similar to: can I add to a plot and auto-re-scale axes?

I am sorry that this is another novice question. I am having trouble using "legend" with the survival curve plot from the survival package, and I wonder if it is because I have rescaled my plot. Here is the relevant segment of code: > plot(survfit(Surv(OS,Status)~shortishcr1),main='Overall Survival by factor', + xlab='Years',ylab='%

Deepayan: Sorry for the additional email, but I'm concerned that I wasn't being clear. In the end, I would like a graphic that portrays my y-values on a log scale in their native data range. That is to say, I would prefer to see the axis labels as 1000 rather than 3 (log10 of 1000). Thank you for your kind assistance, Hobie Perry St. Paul, MN Hobie Perry <hobie_perry@yahoo.com>

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 =

Good Evening- I have a set of nine scenarios I want to plot to see how the distribution is changing, if one tail is getting larger in certain scenario, currently I am using this code colnames<-dimnames(sag_pdfs)[[2]] par(mfrow=c(3,3)) for(i in 1:9) { d<-density(sag[,i]) plot(d,type="n", main=colnames[i]) polygon(d,col="red",border="grey")} where sag is

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

Hi, I have a consultants nightmare -- I was given a project that another consultant did and I was told to do the same calculations, but there's no documentation on what he did. Basically, I have yes/no answers to survey questions about the effectiveness of product attributes by brands. There are 44 attributes and 13 brands. The other guy scaled the proportion of respondents who said

Dear R users, I'm plotting housing prices in City A over past 30 years in ggplot2. The Xs are years since 1980. I have two housing price variables: new home prices and old home prices, both of them measured by $/sqft. I have searched related threads on multiple Y axes in ggplot2 and I understand that multiple Y axes in different scales are not possible. I'm wondering if it is possible to

Dear R users, I'm plotting housing prices in City A over past 30 years in ggplot2. The Xs are years since 1980. I have two housing price variables: new home prices and old home prices, both of them measured by $/sqft. I have searched related threads on multiple Y axes in ggplot2 and I understand that multiple Y axes in different scales are not possible. I'm wondering if it is possible to

Problem: How can I format tickmark labels of log scaled axes of lattice graphics in the usual `xxx'-Format (and not in the scientific format). Example: (according to the help-page of xyplot): In the first plot I get the xxx-Format, in the second plot I get the scientific format (10^xxx): data(sunspot) plot( 1:37, sunspot, log='y',type='l') xyplot( sunspot ~ 1:37,

First, thanks to all who helped me with my question about rescaling axes on the fly. Using unlist() and range() to set the axis ranges in advance worked well. I've since plotted about 300 datasets with relative ease. Now I'm trying to fit a lossy oscillator resonance to (the square root of) a lorentzian (testframe$y is oscillator amplitude, testframe$x is drive frequency): lorentz

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)

After a long and winding road (sorry but I'm a novice) I get to a final result which is quite close to what I need; nevertheless I would like to tweak a little further the xyplot so that I can get ***for each single panel defined by variable z*** a finer control over: -the x and y the limits: I would like to be the same for both axes; -the number of thick marks: again I would like to be same

Hi, I am trying to put on one plot two different logarithmic scales, using the bottom and top X-axes. Below there is an example of what I am trying to achieve, using axTicks() -- and fails. I already spent few hours on that, and cannot figure out from ?par and ?axTicks what I am doing wrong. Example follows: ############################################################ #### Data x <-

Dear useRs, I'd like to produce some scatter plots where N units on the X axis are equal to N units on the Y axis (as measured with a ruler, on screen or paper). This approach x <- sample(10:200,40) ; y <- sample(20:100,40) windows(width=max(x),height=max(y)) plot(x,y) is better than plot(x,y) but doesn't solve the problem because of the other parameters (margins etc). Is

The following output results from fitting models using lmer and lm to data arising from a split-plot experiment (#320 from "Small Data Sets" by Hand et al. 1994). The data is given at the bottom of this message. My question is why is the sum of squares for variety (V) different in the ANOVA table generated from the lmer model fit from that generated by the lm model fit. The

I have been making some segment plots with five variables. They work great, especially when I used a different scale function, which scaled them by area of the circle rather than radius scale <- function(x, Mr = 1 , Mx = 100) { ((x/Mx)^.5)*Mr} Where x is the the value, Mr is the Maximum radius, and Mx is the maximum data value. You could change the exponent .5 to .57 if you wanted Flannery

Though I have read quite a bit, and tried quite a bit, I have yet to find a nice way to overlay 2 or more curves in the same plot, with different ranges. Here is simplified sample code to demonstrate the question: > plot(2*(seq(1,5)), type="l", axes=FALSE) > curve(2*(seq(1,5)), type="b", add=TRUE) Error in curve(2 * (seq(1, 5)), type = "b", add = TRUE) :

I have grades data. I read them from a csv in letter-grade format. I then converted them to levels levels(grades$grade)=c('A+','A','A-','B+','B','B-','C+','C','C-','D+','D','D-') And then to numbers grades$gp=grades$grade levels(grades$gp)=c(4.3,4.0,3.7, 3.3,3.0,2.7, 2.3,2.0,1.7, 1.3,1.0,0.7)

Please, could someone help me figure out what seems to be a very simple problem (and is still taking me hours...). I want to draw a simple scatterplot but with 'equal' axes, i.e. I want both axes to go from -3 to 3. Values for x lie between -2 and 0.5, values for y between -2.2 and 3. I have tried 'usr' and 'eqscplot' and a few other options, but it doesn't give me the

Dear all, This is the first time I am posting to the r-devel list. On StackOverflow, they suggested that the strange behaviour of integrate() was more bug-like. I am providing a short version of the question (full one with plots: https://stackoverflow.com/q/55639401). Suppose one wants integrate a function that is just a product of two density functions (like gamma). The support of the