similar to: glht (multicomparisons) with a binomial response variable

Hello everyone, Like a lot of people, I have been looking for functions in R doing ANOVA (ok) and performing multicomparisons (like Student-Newman-Keuls, etc.). As I have been a little bit disappointed, I have bee looking through the net for such "open source" softwares. I found one in: http://www.statpages.org/miller/openstat/OS4.html I have begun to use it, and it seems good and

Hi, i was working with this model > mq<-glm(rojos~edadysexo*zona*estacion,quasipoisson) and i get this minimal adequate model > anova(mq5,test="F") Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL 518 64799 edadysexo 2 1556.5 516 63243 8.9434 0.0001524 *** zona 4

Hi, I have data with binomial response variable (survival) and 2 categorical independent variables (site and treatment) (see below).? I have run a binomial GLM and found that both IVs and the interaction are significant.? Now I want to do a post-hoc test for all pairwise comparisons to see which treatment groups differ.? I tried the glht function in the multcomp package, but I get surprising

Hi dears, I do > CHOL<-lme(chol~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ haartd+hivdur+factor(arv), random= ~rt|id, na.action=na.omit) ...runs sweet,..then ....try a multicomparisons approach for the categorical rf > summary(glht(CHOL, linfct=mcp(rf="Tukey"))) * Error in model.frame.default(object, data, xlev = xlev) : l'oggetto non è una matrice

Dear list, I'm stuck with looking for a function of the *apply family, which I suppose exists already ? just I can't find it: What I'm looking for is somewhere between sweep and mapply that does a calculation vectorized over a matrix and a vector: It should work complementary to sweep: for each row of the matrix, a different value of the vector should be handed over. Close to

Dear Metafor users, I'm conducting a metaanalysis of prevalence of a particular behaviour based on someone elses' code. I've been labouring under the impression that this: summary(rma.1<-rma(yi,vi,mods=cbind(approxmeanage,interviewmethodcode),data=mal,method="DL",knha=F,weighted=F,intercept=T)) is doing the multivariate analysis that i want, but have read that

Hola. Tengo la matriz adjunta, en un formato incómodo (dos columnas).- Quisiera ordenarla, tal que resulten 9 variables (columnas) con los siguientes valores, que actualmente están en la V1, repetidos 21 veces: Leading Companies Sales (M AR$) Market % Total Growth Mature Portf New Products Extensions Leading Lógicamente, en los filas quisiera los valores que actualmente están en la V2.- ?No logro

On Tue, Jul 30, 2019 at 11:54:53AM -0400, Michael S. Tsirkin wrote: > On Tue, Jul 30, 2019 at 05:43:29PM +0200, Stefano Garzarella wrote: > > This series tries to increase the throughput of virtio-vsock with slight > > changes. > > While I was testing the v2 of this series I discovered an huge use of memory, > > so I added patch 1 to mitigate this issue. I put it in this

Hi dear sirs, I am wondering why the fit of the time serie x with an arima and the fit of diff(x) with an arma (same coeff p & d) differ one from another here are the output of R: %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% > modelarma<-arma(diff(x),c(7,5)) > modelarma Call: arma(x = diff(x), order = c(7, 5)) Coefficient(s): ar1 ar2 ar3 ar4 ar5 ar6 ar7 ma1 ma2 0.06078

Hi all! I keep getting the "too many arguments" error when I insert the xaxt="n", axis(1, at=seq (0, 183, 365, 549, 732, 915, 1095)) statement in the code below for a survival plot. How can I go around this issue? Is there another way for me to change the x-axis? Also, I would prefer the y-axis to be represented by percentages (10, 20, 30..) rather than decimals (.10,

hello everyone, I'm modelling in lmer an average chick weight defined as "Total.brood.mass ~ offset(chick.number), with three fixed and two random effect. Next, I want to use function dredge from MuMIn package for model averaging. Not sure why, but in consequence the offset variable is treated as a predictor, so I get a table that mixes models with and without that offset term (the first

Hi r users,   I would like sort of   cdf         seq   rand 0.00E+00    0     0.262123478 1.56E-03    20    0.964293344 1.55E-02    40    0.494827113 5.30E-02    60    0.733726005 1.16E-01    80    0.800408948 1.97E-01    100   0.925748466 2.88E-01    120   0.047578356 3.80E-01    140   0.266060366 4.68E-01    160   0.125522629 5.48E-01    180   0.701193274 6.18E-01    200   0.915799432

Hola compañeros de la lista. Tengo el siguiente set de datos: Repeticiones <- c(rep("RI", 14), rep("RII", 14), rep("RIII", 14)) Tiempo <- rep(c(0, 2, 4, 6, 8, 10, 12, 24, 36, 48, 60, 72, 96, 120), 3) Concentracion_celular <- c(0.4862, 0.5375, 0.4309, 0.4390, 0.4603, 0.4733, 0.3936, 0.9085, 0.5838, 0.5477, 0.6331, 0.8693, 1.0092, 0.6341, 0.5350,

On Wed, Jul 17, 2019 at 01:30:25PM +0200, Stefano Garzarella wrote: > This series tries to increase the throughput of virtio-vsock with slight > changes. > While I was testing the v2 of this series I discovered an huge use of memory, > so I added patch 1 to mitigate this issue. I put it in this series in order > to better track the performance trends. Series: Acked-by: Michael S.

On Tue, Jul 30, 2019 at 05:43:29PM +0200, Stefano Garzarella wrote: > This series tries to increase the throughput of virtio-vsock with slight > changes. > While I was testing the v2 of this series I discovered an huge use of memory, > so I added patch 1 to mitigate this issue. I put it in this series in order > to better track the performance trends. > > v5: > - rebased

This series tries to increase the throughput of virtio-vsock with slight changes. While I was testing the v2 of this series I discovered an huge use of memory, so I added patch 1 to mitigate this issue. I put it in this series in order to better track the performance trends. v5: - rebased all patches on net-next - added Stefan's R-b and Michael's A-b v4:

This series tries to increase the throughput of virtio-vsock with slight changes. While I was testing the v2 of this series I discovered an huge use of memory, so I added patch 1 to mitigate this issue. I put it in this series in order to better track the performance trends. v5: - rebased all patches on net-next - added Stefan's R-b and Michael's A-b v4:

I have two variables which show a typical quantile relation I would like to fit quantile regression models based on both logarithm and polynomial of second order functions within quantreg. Any help appreciated... Cheers Duccio Herewith the values: ------ var1 var2 0.96429 0.00138 1 0.02316 1.03145 0.09323 1.24088 0.77128 1.39869 0.86732 1.33728 0.63674 1.48299 0.96194

Dear all, This question is partly statistics and partly R and I apologise in advance for my (usual) verbosity! My data is a little more complicated that this suggests, but essentially I have proportion data from different studies (id), each from a specific country and region of the World. I would like to examine the variables that affect the proportion, but these factors are hierarchical. In case

I discovered that IPv6 is sort of working when I got an email rejection from Comcast for not having an IPv6 PTR record. I discovered I could telnet to port 25 on their MX server over IPv6! I then found I could tracroute6 to them, but I couldn't to my Linode VPS in Fremont. It gets to the data center and stops. Going the other way, my Linode can traceroute6 almost to my AT&T-hosted