similar to: Questions about bwplot

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

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

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,

? Hallo, which function can i use to do (baseline) logistic regression + goodness of fit tests? so far i found: # logistic on binary data lrm combined with resid(model,'gof') # logistic on binary data glm with no gof-test # baseline logit on binary data

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

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,

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,

I'm trying to do a Hosmer-Lemeshow 'goodness of fit' test on my logistic regression model. I found some code here: http://sas-and-r.blogspot.com/2010/09/example-87-hosmer-and-lemeshow-goodness.html The R code is above is a little complicated for me but I'm having trouble with my answer: Hosmer-Lemeshow: p=0.6163585 le Cessie and Houwelingen test (Design library): p=0.2843620

Dear R-users, is there a function for the Chi-square Goodness of Fit Test in R (compare function chisq.gof in S/S-PLUS). Thanks for help. Matthias Seitzinger -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

Hi, I am trying to create a function for a goodness-of-fit test for the Pareto Distribution for some loss data that I have. So far I have the following: function(X=OTOL) { n <- length(X)-1 #calculated the number of values (extra as 0 included) i <- 2:640 #values of i j <- 1:639 #values of i-1 Y <- (n-j+1)*((X[i])-(X[j])) #First part of GoF model Y } Where OTOL is the ordered loss

TOPIC My question regards the philosophy behind how R implements corrections to chi-square statistical tests. At least in recent versions (I'm using 2.13.1 (2011-07-08) on OSX 10.6.8.), the chisq.test function applies the Yates continuity correction for 2 by 2 contingency tables. But when used as a goodness of fit test (GoF, aka likelihood ratio test), chisq.test does not appear to implement

I've installed samba 4 fine but get error msg when do a /usr/local/samba/bin/samba-tool domain provision \ Unknown parameter encountered: "idmap domains" Ignoring unknown parameter "idmap domains" Unknown parameter encountered: "idmap alloc backend" Ignoring unknown parameter "idmap alloc backend" Unknown parameter encountered: "display

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 ) {

Hola, con vistas a docencia, me gustaría saber si hay alguna librería que haga el test de la chi cuadrado para variables continuas, que no sean la normal. Lo encontré para variables discretas y para la normal. También, obviamente, se puede hacer un pequeño programa que lo calcule "como a mano", pero a los alumnos les obliga a entrar en detalles de programación... :-/ La idea sería

Hopefully this isn''t completely inappropriate, but lately I''ve been curious about what books people have found most helpful in their development as programmers. If one of the icons like Dave Thomas, DHH, Mike Clark, Andy Hunt et al could weigh in that''d be awesome :) Here are some of my favorites: Refactoring, Martin Fowler Mastering Regular Expressions, Friedl

I am using the ts package to fit ARIMA models, and the tsdiag() function to plot diagnostics. In doing so I'm generating an awful lot of diagnostic plots of different models and different data sets all within the same R session. So my question is, is there an option in tsdiag() similar to <main="Title"> that I can use? This would be quite helpful when I print out the plots,

Hi, I would like to implement the empirical copula in R, does anyone know if it is included in a package? I know it is not in the "Copula" package. This one only includes a gof-test based on the empirical copula process. Thanks for your help! Gregor -- View this message in context: http://www.nabble.com/Empirical-copula-in-R-tf4018319.html#a11412335 Sent from the R help mailing list

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

Hi, Dear all, When I run xpose4() in R and select "2: Basic goodness of fit plots", I get an error like this " Selection: 2Error in print(basic.gof(.cur.db)) : error in evaluating the argument 'x' in selecting a method for function 'print': Error in xvardef(v, object) : object '.cur.db' not found Could you please tell me how to treat it? Thanks,