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Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My

Well pointed out, Jim! It is infortunate that the documentation for options(digits=...) does not mention that these are *significant digits* and not *decimal places* (which is what Joshua seems to want): "?digits?: controls the number of digits to print when printing numeric values." On the face of it, printing the value "0,517" of 'ccc' looks like printing 4

Dear friends, After running the lm() model, we can get summary resluts like the following: Coefficients: Estimate Std. Error t value Pr(>|t|) x1 0.11562 0.10994 1.052 0.2957 x2 -0.13879 0.09674 -1.435 0.1548 x3 0.01051 0.09862 0.107 0.9153 x4 0.14183 0.08471 1.674 0.0975 . x5 0.18995 0.10482 1.812 0.0732 . x6 0.24832 0.10059 2.469 0.0154 * x7

>>>>> Ted Harding >>>>> on Thu, 31 May 2018 07:10:32 +0100 writes: > Well pointed out, Jim! > It is infortunate that the documentation for options(digits=...) > does not mention that these are *significant digits* > and not *decimal places* (which is what Joshua seems to want): Since R 3.4.0 the help on ?options *does* say

Hello everybody, I have data in a data.frame "data" whose structure is given below. I would like to save them into a MySQL database. It works quite well with dbWriteTable but with this function, it also save the number of the rows (see what I get from MySQL below the data.frame structure. Is there a way to avoid this, that is to save my 15 columns and not 16 ? Thanks in advance for

Hi Joshua, Because there are no values in column ddd less than 1. itemInfo[3,"ddd"]<-0.3645372 itemInfo aaa bbb ccc ddd eee skill 1.396 6.225 0.517 5.775 2.497 predict 1.326 5.230 0.462 5.116 -2.673 waiting 1.117 4.948 NA 0.365 NA complex 1.237 4.170 0.220 4.713 5.642 novelty 1.054 4.005 0.442 4.260 2.076 creative 1.031 3.561 0.362 3.689

Dear R-users, I computed a simple mixed models which was: mod<-lmer(nb ~ site + (1|patelle),tr) The output was: Linear mixed-effects model fit by REML Formula: nb ~ site + (1 | patelle) Data: tr AIC BIC logLik MLdeviance REMLdeviance 1157.437 1168.686 -574.7184 1164.523 1149.437 Random effects: Groups Name Variance Std.Dev. patelle

Folks, I have a small dataset of counts of recoveries on defaulted loans: recoveries<-structure(c(0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 0, 0, 0, 0, 0, 4, 0, 1, 2, 2, 12), .Dim = c(11L, 2L), .Dimnames = list( NULL, c("pcts", "counts"))) Here is the data in columnar form: pcts counts [1,] 0.0 0 [2,] 0.1 0 [3,] 0.2 0 [4,] 0.3

R version 3.5.0 (2018-04-23) -- "Joy in Playing" Platform: x86_64-pc-linux-gnu (64-bit) options(digits=3) itemInfo <- structure(list("aaa" = c(1.39633732316667, 1.32598263816667, 1.11658324066667, 1.23651072616667, 1.05368679983333, 1.03100737383333, 0.9630728395, 0.7483865045, 0.620086646166667, 0.5411017985, 0.496397607833333, 0.459528044666667, 0.427877047833333,

Hi, I am using the sim1000G R package to simulate data for case/control study. I can not figure out how to manipulate this code to be able to generate 10% or 50% causal SNPs in R. This is whole code provided as example on GitHub: library(sim1000G) vcf_file = "region-chr4-357-ANK2.vcf.gz" #nvariants = 442, ss=1000 vcf = readVCF( vcf_file, maxNumberOfVariants = 442 ,min_maf =

Dear all I need to compute percentage changes of my data, but unfortunately they contain both negative and zero values, and I am quite confused on how to proceed. Searching the internet I found that many people ran into similar issues, with no obvious solution available. The last couple of weeks I've been playing with all the data transformations that I could think of. Below I will expose on

I have 11 possible predictor variables and use them to model quite a few target variables. In search for a consistent manner and possibly non-manual manner to identify the significant predictor vars out of the eleven I thought the option "select=T" might do. Example: (here only 4 pedictors) first is vanilla with "select=F" >

Hi, I am getting a warning message when I am fitting a generalized linear mixed model (m1.2 below). CHOLMOD warning: matrix not positive definite Error in objective(.par, ...) : Cholmod error `matrix not positive definite' at file:../Supernodal/t_cholmod_super_numeric.c, line 614 Any idea? Thanks for your help, Reza > sessionInfo() R version 2.4.1 (2006-12-18) i386-pc-mingw32

Hi, I have a Community (COM) composed of 6 species: A, B, C, D, E & F. The density of my Community is thus (Eq.1): dCOM = dA + dB + dC + dE + dF I would like to calculate and plot a linear regression between the density of each of my species and the density of the whole community (illustrating how the density of each species varies with variations of the whole community). For example, I would

A most puzzling network conundrum has arisen while I was attempting to create a virtual network behind a virtual router which in turn connects the virtual network to my real network. My machine (192.168.103.23) is on the network with my router (192.168.103.1). The virtual router, tiara, has to connect my 192.168.103.* network with the virtual 10.0.0.* network which comprises two other virtual

Hello, could anyone explain what am I doing wrong. When I use colldiag function from package perturb I get different Variance Decomposition Proportions matrix in R than in SAS, although the eigenvalues and indexes are the same. Thanks for your attention. Results: in R: eigen(cor(indep2)) $values [1] 4.197131e+00 6.674837e-01 9.462858e-02 4.070314e-02 5.323022e-05 colldiag(indep2,c=T)

I'm working on some functions for generalized canonical discriminant analysis in conjunction with the heplots package. I've written a candisc.mlm function that takes an mlm object and computes a candisc object containing canonical scores, coeficients, etc. But I'm stumped on how to construct a mlm for the canonical scores, in a function using the *same* right-hand-side of the model

Dear R users, I am using lmer combined with AIC model selection and averaging (in the MuMIn package) to try and assess how isotope values (which indicate diet) vary within a population of animals. I have multiple measures from individuals (variable 'Tattoo') and multiple individuals within social groups within 4 locations (A, B, C ,D) crucially I am interested if there are

# I have a question about how factanal is calculating the regression factor # scores based on an oblique rotation (promax) of the factors. # # As is explained in the help file, regression factor scores are # obtained as # # hat f = Lambda' Sigma^-1 x # # However, according to Harman's "Modern Factor Analysis" (e.g. second # edition, pp. 351-352) the formula is # # hat f = Phi

Hi, I am getting a warning message when I am fitting a generalized mixed model (mod_2) and I don't understand why because when I add just an interaction factor the model works perfectly (mod_1). Does anyone know what it happpens ? Thanks, Aïda   > mod_1<-lmer(sur15~soeviv15_4plus+frviv15_4plus+frat_15death+dad_class_new+soeviv15_4plus:dad_class_new +frviv15_4plus:dad_class_new+