similar to: Re: follow up on "pairewise plots"

Dear R users, I have a data generated as the following, dat <- data.frame(matrix(sample(24), nrow=4)) dimnames(dat) <-list(rownames=c('g1','g2','g3','g4'), colnames=c("A_CH1","A_CH2","B_CH1","B_CH2","C_CH3","C_CH3")) » dat A_CH1 A_CH2 B_CH1 B_CH2 C_CH3 C_CH3 g1 16 24 7 9 14

Dear R-users Can anyone inform me of a library or more specifically functions that can maximise (or calculate) a Pairwsie likelihood from a data. Better still, i would like to know if there is a function (library) that fits regression models based on pairwise likelihoods. Thanks Pryseley --------------------------------- [[alternative HTML version deleted]]

Hi Dimitris: Thanks for your help, I will try. BR Yiyao -----ԭʼÓʼþ----- ·¢¼þÈË: Dimitris Rizopoulos [mailto:dimitris.rizopoulos at med.kuleuven.ac. be] ·¢ËÍʱ¼ä: 2004Äê10ÔÂ25ÈÕ 15:39 ÊÕ¼þÈË: YiYao_Jiang ³­ËÍ: r-help at stat.math.ethz.ch Ö÷Ìâ: Re: [R] Multiple formula in one block Hi YiYao, you need the `?panel.abline()' function, somehing like: panel=function(x, breaks,

Hi Xavier, the reason you observe this feature is that in the 'constraint' argument you should specify the values under the additive parameterization, i.e., when in the second column of the matrix supplied in 'constraint' you specify 2, then you need to provide the easiness parameters (not the difficulty parameters) in the third column. Check the Details section of ?tpm() and

Thanks for the help Dimitris, However I still have a question, this time I'll be more specific, the following is my SAS code proc mixed data=Reg; class ID; model y=Time Time*x1 Time*x2 Time*x3 /S; random intercept Time /S type=UN subject=ID G GCORR V; repeated /subject = ID R RCORR; run; ** (Type =UN for random effects) The eqivalent lme statement I

I have a somewhat related question. A while back I was doing some simulations using for() loops, and I wanted to keep track of the iterations using a line of code quite similar to what Dimitris presented below. Instead of printing the iteration message at the end of each iteration (actually, at the end of every 100th), nothing was printed until the for() loop was complete, and *then* all

Thanks Dimitris!!! That's much clearer now. Still have a lot of work to do this weekend to understand every bit but your code will prove very useful. Cheers, Aziz -----Original Message----- From: Dimitrios Rizopoulos [mailto:Dimitris.Rizopoulos at med.kuleuven.be] Sent: May 12, 2006 4:35 PM To: Chaouch, Aziz Subject: RE: [R] Maximum likelihood estimate of bivariate

Hey, Thanks for your great inputs. While "index = apply(mat == 0, MARGIN = 1, any)" gives you an idea of the rows containing zero(s), "index<-data[!apply(data==0,MARGIN=1,any),]" does the actual job of removing the rows with zeros. Kind regards Ogbos On 9 March 2010 15:12, Paul Hiemstra <p.hiemstra@geo.uu.nl> wrote: > Dimitris Rizopoulos wrote: > >>

Dear R-users, We'd like to announce the release of our new package "grouped" (available from CRAN), for fitting models for grouped or coarse data, under the Coarsened At Random assumption. This is useful in cases where the true response variable is known only up to an interval in which it lies. Features of the package include: power calculations for two-group comparisons,

Dear R-users, We'd like to announce the release of our new package "grouped" (available from CRAN), for fitting models for grouped or coarse data, under the Coarsened At Random assumption. This is useful in cases where the true response variable is known only up to an interval in which it lies. Features of the package include: power calculations for two-group comparisons,

Dear R helpers: Thanks for the previous reply. I am using Friedman racing test. According the the book "Pratical Nonprametric Statistic" by WJ Conover, after computing the statistics, he suggested to use chi-squared or F distribution to accept or reject null hypothesis. After looking into the source code, I found that R uses chi-sqaured distribution as below: PVAL <-

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Dear R-users, I'd like to announce the release of the new version of package JM (soon available from CRAN) for the joint modelling of longitudinal and time-to-event data using shared parameter models. These models are applicable in mainly two settings. First, when focus is in the time-to-event outcome and we wish to account for the effect of a time-dependent covariate measured with

Hi all, Using R v2.7.1, platform i386-pc-mingw32 Can someone please shed some light on the behaviour of ifelse() for me? My intent is to calc relative proportions of z$b, at the same time subsetting z$b based on z$a. I could attack the problem other ways (suggestions welcome) but I am also intrigued by the _order_ in which ifelse seems to assign values, and how recycling works. For instance,

Dear R-users, I'd like to announce the new version of package 'ltm' for Item Response Theory analysis. The function grm() (along with supporting methods, i.e., anova, margins, factor.scores, etc.) has been added for fitting the Graded Response Model for ordinal polytomous manifest variables. An extra feature of the plot method for classes 'grm', 'ltm' and

Dear R-users, I'd like to announce the new version of package 'ltm' for Item Response Theory analysis. The function grm() (along with supporting methods, i.e., anova, margins, factor.scores, etc.) has been added for fitting the Graded Response Model for ordinal polytomous manifest variables. An extra feature of the plot method for classes 'grm', 'ltm' and

Hi to all, > dlogis(-2000) [1] NaN Warning message: NaNs produced in: dlogis(x, location, scale, log) > dnorm(-2000) [1] 0 Is this an expected behaviour of `dlogis()'? Thanks in advance for any comments, Dimitris platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 9.1

Dear R-users, I'd like to announce the release of my new package "ltm" (available from CRAN), for fitting Latent Trait Models (including the Rasch model) under the Item Response Theory approach. The latent trait model is the analogous of the factor analysis model for Bernoulli response data. "ltm" fits the linear one- and two-factor models but also allows for

Dear R users, I'd like to announce the new version of the package "ltm" (available from CRAN), for fitting Latent Trait Models (including the Rasch and two-parameter logistic models) under the Item Response Theory approach. Three main extra features have been added: (i) now both ltm() and rasch() permit general fixed-value constraints (e.g., useful for scaling purposes), (ii)

Dear R users, I'd like to announce the new version of the package "ltm" (available from CRAN), for fitting Latent Trait Models (including the Rasch and two-parameter logistic models) under the Item Response Theory approach. Three main extra features have been added: (i) now both ltm() and rasch() permit general fixed-value constraints (e.g., useful for scaling purposes), (ii)