similar to: (baseline) logistic regression + gof functions?

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)

Hallo, i get a warning message that NAs are introduced by coercion, so my idea is to write a function to see which values are turned into NA For this i need to write a function to go through (loop) the original data and the transformed (with the introduced na) to see which data were transformed to NA. So the return of this function should be a 2*many matrix like structure, eg names:

Dear R-users, I want to perform a One-Sample parametric bootstrapped Kolmogorov-Smirnov GoF test (note package "Matching" provides "ks.boot" which is a 2-sample non-parametric bootstrapped K-S version). So I wrote this code: ---[R Code] --- ks.test.bootnp <- function( x, dist, ..., alternative=c("two.sided", "less", "greater"), B = 1000 ) {

I'm working on a quick tutorial for my students, and was planning on using Mann-Whitney U as one of the tests. I have the following (fake) data grade <- c("MVG", "VG", "VG", "G", "MVG", "G", "VG", "G", "VG") sex <- c( "male", "male", "female", "male",

Hello, I was looking for a way to evaluate the goodness-of-fit of a logistic regression model. After googling, I found that I could use "resid(fit, 'gof')" method implemented in the rms package. However, since I am not used to the "le Cessie-van Houwelingen normal test statistic," I do not know which statistic from the returned from the "resid(fit,

On Wed, 4 May 2016, Nux! wrote: > Direct links > > https://www.imagemagick.org/discourse-server/viewtopic.php?f=4&t=29588#p132726 > https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2016-3714 > > Mitigation: > > As a workaround the /etc/ImageMagick/policy.xml file can be edited to disable > processing of MVG, HTTPS, EPHEMERAL and MSL commands within image files,

Hi Folks, Hopefully this question has enough R and not too much stats to be appropriate for this list. Based on,* Hosmer et al. 2013. Logistic regression for matched case-control studies. Applied Logistic Regression *(eqtn. 7.8)*, *I am assessing GOF of conditional (or matched) logistic regression models with the *standardized Pearson residuals*. The authors define ?large? as delta chi-squared

Dear R-experts, I have some questions about boxplots with lattice. My data is similar as in the example below, I have two factors (Goodness of Fit and Algorithms) and data values but in each panels the scales are quite different, therefore the normal boxplots produced by set.seed(1) GOF <- factor(rep(c("GOF1","GOF2","GOF3"),each=40)) Alg <-

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

Hi, I'm not a statistician so sorry for possible trivial questions ... I want to perform a GoF test on sample data against several distribution (like Extreme Value, Phase Type, Pareto, ...). Since I suspect a long-tailed behaviour on data I want to use Anderson-Darling (AD) GoF test because it's well known it's more sensible to tail data. Looking at R packages the only AD test is

Hi, I'm not a statistician so sorry for possible trivial questions ... I want to perform a GoF test on sample data against several distribution (like Extreme Value, Phase Type, Pareto, ...). Since I suspect a long-tailed behaviour on data I want to use Anderson-Darling (AD) GoF test because it's well known it's more sensible to tail data. Looking at R packages the only AD test is

Dear list members, Within a loop, I need to create an xyplot with only a legend, not even with the default external box drawn by lattice. I already managed to remove the axis labels and tick marks, but I couldn't find in the documentation of xyplot how to remove the external box. I would really appreciate any help with this ------------- START ----------- library(lattice) x<-1:100

I am trying to test goodness of fit for my legalistic regression using several options as shown below.  Hosmer-Lemeshow test (whose function I borrowed from a previous post), Hosmer–le Cessie omnibus lack of fit test (also borrowed from a previous post), Pearson chi-square test, and deviance test.  All the tests, except the deviance tests, produced p-values well above 0.05.  Would anyone please

All, This may be a bit off topic so feel free to flame me ... my defence is that I am using R. I have data with case counts per family. I arrange the data in a simple table of frequency classes (e.g. how many families with 0 cases, how many with 1 case, &c.) and then GoF to Poisson and negative binomial. I treat each family as a natural sampling unit but families are of different size. I can

Hi all, I need to test gof of 3-dimensional t copula for my trivariate observed data set. So I used the command t.cop <- tCopula(c(0.785,0.283,0.613),dim=3,dispstr="un",df=6,df.fixed = TRUE) where c(0.785,0.283,0.613) is the correlation pattern of my data with 0.785 pearson correlation between variable 1-2, 0.283 correlation between 1-3 and 0.613 is the correlation between variable

Dear R-List, to assess the model fit for 2PL-models, I tried to mimic the bootstrap-approach chosen in the GoF.rasch()-function. Not being a statistician, I was wondering whether the following simplification (omit the "chi-squared-expressed model fit-step") would be appropriate: GoF.ltm <- function(object, B = 50, ...){ liFits <- list() for(i in 1:B){ rndDat <-

I have used Sweave a lot the latest year, but never really used any long function calls. If I have code which look like this ------------------------------------------------------------- gof <- benthic.flux(ID="Gulf of Finland", meas.conc=conc, bw.conc=bw.conc, time=times,

Dear all, I am facing some problem with how to fit a "Baseline category Logit model" with R. Basically I am considering famous "Alligator" data as discussed by Agresti. This data can also be found here: https://onlinecourses.science.psu.edu/stat504/node/174 (there is also an accompanying R file, however the underlying R code could not load the data properly!!!) Below are

R-users, Attached is the file (SR_use2.txt) I'd like to include and includes column headers. nat_est is the response variable and is the number of species at a particular point. The other variables are the explanatory vars (vark, var2, var1, UK, U2, U1, GK, G2, G1, PK, P2, P1). Here is Walsh's sample code for hier.part: data(urbanwq) env <- urbanwq[,2,8] hier.part(urbanwq$lec,

Dear All, I have just estimated this model: ----------------------------------------------------------- Logistic Regression Model lrm(formula = Y ~ X16, x = T, y = T) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 82 LR chi2 5.58 R2 0.088 C 0.607 0