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Using the following lattice plot as an example, I would like to add horizontal lines where y=0: library(lattice) library(grid) fac <- gl(4,12) x <- letters[rep(1:3,16)] y <- runif(48,min=0.0) dotplot(y~x|fac) I've tried it with grid.lines using npc and native units, which works fine unless I change the size of the output device - then the lines are in the wrong place. Is there a

Here's a simple example: x <- 1:5 plot(x,x^2) lines(x,x^2) points(x,x,cex=2) lines(x,x,lw=3) legend("topleft",legend=c("y=x^2","y=x"),pch=1,pt.cex=1:2,lw=c(1,3)) The thickness of the circles in the legend changes with lw. If you change the lw argument in legend to lw=c(1,1) then the thickness of the circles goes back to normal. How can I make the above

What functions exist for differentiating a numeric vector (in my case spectral data)? That is, experimental data without an analytical function. ie, > x <- seq(1,10,0.1) > y=x^3+rnorm(length(x),sd=0.01) #although the real function would be nothing simple like x^3... > derivy <- .... I know I could just use diff(y) but it would be nice to estimate derivatives at the

On 22 November 2007 at 20:00, leviwaldron at gmail.com wrote: | Full_Name: Levi Waldron | Version: 2.5.1 | OS: Ubuntu Gutsy | Submission from: (NULL) (206.248.157.88) | | | I installed libgsl0-dev then tried to install the gsl cran package, | unsuccessfully (see output below). As a workaround I created a symbolic link to | /usr/bin/gcc-...

I have a dataframe with several different treatment variables, and would like to calculate the mean and standard deviation of the replicates for each day and treatment variable. It seems like it should be easy, but I've only managed to do it for one treatment at a time using subset and tapply. Here is an example dataset: > `exampledata` <- structure(list(day = c(1L, 1L, 1L, 1L, 1L,

I am creating a timeline plot, but running into a problem in positions where the values to plot are too close together to print the text without overlap. The simplest way I can think of to solve this (although there may be other ways?) is to create a new numeric vector whose values are as close as possible to the original vector, but spread out to a given minimum difference. For example, take

This one should be easy but it's giving me a hard time mostly because tapply puts the results in a list. I want to calculate the cumulative sum of a variable in a dataframe, but with the accumulation only within each level of a factor. For a very simple example, take: > df <- data.frame(x=c(rep(1,5),rep(2,5),rep(3,5)),fac=gl(3,5,labels=letters[1:3])) > df x fac 1 1 a 2 1 a

Full_Name: Levi Waldron Version: 2.5.1 OS: Ubuntu Gutsy Submission from: (NULL) (206.248.157.88) I installed libgsl0-dev then tried to install the gsl cran package, unsuccessfully (see output below). As a workaround I created a symbolic link to /usr/bin/gcc-4.2, ie: sudo ln -s /usr/bin/gcc-4.1 /usr/bin/gcc-4.2 The correct behavior of the gsl package should be to use the executable

I am trying to model the observed leaching of wood preservative chemicals from treated wood during an outdoor experiment where leaching is caused by rainfall events. For each rainfall event, the amount of rainfall was recorded as well as the amount of preservative chemical leached. A number of climatic variables were measured, but the most important is the amount of rainfall. I have tried a

I want to take the first row of each unique ID value from a data frame. For instance > ddTable <- data.frame(Id=c(1,1,2,2),name=c("Paul","Joe","Bob","Larry")) I want a dataset that is Id Name 1 Paul 2 Bob > unique(ddTable) Will give me all 4 rows, and > unique(ddTable$Id) Will give me c(1,2), but not accompanied by the name column.

I'm interested in restricting the pairwise comparisons of interaction effects in a multi-way factorial ANOVA, because I find comparisons of interactions between all different variables different to interpret. For example (supposing a p<0.10 cutoff just to be able to use this example): > summary(fm1 <- aov(breaks ~ wool*tension, data = warpbreaks)) Df Sum Sq Mean Sq F

I am wondering how to customize a pairwise comparisons plot of a factorial ANOVA, without doing a lot of manual manipulation of a TukeyHSD object. The customizations I'd like are: 1. The aov used log-transformed response data, but I'd like to plot the intervals on their original, untransformed scales 2. Plot all the main and interaction effects together, rather than in a separate