search for: gof

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 <- rep(factor(rep(c("A1","A2","A3","R1"),each=10)),3) Value <- c(runif(40),rnorm(40),rnorm(30,10,3),rnorm(10,20,3)) test.data <- data.frame(Alg=Alg,GOF=GOF,Value=Val...

...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 I get a warning message when I do that with fit.multiple.impute. a<-aregImpute(~med.hist.err+ med.discr+newLiving+No.drugs+Days.categ+Los+Age+Ward+Sex, n.impute=20, nk=0,data=med.err) ddist<-datadist(Age,No.drugs,Days.categ, Sex, Livin...

? 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 multinom with no gof-test (# also, what if the data are not binary and more than one predictor in the model?) Hint...

...the documentation of xyplot how to remove the external box. I would really appreciate any help with this ------------- START ----------- library(lattice) x<-1:100 cuts <- unique( quantile( as.numeric(x), probs=c(0, 0.25, 0.5, 0.75, 0.9, 0.95, 1), na.rm=TRUE) ) gof.levels <- cut(x, cuts) nlevels <- length(levels(gof.levels)) xyplot(1~1, groups=gof.levels, type="n", xlab="", ylab="", scales=list(draw=FALSE), key = list(x = .5, y = .5, corner = c(0.5, 0.5), title="legend",...

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 values > 4. For a 1:1 study, I can fit a conditional logistic model via an unconditional routine by making the response variable all 1?s, taking the...

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, 'gof')" call that I could use to evaluate the goodness of fit....

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 <- rmvlogis(nrow(obje...

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 t...

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 i...

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 = 100...

...g - 2, "P(>Chi)" = P) }   > hosmerlem(no.NA$repcnd, fitted(mod.fit))  X^2                       Df                           P(>Chi) 7.8320107            8.0000000            0.4500497     > # Option 2 - Hosmer–le Cessie omnibus lack of fit test: > library(Design) > lrm.GOF <- lrm(formula = no.NA$repcnd ~  no.NA$svl, data =  no.NA, y = T, x = T) > resid(lrm.GOF,type = "gof") Sum of squared errors     Expected value|H0        SD                     Z                     P                  48.3487115                          48.3017399                  ...

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 do the above analysis for each family size but would like to pool the results. Which way forward ... I am guessing that I could use some extension of Mantel-Haenszel methods or...

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....

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, substance=expression(DIC~(mmol~m^{-3})) ) --------------------------------------------------------...

...le 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$lec, env, fam="gaussian", gof="Rssqu") The code I wrote is library(hier.part) SRUSE<- read.table("F:\\GEORGIA\\species_richness\\SR_use2.txt", sep=" ", header = TRUE, row.names = 1) TEMP<- SRUSE[2:13] hier.part(SRUSE$nat_est,TEMP, family="NegBin", gof="logLik", barplot=...

...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 any corrections for widely recognized problems, such as small sample size, non-uniform expected frequencies, and one D.F. From the help page: "In the goodness-of-fit case simulation is done by random sampling from the discr...

...#############################################33 # Compute the le Cessie and Houwelingen test f <- lrm(Collaterals ~ CHF + Age + CABG + relevel (as.factor (num.obst.vessels),"one") + Current.smoker + DM + HTN + ace.inhibitor + MI, x=TRUE, y=TRUE) library(Design) resid(f, 'gof') Output: > hosmerlem(y=Collaterals, yhat=fitted(cd.full_model)) $chisq [1] 6.275889 $p.value [1] 0.6163585 > resid(f, 'gof') Sum of squared errors Expected value|H0 SD 118.5308396 118.3771115 0.1435944...

...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 data (decreasing), and Y is a vector of length 639 What I need to do next is create another vector TY (of the same length) that is the the sum of part of the Y vector. So TY[1]=Y[1] TY[2]=Y[1]+Y[2] TY[3]=Y[1]+Y[2]+Y[3] and so on. I have tried to do a s...

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 "body&...

Dear R developers, there appears to be a small problem with function cmdscale: for non-Euclidean distance matrices, using option add=FALSE (the default), cmdscale misses the smallest eigenvalue. This affects GOF statistic g.1 (See Mardia, Kent + Bibby (1979): Multivariate Analysis, eq. (14.4.7). The corresponding formula in Cox + Cox (2001): Multidimensional Scaling, 2nd ed., p 38, would seem to contain a misprint, it should be n instead of n-1.) Example: R> cmdscale(eurodist, eig=TRUE)$GOF [1] 0....