similar to: t-test

Hello! I am using R 2.5.1 on a Apple Power Book G4 with Mac OS X 10.4.10 and I am still R beginner. I try to calculate a t.test() using this code: TTest75<-t.test(Fem75, Mal75, alternative= "two.sided", paired= TRUE) This works properly, but I have two variables with a lot of missing data and therefore get the error message: TTest66<-t.test(Fem66, Mal66, alternative=

Hi, I am using SAM (from siggenes_1.2.11 package) method to select genes from a microarray data set. After installing the latest R2.4.0 on my computer, to my surprise the results are totally different from that calculated using R2.1.1. Even the example code doesn't work the same way under these two versions of R. Does anybody know what is going on? Thanks for any suggestions.

Dear R mailing-list comunity! I'm currently trying to implement an R method. I have two sets of data that I convert into a data.frame each. These data.frames I'd like to append to a list: # generate a list listTable<-list() # add one set of data x<-1000 ;y<-1 ;listTable[[length(listTable) + 1]] <- data.frame(matrix(rnorm(x*y), nrow=y)); rm(x); rm(y) # add another set of

Hi. I'd like to do a t-test to compare the Delta values of items with Crit=1 with Delta values of items with Crit=0. What is the t.test syntax? It should produce a result like this below (I can't get in touch with the person who originally did this for me) Welch Two Sample t-test data: t1$Delta by Crit t = -3.4105, df = 8.674, p-value = 0.008173 alternative hypothesis: true

Does anyone know of an R-function or method to compare two C-statistics (Harrells's C - rcorr.cens) obtained from 2 different models in partially paired datasets (i.e. some similar and some different cases), with one continuous independent variable in each separate model? (in a survival analysis context)? I have noticed that the rcorrp.cens function can be used for paired data. Thanks

Dear all, I am running R 2.4.1. > library(siggenes); > library(multtest); > cl<-rep(c(0,1),c(3,3)); > sub<-exprs(AffyExpData[,c(1:3,7:9)]); > gn<-geneNames(AffyRAwData); > sam.out<-sam(sub,cl,rand=123,gene.names=gn); We're doing 20 complete permutations > sam.out SAM Analysis for the Two-Class Unpaired Case Assuming Unequal Variances Delta p0

Hi I have a vector x of length n. I am interested in x[1] being different from the other observations (ie x[-1]). My null hypothesis is that x[1] is drawn from a Gaussian distribution of the same mean as observations x[-1], which are assumed to be iid Gaussian. The (unknown) variance of x[1] is assumed to be the same as the variance of x[-1]. This should be an unpaired t-test. But

Hello list, I am trying to apply the paired t.test between diseased and not diseased patients to identify genes that are more expressed in the one situation under the other. In order to retrieve the genes that are more expressed in the positive disease state I do: p.values<-c() for(i in 1:length(Significant[,1])){ p.values[i]<-try(t.test(positive[i,],negative[i,],alternative

Dear R help; I'll preface this by saying that the example I've provided below is pretty long, turgid, and otherwise a deep dive into a series of functions I wrote for a simulation study. It is, however, reproducible and self-contained. I'm trying to do my first simulation study that's quite big, and so I'll say that the output of this simulation as I'd like it to be is

Dear R users, I would like to know if there is a way in R to execute a post-hoc test (factor levels comparison, like Tukey for ANOVA) of a non-parametric analysis of variance with kruskal.test() function. I am comparing three different groups. The preliminary analysis using the kruskal-wallis-test show significance, but I still don''t know the relationship and the significance level

Hi, I have just found this forum, and it looks like a great place to get some help (I hope) For my dissertation, which is due way too soon, I am doing a survey, comparing attitudes of 2 independent groups, with 5 scale likert questions. Basically I want to show if they have similar or different attitudes. I am testing 4 hypotheses, and have in total about 20 questions. I have to say my

There's a funny inconsistency in how t.test handles paired=T or paired=F. If x and y parameters are lists, paired=F works, but paired=T doesn't. > lg=read.csv("my.csv") > a = subset(lg, condition=="a")["score"] > b = subset(lg, condition=="b")["score"] > t.test(a,b) > t.test(a,b, paired=TRUE) Error in `[.data.frame`(y, yok)

Hi, I recently ran into the problem that I needed a Siegel-Tukey test for equal variability based on ranks. Maybe there is a package that has it implemented, but I could not find it. So I programmed an R function to do it. The Siegel-Tukey test requires to recode the ranks so that they express variability rather than ascending order. This is essentially what the code further below does. After the

Buenos días. Tengo dos mapas que hago con ggplot (abajo) y necesito superponer el 2º al 1º. En el 1º se representa la variable PA, binomial (0, 1), en gris, y en el 2º la variable ExtTG7085, que es numérica y va de 0 a 200. La 2ª es un gradiente en rojo. Lo ideal sería que donde coinciden el rojo de la 2ª se viera más oscuro, pero supongo que eso no es fácil. Podría también ser en otro color, pero

Hola a todos. Un problema dos pasos. Quiero desde RStudio trabajar con dos versiones de R (pongamos R2.13 y R2.15) desde Linux, Ubuntu 10.04 (lucid). Ya tengo instalada la R.2.15.1. Por defecto trabajo con ella. Pasos que creo he de dar: 1º. Instalar dos versiones de R en la misma maquina. ¿Como se hace?. Me estoy metiendo con el PATH y tal... hay algún protocolo clarito por ahí? 2º.

I am trying to use a new (to me) package (samr) and even when I try to run a very simple example, I get this "cannot open the connection" error. The reason I am writing to r-help rather than to the authors of samr is I think this may be a more general R problem rather than a samr-specific problem. Perhaps something with my installation and write access to some particular place ? I am

Hi, I am not sure which kind of test is applied to the data if you use cor.test(x, y) ? Is it an unpaired t-Test? Regards -- View this message in context: http://www.nabble.com/cor.test%28x%2Cy%29-tp22492993p22492993.html Sent from the R help mailing list archive at Nabble.com.

Hello, I have three numerical variables and I would like to test if their correlation is significantly different. I have seen that there is a package that "Test the difference between two (paired or unpaired) correlations". [https://www.personality-project.org/r/html/paired.r.html] However, there is the need to convert the correlations to "z scores using the Fisher r-z

Does anyone know an easy way to convert all the zero values in a imported csv table into NA's [[alternative HTML version deleted]]

Hola a tod@s: Supongamos una lista como ésta: l <- list() l$nombre <- "Ana" l$apellido <- "Pérez" Ahora supongamos que tengo esta variable: parametro <- "nombre" Necesito saber dos informaciones: 1º) Saber si existe la función nombreOnValidate. Es decir, debo concatenar el valor de parametro con la cadena "OnValidate" y hacer un exists,