similar to: How to alter circle size

I suggest to add a panel function to levelplot (or perhaps to an other 3d lattice function) which is able to translate the z values into the size of the rectangles. It could be used to display categorical data. I append the proposed code and two examples: - panel.catlevelplot() - example1.catlevelplot.esoph() - example2.catlevelplot.esoph() Wolfram Fischer #------ CODE

Dear list, I have a problem with the "corrgram" function. It does not seem to "color" large negative correlations, while the same correlation, if positive, provides no problems. Is this a bug? require(corrgram) a = seq(1,100) b = -jitter(seq(1,100), 80) cor(a,b) # r about -.96 c=as.data.frame(cbind(a,b)) corrgram(c, order=NULL, lower.panel=panel.pie,upper.panel=NULL,

I'm trying to make correlograms using corrgram. See below for a simple example. #### library(corrgram) data(baseball) vars1 <- c("Assists","Atbat","Errors","Hits","Homer","logSal") vars2 <- c("Putouts","RBI","Runs","Walks","Years")

In the example: x = rep(c(0,0,1,1),4) y = rep(c(0,1,0,1),4) z = c(1,0,1,0,0,0,1,1,0,1,0,0,1,1,1,1) f = as.factor(c(rep("a",4),rep("b",4),rep("c",4),rep("d",4))) levelplot(z~x+y|f,data.frame(x=x,y=y,z=z,f=f)) I noted that the last ("d") plot remains empty. I guess the reason for this is that the values are constant (1), but I consider it more

Hello, I am trying to plot a few correlograms on the same figure, with the function corrgram() from the package corrgram. However, the function does not seem to use the base graphic system, as setting out the multiple figure layout with, e.g., par(mfrow=c(2, 2,)) does not work. Does anybody know a workaround for this? Many thanks in advance for any advice best giuseppe -- Giuseppe Pagnoni,

given this example library(corrgram) corrgram(mtcars[2:6], order=TRUE, upper.panel=panel.conf, lower.panel=panel.pie, diag.panel=panel.minmax, text.panel=panel.txt) how can I change the variable names in main diagonal? (so that I can put more informative names of variables) I think to understand that this should be done by modifing the panel.txt function but

Hello all. I am trying to run PAN, multilevel multiple imputation program, in R to impute missing data in a longitudinal dataset. I could successfully run the multiple imputation when I only imputed one variable. However, when I tried to impute a time-varying covariate as well as a response variable, I received an error message, “Error: subscript out of bounds.” Can anyone tell if my commands

Hello, are there fixed threshold values for the changes between different shades of blue in the corrgram? Or is a chang relative depending on the data? I didnt get it in manual. Thank you very much, best regards Claudia

If you found my subject heading to be confusing then I'm sure you'll enjoy the example I've included below. I find the apply type functions to be wonderful for avoiding loops but when I use them with existing functions, I end up using loops anyway to extract the vectors I want. I would appreciate it if someone could show me how to avoid these loops. Thanks. EXAMPLE:

Hello everyone, I'm trying to estimate the parameters of the returns series attached using the GARCH code below, but I get the following error message: Error in solve.default(Hessian) : system is computationally singular: reciprocal condition number = 0 Error in diag(solve(Hessian)) : error in evaluating the argument 'x' in selecting a method for function 'diag' Can

Hello! I have 2 maps - both created in ssplot and both identical in terms of outline. Is there any way to superimpose Map1 (which has black borders between Canadian provinces) onto Map2 (which is also a map of Canada)? Thanks a lot for your hints! Dimitri ### A. Reading in Canada data at the province and then at the county level: library(raster) getData('ISO3') # Canada's code is

Hi everyone I want a square scatterplot with abline(0,1) going exactly through the SW and NE corners. By "square" I mean that the plotting region is exactly square, and that the axis limits are identical. x <- 1:20 y <- x+rep(c(-1,1),10) lims <- range(c(x,y)) None of the following do this: plot(x,y) ; abline(0,1) #not square plot(x,y,asp=1);abline(0,1) #diagonal

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

I am attempting to lower the selectCC instruction to the instruction set of the backend I'm working on and I cannot seem to find a way to correctly implement this instruction. I know how this instruction should get implemented; I just have yet to find a way to do it. I want the select_cc instruction to be lowered into a comparison followed by a conditional move. I've attempted to use a

I define the following function to convert a t-value with degrees of freedom DF to another t-value with different degrees of freedom fullDF: tConvert <- function(tval, DF, fullDF) ifelse(DF>=1, qt(pt(tval, DF), fullDF), 0) It works as expected with the following case: > tConvert(c(2,3), c(10,12), 12) [1] 1.961905 3.000000 However, it gives me warning for the example below although the

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

I really can't figure out how to get and set array indices from the c interface. so to get an element I'm calling tindex = *fn\SymbolTable(*index\name) index = LLVMBuildLoad(builder,tindex,"index") arr = *fn\SymbolTable(*array\name) arrptr = LLVMBuildLoad(Builder,arr,"arrayptr") tmp = LLVMBuildGEP(Builder,arrptr,index,0,"ptr") ptr =

Hi, I would like to display inter-parameter scatter plots like those with the corrgram package (see upper triangle here: http://www.statmethods.net/advgraphs/images/corrgram2.png ), just that I would like to plot two datasets instead of one. Say one with black and one with red dots. Or a merged dataset where an indicator column is used to assign different colors to particular dots - with still

I have tried the following code to plot and save many box plots in files. The code works by itself, but does not run in a loop. The error indicates that there is no device. The error is printed below the code. Code: for(i in 1:len) { A = as.numeric(delta[Delta[i,1]]) B = as.numeric(delta[Delta[i,2]]) C = as.numeric(delta[Delta[i,3]]) D = as.numeric(delta[Delta[i,4]]) data

Let's say you have a dataframe of car trade-ins. For example, each row contains oldcar newcar qty and a typical entry could be lexus bmw 1 I put the qty column to allow for fleet purchases, where one purchase may convert multiple cars at once. I'd like to show what's going on. I could do a histogram of newcar to show the frequency each type of car is bought. If there