similar to: Woolf's test, Odds ratio, stratification

Hi all, Could somebody be so kind to explain to me what is the saturated model on which deviance and degrees of freedom are calculated when fitting a binomial glm? Everything makes sense if I fit the model using as response a vector of proportions or a two-column matrix. But when the response is a factor and counts are specified via the "weights" argument, I am kind of lost as far as

Hi, does anyone know of a function to calculate odds ratios in multiway tables (stratified) (+ the other usual statistics involved) i mean: say we have a table r*c*d, For every d (depth) we have a r*c table, and in this table the odds ratio's are calculated for every 2*2 subtable in it. logically this function would look like): ORs(multiwaytable) or ORs(data$var1r,data$var2c,data$var3d)

Dear all, I have been trying to find a function to calculate the Breslow-Day test for homogeneity of the odds ratio in R. I know the test can be preformed in SAS but i was wondering if anyone could help me to perform this in r. In addition i have the fullrefman file to search for functions in the basic R packages, does anyone have any suggestions of an efficient way of searching for

I apologize in advance if this is the wrong forum for this report/request, and for the fact that I have not read the code for ftable.formula in any detail. >From reading the documentation for ftable.formula, I expected that the following two calls to ftable would produce the same results: data(UCBAdmissions) ftable(UCBAdmissions, row.vars = "Dept", col.vars = c("Gender",

Suppose I have a table constructed from structable or simply just an object of class table. How can I convert it to a latex object? I looked in RSiteSearch, but only found info about matrices or data frames. Steve For example, here is a table t2 > str(t2) table [1:2, 1:2, 1:2] 6 8 594 592 57 ... - attr(*, "dimnames")=List of 3 ..$ Hospital : chr [1:2] "A"

Hi there, > Value=c(709,679,699,657,594,677,592,538,476,508,505,539) > Lard=rep(c("Fresh","Rancid"),each=6) > Gender=rep(c("Male","Male","Male","Female","Female","Female"),2) > Food=data.frame(Value,Lard,Gender) > Food Value Lard Gender 1 709 Fresh Male 2 679 Fresh Male 3 699 Fresh

I'm looking for an R method to produce latex versions of tables for table/array objects of 3 or more dimensions, which, of necessity is flattened to a 2D display, for example with ftable(), or vcd::structable, as shown below. I'd be happy to settle for a flexible solution for the 3D case. > UCB <- aperm(UCBAdmissions, c(2, 1, 3)) > ftable(UCB) Dept A B

Hello, I was using the mantelhaen.test function (2x2 J tables for conditional independence testing). I noticed that the option for the continuity correction (correction=T or correction=F) sometimes made a difference while sometimes it did give the same results regardless of correction=T or correction=F. Does the continuity correction apply only in certain cases or in certain strata with some

i am using sarima() function as below ___________________________________________________________________________________________ sarima=function(data,p,d,q,P=0,D=0,Q=0,S=-1,tol=.001){ n=length(data) constant=1:n xmean=matrix(1,n,1) if (d>0 & D>0) fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q), period=S),

Dear List, I'm having problem with an exercise from The R book (M.J. Crawley) on page 567. Here is the entire code upto the point where I get an error. data(UCBAdmissions) x <- aperm(UCBAdmissions, c(2, 1, 3)) names(dimnames(x)) <- c("Sex", "Admit?", "Department") ftable(x) fourfoldplot(x, margin = 2) dept<-gl(6,4) sex<-gl(2,1,24)

Can someone please explain what the $minimum result of the optimize function actually is? I'm trying to optimize the function: fitIT<-function(ampFac,ts_wave1,ts_template){ template<-stretchWaveTime(ts_template,ampFac) fit<-calcFit(ts_wave1,template) return(fit) } with >optimize(f=fitIT,interval=c(0.5,4),ts_wave1=test.data[,1],ts_template=test.data[,1]) $minimum [1]

> addmargins(UCBAdmissions, FUN = list(Total=sum)) works with no problems, but consider: > myFUN <- list(Total=sum) > addmargins(UCBAdmissions, FUN = myFUN) Error in "names<-.default"(`*tmp*`, value = "") : names() applied to a non-vector Is this a bug? > R.version _ platform i386-pc-mingw32 arch i386 os mingw32 system

Hello, Currently it's possible to convert an object of class table to a data frame with as.data.frame.table(), but there's no ready-made function, AFAIK, to do the reverse operation, i.e. conversion of a data frame to a table. Do you think it would be a good idea to add a data.frame method to as.table(), to allow such conversions? The idea is that if `x' is a table and `y <-

Hello Helpers, I have some problems with fitting the model for my data... -->my Literatur says (crawley testbook)= Non-normality of errors-->I get a banana shape Q-Q plot with opening of banana downwards Structure of data: origin wt pes gender 1 wild 5.35 147.0 male 2 wild 5.90 148.0 male 3 wild 6.00 156.0 male 4 wild 7.50 157.0 male 5 wild 5.90

I am fairly new to log-linear modelling, so as opposed to trying to fit modells, I am still trying to figure out how it actually works - hence I am looking at the interpretation of parameters. Now it seems most people skip this part and go directly to measuring model fit, so I am finding very few references to actual parameters, and am of course clear on the fact that their choice is irelevant for

I need to change the font(s) used in mosaic from package vcd. The help file and the vignette do not give very explicit examples for doing that. The easiest solution would be changing the font for everything on the graph: var labels, var names, title, subtitle, and cell labels. What is the easiest way of accomplishing this?

Hi R-helpers, I was trying to put gender='Male' in newdata to create a expected survival curve for a pseudo cohort by using survfit based on Cox regression. My codes are shown below: fit<- coxph(Surv(end, status2)~gender, data=wlwsn1) Summary(fit) coef exp(coef) se(coef) z p genderMale 0.204 1.23 0.0912 2.23 0.025

aperm() was designed for multidimensional arrays, but is also useful for table objects, particularly with the lattice, vcd and vcdExtra packages. But aperm() was designed and implemented before other related object classes were conceived, and I propose a small tune-up to make it more generally useful. The problem is that aperm() always returns an object of class 'array', which

Dear all, I'm new to R and time series analysis. I'd appreciate if you could shed light on my problem. Here is what I have been trying to do: 1. I fit the model ARIMA(1,0,0) with the training dataset xdata[1:100] fitit = arima(xdata, order=c(1,0,0) 2. I have some current observations in the buffer. Say that buf = xdata_new[1:20] 3. I'm trying to forecast the xdata_new[21] based on

Hi I''m anxious to get puppet into production, and although I''d prefer installing puppet as a .deb package (as most of the servers in our shop runs Debian), I think I''ll try to get some of my not-so- important servers into my test setup by installing puppet with the .tgz files. Upon the first installation I realize that puppetd and puppetmasterd is copied to