search for: ndat

Dear R-Help listers, I am trying to estimate an proportional odds logistic regression model (or ordered logistic regression) and then make predictions by supplying a hypothetical x vector. However, somehow this does not work. I guess I must have missed something here. I first used the polr function in the MASS package, and I create a data frame and supply it to the predict function (see below):

...e following code to fit an exponential model to my data. I have 4 different datasets. For 3 datasets nls seems to work fine and I have no error messages. But for 1 dataset I am getting the "world known" singular gradient error. xfit.dNEE <- nls(vario.dNEE~V*(1-exp(-1*dist/L)),data=ndat,start=list(V=vstart,L=lstart),trace=T) I tried also with different starting values but still the same error... Any help would be highly welcome. Thank you in advance. -- View this message in context: http://r.789695.n4.nabble.com/nls-singular-gradient-as-always-tp4669859.html Sent from th...

....82,-3.02,0.99,1.1,-3.31))) rownames(dat)=c("hsa-miR-200c","hsa-miR-520b","hsa-miR-199a-3p","mmu-miR-124a","hsa-miR-302a","hsa-miR-454","mmu-miR-137") colnames(dat)=c("24", "72") heatmap.3(dat, srtCol=70) ndat<-as.matrix(dat) heatmap.3(ndat, Rowv=FALSE, Colv=FALSE)

In the R code of Mardia's test, what does the line " x1 = x[x[, p] == i, -p]" mean? Thanks a lot! function (x) { p = dim(x)[2] f = p - 1 clases = length(table(x[, p])) for (i in 1:clases) { x1 = x[x[, p] == i, -p] ndat = dim(x1)[1] mo3 = mo3(x1) mard1 = ndat * mo3/6 cat("Mardia's test for class", i, "\n") cat("mard1=", mard1, "\n") p1 = 1 - pchisq(mard1, df = f * (f + 1) * (f + 2)/6) cat("pvalue for m3=", p1, &qu...

...9, 1.310990, 2.043032, 0.449906,-0.951917,-0.077104, -0.356833,-0.286042, 0.065750, 0.159677,-0.075792) x2=c(-0.3050, 1.0125, 0.2050, 0.1025, 0.9550, 0.6975, 1.5550, 0.0225, 0.2575, 0.3725, 2.0075, 2.1275, 0.7200, 0.2950, 0.2875, -0.2800,-0.6050, 0.2125,-0.5525,-1.7850) ndat=length(y) ybk=y x1bk=x1 x2bk=x2 ######## Joint probability function of y, x1 and x2 # fit joint probability function fityxv=locfit(~y+x1+x2,alpha=1,deg=1) fyxv=predict(fityxv,where="data") ######## Marginal distribution of gxv # fit marginal distribution of y fitxv=locfit(~x1+x2,alpha=0....

Dear everybody: We used the fine foreign library to bring in an SPSS dataset that was about 9 megabytes and I can squeeze it into a much smaller R object using compression with save(ndat, file="NatAnnES2000.rda", compress=T). I can use load() to get the "ndat" dataframe back, that's all good as far as I can see. If I put that file in the data subdirectory, then the data() command finds it and I can load it. Seems fine, but then I started wondering if...

...06,-0.951917,-0.077104, > -0.356833,-0.286042, 0.065750, 0.159677,-0.075792) > x2=c(-0.3050, 1.0125, 0.2050, 0.1025, 0.9550, > 0.6975, 1.5550, 0.0225, 0.2575, 0.3725, > 2.0075, 2.1275, 0.7200, 0.2950, 0.2875, > -0.2800,-0.6050, 0.2125,-0.5525,-1.7850) > > ndat=length(y) > ybk=y > x1bk=x1 > x2bk=x2 > ######## Joint probability function of y, x1 and x2 > # fit joint probability function > fityxv=locfit(~y+x1+x2,alpha=1,deg=1) > fyxv=predict(fityxv,where="data") > > ######## Marginal distribution of gxv > # fit margi...

....table does pretty much what you want, [not tested] newtab <- as.data.frame(table(a,b,c)) cat("dim\n") for(i in seq(1, ncol(newtab)-1) { cat(nlevels(newtab[,1]," ") } cat("\nlab ") for(i in seq(1, ncol(newtab)-1) { cat(letters[i], " ") } cat("\ndat [", newtab[,"Freq"], " ]\n") -- | David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph: INT+61+7+3362-0217 fax: -0101 / * | Epidemiology Unit, Queensland Institute of Medical Research \_,-._/ | 300 Herston Rd, B...

Hello Sir, I am using mgcv package for my data. My model is y~x1+f(x2),I want to find out the function f(x2) . Following is the code.   sm1=gam(y~x1+s(x2),family=binomial, f) summary(sm1) plot(sm1,residuals=TRUE, xlab="AGE",pch=20)   In this plot I am getting S(x2,1.93) on y axixs  How should I get the function for x2 from this plot.or Is there anyother procedure in R  to get this

Hi Friends, I'm new to R.I have a data frame : xxx having columns color name values R XXX 10 G YYY 4 Y ZZZ 5 G XXX 2

...logreg1<-vector("list", length(l)) logreg.anov1<-vector("list",length(l)) logreg.summ1<-vector("list",length(l)) logreg.conf1<-vector("list",length(l)) for (i in c(13:16)){ logreg1[[i-x]]<-glm(dat[,i]~group + age, family=binomial, data=Ndat) logreg.anov1[[i-x]]<-anova(logreg1[[i-x]]) logreg.summ1[[i-x]]<-summary(logreg1[[i-x]]) logreg.conf1[[i-x]]<-logistic.display(logreg1[[i-x]], crude=FALSE) } --------------------------------- Går det långsamt? Skaffa dig en snabbare bredbandsuppkoppling. Sök och...

...een able to locate any code. Below is my attempt at some code: chgpt = function(a1,a2,a3,gam,wave){ yht=numeric(10) y1=(wave <= gam)*(a1+(a2*wave)) y2=(wave > gam)*((a1+(a2-a3)*gam)+a3*wave) yhat=y1+y2 return(yht) } nl.dat <- nlme(y ~ chgpt(a1,a2,a3,gam,wave)~wave|id, data = ndat, fixed = a1 + a2 + a3 + gam, random = a1 + a2 + a3, start = c(a1=10,a2=5,a3=1,gam=4.5)) summary(n1.dat) I would appreciate any advice on this code or guidance as to where to search for example code to fit this nonlinear mixed effects model. Many thanks, Jeff H...

...igure out. y = rnorm(100) x = c(rep("0",1), rep("1",99)) d = data.frame(won=x, bid=y) d mod1 <- zelig(y~x, model="relogit", data=d, tau=1/100, case.correct="prior", bias.correct=TRUE, robust=TRUE) > mod1 <- zelig(y~x, model="relogit", data=ndat, tau=1/100, case.correct="prior", bias.correct=TRUE, robust=TRUE) <simpleError in family$linkfun(mustart): Value 1.16885 out of range (0, 1)> Error in eval(expr, envir, enclos) : object 'form' not found In addition: Warning messages: 1: In model.matrix.default(mt, mf, contra...