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Hi, I tried to do a nested Anova with the attached Data. My response variable is "survivors" and I would like to know the effect of (insect-egg clutch) "size", "position" (of clutch on twig) and "clone" (/plant genotype) on the survival of eggs (due to predation). Each plant was provided with three different sizes of clutches (45,15,5) and had

Hello everyone :) Love the mailing list! I read it as much as I can! Thanks all you RoR guru''s for providing so much help. Anyways... So say I have Users, and each user can have a list of Plants that they like. I would want to give each user a table that has many entries to show me which plants they like. How could I do this? I don''t know if there''s a way to do

Dear R-community, my data set looks like 'mat' below. Plant<-c(NA,1,1,1,NA,NA,NA,NA,NA,1); Value1<-rnorm(1:10); Value2<-rnorm(1:10); mat<-cbind(Plant,Value1,Value2); I receive data from two different sites. One site is identified by an interger number, the other site has no data in column Plant=NA. My pb: I'm trying to assign labels "A" or "B" to

The formula attribute of the builtin CO2 dataset seems a bit strange: > formula(CO2) Plant ~ Type + Treatment + conc + uptake What is one supposed to do with that? Certainly its not suitable for input to lm and none of the examples in ?CO2 use the above.

Hi, I'm using metafor to perform a mixed-effects meta-analysis. I'd like to test whether the effect is different for animals and plants/whether "group" (animal/plant) influences the effect size, but am having trouble interpreting the results I get. I've read previous posts about QM in metafor, but I'm still a bit confused. I've dummy-coded the factors:

I saw different specifications for Random Effects and I'm confused about the use of "/" and the use of "(0+...|)" . Let say we have a nested structure where some countries have some several plants in different states and we measure the reaction to a drug. The list of Countries = USA, France, Italy The States for USA = Michigan, Florida, California The States for France

I am trying to run the following model in R > lmer(leaves.eaten~Geocytotype+(1|TEST/ PLANT),data=cyphoplantfeeding,family=poisson) My experimental setup is 41 replicates (TEST) of an experiment in which there are three Geocytotypes of a plant species in each TEST, and two plant pseudoreplicates per Geocytotype in each test (i.e. 3*2=6 plants per test). So my random factors are trying

Dear R-people, I hope asking this is not too cheeky, but I do have a R Problem. I hope that some of you like to play around with R and can help me. Its like this. I have several plant species (A,B,C) and 10 replicates per species. 5 plants per species are damaged, 5 not. I let a caterpillar feed on each plant and measured the growth of the caterpillars on control plants (CR) and on damaged

I have never taken a statistics class nor read a statistics text, but I am in dire need of help with a trivial data analysis problem for which I need to write a report in two hours. I have spent 10,000 hours of study in my field of expertise (high frequency noise-making plant biology) but I've always thought that statistics is something that can be mastered on short notice. Briefly, I have

...e of soil ( soil F and soil D), each half of them were subjected to steam sterilize (result in FS and DS soil). A equal volume of soil from two of the four soil types (F, D, FS, DS) were mixed as follows: F+F, F+D, F+FS, F+DS, D+F, D+FS, D+DS, FS+DS (eight treatment). Two type of plant (A, B) were planted in the eight treatment of soil in pot. There were 40 pots divided into 5 groups (8 pots for each group) for each treatment*plant combination. Finally there were 80 groups for plant A and B in total. The 40 groups for plant A were randomly arranged in plot 1, and for plant B in plot 2. The experim...

Dear R users, I have a data frame in the form below, on which I would like to make normality tests on the values in the ExpressionLevel column. > head(df) ID Plant Tissue Gene ExpressionLevel 1 1 p1 t1 g1 366.53 2 2 p1 t1 g2 0.57 3 3 p1 t1 g3 11.81 4 4 p1 t2 g1 498.43 5 5 p1 t2 g2 2.14 6 6 p1 t2 g3 7.85 I

?s 17:38 de 30/11/2023, Robert Baer escreveu: > I am having trouble using back ticks with the R extractor function > 'predict' and an lm() model.? I'm trying too construct some nice vectors > that can be used for plotting the two types of regression intervals.? I > think it works with normal column heading names but it fails when I have > "special"

Hello, I am a beginner in R and statistics, so my question may be trivial. Sorry in advance. I performed a Cox proportion hazard regression with 2 categorical variables with cph{design}. Then an anova on the results. the output is > anova(cph(surv(survival, censor) ~ plant + leaf.age + plant*leaf.age, > Mpnymph) Wald Statistics Response: Surv(survival, censored)

Hi. I had successful confiured my Asterix PBX, but now I need to connect it to a Nortel Meridian phone plant, I got a Digium T100P on the machine I got the Asterisk and I already connect it with the phone plant, I need to know what configuration do I have to do so the ip extensions can make calls to the extensions of my plant Nortel. And extensions from the Nortel plant can call ip extensions of

I am having trouble using back ticks with the R extractor function 'predict' and an lm() model.? I'm trying too construct some nice vectors that can be used for plotting the two types of regression intervals.? I think it works with normal column heading names but it fails when I have "special" back-tick names.? Can anyone help with how I would reference these?? Short of

Hello everybody I want to compare the proportions of germinated seeds (seed batches of size 10) of three plant types (1,2,3) with a glm with binomial data (following the method in Crawley: Statistics,an introduction using R, p.247). The problem seems to be that in two plant types (2,3) all plants have proportions = 0. I give you my data and the model I'm running: success failure

Hi all My data table (g) contains a continues data column (plant.height) and other columns (columns 8 to 57), each with number of levels of different factors. ANOVA test was done and the p-values were extracted as follos: a <- function(x) anova(lm(plant.height ~ x))$"Pr(>F)"[1] r<- apply(g[,8:57],2,a) If I try to do a Kruskal-Wallis test : kw <- function(x)

I'm trying to update a package and would like to crush a WARNING message for a clean build. I've been struggling with this question and haven't gotten any traction on the web either. I've got a document file (Rd) that contains the following \usage statement: \name{sample.data} \alias{sample.data} \title{CONIFERS forest growth model sample data} \description{ A list object of

Hello R-help, I'm studying an example in the R book.? The data file is available from the link below.http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/fertilizer.txt Could you explain Why the results from lme() and lmer() are different in the following case? In other examples, I can get the same results using the two functions, but not here...? Thank you.Miya library(lme4)library(nlme)#

Dear list, How to convert a character to a filename? such as: x <- "height" # "height" here is actually a name of a colume in a data frame filename <- paste("plant,x") write.csv (data, file="C:/plant/filename.csv) # having trouble with this statement, how to 'write' the filename here? All I want from above is to write 'data' to a file