similar to: bootstrapping quantile regression

Dear all, I have a problem with interactions in lmer. I have 2 factors (garden and gebiet) which interact, plus one other variable (home), dataframe arr. When I put: / lmer (biomass ~ home + garden:gebiet + ( 1|Block), data = arr)/ it writes: /Error in lme4::lFormula(formula = biomass ~ home + garden:gebiet + (1 | : rank of X = 28 < ncol(X) = 30/ In the lmer help I found out that if not

Dear anyone who knows more about R than me (so everyone). I have been bashing my head on the keyboard all day trying to do something with my table. I have some data, like so: yyyy-mm Rainfall(mm) 1 1977-02 17.4 2 1977-03 34.0 3 1977-04 26.2 4 1977-05 42.6 5 1977-06 58.6 6 1977-07 23.2 7 1977-08 26.8 8 1977-09 48.4 9

Hello, I have the following data.frame structure(list(Study = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,

Hi all, I am using a 'tricked' Cox Hazard regression model for discrete choice habitat modelling. However, I'm having a hard time understanding the meaning of the first line the following part of the summary() output: Rsquare= 0.307 (max possible= 0.475 ) Likelihood ratio test= 91.8 on 12 df, p=2.23e-14 Wald test = 26.3 on 12 df, p=0.00977 Score (logrank) test = 58.6 on 12 df,

Full_Name: Dennis Wolf Version: 1.9.0 OS: Mac OS 10.3.3 Submission from: (NULL) (160.91.76.23) platform powerpc-apple-darwin6.8 arch powerpc os darwin6.8 system powerpc, darwin6.8 status major 1 minor 9.0 year 2004 month 04 day

The partial R-square (or coefficient of partial determination, or squared partial correlation coefficients) measures the marginal contribution of one explanatory variable when all others are already included in multiple linear regression model. The following link has very clear explanations on partial and semi-partial correlation: http://www.psy.jhu.edu/~ashelton/courses/stats315/week2.pdf In

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 =

If I'm not mistaken, partial R-squared is the R^2 of the quantities plotted in a partial residual plot, so you can base the computation on that. Prof. Fox's `car' package on CRAN has a function for creating those plots, but you need to figure out the way to extract the quantities being plotted. [In any case, the basic tools for doing such computations are all in R, and it

Hi, While executing the make after successful configuration (./configure --with-readline=no --without-iconv), the following error occurs: mbf2n11s (Regatta) /saswork/R/R-2.7.0$ make Target "R" is up to date. Target "R" is up to date. Target "R" is up to date. Target "R" is up to date. Target "Makedeps" is up to date. Target "libbz2.a"

2017-12-09 12:54 GMT-08:00 Chris Lattner via llvm-dev < llvm-dev at lists.llvm.org>: > > > On Dec 8, 2017, at 5:01 PM, Mikhail Zolotukhin via llvm-dev < > llvm-dev at lists.llvm.org> wrote: > > Hi, > > I tweaked my scripts to avoid removing includes when it doesn't give any > significant benefits, which made the patches significantly smaller. This >

Dear R users. I have zoo object "size_june" containing market-capital values: > dim(size_june) # market-cap data of 625 firms for 20 years [1] 20 625 > class(size_june) [1] "zoo" > size_june # colnames = "size.firmcode" size.34020 size.4710 size.11050 size.10660 size.9540 size.8060 size.16160 size.8080 size.9280 1988-06-30 NA

Hi, I tweaked my scripts to avoid removing includes when it doesn't give any significant benefits, which made the patches significantly smaller. This time the patches should not try to remove includes of header files, which are transitively included from other included header files. The gains mostly remained the same (plus/minus noise), the tables are in the end of the email. I also included

Send CentOS-announce mailing list submissions to centos-announce at centos.org To subscribe or unsubscribe via the World Wide Web, visit http://lists.centos.org/mailman/listinfo/centos-announce or, via email, send a message with subject or body 'help' to centos-announce-request at centos.org You can reach the person managing the list at centos-announce-owner at centos.org When

I apologize that I've asked a similar question before, but being new to R I don't think I did a very good job of formating the question. I've included a text file since the date set is somewhat large. What I have is a huge string of numbers in a text file. The numbers are all separated by comma's and the groups are separated by a semicolon. What I would like to do is read each

I gave the AMD64 version of 7.2 RC2 a spin and all installed as expected off the dvd INTEL S3200SHV MB, Core2Duo, 4G of RAM In the past it had been suggested that for zfs tuning, something like vm.kmem_size_max="1073741824" vm.kmem_size="1073741824" vfs.zfs.prefetch_disable=1 However doing a simple test with bonnie and dd, there does not seem to be very much difference in

Hi, I've two data.frame: ind_comp and dati_area > ind_comp INDEX indice 1 1 0.3081856 2 2 0.1368007 3 3 0.1290952 4 4 0.2905484 5 5 0.2686706 6 6 0.1122784 7 7 0.4493264 8 8 0.1932665 9 9 0.1982783 10 11 0.3724666 > dati_area X_COORD Y_COORD DBH AREA ID ind_comp 1 0.0000000 0.0000000 70.0 1 1 0 2

Hello, dear R-community. This is a question about TukeyHSD between factor combinations of a Three-Way ANOVA, which is - since it is a multi measure ANOVA - not a simple ANOVA but a Generalised Linear Mixed Model (GLMM), calculated with "lmer". > growth <- groupedData(length~meas|box_id,outer=~spec*comp*water,data=all.spec) > model <-

Good afternoon, I have a 63 small .csv files which I process daily, and until two weeks ago they processed just fine and only took a matter of moments and had non noticeable memory problem. Two weeks ago they have reached 318 lines and my script "broke". There are some missing-values in some of the files. I have tried hard many times over the last two weeks to create a

Hi, Here in R, I need to load a huge file(.csv) , its size is 200MB. [may come more than 1GB sometimes]. When i tried to load into a variable it taking too much of time and after that when i do cbind by groups, getting an error like this " Error: cannot allocate vector of size 82.4 Mb " My requirement is, spilt data from Huge-size-file(.csv) to no. of small csv files. Here i will give

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening