similar to: strsplit

If my sample size is small is there a particular switch option that I need to use with t.test so that it calculates the t ratio correctly? Here is a dummy example? á =0.05 Mean pain reduction for A =27; B =31 and SD are SDA=9 SDB=12 drgA.p<-rnorm(5,27,9); drgB.p<-rnorm(5,31,12) t.test(drgA.p,drgB.p) # what do I need to give as additional parameter here? I can do it manually but

If I have two vectors X<-1:10 Y<-1:5 When I combine them using cbind, the shorter one is repeated and both are made of the same length. Is there a methods that does this without duplicating the shorter one. I want to use this to store the data back to a file. Thanks ../Murli [[alternative HTML version deleted]]

I am trying to calculate coordinate transformations and in the process of debugging my code using debug I found the following Browse[1]> direction[i] [1] -1.570796 Browse[1]> cos(direction[i]) [1] 6.123032e-17 Browse[1]> cos(-1.570796) [1] 3.267949e-07 Browse[1]> direction[i] [1] -1.570796 Browse[1]> cos(direction[i]) [1] 6.123032e-17 Browse[1]> cos(-1.570796) [1] 3.267949e-07

Can I insert an element in an array at a particular position without destroying the already existing element? X<-c(1,2,3,4,5) I want to insert an element between 1 and 2. Thanks ../Murli [[alternative HTML version deleted]]

Hi!! I am interesting in computing the radius of an arc that best approximates a curve. Is there an R function that I can use to draw an arc? Nothing useful came up when I searched help.search. Does anyone have any suggestion to do this? Thanks ../Murli

Hi all, I have a time series which contain data collected weekly from week 26 to week 25 the following year. How do I plot this data, so that the x-axis is displaying the week numbers, ordered as in the data? Thanks in advance, Gustaf --- x<-c(26:52,1:25) y<-rnorm(52)+1:52 plot(x,y) ## How do I get the x axis to be ordered by the current ordering of x? -- Gustaf Rydevik, M.Sci. tel:

Is there a function that calculates the inter-rater agreement index (kappa) in R? Thanks ../Murli [[alternative HTML version deleted]]

Hi!! I am trying to figure out how to use the string kernel "stringdot" in kernlab. k <- function(x,y) { (sum(x*y) +1)*exp(-0.001*sum((x-y)^2)) } class(k) <- "kernel" data(promotergene) ## train svm using custom kernel gene.k <- ksvm(Class~.,data=promotergene,kernel=k,C=10,cross=5) # works fine in this case gene.rbf <-

Which is the parameter that is used to decrease the size of ylabs plotted in biplot? I tried playing with cex and cex.lab I am not getting it right pc <- princomp(USArrests) biplot(pc, xlabs = rep("", nrow(USArrests)),ylabs=(colnames(USArrests))) Thanks../Murli [[alternative HTML version deleted]]

I used the multcomp package sometime back for doing multiple comparisons. I see that it has been updated and the methods like simint are no longer supported. When I run the program it prompts to me to use glht. How do I get the lower and upper conf int and the pValues using glht? Does anyone have an example? Thanks ../Murli [[alternative HTML version deleted]]

I am trying to add text at specific location on my graph. I know this can be done in R but I can't recollect. I was trying using locator() to identify the position and use identify() but I can get it to work. Can someone jog my memory? Thanks ../Murli [[alternative HTML version deleted]]

I am running a recursive feature selection wrapper on svm for a large data set. The routine gets progressively slower. I am deleting the objects not required. Is there anything else you advise me look for? I tried running it from the command line but it does not print anything on the screen. I did not specify any output file and was expecting the screen to be the default. Can any one help me

I am trying to generate artificial data for feature selection. Basically trying to generate a total of 1000 features with 100 that are informative and rest are uninformative. Informative.data.class1<-rnorm(100,0.25,1) Uninformative.data.class1<-rnorm(900,0,1) Informative.data.class2<-rnorm(100, -0.25,1) Uninformative.data.class2<-rnorm(900,0,1) The above will give me one set of data

I am trying to plot the following data using dotchart intersect.data<-structure(list(X = structure(c(1L, 3L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 2L, 4L, 5L, 6L, 7L), .Label = c("1-100", "1001-1100", "101-200", "1101-1200", "1201-1300", "1301-1400", "1401-1500", "201-300", "301-400", "401-500",

Hi, I am trying to plot the following data so that it can be visually represented well. I tried the dotchart but I felt it was too spread out. Then I tried the barplot which is good enough for me. Is there a way to give the labels for the y-axis as in the dot chart? Also, I feel the grey level is confusing, so is there options for designs within the bars? I cannot use color as the journal wants

Hi!! I am trying to multiply 5 matrices and then using the inverse of that matrix for further computation. I read about precision problems from the archives and the suggestion was to use as.numeric while computing the products. I am still having problems with the results. Here is how I am using it #Mn.mat<-(T.mat %*% Rz.mat %*% Q.mat %*% Rz.mat %*% T.mat) # I was doing this in one step

I am interested in plotting histograms for the following data Isoform Tumor_65_198 Tumor_50_192 Tumor_80_167 Tumor_80_204 Tumor_95_197 Tumor_70_189 Tumor_90_202 Tumor_40_177 Tumor_60_21 Tumor_70_174 Tumor_70_147 Tumor_50_5 ABCC4-2007 1 1 1 6 1 9 10 1 2 0 10 1 ABCC4-2008 5 8 7 5 3 10 5 5 7 3 10 3 ABCC4-2009 0 0 0 0 0 0 0 0 0 0 0 0 ABCC4-2010

I am trying to find the best way to save the follwoing object I am creating library(multcomp) data(recovery) Dcirec<-simint(minutes~blanket, data=recovery, conf.level=0.9, alternative="less") I am probably not doing it the most efficient way I think. Here is what I am doing a<-print(Dcirec) write(a,file="mult_test.dat", append=T) or save(Dcirec,

I want the levels to appear in the boxplot instead of 1 and 2. What do I need to do for that? Here is the dummy code. x<-runif(100,50,80) x1<-runif(100,70,80) True.positives<-c(x,x1) splice<-factor(c(rep("Human.AA.200",100),rep("Human.AA.100",100))) splice<-factor(splice,levels=c("Human.AA.200","Human.AA.100"))

This might be easy (but I didn't find an answer in the archives). I'm trying to make nice looking images (using image()). To make them look nice (not jagged), it usually takes me at least 100x100 points. This can be slow for frequent redraws. Is there a smarter (less point intensive) way? I was thinking in terms of hexabins, for example, which for much less jagged looking regions,