similar to: analysis of life tables

Dear list, I have data on insect survival in different cages; these have the following structure: deathtime status id cage S F G L S 1.5 1 1 C1 8 2 1 1 1 1.5 1 2 C1 8 2 1 1 1 11.5 1 3 C1 8 2 1 1 1 11.5 1 4 C1 8 2 1 1 1 There are 81 cages and

Now I try to add some C and Fortan code to my package, so the NAMESPACE file is useDynLib(eha) importFrom(survival, Surv) export(mlreg.fit, risksets) but I get ..... * checking R files for library.dynam ... OK * checking S3 generic/method consistency ... WARNING Error in .try_quietly({ : Error in library(package, lib.loc = lib.loc, character.only = TRUE, verbose = FALSE) :

I'm trying to learn how to use namespaces, so I have created a small package with two functions. My NAMESPACE file is importFrom(survival, Surv) export(mlreg.fit, risksets) because 'mlreg.fit' uses 'Surv' from 'survival'. However I get * checking package dependencies ... WARNING Namespace dependencies not required: survival Why? (If I remove 'importFrom' I

I'm getting unexpected results from the coxph function when using weights from counter-matching. For example, the following code produces a parameter estimate of -1.59 where I expect 0.63: d2 = structure(list(x = c(1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1), wt = c(5, 42, 40, 4, 43, 4, 42, 4, 44, 5, 38, 4, 39, 4, 4, 37, 40, 4, 44, 5, 45, 5, 44, 5), riskset =

Hello. I apologize in advance for the VERY lengthy e-mail. I endeavor to include enough detail. I have a question about survival curves I have been battling off and on for a few months. No one local seems to be able to help, so I turn here. The issue seems to either be how R calculates Kaplan-Meier Plots, or something with the underlying statistic itself that I am misunderstanding. Basically,

Hi, If I have the follow situation: A dependent variable (i.e. number of insects) that is affected by an independent variable (i.e. rain). The problem is that the measure of rain affect the population in other moment. So there exit a lag between the rain and the number of insects. Exist in R any tool to find what is this lag? Explain better. Suppose that I have a linear relationship

Hi, I have a small simple data frame (attached) - to compare diversity of insects encountered in disturbed and unditurbed site. What i have is the count of insects - the total number of times they were encountered over 30 monitoring slots. Can someone please check for me to make sure how the 'community data matrix' for the diversity function needs to be oriented so that i'm

plot(1,2, ylab= paste("insects", expression(m^2), sep=" ")) I get insects m^2 I would like m to the 2 what is the problem? -- Let's not spend our time and resources thinking about things that are so little or so large that all they really do for us is puff us up and make us feel like gods. We are mammals, and have not exhausted the annoying little problems of being

Dear R users, suppose I have a matrix of observations for which I calculate all pair-wise correlations: m=matrix(sample(1:100,replace=T),10,10) w=cor(m,use="pairwise.complete.obs") How do I extract only those correlations that are >0.6? w[w>0.6] #obviously doesn?t work, and I can?t find a way around it. I would very much appreciate any help! Best wishes Christoph (using R

Dear all, I have a data frame that looks like this: c1 c2 c3 A B C B C A A A B and so on; I?d like to produce one single vector consisting of the columns c1,c2, c3, such that vector=("A","B","A","B","C","A","C","A","B") I guess it?s easy to do but I don?t know how...Can anyone

Dear members, here is my trouble: My data consists of counts of trapped insects in different attractive traps. I usually use GLMs with a poisson error distribution to find out the differences between my traitments (and to look at other factor effects). But for some dataset where one traitment contains only zeros, GLM with poisson family fail to find any difference between this particular traitment

I read through Harrington and Fleming (1982) but it is beyond my statistical comprehension. I have survival data for insects that have a very finite expiration date. I'm trying to test for differences in survival distributions between different groups. I understand that the medical field is most often dealing with censored data and that survival analysis, at least in the package survival,

Dear all, How can I replace text in objects that are of class "formula"? y="a * x + b" class(y)="formula" grep("x",y) y[1] Suppose I would like to replace the "x" by "w" in the formula object "y". How can this be done? Somehow, the methods that can be used in character objects do not work 1:1 in formula objects... Many

Dear all, For an introductory course on glm?s I would like to create an example to show the difference between glm and transformation of the response. For this, I tried to create a dataset where the variance increases with the mean (as is the case in many ecological datasets): poissondata=data.frame( response=rpois(40,1:40), explanatory=1:40) attach(poissondata) However, I have run into

Dear R users, Let?s say I have 10 models, each named m1,m2,m3..., and I would like to summarize them automatically and simultaneously - e.g., to extract parameter estimates later on from all models; how can I do that? I have tried: x=1:10 #this creates some example data y=rnorm(10) m1=lm(x~y) m2=lm(x~1) sum.lms=function(x)summary(paste("m",x,sep="")) sum.lms(1:2) but

Dear R users, I??ve got a simple question but somehow I can??t find the solution: I have a data frame with columns 1-5 containing one set of integer values, and columns 6-10 containing another set of integer values. Columns 6-10 contain NA??s at some places. I now want to calculate (1) the number of values in each row of columns 6-10 that were NA??s (2) the sum of all values on columns 1-5

Hi R-helpers, I have a dataframe (called data) with trees in rows (n=100) and insect species (n=10) in columns. My tree IDs are in a column called TREE and each species has a column labeled SPEC1, SPEC2, SPEC3, etc... I wish to randomize the values in my dataframe such that row and column totals are held constant, i.e. in my randomized data each tree will have the same number of individual

Please pardon the simplicity of this question of biological nature. I'm trying to calculate a statistic, px, the proportion of a cohort that survives through the interval x:x+1. I have the vector from which the calc is to be made but I can't figure out how to tell R to take the current value and divide it by the next value. The formula is P0=L1/LO The following is an example of the

Sorry, I forgot to write that the SIP peer must keep ringing while the announcement is being played. Le 22/08/2016 ? 17:42, John Kiniston a ?crit : > This seems like the obvious answer but maybe I'm misunderstanding the > question. > > exten => s,1,Dial(SIP/alice,20) > same => n,Playback(myannouncement) > same => n,NoOP(Whatever else you want to do goes

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to