similar to: questions on unstack()

Hi, I have three original curves as follows, n<-seq(20,200,by=10) t<-c(0.1138, 0.1639, 0.2051, 0.2473, 0.2890, 0.3304, 0.3827, 0.4075, 0.4618, 0.4944, 0.5209, 0.5562, 0.5935, 0.6197, 0.6523, 0.6771, 0.6984, 0.7209, 0.7453) es<-c(0.3682, 0.4268, 0.5585, 0.6095, 0.7023, 0.7534, 0.8225, 0.8471, 0.8964, 0.9098, 0.9371, 0.9514, 0.9685, 0.9747, 0.9812, 0.9859, 0.9905, 0.9923, 0.9940)

* * Hi , Can anyone help me please with this problem?* * *CASE-I* all_raw_data_NAomitted is my data frame.It has columns with names i1 ,i2, i3,i4…, till i15.It has 291 rows actually ,couldn’t show here. The data frame looks like this:-- i1 i2 i3 i4 i5 i6 i7 i8 i9 i10 i11 i12 i13 i14 i15 2 2 2 2 2 2 2 2 2 2 2 1 2 2 3 2 3 2 2 2 2 3 2 2 3 3

Hi, does the shapiro wilk test in R-1.3.0 work correctly? Maybe it does, but can anybody tell me why the following sample doesn't give "W = 1" and "p-value = 1": R> x<-1:9/10;x [1] 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 R> shapiro.test(qnorm(x)) Shapiro-Wilk normality test data: qnorm(x) W = 0.9925, p-value = 0.9986 I can't imagine a sample being

Okay, I deleted most of this thread, so I was waiting for another message to respond to, so unfortunately this will be out of place in the thread. This is in response to Miroslav's idea about variable block sizes. I may be a bit out of my league here as I'm just starting to look at how the actual encoding gets done. But it seems to me that you could make a decent guess about when

I have the R code at the end. The last command gives me "1 observation deleted due to missingness". I don't understand what this error message. Could somebody help me understand it and how to fix the problem? > summary(afit) Df Sum Sq Mean Sq F value Pr(>F) A 2 0.328 0.16382 0.1899 0.82727 B 3 2.882 0.96057 1.1136 0.34644 C

Dear R-devel, There seems to be a discrepancy in the order in which lm and rlm evaluate their arguments. This causes rlm to sometimes produce an error where lm is just fine. Here is a little script that illustrate the issue: > library(MASS) > ## create data > n <- 100 > dat <- data.frame(x=rep(c(-1,0,1), n), y=rnorm(3*n)) > > ## call lm, works fine > summary(lm(y ~

Hi Everyone, I am trying to understand the unstack() function but after struggling for two days, I have given up. More specifically, I am trying the exercises at the end of Chapter 1 of Data Analysis and Graphics Using R by Maindonald and Braun, 2nd ed. Exercise 18 (p. 41) asks to unstack the Rabbit data frame from the MASS package to get a certain data frame that is shown in the exercise.

Hi all, I'm having a problem restructuring my data the way I'd like it. I have data that look like this: Candidate.ID Specialty Office Score 110002 C London 47 110002 C East 48 110003 RM West 45 110003 RM

I've been reading the R mail archives and I've found a lot of messages with this same kind of problem, but I can't understand the answers. Can one of you try to explain this to me? Here's my example. Given a regression model and a variable, I want to use unstack() on the vector of residuals and make some magic with the result. But unstack hates me. PCSE <- function

On Thu, Oct 17, 2002 at 09:51:02AM -0500, Brady Patterson wrote: > > Okay, I deleted most of this thread, so I was waiting for another message to > respond to, so unfortunately this will be out of place in the thread. > > This is in response to Miroslav's idea about variable block sizes. I may be a > bit out of my league here as I'm just starting to look at how the

Dear All, I?m using R package ?msm? to fit a multi state model to infection history data (counts of infections per month upto diagnosis of a particular disease (sink state is state 11). The observed transitions are as follows: to from 1 2 3 4 5 6 7 8 10 11 1 35192 3806 899 233 46 11 3 0 1 534 2 3801 790 249 69 15

I am trying to show some techniques to my graduate regression class. The textbook mentioned using bootstrap samples of regression coefficients for assessing variability. I decided to show them reasonably effective ways of doing the resampling. The following is a function I wrote to create bootstrap samples of coefficients from a fitted linear regression model. bsCoefSample <- ##

--- Miroslav Lichvar <lichvarm@phoenix.inf.upol.cz> wrote: > On Thu, Oct 17, 2002 at 09:51:02AM -0500, Brady Patterson wrote: > > ... But it seems to me that you could make a decent guess > > about when something "new" happens based on the second derivative > of the signal > > (where the first derivative is the difference between a given > sample and the

I have been attempting to use cph models to get better calibration of my models for which I had originally used logistic regression. I tried running with 40 repetitions and got an error. I then tried 500 repetitions (thinking that the NaNs in the output below might be caused by that choice) and then let my computer crunch for several hours and got only the same error message and

Hello list, I've seen the recent discussions documenting problems with lm. I have encountered the following problem. I use WinXP Pro with service pack 1, and R 1.9.0, on a XEON 2GHz, with 1GB of RAM. > eff.fro std.dev mean NSTRDSP 7.403749e-01 1.215686e-01 CPFGEP 9.056763e+00 1.815686e+00 WSWOLF 4.703588e+05 1.112832e+05 NPILGRIM 1.017640e+06 2.134335e+05

Greetings, I am trying the get the standard errors of multiple comparisons using Tukey's HSD. These are not reported by the function TukeyHSD. When I apply the following code to the data, which I store as PROLE4.TXT, several unexpected things happen. First, the function TukeyHSD works for all the comparisons but the function simint doesn't. Second, after the application of na.omit