similar to: Error in stepAIC function using a survival model

----- Forwarded message from Petros Petrou <ppetrou@redhat.com> ----- Date: Mon, 20 Apr 2020 09:29:37 +0100 From: Petros Petrou To: Richard Jones Subject: Guestfish Ansible Modules using Python Bindings Hi Richard, I have been experimenting with guestfish and rhel qcow2 cloud images the last few months. I was challenged in a recent assignment on how to customize the RHEL 8 qcow image

Hallo, I have this dataset with repeated measures. There are two within-subject factors, "formant" (2 levels: 1 and 2) and "f2 Ref" (25 levels: 670, 729, 788, 846, 905, 1080, 1100, 1120, 1140, 1170, 1480, 1470, 1450, 1440, 1430, 1890, 1840, 1790, 1740, 1690, 2290, 2210, 2120, 2040, 1950), and one between-subject factor, lang (2 levels:1 and 2). The response variable

Dear ReXperts, I have the below text file output. I need to extract the T, QC, QO, QO-QC and WT columns for the data between T = 10 and T=150. Any ideas? Thanks in advance. ======================================================================================== 1 D C ---CAT-- T THETA QC QO QO-QC QC/QO WT FSD 8 1 0 1.0000E+01

The "merge" command is creating duplicate column names in a dataframe that is the result of the merge. The following is the 'merge' command: x <- merge(invType , allocSlots , by.x = 'index' , by.y = 'indx' , all.x = TRUE ) The 'invType' dataframe was the result of a previous merge and has the following column names that are

Hello, We use drc to fit dose-response curves, recently we discovered that there are quite different standard error values returned for the same dataset depending on the drc-version / R-version that was used (not clear which factor is important) On R 2.9.0 using drc_1.6-3 we get an IC50 of 1.27447 and a standard error on the IC50 of 0.43540 Whereas on R 2.7.0 using drc_1.4-2 the IC50 is

dear R users, the data frame (read in from a csv) looks like this: TreeTag Census Stage DBH 1 CW-W740 2001 juvenile 5.8 2 CW-W739 2001 juvenile 4.3 3 CW-W738 2001 juvenile 4.7 4 CW-W737 2001 juvenile 5.4 5 CW-W736 2001 juvenile 7.4 6 CW-W735 2001 juvenile 5.4 ... 1501 1.00E-20 2001 adult 32.5 i would like to

Hi Everyone, I'm wondering if anyone here write AGI's in compiled binaries. I'm writing a small Cepstral AGI in Freepascal/Lazarus. I know there are some other AGI's out there, but I wanted to add some more functionality than what is available such as having the AGI determine if the "data" argument is plain text or a path to a text file and act accordingly. The

Dear R-list users, I'm new to principal components and factor analysis. I thought this method can be very useful for me to find relationships between several variables (which I know there is, only don't know which variables exactly and what kind of relation), so as a structure detection method. Now, I'm experimenting with the function prcomp from the mva package. In my source code

Hi everyone, I have carried out a multiple imputation in R using Amelia II and have created 5 multiply imputed datasets. The purpose of my research is to fit a Poisson Model to the data to estimate numbers of hospital admissions. Now that I have 5 completed datasets and I have to pool all the 5 datasets to get one combined output for a poisson model. I have checked previous queries about

Hi Eric, you can use an annotate-layer, eg ind<-which(sig>0,arr.ind = T) ggplot(m1.melted, aes(x = Month, y = Site, fill = Concentration), autoscale = FALSE, zmin = -1 * zmax1, zmax = zmax1) + geom_tile() + coord_equal() + scale_fill_gradient2(low = "darkred", mid = "white", high = "darkblue",

A couple of suggestions: 1. This screams out for do.call. Try jj <- do.call("rbind", t1). 2. Use rowSums() instead of apply(..., 1, sum). Andy _____ From: r-help-bounces at stat.math.ethz.ch on behalf of Mike Nielsen Sent: Sat 7/8/2006 7:20 PM To: r-help at stat.math.ethz.ch Subject: Re: [R] Combining a list of similar dataframes into a single dataframe [Broadcast] Well,

Hi I have found a little problem with an R script. I am trying to merge some data and am finding something unusual going on. As shown below I am trying to assign (MatchedValues[Value2,Value]) to (ClusteredData[k,Value]) which are two separate dataframes. 1) By the following command you can see that the value im transferring is 481844.03 > MatchedValues[Value2,Value] [1] 481844.03 6618

I would be very grateful to anyone who could point to the error of my ways in the following. I have a dataframe called net1, as such: > str(net1) `data.frame': 114192 obs. of 9 variables: $ server : Factor w/ 122 levels "AB93-99","AMP93-1",..: 1 1 1 1 1 1 1 1 1 1 ... $ ts :'POSIXct', format: chr "2006-06-30 12:31:44"

Using the small reproducible example below, I'd like to know if one can somehow use the matrix "sig" (defined below) to add a black outline (with lwd=2) to all pixels with a corresponding value of 1 in the matrix 'sig'? So for example, in the ggplot2 plot below, the pixel located at [1,3] would be outlined by a black square since the value at sig[1,3] == 1. This is my first

Problem: I am sorting through model selection process for first time and want to make sure that I have used glm, stepAIC, and update correctly. Something is strange because I get a different result between: 1) a glm of 12 predictor variables followed by a stepAIC where all interactions are considered and then an update to remove one specific interaction. vs. 2) entering all the terms