similar to: fitting the gamma cumulative distribution function

Is there a conventional way to test for nested factors? I.e., if 'a' and 'b' are lists of same-length factors, does each level specified by 'a' correspond to exactly one level specified by 'b'? The function below seems to suffice, but I'd be happy to know of a more succinct solution, if it already exists. Thanks, Tim. --- "%nested.in%" <-

Suppose I have a list of logicals, such as returned by lapply: Theoph$Dose[1] <- NA Theoph$Time[2] <- NA Theoph$conc[3] <- NA lapply(Theoph,is.na) Is there a direct way to execute logical "or" across all vectors? The following gives the desired result, but seems unnecessarily complex. as.logical(apply(do.call("rbind",lapply(Theoph,is.na)),2,"sum"))

Hi. I'm trying to find a systematic way to prevent assignment to names of existing functions. I've tried reassigning to the assignment operator, with mixed results. The function definition for "<-" below works as hoped for the demonstrated assignments to a and c. However, for the assignment based on the test function, it appears that the formal argument is not

Suppose I want to delete everything in my working directory that is not a function. It seems that sapply(ls(),is.function) always returns FALSE, because ls() returns objects of mode character. How do I evaluate is.function(), not on a character string, but on the object that character string represents? Thanks, Tim

Hi. I frequently convert date and time data to and from character representations. I'm frustrated with chron, because 'seconds' are required to create a time object (my input data never has seconds). More importantly, I cannot make chron print the format 12/30/2006 (which my output data requires). I really like the format flexibility of strftime() and strptime(), but of course

Hi. I'm maintaining a package that creates an object that is essentially a classed version of numeric. I updated recently from 2.7.1 to 2.9.0, and merges involving my class suddenly took a huge performance hit. I've traced the problem to something near rank(). From NEWS, it seems rank() etc. changed in 2.8.0. Methods for xtfrm() are supposed to help, but I've had no success. There

I see in the archives significant discussion about SAS, CDISC formats etc. for FDA, but no direct suggestion of adding a write.xport method to the foreign package. Are there significant barriers to doing so? [[alternative HTML version deleted]]

r-devel, In a development version of the CRAN package metrumrg, I write ... require(reshape) setGeneric('cast') setOldClass(c('keyed','data.frame')) setMethod('cast','keyed', function ...) The result is satisfactory when sourcing the code directly, but when checking the package (which has 'reshape' as a dependency in the DESCRIPTION file) I get

Hello. Here is my R code. I used the functional data . Now I need to use the functional data by applying the kernels instead of the xi, yi functions. Bonjour. Voici mon code en R . J'ai utiliser les donn?es fonctionnelles . Maintenant j'ai besoin d'utiliser les donn?es fonctionnelles en appliquant les noyaux ? la place des fontions xi, yi library(MASS)

Un texte encapsul? et encod? dans un jeu de caract?res inconnu a ?t? nettoy?... Nom : non disponible Url : https://stat.ethz.ch/pipermail/r-help/attachments/20071006/065906cc/attachment.pl

Le 04/12/2018 ? 11:27, I?aki Ucar a ?crit?: > On Tue, 4 Dec 2018 at 11:12, <qweytr1 at mail.ustc.edu.cn> wrote: >> function ppois is a function calculate the CDF of Poisson distribution, it should generate a non-decreasing result, but what I got is: >> >>> any(diff(ppois(0:19,lambda=0.9))<0) >> [1] TRUE >> >> Actually, >> >>>

Maybe its slightly off-topic, but can anybody help with computing entropy on matrix of probabilities? Guess we have a matrix of probabilites, A, 2x2, something like this: z x 0 1 2 3 4 0 0.063 0.018 0.019 0.016 0.000 1 0.011 0.162 0.040 0.042 0.003 2 0.015 0.030 0.164 0.033 0.002 3 0.012 0.035 0.036 0.159 0.002 4 0.004 0.021 0.018 0.013 0.082 sum(A)=1 Can i

Hi, I am interested in using a bayesian network as a predictor (machine learning); however, I can't get any of the implementations (deal, nblearn) to learn & predict stuff. Shouldn't there also be probabilites for each node after the learning phase, how can I access these? Cheers, Stephan -- View this message in context:

Hi, my question is probably amateurish but I can't seem to find the answer anywhere. In what metric are the MCMCglmm package's posterior means for family = "categorical"? I suppose that they can't be odds ratios and probabilites as my numbers are outside their bounds. So I'm thinking ? are they just basic regression coefficients conceptually equal to those obtained by

Dear experts, I am looking for a package that does logistic regression with corssvalidation and gives me the probabilites of all the corssvalidations so that I can plot them in ROCR. Would also like to know if the corssvalidate model would give me a summary coefficient for the intercept and my to predictors. Thank you. Best regards, Jon Toledo, MD Postdoctoral fellow University of Pennsylvania

Hi, I have a question about logistic regression in R. Suppose I have a small list of proteins P1, P2, P3 that predict a two-class target T, say cancer/noncancer. Lets further say I know that I can build a simple logistic regression model in R model <- glm(T ~ ., data=d.f(Y), family=binomial) (Y is the dataset of the Proteins). This works fine. T is a factored vector with levels cancer,

I have the following contingency table dat <- matrix(c(1,506,13714,878702),nr=2) And I want to test if their is an association between events A:{a,not(a)} and B:{b,not(b)} | b | not(b) | --------+-----+--------+ a | 1 | 13714 | --------+-----+--------+ not(a) | 506 | 878702 | --------+-----+--------+ I am worried that prop.test and chisq.test are not valid given the

Hi, For fun, I'm trying to throw some horse racing data into either an svm or lrm model. Curious to see what comes out as there are so many published papers on this. One thing I don't know how to do is to standardize the probabilities by race. For example, if I train an LRM on a bunch of variable I get a model. I can then get probability predictions from the model. That works.

Hi, In continuation of the discussion of melange comments,about negative value returned in matcher due to logarithm of probabilities. *I**f we make K suitably large, we could clamp each log(K.Pi) to be >= 0, and this change will only affect really low probability terms (those with Pi < 1/K, so you can adjust K to suit):* *W' = sum(i=1,...,n, max(log(K.Pi), 0))* Did you mean for low

Hi R-users, I have this code to uniformise the data using gamma: > length(dp1) [1] 696 > dim(dp1) [1] 58 12 > dim(ahall) [1]  1 12 > dim(bhall) [1]  1 12 > trans_dt <- function(dt,a,b) + { n1 <- ncol(dt) +   n2 <- length(dt) +   trans  <- vector(mode='numeric', length=n2) +   dim(trans) <- dim(dt) +   for (i in 1:n1) +   {  dt[,i] <- as.vector(dt[,i])