search for: generalises

Dear R users, I'm hoping that more experienced users will be able to assist me in examining the model fit of a mixed generalised linear model. The example using the data 'bacteria' within the MASS package will hopefully illustrate what I would like to acheive; library(MASS) library(nlme) attach(bacteria) # y being output and the trt - treatment group being an explanatory variable.

Dear list, Is there a generalised form of rbind/cbind for combining matrices/arrays into higher-D structures? ie. if I have: a <- matrix(2,2,2) b <- matrix(3,2,2) how can I get array(rep(c(3,2), each=4), c(2,2,2)) ? It seems like this would be the job of a generalised abind function: abind(a,b, along=1) == rbind(a,b) abind(a,b, along=2) == cbind(a,b) abind(a,b, along=3)

Hi R-helpers, I have two queries relating to generalising to n dimensions: What I want to do in the first one is generalise the following statement: expand<-expand.grid(1:x[1],1:x[2],...1:x[n]) where x is a vector of integers and expand.grid gives every combination of the set of numbers, so for example, expand.grid(1:2, 1:3) takes 1,2 and 1,2,3 and gives 1,1 2,1 1,2 2,2 1,3 2,3 My x

Dear R users, I have used the following model: M1 <- gls(Nblad ~ Concentration+Season + Concentration:Season, data=DDD, weights=varIdent(form=~ 1 | Season*Concentration)) to assess the effect of Concentration and Season on nitrogen uptake by leaves (Nblad). I accounted for the difference in variance across the factor levels by using the varIdent function. Then I wanted to perform multiple

Hello, I would like some help with my Generalised Linear Model in R-Studio I am a little confused about what family to use and for my data My outcome variable (response) is categories 0,1,2,3 but my data i dont have any individuals that have fallen into the top group "3" What i have read about Generalised Models is that Biomial data is only two option not the 4 that i have. What

Readers, I have been reading 'the r book' by Crawley and think that the generalised additive model is appropriate for this problem. The package 'gam' was installed using the command (as root) install.package("gam") ... library(gam) > library(gam) Loading required package: splines Loading required package: akima > library(mgcv) This is mgcv 1.3-25 Attaching

Dear R users I am analysing data from a group of twins and their siblings. The measures that we are interested in are all correlated within families, with the correlations being stronger between twins than between non-twin siblings. The measures are all calculated from survey answers and by definition have limited ranges (e.g. -5 to +5), though within the range they are approximately normally

Hi, I am trying to figure out the observed acceptance rate and M, using generalised rejection sampling to generate a sample from the posterior distribution for p. I have been told my code doesn't work because I need to "take the log of the expression for M, evaluate it and then exponentiate the result." This is because R is unable to calculate high powers such as 545.501.

Hi, I have recently been attempting to find the LD50 from two predicted fits (For male and females) in a Generalised linear model which models the effect of both sex + logdose (and sex*logdose interaction) on proportion survival (formula = y ~ ldose * sex, family = "binomial", data = dat (y is the survival data)). I can obtain the LD50 for females using the dose.p() command in the MASS

Thanks, this makes sense. Is there a similar precedence reasoning behind why operator functions (`+` etc) can't be piped into? On Fri, Apr 21, 2023 at 11:52?PM Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > On 21/04/2023 4:35 a.m., Michael Milton wrote: > > I just checked out R-devel and noticed that the new "pipe extractor" > > capability coming in 4.3

On 21/04/2023 11:33 a.m., Michael Milton wrote: > Thanks, this makes sense. Is there a similar precedence reasoning behind > why operator functions (`+` etc) can't be piped into? Yes: > identical(quote(1 + 1), quote(`+`(1, 1))) [1] TRUE Duncan Murdoch

I'm afraid I don't understand. I know that parsing `+`(1, 1) returns a result equivalent to `1 + 1`, but why does that impose a restriction on parsing the pipe operator? What is the downside of allowing arbitrary RHS functions? [[alternative HTML version deleted]]

To whom this may concern, I would be very grateful if someone could give me some advice on where I am going wrong with a logistic regression I am trying to run. I am trying to run a logistic regression on an aggregated data set and have input the command: logistic.mod<-glm(x~Frequency+Location+Sex+Age.Group,family=binomial(link="logit"),data=earsag1.dat) where x is the count of my

I just checked out R-devel and noticed that the new "pipe extractor" capability coming in 4.3 only works for the 4 extractor operators, but no other standard operators like +, *, %*% etc, meaning that e.g. mtcars |> as.matrix() |> _ + 1 |> colMeans() is a syntax error. In addition, we are still subject to the restriction that the functions on the RHS of a pipe can't have

Hi, I am working with a friend on a master's project. Our laboratory does a lot of statistical analysis using the R stats package and we also have a lot of under-utilised nvidia cards sitting in the back of our networked linux machines. Our idea is to coerce the linux nvidia driver to run some of our statistical analysis for us. Our first thought was to specifically code up a version of

On 21/04/2023 12:16 p.m., Michael Milton wrote: > I'm afraid I don't understand. I know that parsing `+`(1, 1) returns a > result equivalent to `1?+ 1`, but why does that impose a restriction on > parsing the pipe operator? What is the downside of allowing arbitrary > RHS functions? I thought the decision to exclude "_ + 1" happens after enough parsing has

On 21/04/2023 4:35 a.m., Michael Milton wrote: > I just checked out R-devel and noticed that the new "pipe extractor" > capability coming in 4.3 only works for the 4 extractor operators, but no > other standard operators like +, *, %*% etc, meaning that e.g. mtcars |> > as.matrix() |> _ + 1 |> colMeans() is a syntax error. In addition, we are > still subject to

Greetings All, Out of curiosity, I've just done a very primitive experiment: Obj <- list(Fun=sum, Dat=c(1,2,3,4)) Obj$Fun(Obj$Dat) # [1] 10 That sort of thing (much more sophisticated) must be documented mind-blowingly somewhere. Where? Where I stand right now: The above (and its immediately obvious generalisations, like Obj$Fun<-cos) is all I know about it so far. Ted.

Hi there, I am trying to fit a generalised linear model to some loan application and default data. The purpose of this is to eventually work out the probability an applicant will default. However, R seems to crash or die when I run "glm" on anything greater than a 5-way saturated model for my data. My first question: is the best way to fit a generalised linear model in R to fit the

Hi All, I was looking for a function to find a small matrix inside a larger matrix in R similar to the one described in the following link: https://www.mathworks.com/matlabcentral/answers/194708-index-a-small-matrix-in-a-larger-matrix I couldn't find anything. The above function can be seen as a "generalisation" of the "which" function as well as the function described