similar to: Write to multiple connections or multiple text files

Dear R-ers, I conducted a regression analysis, and then intended to add the regression function (y=4.33+1.07x) in a graph. But the following code can only give me a text like y=a+bx. Who can help me out? Thank you very much in advance. CODE # Read data x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9) y <- c(6, 5, 8, 9, 11, 10, 11, 12, 15) data01 <- data.frame(x, y) #

I am using expression() to incorporate text into graphics. To create a superscript, I use the '^' character. Can someone please tell me the character to use to create a subscript? Thank you ----------------------------------------------------- Christine Donnelly Statistical Consulting Unit The Graduate School John Dedman Mathematical Sciences Building (Bldg 27) Australian National

Estimate region of highest probabilty density Dear R-community I have data consisting of x and y. To each pair (x,y) a z value (weight) is assigned. With kde2d I can estimate the densities on a regular grid and based on this make a contour plot (not considering the z-values). According to an earlier post in the list I adjusted the kde2d to kde2d.weighted (see code below) to estimate the

heya, i am fitting linear mixed effect model to a response Y. Y shows an s-shaped distribution when using QQ-plots (some zero values and some very high values). hence, which transformation should i apply that Y follows a normal distribution? any r-function/package available to do this? thanks for any hint, regards, lukas ??? Lukas Indermaur, PhD student eawag / Swiss Federal Institute of

Hello Instead of testing against 0 i would like to test regression slopes against -1. Any idea if there's an R script (package?) available. Thanks for any hint. Cheers Lukas ??? Lukas Indermaur, PhD student eawag / Swiss Federal Institute of Aquatic Science and Technology ECO - Department of Aquatic Ecology ?berlandstrasse 133 CH-8600 D?bendorf Switzerland Phone: +41 (0) 71 220

Hi, Fitting all possible models (GLM) with 10 predictors will result in loads of (2^10 - 1) models. I want to do that in order to get the importance of variables (having an unbalanced variable design) by summing the up the AIC-weights of models including the same variable, for every variable separately. It's time consuming and annoying to define all possible models by hand. Is there a

Hi, I would like to evaluate the frequency of the variables within the best selected model by AIC among a set of 12 competing models (I fit them with GLM) with a bootstrap procedure to get unbiased results. So I would ike to do the ranking of the 12-model-set 10'000 times separately and calculate the frequency of variables of the 10'000 best ranked models. I wrote a script doing the model

A dataframe holds 3 vars, each checked true or false (1, 0). Another var holds the grouping, r and s: ### start:example set.seed(20) d <- data.frame(sample(c(0, 1), 20, replace=T), sample(c(0, 1), 20, replace=T), sample(c(0, 1), 20, replace=T)) names(d) <- c("A", "B", "C") e <- rep(c("r", "s"), 10) ### end:example How do I get the

A Likert scale may have produced counts of answers per category. According to theory I may expect equality over the categories. A statistical test shall reveal the actual equality in my sample. When applying a chi square test with increasing number of repetitions (simulate.p.value) over a fixed sample, the p-value decreases dramatically (looks as if converge to zero). (1) Why? (2) (If

I have created a function to do something: i <- factor(sample(c("A", "B", "C", NA), 793, rep=T, prob=c(8, 7, 5, 1))) k <- factor(sample(c("X", "Y", "Z", NA), 793, rep=T, prob=c(12, 7, 9, 1))) mytable <- function(x){ xtb <- x btx <- x # do more with x, not relevant here cat("The table has been created,

Hello I want to batch job the calculation of many GLM-models, extract some values and store them in a file. Almost everything in the script below works (read file, extract values and write them to file) except I fail in indexing the GLM with the modelstructure it should run. Running GLM's conventionally is no problem. Conventionally a GLM is calculated as:

How to I "recode" a factor into a binary data frame according to the factor levels: ### example:start set.seed(20) l <- sample(rep.int(c("locA", "locB", "locC", "locD"), 100), 10, replace=T) # [1] "locD" "locD" "locD" "locD" "locB" "locA" "locA" "locA"

A list contains several matrices. Over all matrices (list elements) I'd like to access one matrix cell: m <- matrix(1:9, nrow=3, dimnames=list(LETTERS[1:3], letters[1:3])) l <- list(m1=m, m2=m*2, m3=m*3) l[[3]] # works l[[3]][1:2, ] # works l[[1:3]][1, 1] # does not work How can I slice all C-c combinations in the list? S?ren -- S?ren Vogel, Dipl.-Psych. (Univ.), PhD-Student, Eawag,

Dear All Where can I lookup good methods to compute p from q=bin(m,n)p^n*(1-p)^(m-n) such that q<=alfa, alfa small. Are there such libs, code and source in R? Best Regards -- Jan Burse SIAM, EAWAG Scheuchzerstr. 67 ?berlandstr. 133 8006 Z?rich 8600 D?bendorf tel: +41-1-364 17 66 tel: +41-1-823 55 34

Heya I am fitting linear mixed effects model in R and want to assess the model fit (with Animal number as random factor; repeated measures for Animals): ts.model <- lme(LOG_FOC_MW ~ R_DN_SUM + ANIMAL + SEX+ YY, data = t.data, random = ~ 1 | ANIMAL, correlation=corCAR1(0.2, form = ~1 | ANIMAL ), method='ML', na.action=na.omit)). Is there a possability to easly compute an

Hello a <- c("Mama", "Papa", "Papa; Mama", "", "Sammy; Mama; Papa") a <- strsplit(a, "; ") mama <- rep(F, length(a)) mama[sapply(a, function(x) { sum(x=="Mama") }, simplify=T) > 0] <- T papa <- rep(F, length(a)) papa[sapply(a, function(x) { sum(x=="Papa") }, simplify=T) > 0] <- T # ... more

In https://stat.ethz.ch/pipermail/r-help/2008-March/156868.html I found what linear separability means. But what can I do if I find such a situation in my data? Field (2005) suggest to reduce the number of predictors or increase the number of cases. But I am not sure whether I can, as an alternative, take the findings from my analysis and report them. And if so, how can I find the linear

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Is there interest in ipp2p support in Shorewall? While the ipp2p code is not part of the standard kernel.org distributions, my experience is that it is very easy to install and I would be willing to provide support for it if there is interest. See http://rnvs.informatik.uni-leipzig.de/ipp2p/index_en.html for information about ipp2p. - -Tom - -- Tom

### example:start v <- sample(rnorm(200), 100, replace=T) k <- rep.int(c("locA", "locB", "locC", "locD"), 25) tapply(v, k, summary) ### example:end ... (hopefully) produces 4 summaries of v according to k group membership. How can I transform the output into a nice table with the croups as columns and the interesting statistics as lines? Thx,

Hello The "Craddock-Flood Test" is recommended for large tables with small degrees of freedom and low-frequency cells. Is there an R procedure and/or package which does the test? Thank you for your help! S?ren Vogel -- S?ren Vogel, Dipl.-Psych. (Univ.), PhD-Student, Eawag, Dept. SIAM http://www.eawag.ch, http://sozmod.eawag.ch