similar to: How to draw different series for different groups in xyplot

Dear R help, I have some readings in three dimensions (x, y, z) and an amplitude for each. I'd like to visualize the data using ggplot, using tile plots, as I have some additional point data I would like to eventually overlay on the tile plots. I would like to subset the data by sections, slices if you will, in the z dimension, and plot the data for that slice. I can do all of this, but am

Doh! Thanks very much. sg On 11/6/23 5:17 PM, John Fox wrote: > Dear Spencer, > > You need the t distribution with correct df, not the standard-normal > distribution: > > > pt(-z.confInt/2, df=13) > ??? 1???? 2???? 3???? 4???? 5???? 6???? 7???? 8???? 9??? 10??? 11 > 0.025 0.025 0.025 0.025 0.025 0.025 0.025 0.025 0.025 0.025 0.025 > ?? 12??? 13 > 0.025

Hi, Please see the email exchanges below. I am having trouble generating output that is large enough for our needs, specifically when using the GaussRF function. However, when I wrote Dr. Schlather (the author of the GaussRF function), he indicated that there is also a limit imposed by R itself. Is this something that we can overcome? Thank you very much for any assistance you may provde.

I have a file: x y z 0.025 0.025 1.65775 0.025 0.050 1.62602 0.025 0.075 1.63683 0.025 0.100 1.91847 0.025 0.125 2.00913 0.025 0.150 1.82222 0.025 0.175 1.70901 0.025 0.200 1.39759 0.025 0.225 1.39089 0.025 0.250 1.04762 If I read the file like this: data<-read.table("file.dat") How do I access the vectors x,y,z that are inside the dataframe data? I studied Venables and

Hi all, I have a hypothesis test of proportion question without using R. I hope you could help to answer my question. Here is the statement. ********************* Preliminary data from a feasibility study showed a potential failure level of 1%. A sample size was calculated to detect a potential shift from this 1% level to a 2.5%, at an alpha level of 0.025 and beta of 0.1. The sample size

I am trying to use t.test on the following data: date type INTERVAL nCASES MTF SDF MTO SDO nFST MF nOBS MO MB BIASCV BIASEV ME MAE RMSE CRCF 2001-06-15 avn GE1.00 4385 0.246 0.300 1.502 0.556 1367 1.373 4385 1.502 1.471 0.285 0.164 -1.256 1.266 1.399 0.056 2001-06-15 avn

Good morning, suppose the following: m <- round(matrix(rnorm(16), ncol=4), 3) a <- rev(c(0.01, 0.025, 0.05, 0.1)) rownames(m) <- a colnames(m) <- c("0.25,0.75", "0.4,0.6", "0.1,0.9", "0.4,0.9") m 0.25,0.75 0.4,0.6 0.1,0.9 0.4,0.9 0.1 1.034 -0.119 -1.213 0.619 0.05 0.035 1.074 0.525 1.671 0.025 -1.687 0.960

Hello, I hesitate to call this a bug, because I could have forgotten something important, but the MarkowitzPortfolio example in fPortfolio does not work for me. Here's my code: > library(fPortfolio) > >xmpPortfolio("\nStart: Load monthly data set of returns > ") > data(berndtInvest) > # Exclude Date, Market and Interest Rate columns from data

Hi, I have a question about the computation of confidence intervals in the zyp package, in particular using the functions zyp.sen and confint.zyp, or zyp.yuepilon. (1) I'm a bit confused about the confidence intervals given by zyp.sen and confint.zyp. When I request a certain confidence interval in the function, the R output seems to deliver another confidence interval, e.g. when I set

Hi All, I have a dataframe, divs, that looks like this: > divs date ticker dividend 263 20010322 ADBE 0.025 264 20010628 ADBE 0.025 265 20010927 ADBE 0.025 4308 20010212 ED 0.550 4309 20010514 ED 0.410 5416 20010330 GE 0.137 5417 20010629 GE 0.137 5418 20010928

Sadly you failed to set your email program to send plain text and the data is corrupted at my end. I also think you need to reduce the size of the data set... the intent here is to increase your understanding, not debug your particular analysis. I will say that I am having a very challenging time understanding what you are trying to accomplish though. What are the equations that you think need

Hi, I want to compute the quantiles of Chi^2 distributions with different degrees of freedom like x<-cbind(0.005, 0.010, 0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975, 0.99, 0.995) df<-rbind(1:100) m<-qchisq(x,df) and hoped to get back a length(df) times length(x) matrix with the quantiles. Since this does not work, I use x<-c(0.005, 0.010, 0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975,

hi ladyes and gentlemans of the R comunity. i have the following graphical issue: # say y is a matrix or a dataframe with dim(51,nrow=17),hence 3 columns; l<-51 # now the 3 colums represents 3 different aspects of my continuous variable (3 factors),plotted against a categorical one; n<-levels(factor(x)) # i want to plot those 3 factors adjacently using the barplot(), and possibly show

I have a data frame (narrow) with 431 rows and 6 columns containing information on chromosome, position, lod1, lod2, lod3, lod4, looking like this: > narrow chr pos lod1 lod2 lod3 lod4 1 1 3.456 -0.025 -0.003 -0.209 -0.057 2 1 5.697 -0.029 -0.005 -0.200 -0.058 3 1 8.434 -0.049 -0.012 -0.247 -0.092 4 1 9.466 -0.074 -0.025 -0.300 -0.136 5 1 9.706

I have a data.frame with three columns + the row number Sample MidPoint Count [1,] 1 0.025 1 [2,] 1 0.075 3 [3,] 1 0.125 15 [4,] 1 0.175 12 [5,] 1 0.225 5 [6,] 1 0.275 8 When I export the data to a csv the column names are shifted because the row numbers are being exported as well. Sample MidPoint

Hi there, I am doing multiple comparisons for data that is not normally distributed. For this purpose I tried both functions kruskal{agricolae} and kruskalmc{pgirmess}. It confuses me that these functions do not yield the same results although they are doing the same thing, don't they? Can anyone tell my why this happens and which function I can trust? kruskalmc() tells me that there are no

Dear R users, I've written a loop to generate Moran's test (spdep package) on serval subsamples of a large dataset. See below a short example. My loop is working fine, however I would like to be able to store the test results as lines of a matrix, that I would latter be able to export as a dataset. My problem is that I'm not sure how I could do this using R. Any help will be much

I can't get my simultaneous equations to work using system fit. Please help. #Reproducible script Empdata<- read.csv("/Users/ngwinuiazenui/Documents/UPLOADemp.csv") View(Empdata) str(Empdata) Empdata$gnipc<-as.numeric(Empdata$gnipc) install.packages("systemfit") library("systemfit") pdata <- plm.data(Empdata,

I have been checking my package coda with the current R-devel using "R CMD check". As usual, this has uncovered a large number of errors and inconsistencies in my documentation, for which I am very grateful. The only problem I have is with code/documentation mismatches when I have written a method for a generic function, e.g. * checking for code/documentation mismatches ... WARNING

[Sent to R-help by mistake. Sorry] I have been checking my package coda with the current R-devel using "R CMD check". As usual, this has uncovered a large number of errors and inconsistencies in my documentation, for which I am very grateful. The only problem I have is with code/documentation mismatches when I have written a method for a generic function, e.g. * checking for