similar to: Question about which kind of plot to use

Dear Community, I have been using two types of survival programs to analyse a data set. The first one is an R function called aftreg. The second one an STATA function called streg. Both of them include the same analyisis with a weibull distribution. Yet, results are very different. Shouldn't the results be the same? Kind regards, J -- View this message in context:

Hello, I have the following data.frame structure(list(Study = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L,

Greetings, I'm learning R and I'm stuck on a basic concept: how to specify a logical condition once and then perform multiple transformations under that condition. The program below is simplified to demonstrate the goal. Its results are exactly what I want, but I would like to check the logical state of gender only once and create both (or any number of) scores at once.

Hi, all. I'd like to add legend on my graph but I can't. My code is follows. library(ggplot2) score1<-rnorm(100,0,5) score2<-rnorm(400,10,15) mydata<-data.frame(score1,score2) ggplot(mydata,aes(y=score2,x=score1))+geom_point()+stat_quantile(quantiles=c(0.50),col="red")+stat_quantile(quantiles=c(0.90),col="blue",size=2) I like to add legend indicating the

hi all - i'm having trouble using lme to specify a mixed effects model. i'm pretty sure this is quite easy for the experienced anova-er, which i unfortunately am not. i have a data frame with the following columns: col 1 : "Score1" (this is a continuous numeric measure between 0 and 1) col 2 : "Score2" (another continuous numeric measure, this time bounded between 0

I would like to know if you have any suggestions how to visualize the results from a paired t-test (see the example data below). I tried to produce plots that show the mean and CI's from the original data and the estimate of the difference between means and the confidence intervals (see below) from the t-test. I really don't know what would be the best way to graphically display the

Hello, It's an alternative to use SAS by function in R? I want to plot d histograms by plot.from example bellow: Thank you! plot d 1 1 16.3 2 1 25.0 3 1 57.8 4 1 17.0 5 2 10.8 13 2 96.4 17 3 76.0 18 3 32.0 19 3 11.0 20 3 11.0 24 3 106.0 25 3 12.5 21 4 19.3 22 4 12.0 26 4 15.0 27 5 99.3 32 7 11.0 36

Dear colleagues, I have a problem which has bitten me occasionally. I often need to prepare graphs for many variables in a data set, but seldom for all. or for any large number of sequential or sequentially named variables. Often I need several graphs for different subsets of the dataset for a given variable. I run into similar problems with other needs besides graphing. What I would like to

Dear all- I have a dataset (see a sample below - but the whole dataset is June 2005 - June 2008). The "LST" format is "YYMMDDHHmm" and I would like to get the hourly average of the "mph" for the summer months (spanning all years). I have been trying to use "aggregate" but am not having much success at all! any thoughts would be greatly appreciated.

Hi! I have 2 data.frames: "fish" and "popn": >fish xloc yloc id birth size weight energy gonad 20 15 15 54 -60 107.9 63.0 15952.9 8.0 21 15 15 32 -60 105.1 61.4 15538.8 7.8 91 4 43 96 -60 118.9 69.4 17573.2 8.8 71 32 4 64 -60 121.6 71.0 17976.0 9.0 34 2 64 20 -60 116.2 67.9 17173.0 8.6 95 6 20 58 -60 106.5

Dear friends, In S-PLUS, we can use the following argument, but not in R. mode12 <- gam(score1 ~ lo(latitude) + lo(longitude)) I searched the help in S-PLUS, it says lo() Allows the user to specify a Loess fit in a GAM formula, but i didn't find the correponding function in R. Anybody knows how to do the similar task in R? Thanks very much. -- With Kind Regards, oooO:::::::::

Hi, I am very new to R and I've been trying to work through the R book to gain a better idea of the code (which is also completely new to me). Initially I imputed my data from a text file and that seemed to work ok, but I'm trying to examine linear relationships between gdist and gair, gdist and gsub, m6dist and m6air, etc. This didn't work and I think it might have something to do

Dear useRs, i want to split a matrix having 1116rows and 12 columns. i want to split that matrix into 36 small matrices each having 12 columns and 31 rows. The big matrix should be splitted row wise. which means that the first small matrix should copy values which are in first 31 rows and 12 columns of the big matrix. similarly 2nd small matrix should contain values from 32nd to 63rd row of the

Dear R-Users, I have a big data set "mydata" with repeated observation and some missing values. It looks like the format below: userid sex item score1 score2 1 0 1 1 1 1 0 2 0 1 1 0 3 NA 1 1 0 4 1 0 2 1 1 0 1 2 1 2 NA 1 2 1 3 1

Suppose I have a data frame like "dat" below. For some context, this is the format that represents student's taking a computer adaptive test. first.item is the first item that student was administered and then score.1 is the student's response to that item and so forth. item.pool <- paste("item", 1:10, sep = "") set.seed(54321) dat <- data.frame(id =

  Hi r-users,   I would like to use gev and my data (annual rainfall ) is as follows:   > head(dat,20) A B C D E F G H I J 1 45.1 41.5 58.5 50.1 46.0 49.1 37.7 49.1 59.8 54.0 2 50.3 39.8 49.4 56.4 49.4 48.8 42.1 49.8 49.4 58.3 3 41.7 39.3 44.6 39.1 35.7 41.5 40.8 40.8 38.5 45.6 4 50.7 33.9 48.4 28.2 35.5 39.1 61.4 17.0 30.7 38.3 5 39.3 30.6 46.9 23.8 25.8

Dear All, I'm looking for?a way on computing the derivative of first and second order?of a smoothing curve produced by a nonprametric regression. For instance, if we run the R script below, a smooth nonparametric regression curve is produced. provide.data(trawl) Zone92?? <- (Year == 0 & Zone == 1) Position <- cbind(Longitude - 143, Latitude) dimnames(Position)[[2]][1] <-

Hi I'm hoping someone can help me I am a relative newbie to R. I have data that is in a similar format to this... Experiment Score1 Score2 X -0.85 -0.02 X -1.21 -0.02 X 1.05 0.09 Y -1.12 -0.07 Y -0.27 -0.07 Y -0.93 -0.08 Z 1.1 -0.03 Z 2.4 0.09 Z -1.0 0.09 Now I can easily have a look at the overall correlation of score 1 and 2 by doing this plot(data[,2], data[,3]) or fit <-

2017-12-09 12:54 GMT-08:00 Chris Lattner via llvm-dev < llvm-dev at lists.llvm.org>: > > > On Dec 8, 2017, at 5:01 PM, Mikhail Zolotukhin via llvm-dev < > llvm-dev at lists.llvm.org> wrote: > > Hi, > > I tweaked my scripts to avoid removing includes when it doesn't give any > significant benefits, which made the patches significantly smaller. This >

Hi, I want to fit some psychophysical data with cumulative gaussians. There is quite a convenient toolbox for matlab called 'psignifit' (formerly known as 'psychofit'). It allows the lower bound of the sigmoid to vary slightly from zero, aswell as the upper bound to vary from one. with these two free parameters, the fitted function is less sensitive to noisy data and outliers.