similar to: problem with se.contrast()

Hello, I have two data frames, NameAndConc.df and WhichOnes.df. NameAndConc.df has two columns, the first column is the names of some material, and the second column is the concentration of active ingredient. > NameAndConc.df name conc 1 material1 0.8 2 material2 0.5 3 material3 0.4 WhichOnes.df has two columns, each of which specifies which material. > WhichOnes.df

I've tried without success to calculate interaction and main effects contrasts using R. I've found the functions C(), contrasts(), se.contrasts() and fit.contrasts() in package gmodels. Given the url for a small dataset and the two-way anova model below, I'd like to reproduce the results from appended SAS code. Thanks. --Dale. ## the dataset (from Montgomery) twoway <-

Would someone be so kind as to explain in English what the ANOVA code (anova.lm) is doing? I am having a hard time reconciling what the text books have as a brute force regression and the formula algorithm in 'R'. Specifically I see: p <- object$rank if (p > 0L) { p1 <- 1L:p comp <- object$effects[p1] asgn <-

Full_Name: Yves Rosseel Version: 2.2.1 OS: i686-pc-linux-gnu Submission from: (NULL) (157.193.116.152) Dear developers, For the single-model case, the anova.mlm() function does not seem to handle multi-parameter predictors (eg factors) correctly. A toy example illustrates the problem: Y <- cbind(rnorm(100),rnorm(100),rnorm(100)) A <- factor(rep(c(1,2,3,4), each=25)) fit <- lm(Y ~ A)

I have code that constructs a plot using the lattice package that looks something like the following toy example: library(lattice) Start <- factor(rbinom(100,1,.5)) Answer <- 2 - rbinom(100,1,.7) histogram(~Answer | Start, breaks=c(1, 1.4 ,1.6,2), scales=list(x=list(at=c(1.2,1.8),labels=c("Yes","No"))), xlab="",ylab="")

I am trying to compile the R-dev sources on WinXP using mingw 3.1.0-1 and fpTeX 0.7. In the past I have been able to build the sources fine but now I receive an error in the process that I havent seen reported on the list before. Everything works fine until I get to 'make docs'. I now get the message below while building the R-admin manual (refman.pdf builds fine) and the make process

Hi, In R I get the following: > x <- 1:9 > f <- as.factor(1:9) > i <- model.matrix(~x+f) > > attr(i, "assign") [1] 0 1 2 2 2 2 2 2 2 2 whereas in S I get the following: > attr(i, "assign") $"(Intercept)": [1] 1 $x: [1] 2 $f: [1] 3 4 5 6 7 8 9 10 Has anybody written a model.matrix function to get the same result as S?

Hi I just started using RGui.exe under widnows. I have a text file containing date arranged in columns and rows, each column has the same format, each row with different formats. 3 of the columns are something like this 1/12/2006 3:59:45 PM I need to calculate the different in seconds between 2 selected periods using their row’s index My solution: Read the file in a data frame and

Hello :) I have two data frames, one has properties taken on a piece by piece basis and the other has performance on a lot by lot basis. I wish to combine these two data frames but the problem is that each lot has multiple pieces and hence i need to take a mean of the properties of multiple pieces and match it to the row having data about the lot. I was wondering if there is a simple commmand,

For the following code below, the x-axis ticks are 1,2,3,4,5,6,7 when I was expection them to be 1,2,8,9,10,11,12. Please help me figure out where is the mistake. library(lattice) testdata <- as.data.frame(t(structure(c( 1,2005,9.24,6.18,634, 2,2005,8.65,6.05,96, 8,2004,6.81,6.51,16, 9,2004,9.0,7.29,8, 10,2004,8.84,6.18,524, 11,2004,8.54,6.35,579, 12,2004,9.97,6.3,614, 12,2005,8.75,5.84,32,

Dear R-core There is a different output for the apply function due to the change of unlist as mentioned in the R news. Newly, applying as.factor() (or factor()) in str(dat <- data.frame(x = 1:10, f1 = gl(2,5,labels = c("A", "B")))) (d1 <- apply(dat,2,as.factor)) newly returns a character matrix while in R-2.3.1 the same command resulted in an integer matrix that was

Hi... With samba4 I sometimes feel like a bloody beginner even I use samba since ages. Miracle of the day: Users connecting using samba4 cannot write to group writeable folders even they should be able to. I upgraded one 3.6 fileserver to 4.2.3. PDC is always a samba 4.2.3 instance. I am using samba4 in classic PDC mode for a couple of reasons. On my fileserver I have a folder called

Hello everbody, i'm currently using the anova()-test for a small data.frame of 40 rows and 2 columns. It works well, but is there any preconditions for a valid variance analysis, that i should consider? Thank you for your answer, Daniel

Hi All.. Have a question.. For the following r code testdata <- as.data.frame(t(structure(c( "1/1/04","LV1",3.8,2,87, "2/1/04","LV1",3.2,3,28, "3/1/04","LV1",3.4,3,88, "4/1/04","LV1",3,2,26, "5/1/04","LV1",3.8,2,87, "6/1/04","LV1",3.2,3,28,

I have a text string that contains text within two brackets. e.g. "testdata[3]" "testdata[-4]", "testdata[-4g]", I wish to "extract" the string enclosed in brackets? What is a good way to do this? e.g. fun(testdata[3]) = '3' fun(testdata[-4g]) = '-4g' --------------------------------- Moody friends. Drama queens. Your life?

Hello All, I was wondering if it's possible to reshape data from long to wide in R without using a "timevar". I've pasted some sample data below along with some code. The data are sorted by Subject and Drug. I want to transpose the Drug variable into multiple columns in alphabetical order. My data have a variable called "RowNo" that functions almost like a

In playing around with data.frames (and wanting a simple, cheap way to use the variable and case names in plots; but I've solved that with some hacks, yech), I noticed the following behavior with subsetting. testdata <- data.frame(matrix(1:20,nrow=4,ncol=5)) names(testdata) ## expect labels, get them names(testdata[2,]) ## expect labels, get them names(testdata[,2]) ## expect labels, but

Dear all, I have a dataset where the interaction is more than obvious, but I was asked to give a p-value, so I ran a logistic regression using glm. Very funny, in the outcome the interaction term is NOT significant, although that's completely counterintuitive. There are 3 variables : spot (binary response), constr (gene construct) and vernalized (growth conditions). Only for the FLC construct

Dear R-list I have a question concerning the argument "adjustSigma" in the function "lme" of the package "nlme". The help page says: "the residual standard error is multiplied by sqrt(nobs/(nobs - npar)), converting it to a REML-like estimate." Having a look into the code I found: stdFixed <- sqrt(diag(as.matrix(object$varFix))) if (object$method

hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after