similar to: Using predict with glmmPQL

From: Erwan Yvin <erwan.yvin at stericsson.com> Add the CAIF Virtio shared memory driver for talking to a modem. This CAIF Link layer communicates to the modem over shared memory. It is implemented as a virtio_driver. The underlying virtio device is managed by the remoteproc framework. The Virtio queue is used for transmitting data to the modem, and the new vringh is used for receiving

From: Erwan Yvin <erwan.yvin at stericsson.com> Add the CAIF Virtio shared memory driver for talking to a modem. This CAIF Link layer communicates to the modem over shared memory. It is implemented as a virtio_driver. The underlying virtio device is managed by the remoteproc framework. The Virtio queue is used for transmitting data to the modem, and the new vringh is used for receiving

Hello R-help subscribers, I am analyzing a data set using a mixed logit model, and I have recently discovered some curious behavior. I am hoping you all can help. I first ran the following model in December 2012. lmer(Response.binary ~ ItemType.c * Block + (1 | Subject) + (1 | Word), data=lexdec, family="binomial") I then took a break from the data for the holidays. I returned to

Dear R users, I am querying an Oracle database using sqlQuery() from the RODBC package. What I would like to do is embed my R Sql query in a for loop. For example suppose the data.frame "vessel" contains two columns: vessel[,1]= vessel id and vessel[,2]=permit year I am using "vessel" as an input in a SQL based function that require inputs of permit year and vessel id

Dear list, After reading different mails, blogs, and tried a few different codes without any success, I am asking your help! I have the following data frame where each row represent a survey unit with the following variables: > names(RV09) [1] "record.t" "trip" "set" "month" "stratum" "NAFO" [7]

On Fri, Feb 22, 2013 at 1:42 AM, Rusty Russell <rusty at rustcorp.com.au> wrote: > Ohad Ben-Cohen <ohad at wizery.com> writes: >> On Thu, Feb 21, 2013 at 8:37 AM, Rusty Russell <rusty at rustcorp.com.au> wrote: >> What do you think about creating some virtio-level wrappers for the >> vringh handlers? >> >> I don't think we're going to stop

On Fri, Feb 22, 2013 at 1:42 AM, Rusty Russell <rusty at rustcorp.com.au> wrote: > Ohad Ben-Cohen <ohad at wizery.com> writes: >> On Thu, Feb 21, 2013 at 8:37 AM, Rusty Russell <rusty at rustcorp.com.au> wrote: >> What do you think about creating some virtio-level wrappers for the >> vringh handlers? >> >> I don't think we're going to stop

Check on the correct return value from vringh_notify_enable_kern(). It returns false if more packets are available, not true. Signed-off-by: Sjur Br?ndeland <sjur.brandeland at stericsson.com> --- Hi, This patch applies to Rusty's virtio-next branch. Thanks, Sjur drivers/net/caif/caif_virtio.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git

Check on the correct return value from vringh_notify_enable_kern(). It returns false if more packets are available, not true. Signed-off-by: Sjur Br?ndeland <sjur.brandeland at stericsson.com> --- Hi, This patch applies to Rusty's virtio-next branch. Thanks, Sjur drivers/net/caif/caif_virtio.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git

Hello, I want to select same data of a data frame that has information of fish catches: catch, vessel, trip, day, month, year where trip is the fishing operation number done in each day by each vessel, i.e. for each day, any vesel can have several register of catch corresponding to each operation. >From this data frame I want to do: 1. Add the catches of all trip by vessel and day; then

Hi, I'm trying to create a plot showing the density distribution of some shipping data. I like the look of violin plots, but my data is not continuous but rather binned and I want to make sure its binned nature (not smooth) is apparent in the final plot. So for example, I have the number of individuals per vessel, but rather than having the actual number of individuals I have data in the

Han-Lin I don't think I have seen a reply so I will suggest that maybe you could try a different approach than what you are thinking about doing. I believe the current best practice is to use the weights as a covariate in a regression model - and bytheway - the weights are the inverse of the probabilities of selection - not the probabilities. Fundamentally, there is a difficulty in making

I'm a bit puzzled by how to write out additive random effects in glmmPQL. In my situation, I have a factorial design on two (categorical) random factors, A and B. At each combination, I have a binary response, y, and two binary fixed covariates, C and D. If everything were fixed, I would use glm(y ~ A + B + C + D, family = binomial) My first thought was to use glmmPQL(y ~ A + B, random

Dear list, I have been struggling for some time now with this code... I have this vector of unique ID "EID" of length 821 extracted from one of my dataframe (skate). It looks like this: > head(skate$EID) [1] "896-19" "895-8" "899-1" "899-5" "899-8" "895-7" I would like to remove the complete rows in another dataframe

Hello, I am using the R function glmmPQL to fit a logistic GLMM, with weights. I am finding that I get fairly different parameter estimates in glmmPQL from fitting the full dataset (with no "weight" statement) and an equivalent, shorter dataset with the weights statement. I am using the weights statement in the 'glmmPQL' function exactly as in the 'glm' function. I

Hi ! I try to compare some models obtained from glmmPQL. model1 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 +I(freq8_4^2), random=~1|num, binomial); model2 <- glmmPQL(y~red*yellow+I(red^2)+I(yellow^2)+densite8+I(densite8^2)+freq8_4 , random=~1|num, binomial); anova(model1, model2) here is the answer : Erreur dans anova.lme(model1, model2) : Objects must

Dear all, for a cross-validation I have to use predict.glmmPQL() , where the formula of the corresponding glmmPQL call is not given explicitly, but constructed using as.formula. However, this does not work as expected: x1<-rnorm(100); x2<-rbinom(100,3,0.5); y<-rpois(100,2) mydata<-data.frame(x1,x2,y) library(MASS) # works as expected model1<-glmmPQL(y~x1, ~1 | factor(x2),

I have come across the previous communication on this list in September (copied below) because I had received the same error message. I understand from Brian Ripley's reply that anova should not be used with glmmPQL because it is not an adequate method, and that this is now shown with an error message. My question is, what method *should* be used? Using summary does not give me the result

Dear R-help, I recently tried implementing glmmPQL in 2.3.1, and I discovered a few differences as compared to 2.2.1. I am fitting a regression with fixed and random effects with Gamma error structure. First, 2.3.1 gives different estimates than 2.2.1, and 2.3.1, takes more iterations to converge. Second, when I try using the anova function it says, "'anova' is not available

Hi, In glmmPQL in the MASS library, the function uses repeated calls to the function lme(), using ML. Does anyone know how you can change this to REML? I know that in lme(), the default is actually set to REML and you can also specify this as 'method=REML' or 'method'ML' but this isn't applicable to glmmPQL(). I'd appreciate any help or advice! Thanks, Emma