similar to: Combining means

?Dear All I have some data expressed in geometric means and 95% confidence intervals. Can I code them in metafor as: rma(m1i=geometric mean 1, m2i=geometric mean 2, sd1i=geometric mean 1 CI /3.92, sd2i=geometric mean 2 CI/3.92.......etc, measure="MD") All of the studies use geometric means. Thanks! Edward ---------------------------- [[alternative HTML version deleted]]

Inline. -- Bert On Tue, Apr 26, 2016 at 2:25 PM, Andr? Luis Neves <andrluis at ualberta.ca> wrote: > Ok. I`m trying to run a Poisson glmm with an observation-level random > intercept. But I`m getting the following error for the 'Baci' variable: > > 'Error: (maxstephalfit) PIRLS step-halvings failed to reduce deviance in > pwrssUpdate'. I guess this message

I want to use function cbind.na at library(qpcR) I install package qpcR and I can use functions such m1 <- pcrfit(reps, 1, 2, l5) > AICc(m1) [1] -102.5843 but when i try cbind.na(1, 1:7) i take message Error: could not find function "cbind.na" Thanks -- View this message in context: http://r.789695.n4.nabble.com/library-qpcR-cbind-na-tp4023339p4023339.html Sent from the

Ok. I`m trying to run a Poisson glmm with an observation-level random intercept. But I`m getting the following error for the 'Baci' variable: 'Error: (maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate'. I guess this message is because the baci variable is not a an integer, and cannot be transformed into an integer as R has a threshold of 2x10^9 even in

I am using R on a Windows XP professional platform. The following code is part of a bigger one CODE press=function(y,x){ library(qpcR) models.press=numeric(0) cat("\n") dep=y print(dep) indep=log(x) print(indep) yfit=dep-PRESS(lm(dep~indep))[[2]] cat("\n yfit\n") print(yfit) yfit.orig=yfit presid=y-yfit.orig press=sum(presid^2)

>>>>> Rich Shepard >>>>> on Mon, 22 Jan 2024 07:45:31 -0800 (PST) writes: > A statistical question, not specific to R. I'm asking for > a pointer for a source of definitive descriptions of what > types of data are best summarized by the arithmetic, > geometric, and harmonic means. In spite of off-topic: I think it is a good

Dear all, Is there anyway too generate MA plot for 2 qPCR assays (an array of 2x 400). -- View this message in context: http://r.789695.n4.nabble.com/Bioconductor-MA-plot-for-qPCR-array-tp4182805p4182805.html Sent from the R help mailing list archive at Nabble.com.

Hi! Is There a way to manually create an affybatch object from qPCR array data? -- View this message in context: http://r.789695.n4.nabble.com/Creating-affybatch-objects-from-matrix-data-from-qPCR-array-tp3921559p3921559.html Sent from the R help mailing list archive at Nabble.com.

Hi folks, The following are initial virtio-scsi + target vhost benchmark results using multiple target LUNs per vhost and multiple virtio PCI adapters to scale the total number of virtio-scsi LUNs into a single KVM guest. The test setup is currently using 4x SCSI LUNs per vhost WWPN, with 8x virtio PCI adapters for a total of 32x 500MB ramdisk LUNs into a single guest, along with each backend

Hi folks, The following are initial virtio-scsi + target vhost benchmark results using multiple target LUNs per vhost and multiple virtio PCI adapters to scale the total number of virtio-scsi LUNs into a single KVM guest. The test setup is currently using 4x SCSI LUNs per vhost WWPN, with 8x virtio PCI adapters for a total of 32x 500MB ramdisk LUNs into a single guest, along with each backend

Hello, everyone I have a piece of code that looks like this: mrets <- merge(mrets, BMM.SR=apply(mrets, 1, MyFunc, ret="BMM.AV120", stdev="BMM.SD120")) mrets <- merge(mrets, GM1.SR=apply(mrets, 1, MyFunc, ret="GM1.AV120", stdev="GM1.SD120")) mrets <- merge(mrets, IYC.SR=apply(mrets, 1, MyFunc, ret="IYC.AV120",

I have been trying the integrate function in R, a function which would be very useful for a current project of mine. But I am encountering errors integrating the one function I have tried. The function to be integrated is a product of a gamma demsity and a normal density: gamma.by.normal_function(y,x,shape,scale,stdev) return( dgamma(y,shape=shape,scale=scale)*

Dear R-Help, I also send the following post by e-mail to you, however I try to post it here aswell. My name is Henrik and I am currently trying to solve a Maximum Likelihood optimization problem in R. Below you can find the output from R, when I use the "BFGS" method: The problem is that the parameters that I get are very unreasonable, I would expect the absolute value of each

Dear R-Help, my name is Henrik and I am currently trying to solve a Maximum Likelihood optimization problem in R. Below you can find the output from R, when I use the "BFGS" method: The problem is that the parameters that I get are very unreasonable, I would expect the absolute value of each parameter to be bounded by say 5. (furthermore the variable stdev should be greater than zero).

Hi all, I've been trying (unsuccessfully) to modify an xyplot I created using the lattice package. I would like to change default strip colors and locations. I started with numeric data in 4 columns, which look like this: 0.25 2 1 32 0.25 2 2 30 0.25 2 3 27 0.25 2 4 23 0.25 2 5 17 0.25 3 1 30 0.25

Hi, I am looking to build even quintiles for a set of data. I managed to get it done, but I would like to know if there is a more direct way to write the data from my loop output x in the bottom of the code into the "empty" matrix p1, which I filled with NA''s. The way I am doing it at the moment is, more or less adding the matrix x after p1 and then deleting in a second step

Hej, Calling newuoa (from the minqa package) makes R crash when the package rgl is loaded first. This however only on certain selected data. The data used for testing (saved to 'bugs.R'): xvals = c(1,2,4,5,7,8,9,10,11,12,14,15,16,18,19,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36) yvals =

Hi all, I need to check for a difference between treatment groups in the parameter of the geometric distribution, but with a cut-off (i.e. right censored). In my experiment I stimulated animals to see whether I got a response, and stopped stimulating if the animal responded OR if I had stimulated 10 times. Since the response could only be to a stimulation, the distribution of response times

Sorry to prolong a thread on something that is clearly off topic, but when Michael Meyer wrote >by using the geometric mean all asymptotic results no longer apply. that is flat our wrong. It's true that the geometric mean converges to something different that E[X], but it does indeed have an asymptotic distribution and one that makes sense in some contexts. There is no reason that

Hi, Many apologies for sending this twice. I accidentally hit the send button before I finished writing my mail. I am new to R and I hope someone can help me with my problem. I am trying to draw a side by side barplot. There is a main experiment and there are many sub experiments within the main experiment. I would like to draw a bar plot showing the number and type of sub_experiments done for