similar to: offsets again (PR#159)

Hi, I want to use the value of variable to quote elements in matrix. For example, I have a matrix like:               y1   y2m1         1      2m2         3      4 where y1,y2,m1,m2 are column and row names.  I have two random character variable, say x,  that could be either  y1 or y2  and  y that could be either m1 or m2.  So can I  do like   Matrix[y,x] to quote elements?  I've tried this

Hi, I have measured two response variables (y1, y2) at each treatment level (x = 0, 1.5 or 3). Now I would like to show the y1 and y2 against x in a bar plot. However, y1 and y2 differ in scale so I need two y-axises, one on the left side and one on the right side (and I dont want to standardize my responses). This is fairly easy if you want to show points,lines etc, but gets more complicated

hello all I am writing a quite simple script to study dental wear patterns in humans and I wrote this function sqrt(var(Y1)+var(Y2))^2-4(var(Y1)*(var(Y2)-cov(Y1,Y2)^2)) but appear this error message Error: attempt to apply non-function alternatively I wrote this sqrt(var(Y1)+var(Y2)^2)-4[(var(Y1)*(var(Y2)-cov(Y1,Y2)^2))] but this error message appear [1] NA Warning message: NAs introduced

Hi all, I'm extracting the name of the term in a regression model that dropterm specifies as the least significant one, and I'm assigning this name to an object. However, when I use update(), it ignores this object. Is there a way I can make it not ignore it? A reproducible example is below: > lm(x1~1+y1*y2+y3+y4,data=anscombe)->my.lm >

Hi R, y1 <- zoo(matrix(1:10, ncol = 2), 1:5) colnames(y1)=c("a","b") y2 <- zoo(matrix(rnorm(10), ncol = 2), 6:10) colnames(y2)=c("b","a") > y1 a b 1 1 6 2 2 7 3 3 8 4 4 9 5 5 10 > y2 b a 6 0.9070204 0.3527630 7 1.2405943 0.8275001 8 -0.1690653 -0.1724976 9 -0.6905223 -1.1127670 10

Hi all, Using xyplot I want to print to Y variables (y1, y2) versus X, conditional on the group. How can I obtain a line (type="l") for one relationship (ie. y1 ~ x) and points (type="p") for the other (y2 ~ x) ? library(lattice) # create some sample data df<-data.frame(group=as.factor(c(rep("a",4), rep("b",4))), # grouping variable for conditional

Although this should be trivial, I'm having a spot of trouble. I want to make a lattice plot of the format y1+y2 ~ x | grp but then fit a lm to each y variable and add an abline of those models in different colors. If the xyplot followed y~x|grp I would write a panel function as below, but I'm unsure of how to do that with y1 and y2 without reshaping the data before hand. Thoughts

Dear R users, I have two n*1 integer vectors, y1 and y2, where n is very very large. I'd like to compute elbp = 4^(y1) * 5^(y2) * sum_{i=0}^{max(y1, y2)} [{ (y1-i)! * (i)! * (y2-i)! }^(-1)]; that is, I need to compute "elbp" for each (y1, y2) pair. So I made R code like below, but I don't think it's efficient Would you plz tell me how to avoid this "for"

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) {

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Hi - I'm up against a complicated reshape problem. I have data of the form X1,Y1,hr1,hr2,hr3 X1,Y2,hr1,hr2,hr3 X1,Y3,hr1,hr2,hr3 X2,Y1,hr1,hr2,hr3 X2,Y2,hr1,hr2,hr3 X2,Y3,hr1,hr2,hr3 where X and Y are factors and the hr(1,2,3) are values. I need it as ,X1, X2 Y1,hr1,hr1 Y1,hr2,hr2 Y1,hr3,hr3 Y2,hr1,hr1 Y2,hr2,hr2 Y2,hr3,hr3 .., Any hints? I've been at it for hours. p -- View

Dear List, I am trying to find each time a value changes in a dataset. The numbers are variables for day vs. night values, so what I am really getting is the daily sunrise and sunset. A simplified example is the following: x<-seq(1:100) y1<-rep(1,10) y2<-rep(2,10) y<-c(y1,y2,y1,y1,y1,y2,y1,y2,y1,y2) xy<-cbind(x,y) I would like to know each time the numbers change. Correct

hello, I want to simulate 200 times the mean of a joint probability (y1) and 200 times the mean of another joint distribution (y2), that is I'm expecting to get 200 means of y1 and 200 means of y2. y1 and y2 are probabilities that I calculate from the marginal prob. (z1 and z2 respectively) multiple by the conditional prob. (x1 and x2 respectively), which I generaterd from the binomial

Dear all I have a code like x<-1:10 y1<-x+runif(10)*2 y2<-seq(0,50,length.out=10)+rnorm(10)*10 par(mfrow=c(1,2)) plot(y1~x) plot(y2~x) Now I would like to plot y1 and y2 on the same graph, with its two scales (y1 on left and y2 on rigth side). Any help are welcome. Kind regards Miltinho Brazil [[alternative HTML version deleted]]

Hi, I was wondering why the results from lm and dynlm are not the same for what I think is the same model. I have just modified example 4.2 from the Pfaff book, please see below for the code and results. Can anyone tell my what I am doing wrongly? Many thanks, Werner set.seed(123456) e1 <- rnorm(100) e2 <- rnorm(100) y1 <- ts(cumsum(e1)) y2 <- ts(0.6*y1 + e2) lr.reg <- lm(y2

Dear All, I would be very appreciative of your help with the following 1). I am running multivariate multiple regression through the manova() function (kindly suggested by Professor Venables) and getting two different answers for test=c("Wilks","Roy","Pillai") and tests=c("Wilks","Roy",'"Pillai") as shown below. In the

Hello all, I have data in the form of a table: X Y1 Y2 0.1 3 2 0.2 2 1 And I would like to transform in the form: X Y 0.1 Y1 0.1 Y1 0.1 Y1 0.1 Y2 0.1 Y2 0.2 Y1 0.2 Y1 0.2 Y2 Any ideas how? Thanks in advance, IOanna [[alternative HTML version deleted]]

Could I suggest the following extension to plot.formula: plot(cbind(y1,y2) ~ x, ...) should plot (y1 against x) and (y2 against x) on the same plot. The default y axis limits would be determined by the range of c(y1,y2). This would be pretty handy sometimes, replacing 4 lines of code. The current plot.formula evaluates cbind(y1,y2), which is a matrix, so plot.formula looks for

Hi, I have this script: dat <- data.frame(X = halistat$Date,Y1 = halistat$avg,Y2 = halistat$stdev) ggplot(data = dat, aes(x = X, y = Y1, ymin = Y1 - Y2, ymax = Y1 + Y2)) + geom_point() + # points at the means geom_line() + # if you want lines between pints geom_errorbar() # error bars, Y1 - Y2 and Y1 + Y2 halistat$Date values: 29/1/10 21/2/10 30/3/10 30/4/10 30/5/10 In the resulted

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