search for: priory

I want to specify a 2-level mixed model using the lme function in order to test an a priori hypothesis about the between-group values of the slopes but don't know how to do this . Here is the problem. Consider first the case of a single group. The model is: Y_i= a +bX_i + error where I indexes the different values of X and Y in this group . The a priori hypothesis of the slope is: b=K.

Dear R/statistics wizards, This may be more of a statistics question than an R one, but I’m hoping that someone has the time to help. I’ve already consulted a few local statisticians and I’ve not yet received a clear answer. Before I start an experiment I want to conduct an a priori power analysis test for a Generalized Linear Model, family = poisson. The response variables will be counts.

Dear R-helpers, I have some stacked data with which I want to generate orthogonal contrasts - with the caveat that I determine what means comparisons are taking place a-priori. Does it matter for e.g. 'contr.SUM' that the data are stacked, and does 'contr.SUM' allow me to set the contrasts efficiently even if the data are unballanced? Is there an alternative in R I should

Hi All, I have tried to find an answer within documentation, but I cannot: o How can call a class "slot" without knowing the name a priori? E.g., let's say I use the "pcaMethods" library to create a "pcaRes" object. How can I call parts of that object without using the specific names of the object in the call? example code: library(pcaMethods)

Hi All, I woul like to ask you a couple of questions on chisq.test. First, I have 40 flies, 14 males and 26 females and I want to test for an a priori hypothesis that the sex ratio is 1:1 sex<-c(14,26) pr<-c(1,1)/2 chisq.test(se, p=pr, correct=TRUE) Chi-squared test for given probabilities data: sex X-squared = 3.6, df = 1, p-value = 0.05778 If my calculations are correct, this is

> ssh has to set NONBLOCK otherwise it can, well, block - there's > no way for ssh to know a priori how much data it can write to a fd. I don't know anything about how ssh is structured, but I think it must be a bit more complicated than that. Ssh only sets O_NONBLOCK on an fd if isatty(fd) returns false, so it's able to function with blocking input and output if the relevant

HI Folks, I'm trying to create a list with named elements. Only, I don't know the names of the elements a priori (they come from the data being calculated). Currently, my approach is to create an environment, then assign things to the environement, then as.list the environment to get a list. Running the code gives, for example: > e2 <- new.env(FALSE, NULL) >

Hi, I have a matrix that has a variable number of columns. I do not know, a priori, the number of columns. How can I get a sub matrix, for example, from row 10 to the end of the columns? In MatLab I would use something like this: SubMatrix = Matrix[10, 1:end] Thanks a lot. -- View this message in context: http://www.nabble.com/Get-data-from-matrix-tf4629336.html#a13218653 Sent from the R

Dear R-Listeners, as the Sweave faq says: http://www.ci.tuwien.ac.at/~leisch/Sweave/FAQ.html creating several figures from one figure chunk does not work, and for standard graphics, a workaround is given. Now I have a multipage trellis plot with an a-priori unknown number of pages, and I don't see an elegant way of dividing it up into multiple pdf-files. I noted there is a "page"

Dear R-listers, I would like to know if there is a way to programmatically generate parameter start values for the model y~(a*exp(b*x)+c*exp(d*x)) in R. I've scoured the help files and archives for nls() and similar searches, and have read Fox 2002 - the best advice has been to make estimates from a priori knowledge of the data. However, in the Matlab CurveFit tool, reliable start values are

Hi, I'm having a problem with a step that should be pretty simple. I have a dataframe, d, with column names : gene s1 s2 s3. The column "gene" stores an Id; the rest of the columns store intensity data. I would like to extract the rows for gene Ids i1, i2, i3 ( I know a priori that those rows exist). So I do this: subset(d, gene %in% c(i1, i2, i3)). This does not give me the

Hi, I trying to determine the best way to compute the power for a one-sample one-sided binomial test. Specifically I need to sample a population of individuals and ask whether a sample rate of 0% is compatable with a minimum threshold of 3% and how many samples are needed. I have made use of power.prop.test but I am not sure if a) that is the correct (or best) function to use and b) if the

Is there an easy way to do a linear model with an a priori known slope? In essence, I want to minimize the residuals around a line of known slope. -R -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the

A search of the archives did not reveal an answer: For basic tests of association, where one has no a priori knowledge of the form of the relation or of the distributions of the variables, rank correlation seems like a good start. Why is cor.test() with Kendall and Spearman options relegated to the ctest package, rather than in the base package? Does this suggest that the developers consider

Hi all, Imagine I have a matrix and the first colum is a list that repeats the same names, I want to sum the second column on each unique name on first column. Imagine this: Pepe 2 Pepe 3 Pepe 4 Jose 2 Jose 5 Manuel 4 Manuel 2 I want to make a new matrix that calculates and recognizes that there are 3 different names ans sum second column. But a priori I don´t know the list of the different

Hi, I'm designing a experiment in order to compare the growing of several clones of a tree specie. It will be a complete randomized block design. How can I decide what model of mean comparision to choose? LSD, HSD,TukeyHSD, Duncan,...? Thanks in advance

Hi I am running some linear and non-linear mixed effect models and would like to do some planned contrasts (a priori contrasts) I have looked in the help and in many forums and it seems possible to do so but don't understand how to write the function and I couldn't find an example in Pinheiro and Bates. lme {nlme} has a contrasts argument but I can't understand how to code it.

I'm struggling to find a way of parsing a vector of data in this sort of form: A,B,C B,B A,AA,C A,B,BB,BBB,B,B into a matrix (or data frame). The catch is that I don't know a priori how many entries there will be in each element, nor how many characters there will be. strsplit(vec,",") gets me a list, but I can't find a way of turning the list into a matrix. unlistlst)

Hi, I was wondering if someone could help me answer a question that is bound to come up in my Master's defense. I'm using AIC to select models and my question is how do I know that the models I developed a priori contain the 'best' models in the system. How do I not know that some models which I didn't include aren't actually the 'best' model?? Thanks so much

I did this: nb <- naiveBayes(users, platform) pl <- predict(nb,users) nrow(users) ==> 314781 ncol(users) ==> 109 1. naiveBayes() was quite fast (~20 seconds), while predict() was slow (tens of minutes). why? 2. the predict results were completely off the mark (quite the opposite of the expected overfitting). suffice it to show the tables: pl: android blackberry ipad