continuous terms

Hi all! this is a rather statistical question: is it meaningful to consider an interaction effect between 2 continuous covariates? for example: lm(y~x1+x2+x1:x2) Should one of continuous x1, x2 be "transformed" to a categorical variable, i.e. be classified into groups? Is it easier to interpre

[R] level significance

Hi all! I am fitting a (mixed) model with a factor (F) and continuous response and predictor: y~F+F:x (How) can I check the significance of the model at each factor level (i.e. the model could be significant only at one of the levels)? Thank you! ___

[R] autocorrelation by group in mixed model

Hi all! (How) is it possible to fit a mixed model with group specific auto-correlation structure ? For instance, not all my groups display auto-correlation so I would like to use a corMatrix (corAR1) only for the auto-correlated ones. If I construct manually a the corMatrix, is it possible to

[R] mean and variance of ratio

Hi all! I try to estimate a statistic of the form: (x1-x2)/(y1-y2), where x1,x2,y1,y2 represent variable means, so each has an estimate and standard error associated with it. How is it possible to estimate the mean and the variance of this ratio? Thank you! [[alternative HTML v

[R] regression with error in predictor

Hi all! I am trying to run a regression where the predictor values are not real data but each is estimated from a different model. So, for each value I have a mean and variance. Which package/function should I use in this case? Thank you! Irene [[alternative HTML version d

[R] non-linear correlation

Hi all! This is a rather statistical question; Which effect sizes (parametric or not) could I use in order to estimate the amount of non-linear correlation between 2 variables? Is it possible to correct for auto-correlation within the correlated times series? Any suggestions for the ap

[R] non-linear model parameterization

g, so this has to be taken into account in the model formulation. What options do I have? Also, how is it possible to fit a partially linear model? Thank you!! Irene Mantzouni PhD student DIFRES __ R-help@r-project.org mailing list

[R] constructing a self-starting non-linear model

Dear all, I am trying to define a selfStart function for a non-linear model, which is a log-transformed SSmicmen model with multiplicative errors and so it is required to make them additive: log(y)=log(a)+log(x)-log(1+x/b) Any ideas about how to use the "peeling" method to derive the "ini

[R] continue for loop in case of erros

Dear all, I have a for loop which includes nls model estimation. The loop breaks after the first non-convergence error. How can I make the loop continue and try to estimate all models? I suppose it should be sth like: if(...) { next } but I have no idea how to setup the arguements... Thank yo

Re: [R] continue for loop in case of erros

Thank you all! Yes, try works! __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible cod

[R] weird (?) resid(.) from 2-level lme()

Hi all! I am trying to fit a 2-level hierarchical lme(). I can extract ranef() and coef() without problems for levels=1:2. However, when it comes to resid() at level=2, the resulting list has some unnamed entries (label="NA"). I checked with my data (NAs have been omitted) and I found out tha

[R] lme() random form

Dear all, I am trying to fit a lme of the form: m=lme(y~F*x-1, random=...,data=...) F is a 2-level factor since I need the fixed part to differ for 2 groups. The random part depends on subgroups G. I would like to have a random effect only on the slope and I cannot figure out how to formulat

[R] residual s.e. by group in mixed model

Hi all! I would like to extract the residual standard error by group in a lme() model... Is there a direct method? Also, a rather statistical question; I need to estimate the standard error of the mean(residuals) in a model..is this the same as the residual s.e.? Thank you!! Irene _

[R] coef se in lme

Hi all! How is it possible to estimate standard errors for coef obtained from lme? Is there sth like se.coef() for lmer or what is the anaytical solution? Thank you! __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help P

Re: [R] coef se in lme

Let's say that I can estimate se for ranef by group and the se for fixef (modifying the se.fixef and se.ranef functions of "arm" package for lmer). Since, I need the se for coef=ranef+fixef can I estimate it based on the SEs of ranef and fixef? Thank you! Irene __

[R] mixed model with factor

Dear all I am trying to fit a mixed model with a factor and a random effect on a slope: y~F*x+...,random=~x where F is a factor with 2 levels and x the covariate. the random effects for the 2 levels of F should be equal so I am fitting the model like: ex.lme=lme(y~x+F+z*x+F*x-1, random=list(g

[R] missing values in mixed model

Dear all, I have a mixed model of the form: y[it]=a+b*z[i]+ai+(c+c[i])*x[it] ("i" refers to group and "t" to observation in group). zi are group-specific variables available only for a number of groups and "ai", "ci" denote the random effects on the intercept and slope respectively. Is the

Re: [R] coef se in lme

parameter that is linear combination of these quantities? All the best, Irene Από: [EMAIL PROTECTED] εκ μέρους Douglas Bates Αποστολή: Τετ 17/10/2007 10:04 μμ Προς: Doran, Harold Κοιν.: Irene Mantzouni; [EMAIL PROTECTED]; R-SIG-Mixed-Models Θέμα: Re: [R] coef se

[R] hierarchical mixed model

I would like to fit a 2-level mixed model: yit=a+a[i]+a[it] +(b+b[i]+b[it])*xit+eps[it] However, the variance of the second level components should depend on the group, i.e. sigma for a[it] and b[it] should be [i] specific. I do not know whether this is conceptually right in the mixed model

[R] mixed model testing

Is there a formal way to prove the need of a mixed model, apart from e.g. comparing the intervals estimated by lmList fit? For example, should I compare (with AIC ML?) a model with seperately (unpooled) estimated fixed slopes (i.e.using an index for each group) with a model that treats this par

[R] interpretation for multiple regression

Dear all, probably this is quite clear for most of you but for me it is a headache... I am regressing response A against the continuous covariate B and the relationship is clearly quadratic. When I add a second covariate B, the relationship becomes linear for both B and C. So, I expect that

Re: [R] interpretation for multiple regression

Hi! Yes, I think that you understood it right and made it clear enough to me too! thank you! :) Από: Daniel Malter [mailto:[EMAIL PROTECTED] Αποστολή: Κυρ 11/11/2007 10:55 μμ Προς: 'Rolf Turner'; Irene Mantzouni Κοιν.: [EMAIL PROTECTED] Θέ