similar to: Intercept in lm and in library(car): Anova

Hi R lovers I would like to know how to step to the next line in the R console editor without breaking the continuity of my code more clearly : if for example I write a function, so far i have to write the all code inside on the same line wich may become obscure as the function is more and more complex. I would like to do like in the example of the manuels: >twosam <- function(y1, y2) {

I'm trying to figure out R, a piece at a time, hours at a time... I was trying to copy the sample function in, "An Introduction to R" (for version 2.1.0) by W. N. Venables, D. M. Smith, page 42. Section 10.1 "Simple examples" provides a sample function which I tried to duplicate (I'm using Mac OS X 10.3.9, and "R for Mac OS X Aqua GUI v1.11). The following

hello: I have 3 data.frame objects. First df object: Of dim (149,31). Columns 2:31 are marked as T1..T14 and N1..N16. Name T1 T2 N1 T3 N2 N3 N4 T4 mu1 10 10 9 10 9 9 8 10 mu2 11 11 9 11 9 9 9 11 ... muN 12 12 9 11 9 9 8 12 Second df object: of Dim (50000,31). Columns 2:31 are maked as T1...T14 and N1..N16.

Dear R users, Suppose I have two different response variables y1, y2 that I regress separately on the same explanatory variable, x; sample sizes are n1=n2. Is it legitimate to compare the regression slopes (equal variances assumed) by using lm(y~x*FACTOR), where FACTOR gets "y1" if y1 is the response, and "y2" if y2 is the response? The problem I see here is that the

Dear R-experts! Running a meta-analysis (using the magnificent metafor-package), I use cohen's d as a main outcome measure in a random-effects model. For most of the samples cohen's d is derived form a comparison of two groups (A & B). However some studies report results from an ANOVA (one-factor with three levels: C,D,E) whereas two groups (C,D) correspond to one group in the other

I read somewhere that t.test is equivalent to a hypothesis testing for one way ANOVA. But I'm wondering how they are equivalent. In the following code, the p-value by t.test() is not the same from the value in the last command. Could somebody let me know where I am wrong? > set.seed(0) > N1=10 > N2=10 > x=rnorm(N1) > y=rnorm(N2) > t.test(x,y) Welch Two Sample t-test data:

> On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > Hi Bogdan, > Kinda messy, but: > > N <- data.frame(N=c("n1","n2","n3","n4")) > M <- data.frame(M=c("m1","m2","m3","m4","m5")) > C <-

Thank you David. Using xtabs operation simplifies the code very much, many thanks ;) On Tue, Jun 6, 2017 at 7:44 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > > On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > > > Hi Bogdan, > > Kinda messy, but: > > > > N <-

Could some kindly tell me if I am supposed to be getting the same test statistic value with var.equal=TRUE and var.equal=FALSE in t.test ? set.seed(1066) x1 <- rnorm(50) x2 <- rnorm(50) t.test(x1, x2, var.equal=FALSE)$statistic # 0.5989774 t.test(x1, x2, var.equal=TRUE)$statistic # 0.5989774 ??? Here are my own calculations that shows that perhaps the result when var.equal=TRUE is

Simple matrix indexing suffices without any fancier functionality. ## First convert M and N to character vectors -- which they should have been in the first place! M <- sort(as.character(M[,1])) N <- sort(as.character(N[,1])) ## This could be a one-liner, but I'll split it up for clarity. res <-matrix(NA, length(M),length(N),dimnames = list(M,N)) res[as.matrix(C[,2:1])] <-

Here's another approach: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400)) # Rebuild the factors using M and N C$m <-

Hi Bogdan, Kinda messy, but: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400))

Hi! When I force-resync a mailbox of mine, I see following output: # doveadm force-resync -u $USER $PATH doveadm($USER): Warning: mdbox .../storage: Inconsistency in map index ($X,$Y2 != $X,$Y2) doveadm($USER): Warning: fscking index file .../storage/dovecot.map.index doveadm($USER): Warning: mdbox .../storage: rebuilding indexes doveadm($USER): Error: mdbox .../storage: Duplicate GUID $G in

Hello. I'm trying to find a way in R to calculate between and within subject variances and confidence intervals for some analytical method development data. I've found a reference to a method in Burdick, R. K. & Graybill, F. A. 1992, Confidence Intervals on variance components, CRC Press. This example is for Balanced Data confidence interval calculation from Pg 62. The data are

Dear Bert, thank you for your response. here it is the piece of R code : given 3 data frames below --- N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"),

A strange problem with sem: I downloaded the sem library and then, I specified my simple measurement model (below). I highlighted it and ran it. It ran, but it did NOT tell me "22 lines read". And nothing works after that - it looks like it runs, but it does not produce anything... Did I make a mistake somewhere in the model? (notice, TIME has only 1 indicator - t1, and I fixed t1's

Hi everyone, I have dataset which I make a sample of it couple of times and each time I get the mean and standard deviation of each row for each sample. I have a function for that, which takes the name of the file and number of times to sample and then returns the mean and standard deviation for each row in each sample. Sample=function(name, n){

Dear all, please could you advise on the R code I could use in order to do the following operation : a. -- I have 2 lists of "genome coordinates" : a list is composed by numbers that represent genome coordinates; let's say list N : n1 n2 n3 n4 and a list M: m1 m2 m3 m4 m5 2 -- and a data frame C, where for some pairs of coordinates (n,m) from the lists above, we have a

Reproducible example, please. -- In particular, what exactly does C look ilike? (You should know this by now). -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Mon, Jun 5, 2017 at 6:45 PM, Bogdan Tanasa <tanasa at gmail.com>

I would be more strong on this advice: learn to think in R, rather than thinking in Python, when programming in R. R has atomic vectors... Python does not (until you import a package that implements them). I find that while it is possible to import R thinking into Python, Python programmers seem to object for stylistic reasons even though such thinking speeds up Python also. A key step in that