similar to: post hoc in repeated measures of anova

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

Hi everyone, I'm sorry if this turns out to be more a statistical question than one specifically about R - but would greatly appreciate your advice anyway. I've been using a logistic regression model to look at the relationship between a binary outcome (say, the odds of picking n white balls from a bag containing m balls in total) and a variety of other binary parameters:

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

Hi everybody, I'm trying to fit a Garch(1,1) process to the DAX returns. My data consists of about 2300 10day-logreturns in chronologically descending order (see attachment). But if I use the garch function I get a very high alpha_1 and a quite low beta, which doesn't make that much sense. I think I am missing something, but have no idea what it might be. I'd appreciate it a lot

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",

Hi, I am having a problem figuring out how to extract a subset of rows. I have a list with 68 similar data.frames. Each data.frame is 500 rows by 5 columns. I want to take one row from each data.frame based upon the data in a particular column (i.e. it matches a symbol). For example: > str(database) List of 68 $ X2011.01.11:'data.frame': 500 obs. of 5 variables: ..$ Symbol

Dear list members, I am trying to estimate parameters of the AR(1)-GARCH(1,1) model. I have one additional dummy variable for the AR(1) part. First I wanted to do it using garchFit function (everything would be then estimated in one step) however in the fGarch library I didn't find a way to include an additional variable. That would be the formula but, as said, I think it is impossible to add

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

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

Dear all, I would like to use the residuals in a general linear mixed effect model to diagnose model fit. I know that the resid function has been implemented for linear mixed models but not yet for general linear mixed effects. Is there a way to get them out of lmer fit objects? I tried searching the r-help archive and found nothing. I tried and failed to replicate what (I guessed would be

Greetings Everybody: I generated a 1.2MB dta file based on the general social survey with Stata8 for linux. The file can be re-opened with Stata, but when I bring it into R, it says all the values are missing for most of the variables. This dataset is called "morgen.dta" and I dropped a copy online in case you are interested http://www.ku.edu/~pauljohn/R/morgen.dta looks like this

Hi Folks... Œbeen playing with this for a while, with no luck, so I¹m hoping someone knows it off the top of their head... Difficult to find this nuance in the archives, as so many msgs deal with read.csv! I¹m trying to read a data file with the following structure (a little piece of the actual data, they are actually csv just didn¹t paste with the commas): wavelength SampleA SampleB SampleC

Hello!   I estimate vector error correction model (vecm) model. I have only one cointegratio relationship. I write :   joh.vecm.rls <- cajorls(joh.vecm, r=1) The output estimation is : Call: lm(formula = substitute(form1), data = data.mat) Coefficients:                up.d            expl.d        upd.d           r.d      ect1      -1.34e-01   4.55e+02   6.91e+00   2.43e+03 constant 

Ok. I did some quality tests. First off; never do quality tests with ints. I had serious problems interpreting my results until it dawned on me that the signal differences were just 0 or 1. So, after a lot of scratching my head, these are done comparing the result from the _float versions (which is how both resamplers work internally anyway). What I did was this: Load speex_wb.wav as one

Dear all: I converted the columns (Baci, Meti, Fungii, Protozoai) into integers (using excel) and then imported the data (.txt) into R. Interestingly, the other three variables were loaded as INT, but the 'Baci' one continued as Num. I imported the data using the following command line: X <- read.delim(file.choose(), header = TRUE, dec =

Hi, I downloaded both gee and geepack, and I am trying to understand the differences between the two libraries. I used the same data and estimated the same model, with a correlation structure autoregressive of order 1. Surprisingly for me, I found very different results. Coefficients are slightly different in value but sometimes opposite in sign. Moreover, the estimate of rho (correlation

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

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 all, I try to represent a multiple curve graphic where the x-axis is the temperature and the different y-axes are the different X (X22,X43,X44...) some X corresponds to the same molecule (22 and 44 are for CO2 for instance) so I use the same colour for them. I wanna mix the linetype with the colour to be able to visually see the difference between X43 and X45 The best I have done up to now

Hi all, how can I divide two tables of the same dimension so that all names are preserved, ie do not become NA? I have "tab1" and "tab2", each having names in the first column. I want "tab3" with the same names and values "tab1/tab2". Thanks, Serguei