similar to: nlme

Dear List, Some independent variable were missing in calculation using lm and glm (gaussian). (X= Y1+Y2+…..+Y16, Independent number: 16 variable) However, those variables did work well in cor(X, Y) respectively. str(dataframe) was also run to ensure that the variables were all numbers. Moreover, the missing variables were different in lm and glm. In lm, 3 factors were not taken into

Hi - How can I add different notes in different panels? My data looks like ID Dose Visit Value 1 1 0 0.5 -6.5802e-02 2 1 0 1.0 2.4085e-01 3 1 0 1.5 -2.2907e-01 4 1 0 2.0 2.4074e-01 ... ... ... ... ... 270 45 30 3.0 -8.1316e-01 271 46 30 0.5 -2.0786e-01 272 46 30 1.0 -2.9336e-01 273 46 30 1.5 -5.5657e-01 274 46 30 2.0 -2.1659e-01

Dear List, I tried to do cca based on species data and environmental variables (formula instead of community data). However, there was an error saying row sums must be >0. I searched the previous related messages but found few solutions. Please kindly help and thank you in advance. code This is vegan 1.17-3 Warning message: package 'vegan' was built under R version 2.10.1

My question concerns the logic behind hypothesis tests for fixed-effect terms in models fitted with lme. Suppose the levels of Subj indicate a grouping structure (k subjects) and Trt is a two-level factor (two treatments) for which there are several (n) responses y from each treatment and subject combination. If one suspects a subject by treatment interaction, either of the following models seem

Hello, I am using package MuMIn to calculate AIC for a full model with 10 explanatory variables. Thanks in advance in sharing your experience. Q1 In the AIC list of all models, each model is differentiated by model number. Please kindly advise if it is possible to find the corresponding explanatory variable(s) for the model number. Q2 error message I tried to display sub-model with only

I'm using R to manipulate some data for testing nebulisers. I have a data frame as follows: Volt ABal 0.4208 A1 0.3982 AF 0.4353 B1 0.3954 BF 0.4355 C1 0.3975 CF 0.4345 I also have a calibration curve loaded Voltage NaF LogNaF 1 0.3588 20 1.30103 2 0.3990 100 2.00000 3 0.4391 500 2.69897 If I try to

Hi, I'm trying to save heatmaps as PDFs. However, the PDF version of the heatmaps (Heatmap_CAFvsTNF_run2.pdf) is blurred when compared to its counterpart, which was saved manually by using the software "Grab" (Heatmap_CAFvsTNF_run2.tiff). -----R code-------- sample_output <- "stroma_run2" filename <-

In R, I'm imported a data frame of 2,321,123 by 4 called "dataF". I converted the data frame "dataF" to a matrix dataM <- as.matrix(dataF) Does R have an efficient routine to treat the special elements that contain "inf" in them. For example, can you separate the rows that have "inf" elements from the matrix into a separate matrix without

Dear All- I am trying to merge two data files - they have different date formats and different times zones. I need to match up the date/time of the datasets and then invoke a conditional statement, such as: if dataC$mph is >= 12 then keep dataM$co23 for the corresponding time/date stamp. snippets of data files: *dataC.txt* LST in mph Deg DegF DegF2 % volts Deg

In addition to which, I would recommend df <- read.table("DATAM", header = TRUE, fill = TRUE, stringsAsFactors=FALSE) and then converting the Time column to POSIXct date-time values using as.POSIXct() specifying the format using formatting codes found in ?strptime because the times are not in the POSIXct default format. This example might indicate the idea: >

I use the method, df$Time = as.POSIXct(df$Time), but it has the warning message: Error in as.POSIXlt.character(x, tz, ...) : character string is not in a standard unambiguous format On Thu, Dec 14, 2017 at 1:31 PM, MacQueen, Don <macqueen1 at llnl.gov> wrote: > In addition to which, I would recommend > > df <- read.table("DATAM", header = TRUE, fill = TRUE, >

On Thu, Dec 14, 2017 at 1:58 PM, Berend Hasselman <bhh at xs4all.nl> wrote: > >> On 14 Dec 2017, at 19:36, lily li <chocold12 at gmail.com> wrote: >> >> Hi R users, >> >> I have a question about reading from text files. The file has the structure >> below: >> >> Time Column1 Column2 >>

Hello. I want to specify a diagonal structure for the covariance matrix of random effects in the lme() function. Here is the call before I specify a diagonal structure: > fit2<-lme(Ln.rgr~I(Ln.nar-log(0.0011)),data=meta.analysis, + random=~1+I(Ln.nar-log(0.0011)|STUDY.CODE,na.action=na.omit) and this works fine. Now, I want to fix the covariance between the between-groups slopes

library(nlme) data(Phenobarb) na.include <- function(x)x phe1 <- nlme(conc~phenoModel(Subject, time, dose, lCl, lV), data = Phenobarb, fixed = lCl+lV~1, random= pdDiag(lCl+lV~1), start = c(-5,0), na.action = na.include, naPattern = ~!is.na(conc)) phe.ranef <- ranef(phe1,augFrame=TRUE) plot(phe.ranef, form=lCl~Wt+ApgarInd) [Error in max(length(x0),

Hello I have a problem on keeping the format when I export a matrix file with the write.table() function. When I import the data volcano from rgl package it looks like this in R: > data[1:5,] [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14] [1,] 100 100 101 101 101 101 101 100 100 100 101 101 102 102 [2,] 101 101 102 102 102 102 102

Hi, everyone, I wrote a function, which includes an nlme estimation. The problem is sometimes nlme may not converge due to too many random effects. Say a, b are two parameters. if I specify random effects by: random = a+b~1, nlme fails to converge. Then I have to constrain the random effects in a positive definite diagonal matrix by: random = list(pdDiag(a+b~1)) My question is how I can

Hi R users, I have a question about reading from text files. The file has the structure below: Time Column1 Column2 01.01.2001-12:00:00 01.01.2001-24:00:00 12 11 01.02.2001-12:00:00 13 10 01.02.2001-24:00:00 11 12 01.03.2001-12:00:00 15 11 01.03.2001-24:00:00 16 10 ... I

Hi all, I would like to fit a gamm model of the form: Y~X+X*f(z) Where f is the smooth function and With random effects on X and on the intercept. So, I try to write it like this: gam.lme<- gamm(Y~ s(z, by=X) +X, random=list(groups=pdDiag(~1+X)) ) but I get the error message : Error in MEestimate(lmeSt, grps) : Singularity in backsolve at level 0, block 1

This concerns the "Clinical Study of Quinidine" example on page 380 of the book "Mixed-Effects Models in S and S-PLUS" by Pinheiro and Bates (2000). I have tried to reproduce the example, but get an error: > library(nlme) > fm1Quin.nlme <- nlme(conc ~ quinModel(Subject, time, conc, dose, interval, lV, lKa, lCl), + data=Quinidine, +

I'm using pairs() to generate a scatterplot matrix; pairs(~ Fuzzy.gray.white.ratio+Fuzzy.gw.t.score+AgeWhenTested+signal_mean.noise, data=datam,subset=status=="control",main="Controls", labels=c("G/W","Peak Separation","Age","S/N")) How can I add regression lines to the plots?