similar to: Changing the order of the factors in a cumulative logit model

Dear R community, I'm trying to analyze a model with an ordinal response variable. I wonder if clm()s (Cumulative Link Models) are appropriate in my case. The study compares parasite infestation of porpoises in 1995 and 2009. The degree of infestation is a rank (mild to severe, as ordered factor). In some parasite species clm gives meaningful results. But in one case I started to wonder. In

Dear R users, Basically, from the following arbitrary data set: a <- data.frame(id=c(c("A1","A2","A3","A4","A5"),c("A3","A2","A3","A4","A5")),loc=c("B1","B2","B3","B4","B5"),clm=c(rep(("General"),6),rep("Life",4))) > a

Dear List, I am using both the clm() and clmm() functions from the R package 'ordinal'. I am fitting an ordinal dependent variable with 5 categories to 9 continuous predictors, all of which have been normalised (mean subtracted then divided by standard deviation), using a probit link function. From this global model I am generating a confidence set of 200 models using clm() and the

Hello, I hope this mailing list is correct. I've a cluster pacemaker with a clone resource ocfs2 with ocfs2-tools-1.4.1-25.6.x86_64 ocfs2-tools-o2cb-1.4.1-25.6.x86_64 on Opensuse 11.2 After some network problem on my switch I receive on one of 4 nodes of my cluster the following messages Aug 18 13:12:28 nodo1 openais[8462]: [TOTEM] The token was lost in the OPERATIONAL state. Aug 18 13:12:28

Dear R-experts, I need to replace in an expression the character "Cl" by "Cl+beta" But in the following case: form<-expression((Cl-(V *ka) ) +(V *Vm *exp(-(Clm/Vm) *t))) gsub("Cl","(Cl+beta)",as.character(form)) We obtain: [1] "((Cl+beta) - (V * ka)) + (V * Vm * exp(-((Cl+beta)m/Vm) * t))" the character "Clm" has been

Dear list: After reading the following two links: http://luna.cas.usf.edu/~mbrannic/files/regression/Logistic.html http://www.tufts.edu/~gdallal/logistic.htm I've known the mathematical basis for logistic regression.However I am still not so sure about the "logit " For a categorical independent variable, It is easy to understand the procedures how "log

Dear R-helpers, I am very new to R and trying to run the conditional logit model using "clogit " command. I have more than 4000 observations in my dataset and try to predict the dependent variable from 14 independent variables. My command is as follows clmtest1 <- clogit(Pin~Income+Bus+Pop+Urbpro+Health+Student+Grad+NE+NW+NCC+SCC+CH+SE+MRD+strata(IDD),data=clmdata) However, it

I have data from a design in which items are completely nested within subjects. Subject is the only second-level factor, but I have multiple first-level factors (IVs). Say there are 2 such independent variables that I am interested in. What is the proper syntax to fit a mixed-effects model with a by-subject random intercept, and by-subject random slopes for both the 2 IVs? I can

Dear list, I'd like to create a visual plot of a clmm() I've fitted using the 'ordinal' package in R. It's possible to do this with a glm() by using the 'effects' package. For example: library(effects) data(BEPS) mod <- lm(political.knowledge ~ age + gender + vote, data=BEPS) eff <- effect("age", mod, default.levels=100) plot(eff,

Hell all. I¡¯m trying to estimate mixed logit model using MSLE. In order to see that mixed logit model works better than simple logit model ( the logit model with fixed coefficient) I simulated a dataset with random coefficients and tried to fit the data with both mixed logit and simple logit model. Because my mixed logit model contains analytically intractable integrations, I applied

Greetings all, please consider the following data: #Build Data frame Slope<-c(1.291370, 12.208500, 2.110930, 0.578990, 5.019520, 0.807444, 0.554079 , 1.257080, 0.241504 , 0.184337 , 0.383044 , 0.342021) Exposure<-c(790.54, 1167.79 , 845.58 , 1082.47 , 1189.61 , 677.17 , 2058.56 , 469.09 , 112.02 , 803.31 , 254.14 ,1336.16) FwyDist<-c(11809.4222 ,10623.0458, 12279.6271,

This is a simple problem that has stumped me: I'm trying to loop through a few dozen variable names in graphs. I've tried various approaches like this: attach(mydata) ivs <- c("oneiv", "anotheriv", "yetanotheriv") dvs <- c("onedv", "anotherdv", "yetanotherdv") for (iv in ivs) { for (dv in dvs) { graphname <- paste(iv,

# I've tried to make this easy to paste into R, though it's probably so simple you won't need to. # I have some data (there are many more variables, but this is a reasonable approximation of it) # here's a fabricated data frame that is similar in form to mine: my.df <- data.frame(replicate(10, round(rnorm(100, mean=3.5, sd=1)))) var.list <- c("dv1",

I'm trying to fit a model to solve a biological problem. There are multiple independent variables, and also there are multiple responses. Each response is a function of all the independent variables, plus a set of parameters. All the responses depend on the same variables and parameters - just the form of the function changes to define each seperate response. Any ideas how I can fit

I'm fairly new to the list and new to submitting patches. Can someone please verify the attached patch for running a HP-UX Trusted System with PAM and OpenSSH 3.4p1? The problem seemed to be that pam couldn't verify the user via __pamh after the call to permanently_set_uid in session.c. So I called do_pam_session prior to the call and added a function do_pam_set_tty in order to set the

Dear list, I have the following how-to-do it in R, questions. Suppose I have ten independent variables, and one dependent variable. I want to find the Pearson correlation of all the IVs with the DV, but not the correlation between the IVs. What I know so far, about R, that I have to type the cor () function ten times, each time requesting for a correlation between one IV and the DV. I was

Hi, I am trying to carry out a multinomial regression using the cumlogit link function. I have tried using the VGAM package, and have gotten some results... fit1 <- vgam(Y ~ X1 + X2 + X3 + X4, cumulative(link=logit,intercept.apply=FALSE,parallel=TRUE), data = data1 ) The problem arrises when I try to get the information out of the fitted object. I can

Hi, ? I am very new to R. So, pardon my dumb question. I was trying to write my own function to run a different model (perform an ordered logistic regression) using the example in website http://pngu.mgh.harvard.edu/~purcell/plink/rfunc.shtml But R returns a error `R Error in eval(expr, envir, enclos) : object 's' not found' when I run it. What am I doing wrong here? Here's

Hello everyone, I have tried to look for this everywhere and so far have no luck. I have a plotMeans(DV, IV1, IV2, error.bars="se") graph that plots my data (DV-continuous, IVs are factors, IV1 - two levels, IV2-four levels). I am trying to increase a scale of my y-axis (to be consistent with my other graphs), but unfortunately nothing works with "plotMeans" function, which

I've spent several days compiling the following code (I apologize in advance - this code is very inelegant, and I'm sure could be written much more efficiently, but I've stuck with whatever method I could get to work - sometimes the more efficient code I just couldn't get to work without an error, because of my R inexperience). My main motivation for writing the code is that