similar to: Function update problem

Dear all, The background is that I'm trying to fix this bug in the geometry package: https://r-forge.r-project.org/tracker/index.php?func=detail&aid=1993&group_id=1149&atid=4552 Boiled down, the problem is that there exists at least one matrix X for which det(X) != 0 and for which solve(X) fails giving the error "system is computationally singular: reciprocal condition

Hi, I'm having trouble with glmmPQL from the MASS package. I'm trying to fit a model with a binary response variable, two fixed and two random variables (nested), with a sample of about 200,000 data points. Unfortunately, I'm getting an error message that is difficult to understand without knowing the internals of the glmmPQL function. > model <- glmmPQL(primed ~

Hello, I have difficulties to deal with multilevel model. My dataset is composed of 10910 observations, 1237 plants nested within 17 stations. The data set is not balanced. Response variable is binary and repeated. I tried to fit this model model<- glmmPQL( y ~ z1.lon*lun + z2.lat*lun + z1.lon*lar + z2.lat*lar + z1.lon*sca + z2.lat*sca +z1.lon*eta + z2.lat*eta, random = ~ lun + lar + sca

Hi, I was very much hoping someone could help me with the following. I am trying to convert some SAS NLMIXED code to NLME in R (v.2.1), but I get an error message. Does anyone have any suggestions? I think my error is with the random effect "u" which seems to be parametrized differently in the SAS code. In case it's helpful, what I am essentially trying to do is estimate parameters

I get this error while computing partial correlation. *Error in solve.default(Szz) : system is computationally singular: reciprocal condition number = 4.90109e-18* Why is it?Can anyone give me some idea ,how do i get rid it it? This is the function i use for calculating partial correlation. pcor.mat <- function(x,y,z,method="p",na.rm=T){ x <- c(x) y <- c(y)

Hi All, This is my R-version information:--- > version _ platform i486-pc-linux-gnu arch i486 os linux-gnu system i486, linux-gnu status major 2 minor 7.1 year 2008 month 06 day 23 svn rev 45970 language R version.string R version 2.7.1 (2008-06-23) While calculating partial

Hi - I am not R expert and I would appreciate your time if you can help me about my xyplot question. I would like to add text (p-value) in a 4 panels xyplot. I thought panel = function{} should work but I am not sure where I did it wrong. The error message from the following code is "Argument subscripts is missing with no default values" xyplot(GLG ~ PD | factor(TRT) , groups =

>>>>> Fox, John <jfox at mcmaster.ca> >>>>> on Thu, 14 Sep 2017 13:46:44 +0000 writes: > Dear Martin, I made three points which likely got lost > because of the way I presented them: > (1) Singularity is an unusual situation and should be made > more prominent. It typically reflects a problem with the > data or the

Dear Terry, Even the behaviour of lm() and glm() isn't entirely consistent. In both cases, singularity results in NA coefficients by default, and these are reported in the model summary and coefficient vector, but not in the coefficient covariance matrix: ---------------- > mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), + data=longley) >

>>>>> Martin Maechler <maechler at stat.math.ethz.ch> >>>>> on Thu, 14 Sep 2017 10:13:02 +0200 writes: >>>>> Fox, John <jfox at mcmaster.ca> >>>>> on Wed, 13 Sep 2017 22:45:07 +0000 writes: >> Dear Terry, >> Even the behaviour of lm() and glm() isn't entirely consistent. In both cases,

Hello, to plot the result of a singular non linear regression (using nls) I usually use the function plotfit, for example: r.PTG.V<-nls(PTG.P~ fz1(Portata, a,b), data=dati, start=list(a=10, b=10), nls.control(maxiter=200), algorithm='port', trace=TRUE, na.action=na.omit, lower=list(a=0, b=10), upper=list(a=100, b=100)) plotfit(r.PTG.V) I tried to use the function plotfit on the

Hi, The following function does work optimize.W<-function(W,k,G,Groups,cph.call,z){ n<-length(Groups) grp.wt<-rep(0,n) for(i in 1:(length(G))){ ind<-Groups == G[i] if(G[i]!=k){ grp.wt[ind]<-W[i] } elsegrp.wt[ind]<-1 } z<-data.frame(cbind(z,grp.wt=grp.wt)) #needed to make the case weights #part of the data

Hi, I am running simulations that does multiple comparisons to control. For each simulation, I need to model 7 nls functions. I loop over 7 to do the nls using try if try fails, I break out of that loop, and go to next simulation. I get warnings on nls failures, but the simulation continues to run, except when the internal call (internal to nls) of the chol2inv fails.

Dear R users,   I am struggling to fit expo-linear equation to my data using "nls" function. I am always getting error message as i highlighted below in yellow color:     ### Theexpo-linear equation which i am interested to fit my data:       response_variable =  (c/r)*log(1+exp(r*(Day-tt))), where "Day" is time-variable   ## my response variable   rl <-

Hello everyone, I hope this is a fair enough question, but I don’t have access to a copy of Bates and Pinheiro. It is probably quite obvious but the answer might be of general interest. If I fit a fixed effect with an added quadratic term and then do it as an orthogonal polynomial using maximum likelihood I get the expected result- they have the same logLik.

Dear R users, I am struggling to fit expo-linear equation to my data using "nls" function. I am always getting error message as i highlighted below in yellow color:  Theexpo-linear equation which i am interested to fit my data:       response_variable =  (c/r)*log(1+exp(r*(Day-tt))), where "Day" is time-variable my response variable rl <-

Dear R users, I am trying to fit a GLMM to the following dataset; tab a b c 1 1 0.6 199320100313 2 1 0.8 199427100412 3 1 0.8 199427202112 4 1 0.2 199428100611 5 1 1.0 199428101011 6 1 0.8 199428101111 7 0 0.8 199527103011 8 1 0.6 199527200711 9 0 0.8 199527202411 10 0 0.6 199529100412 11 1 0.2 199626201111 12 2 0.8 199627200612 13 1 0.4 199628100111 14 1 0.8

Hello, I'm using the gstat package within R for an automated procedure that uses ordinary kriging. I can see that there is a logical ("singular") atrtibute of some adjusted model semivariograms: .- attr(*, "singular")= logi TRUE I cannot find documentation about the exact meaning and the implications of this attribute, and I dont know anything about the inner calculations

Hi everyone I am using box.tidwell to transform the explanatory variables. However, it appears problematic. The warning I received as follows. box.tidwell(Newresponse ~ FAC2_1 + FAC4_1 + FAC5_1 + FAC6_1 + FAC7_1 + KXI + RECODINC) Warning in log(x) : NaNs produced Warning in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : extra arguments na.rm are just disregarded.

Good evening! I don't know R, but I have to do this work for my diploma. So I'm sorry for the strange message below. Help me anybody decides this issues, please. a part of the code is: for (i in 1:133) { r_squared <- vector() sample_bank <- mydata[((i-1)*311+1):(i*311),] #return for bank return_bank <- diff(sample_bank[,4])/sample_bank[,4][-length(sample_bank[,4])] I've