similar to: Regression model with proportional dependent variable

Hello, I have been using mgcv to run GAM hurdle models, analyzing presence/absence data with GAM logistic regressions, and then analyzing the data conditional on presence (e.g. without samples with no zeros) with GAMs with a negative binomial distribution. It occurs to me that using the negative binomial distribution on data with no zeros is not right, as the negative binomial allows zeros.

in vm_find_vqs() irq has a wrong type so, in case of no IRQ resource defined, wrong parameter will be passed to request_irq() Signed-off-by: Ihor Matushchak <ihor.matushchak at foobox.net> --- Changes in v2: Don't overwrite error code value. drivers/virtio/virtio_mmio.c | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/drivers/virtio/virtio_mmio.c

in vm_find_vqs() irq has a wrong type so, in case of no IRQ resource defined, wrong parameter will be passed to request_irq() Signed-off-by: Ihor Matushchak <ihor.matushchak at foobox.net> --- Changes in v2: Don't overwrite error code value. drivers/virtio/virtio_mmio.c | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/drivers/virtio/virtio_mmio.c

in vm_find_vqs() irq has a wrong type so, in case of no IRQ resource defined, wrong parameter will be passed to request_irq() Signed-off-by: Ihor Matushchak <ihor.matushchak at foobox.net> --- drivers/virtio/virtio_mmio.c | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/drivers/virtio/virtio_mmio.c b/drivers/virtio/virtio_mmio.c index f363fbeb5ab0..60dde8ed163b

in vm_find_vqs() irq has a wrong type so, in case of no IRQ resource defined, wrong parameter will be passed to request_irq() Signed-off-by: Ihor Matushchak <ihor.matushchak at foobox.net> --- drivers/virtio/virtio_mmio.c | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/drivers/virtio/virtio_mmio.c b/drivers/virtio/virtio_mmio.c index f363fbeb5ab0..60dde8ed163b

I have class Debtortrans < ActiveRecord::Base has_many :debtortranstaxes, :foreign_key => ''debtortransid'', :class_name => ''debtortranstaxes'' belongs_to :debtor, :foreign_key => ''debtorno'' and class Debtortranstaxes < ActiveRecord::Base belongs_to :debtortrans, :foreign_key =>

Hi! I am a psychologist who suspects that the only sensible way to analyse a particular data set is to use generalised linear mixed models. I am hoping that someone might be able to point me in the right direction to find some very practical hands on documentation that might be able to talk me through actually doing such an analysis? So far in my searches the most useful document I have turned

I''m having an encoding problem, even though I''m attempting to have all my data always be in utf-8. Here''s the path the data takes: I have a CSV file that I believe is in utf-8, because I''m looking at it in a hex editor. One of the words is: 64 65 62 74 6F 72 E2 80 99 73 which translates to: debtor’s [that is, it contains a smart apostrophe] Now,

Hello, I am currently working with the betareg package, which allows the fitting of a variable dispersion beta regression model (Simas et al. 2010, Computational Statistics & Data Analysis). I was wondering whether there is any package in R that allows me to fit variable dispersion parameters in the standard logistic regression model, that is to make the dispersion parameter contingent upon

Greetings, I am interested in using a generalized linear mixed model with data that best fits a beta distribution (i.e., the data is bounded between 0 and 1 but is not binomial). I noticed that the beta distribution is not listed as an option in the "family objects" for glmmPQL or lmer. I found a thread on this listserve from 2006 ("[R] lmer and a response that is a

Dear all, I am using gamlss (Package gamlss version 4.0-0, R version 2.10.1, Windows XP Service Pack 3 on a HP EliteBook) to relate bird counts to habit variables. However, most models fail because “the global deviance is increasing” and I am not sure what causes this behaviour. The dataset consists of counts of birds (duck) and 5 habit variables measured in the field (n= 182). The dependent

Hello users of the wine! I have a problem with printing in the wine! I am testing with cups but I not obtained to make to function! somewhere could help me! felipe Debtor -- POP. Nem parece internet gr?tis. Seja POP voc? tamb?m! Acesse: http://www.pop.com.br/pop_discador.php e baixe o POPdiscador.

From: Ihor Matushchak <ihor.matushchak at foobox.net> [ Upstream commit 5e663f0410fa2f355042209154029842ba1abd43 ] in vm_find_vqs() irq has a wrong type so, in case of no IRQ resource defined, wrong parameter will be passed to request_irq() Signed-off-by: Ihor Matushchak <ihor.matushchak at foobox.net> Signed-off-by: Michael S. Tsirkin <mst at redhat.com> Reviewed-by: Ivan T.

Hello, I want to determine the regression relationship between a proportion (y) and a continuous variable (x). Reading a number of sources (e.g. The R Book, Quick R,help), I believe I should be able to designate the model as: model<-glm(formula=proportion~x, family=binomial(link="logit")) this runs but gives me error messages: Warning message: In eval(expr, envir, enclos) :

Dear R-helpers: We have had fun using betareg to fit models with proportions as dependent variables. However, in the analysis of these models we found some wrinkles and don't know where is the best place to start looking for a fix. The problems we see (so far) are that 1. predict ignores newdata 2. anova does not work Here is the small working example: ---------------------------- x

Hello, I have a dataset with proportions that vary around a fixed mean, is it possible to use betareg to look at variance in the dispersion parameter while keeping the mean fixed? I am very new to R but have tried the following: svec<-c(qlogis(mean(data1$scaled)),0,0,0) f<-betareg(scaled~-1 | expt_label + grouped_hpi, data=data1, link.phi="log",

Dear all, I get an error using betrag on this data set :http://dl.dropbox.com/u/1866110/dump.csv. I run it like this regression f2.1=betareg(Y~X1+X2,data=dump) summary(f2.1) I get : Call: betareg(formula = Y ~ X1 + X2, data = dump) Standardized weighted residuals 2: Error in quantile.default(x$residuals) : missing values and NaN's not allowed if 'na.rm' is FALSE In addition:

On Tue, Jul 02, 2019 at 05:48:18PM +0300, Ihor Matushchak wrote: > in vm_find_vqs() irq has a wrong type > so, in case of no IRQ resource defined, > wrong parameter will be passed to request_irq() > > Signed-off-by: Ihor Matushchak <ihor.matushchak at foobox.net> Thanks! pls don't make v2 a response to v1 in the future though. > --- > Changes in v2: > Don't

Hi, Quoting Ihor Matushchak (2019-07-02 12:59:18) > in vm_find_vqs() irq has a wrong type > so, in case of no IRQ resource defined, > wrong parameter will be passed to request_irq() > > Signed-off-by: Ihor Matushchak <ihor.matushchak at foobox.net> > --- > drivers/virtio/virtio_mmio.c | 7 ++++++- > 1 file changed, 6 insertions(+), 1 deletion(-) > > diff

Hi, I'm a bit puzzled by the gamlss fitting of exponential and lognormal data. Gamlss seems to think that exponentially distributed data fits better with a lognormal distribution, and vice versa. For example, X <- rexp(1000) Gexp <- gamlss(X~1,family=EXP) # X~1 is X tilde 1 GAMLSS-RS iteration 1: Global Deviance = 2037.825 GAMLSS-RS iteration 2: Global Deviance = 2037.825 Glno