similar to: Discriminating between experiments with xyplot (lattice)

(I'm new to R) Is there a way to add data to an existing plot, and have the plot axes rescaled automatically (i.e. if the new data lie outside the current axes) ? If not, how can I specify multiple datasets at once, so the axes are scaled to accomodate all sets? Details: (Am I using R's data structures in a reasonable way?) I have many small datasets taken under different

Hi there, I've got a set of 10 numeric variables called Mood1 to Mood10 in a dataset called mood. I'm trying to create a set of 10 new variables called m1 to m10 so that m1=Mood1*1, m2=Mood2*2, etc to m10=Mood10*10 Trawling through the internet, I eventually tried the following code: for (i in 1:10){ assign(x=paste0("mood$m",i),

Hi, I'd like to normalize a dataset by dividing each row by the first row. Very simple, right? I tried this: > expt.fluor X1 X2 X3 1 124 120 134 2 165 163 174 3 52 51 43 4 179 171 166 5 239 238 235 >first.row <- expt.fluor[1,] > normed <- apply(expt.fluor, 1, function(r) {r / first.row}) >normed [[1]] X1 X2 X3 1 1 1 1 [[2]] X1 X2 X3 1

Hello All, I have a question regarding how glmmPQL should be specified. Which of these two is correct? summary(fm.3 <- glmmPQL(cbind(response, 100 - response) ~ expt, data = data.1, random = ~ 1 | subject, family = binomial)) summary(fm.4 <- glmmPQL(response ~ expt, data = data.2, random = ~ 1 | subject, family =

Hi r-users,   I would like to extract the data that match.  Attached is my data: I'm interested in matchind the value in column 'intg' with value in column 'rand_no' > cbind(z=z,intg=dd,rand_no = rr)             z  intg rand_no    [1,]  0.00 0.000   0.001    [2,]  0.01 0.000   0.002    [3,]  0.02 0.000   0.002    [4,]  0.03 0.000   0.003    [5,]  0.04 0.000   0.003    [6,] 

hi there, I have a dataframe in the shape vA1, vA2,..., vA11, vB1, vB2,..., VB11,......., VP1, VP2,...., VP11 (so 16 times a sequence of 11 variables) I am trying to build a double loop so that i can apply the function (i-1)*v(i) to the first 10 variables, then the same for the next 10 variables, etc... 16 times. I have tried the following with no luck: iscores<-list() for (j in

I have a data set with the following structure (with many more obs.): var1 expt day diameter 1 1 2 0.5 1 1 3 0.9 1 1 4 1.3 1 1 5 1.7 1 2 2 0.3 1 2 3 0.5 1 2 4 0.9 1 2 5 1.6 2 1 2 0.7 2 1 3 1.2 2 1 4 1.6 2 1 5 2.3 2 2 2 0.4 2 2 3 0.8 2 2 4 1.6 2 2 5 3.2 I can get separate regression analysis for each level of var1 and expt with the command: by(data3, data3$var1:data3$expt, function(x)

I am trying to do Tukey HSD comparisons on a repeated measures expt. I found the following example on r-help and quoted approvingly elsewhere. It is broken. Can anyone please tell me how to get it to work? I am using R 2.4.1. > require(MASS) ## for oats data set > require(nlme) ## for lme() > require(multcomp) ## for multiple comparison stuff > Aov.mod <- aov(Y ~ N + V +

Dear R listers, When using smooth.spline to interpolate data, results are generally good. However, some cases produce totally unreasonable results. The data are values of pressure, temperature, and salinity from a probe that is lowered into the ocean, and the objective is to interpolate temperature and salinity to specified pressures. While smooth.spline provides excellent values at the

Hi All, I have a microarray dataset as follows:                           expt1 expt2 expt3  expt4 expt 5  gene1                val      val     val      val     val gene2                val      val     val      val    val . . .. gene15000       val       val     val      val     val The result is from the same organism in four different experiments.  Also, there are 4 replicates of each

Dear R-users, The numerical integration example given in Gentleman and Ihaka (2000), "Lexical Scope and Statistical Computing," JCGS, 9, 491-508, is very interesting and helpful in understanding how lexical scope is about. However, I got some questions that I just can't figure out. First all, allow me to copy the two functions given by the authors: midpoint <- function(f, a,

I have written some programs in Common Lisp and I have been using SAS to pipe those programs to my lisp compiler in batch mode by using the %xlog and %xlst SAS commands. I wonder if there is in R a similar way to pipe commands to LISP so that all my work would be concentrated in R even when I have to call a LISP program? I have looked at the foreign library but this seems to adjust data types not

Gday, This is a repost since I only had one direct reply and I remain mystified- This may be stupidity on my part but it may not be so simple. In brief, my problem is I'm not sure how to extract parameter values/effect sizes from a nonlinear regression model with a significant interaction term. My data sets are dose response curves (force and dose) for muscle that also have two

It seems like I've seen how to do this, but so far my archives search has come up dry. I have two X100Ps working with a single instance of asterisk, and I want to do different things with incoming calls depending on which line has rung in. Could someone provide me with a quick pointer? Thx. B.

Hi all, I just submitted a patch to 2-3-stable on lighthouse and wanted to bring it to folks general attention. https://rails.lighthouseapp.com/projects/8994-ruby-on-rails/tickets/896-be-a-little-more-discerning-about-which-exceptions-to-handle My explanation is a bit long-winded, but here''s the motivation for the patch, as explained in the lighthouse ticket: Perhaps I should open

FYI, looks like support for Racoon is ending. Does anyone have any experience with the version in ipsec-tools ? ---Mike >Racoon users, > >This is the announcement that the kame project will quit providing >a key management daemon, the racoon, and that "ipsec-tools" will become >the formal team to release the racoon. >The final release of the racoon in the

Hi, I'm using R-2.12 on a linux 64bit machine. When I run a chunk of code inside a foreach() %do% { ...} or %dopar% {...} (with doMC backend) I keep getting a segfault. Running the *same* code within lapply(something, function(x) ... ) doesn't result in any segfaults. I'll paste the output below, but I'm not sure it would be helpful. I'm more curious how to go about smoking

Hi all, I have 2 data frames the first contains a list with repeats of words and an associated response time (RT) measure for each word. The second is a tabulation of each unique word and other information such as the amount and of responses for each word. I need to determine the mean RT for each word and add that as a column in the second data frame. Any help would be appreciated Cheers

With ggplot2, I can plot the glm stat_smooth for binomial data when the response is binary or a two-level factor as follows: data("Donner", package="vcdExtra") ggplot(Donner, aes(age, survived)) + geom_point(position = position_jitter(height = 0.02, width = 0)) + stat_smooth(method = "glm", family = binomial, formula = y ~ x, alpha = 0.2, size=2) But how can I

I have updated my laptop to 7.4 CR this morning and found that there have been a lot of logs on two fronts for the first time. 1. The kernel has indicated overheating: [Sat Aug 26 08:58:21 2017] CPU2: Core temperature above threshold, cpu clock throttled (total events = 1) [Sat Aug 26 08:58:21 2017] CPU4: Package temperature above threshold, cpu clock throttled (total events = 1) [Sat Aug 26