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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

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

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

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 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

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

I keep getting the same error in my survival analysis. I have access to a very large database but am just using small subsets to get some results. In this particular subset there is 50 explanatory variables(both factors of many levels and covariates) and 117 data pieces with some of the data being censored. I am using the stepAIC command to find my model. My initial model is built from all

Hi, I'm estimating a loglogistic aft (accelerated failure time) model, just a simple plain vanilla one (without time dependent covariates), I'm comparing the results that I obtain between aftreg (eha package) and survreg(surv package). If I don't use any covariate the results are identical , if I add covariates all the coefficients are the same until a precision of 10^4 or 10^-5 except

Dear Members of the Help List, Honestly, I feel a little bit stupid - I would like to do something rather simple: fit a non linear model to existing data, to be more precise I wanted to start with simple higher order polynomials. Unfortunately, I do not quite understand the examples in the helpfiles for the nlm, nls and nlsModel commands. Could anyone please provide a simple example to get me

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

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",

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,

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

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,

Anyone know how to get p-values for the t-values from the coefficients produced in vglm? Attached is the code and output ? see comment added to output to show where I need p-values + print(paste("********** Using VGAM function gamma2 **********")) + modl2<- vglm(MidPoint~Count,gamma2,data=modl.subset,trace=TRUE,crit="c") + print(coef(modl2,matrix=TRUE))

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.