similar to: warning message from nlme

Hi all, I just wonder what singular convergence means. Thanks. Yen Lin Error in lme.formula(Data ~ 1, random = ~1 | Wafer/fie/loc, subset = Wafer == : singular convergence (7) [[alternative HTML version deleted]]

Hi all, Suppose I have a 4 x 2 matrix A and I want to select the values in second column such that the value in first column equals to k. I gave the colnames as alpha beta, so I was trying to access the info using A$beta[A[,1]==k], however, I was told it's not a data frame, I can get the object by using dollar sign. I tried data.frame(A), but it didn't work. Any input

Hi all, If I want to write a for loop to loop through a set of factors, which are not coded in 1,2,3, for e.g in character, possible to write the for loop without changing it to categorical variables? I saw the manual mentions for loop will take a list, but I'm not sure how to create a list here. Any input will be appreciated. Thanks. Yen Lin [[alternative HTML version deleted]]

Hi all, I wonder how could I convert a matrix A to a dataframe such that whenever I'm running a linear model such lme, I can use A$x1? I tried data.frame(A), it didn't work. Should I initialize A not as a matrix? Thanks. Yen Lin [[alternative HTML version deleted]]

Hi all, Does lme has function to compute the cook's distance or influence measure like lm? I can't find them. Thanks. Yen Lin [[alternative HTML version deleted]]

Hi all, I have read a data matrix of 304 x 404 using read.table. When I am trying to name the colnames, with first try colnames(L5)<-list(2:305), I keep getting error message such as Error in "colnames<-"(`*tmp*`, value = list(c(2, 3, 4, 5, 6, 7, 8, 9, : length of 'dimnames' [2] not equal to array extent and I don't know why. But, if I look at

I am trying to understand the proper way to encode the nesting structure for data in the context of nlme, in the specific case of individuals nested within species for which each individual is unique. I have searched through Pinheiro & Bates and also past postings, but without success. Take the Wafer data set which has 2 levels: Wafer (8 values) and Site nested within Wafer (10 values for

When I use a model fit with LME, I get an error if I try to use "predict" with a dataset consisting of a single line. For example, using this data: > simpledata Y t D ID 1 -1.464740870 1 0 1 2 1.222911373 2 0 1 3 -0.605996798 3 0 1 4 0.155692707 4 0 1 5 3.849619772 1 0 2 6 4.289213902 2 0 2 7 2.369407737 3 0 2 8 2.249052533 4 0 2 9 0.920044316 1

Dear all, I am planning to use nlme library for analysis of experiments in semiconductor industry. Currently I am using "lm" but plan to move to "lme" to handle within wafer / wafer-to-wafer and lot-to-lot variation correctly. So far everything is working well, but I have a fundamentel question: NLME offers "maximum likelihood" and "restricted maximum

I'm attempting to run an LME model with random intercepts for location and have received an error message. The model and error message are given below: > mixed.distance <- lme (fixed= log(min5inpnc) ~ i93centerline, random=~1|location, data = nosmokecomp) Error in y[revOrder] - Fitted : non-conformable arrays I believe that my dataset may have dimensions that are not working with

Hello dear uesRs, I am working on modeling both level one and level two heteroscedasticity in HLM. In my model, both error variance and variance of random intercept / random slope are affected by some level two variables. I found that nlme is able to model heteroscedasticity. I learned how to use it for level one heteroscedasticity but don't know how to use it to model the level

If you don't get a response it is because you did not read the Posting Guide which indicates that the R-sig-ME mailing list is where this question would have been on-topic. -- Sent from my phone. Please excuse my brevity. On August 16, 2017 6:17:03 AM PDT, b88207001 at ntu.edu.tw wrote: >Hello dear uesRs, > >I am working on modeling both level one and level two

Greetings R-helpers, I am attempting to fit an lme() while specifying a correlation structure, but I'm getting into trouble long before I get to that point. I am receiving the error: Error in y[revOrder] - Fitted : non-conformable arrays It doesn't seem to matter how simple or complex the model I specify is, it always gives this same error message. This makes me suspect something is

A better place for this post would be on R's mixed models list: r-sig-mixed-models . Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Wed, Aug 16, 2017 at 6:17 AM, <b88207001 at ntu.edu.tw> wrote: > Hello dear

I have one fixed effect, sor, with two levels. I have eight lots and three wafers from each lot. I have included the data below. I would like to fit a mixed model that estimates a covariance parameter for wafer, which is nested in lot, and two covariance parameters for lot, one for each level of sor. The following command fits the model that I want, except for it estimates the correlation

Hi helpeRs, I've been working through some examples in Pinhiero & Bates( 2000) trying to understand how to translate to the new Lme4 syntax but without much luck. Below is what I think I should do, but either the answers don't come out the same or I get errors. In the Oxide problems I'm particularly interested in obtaining the levels coeficients but this options no longer seems

Dear All; Attempting to reproduce Figure 4.15 of MEMSS (p. 171) in R using plot(Wafer, outer = ~ Wafer) yields an error: > plot(Wafer, outer = ~ Wafer) Error in order(na.last, decreasing, ...) : Argument lengths differ The plot() produces the figure without problems for all versions of Splus (4.5 to 6.2) on Windows. Noticing that the plot() differs slightly between

Is there a package in R that can do a variability plot? A variability plot is a kind of categorized dot plot. (If there is a lot of data in each category, box plots are used rather than dot plots.) Usually, the categories are factor level combinations. All the dot plots appear in the same window; below the x-axis a hierarchy of factors shows which dot plot corresponds to which factor-level

Could someone identify a function that I might use to perform a components of variance analysis? In addition to the variance attributable to each factor, I would also like to obtain the SE of the variances. Thank you, John John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics Baltimore VA Medical Center GRECC and University of Maryland School of Medicine Claude Pepper OAIC University of

All, I am having trouble with a "read.table()" function that is inside of another function. But if I call the function by itself, it works fine. Moreover, if I run the script on a Mac OS X (with the default Mac OS X version of R installed, rev 2.8), it works fine. But it does not work if I run it on windows vista (also default Windows version of R, rev. 2.8). Again, both