similar to: Question on summing rows within nested variable

Hi, Could someone help with coding this in R? I need to select one row per patient i in clinic j. The data is organized similar to that shown below. Two columns - patient i in column j identify each unique patient. There are two columns on outcome. Some patients have multiple rows with each row representing one visit, coded for in the column, visit. Some patients have just one row indicating

Hello R users, I've used the following help two compare two regression line slopes. Wanted to test if they differ significantly: Hi, I've made a research about how to compare two regression line slopes (of y versus x for 2 groups, "group" being a factor ) using R. I knew the method based on the following statement : t = (b1 - b2) / sb1,b2 where b1 and b2 are the two slope

Hi all, I need to test the significnce of difference between slopes of two regression lines and regression line with theoretical line. I try to use Slope.test function from emu package, but an error occured... library(emu) d1<-data.frame(P1=c(1,2,3,5,7,8,9,13,14,15), P2=c(1,2,5,8,11,13,15,15,18,24), R=c(2,7,8,9,16,21,27,31,33,36)) # First data set m1<-lm(R~P1+P2+P1*P2,data=d1) # Regr.

Hi, I've made a research about how to compare two regression line slopes (of y versus x for 2 groups, "group" being a factor ) using R. I knew the method based on the following statement : t = (b1 - b2) / sb1,b2 where b1 and b2 are the two slope coefficients and sb1,b2 the pooled standard error of the slope (b) which can be calculated in R this way: > df1 <-

Hello, I've written a simple (although probably overly roundabout) function to test whether two regression slope coefficients from two linear models on independent data sets are significantly different. I'm a bit concerned, because when I test it on simulated data with different sample sizes and variances, the function seems to be extremely sensitive both of these. I am wondering if

Dear List, I used rlm to calculate two regression models for two data sets (rlm due to two outlying values in one of the data sets). Now I want to compare the two regression slopes. I came across some R-code of Spencer Graves in reply to a similar problem: http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg06666.html The code was: > df1 <- data.frame(x=1:10, y=1:10+rnorm(10))

Hello, I have spent considerable time trying to figure out that which I am about to describe. This included searching Help, consulting my various R books, and trail and (always) error. I have been assuming I would need to use a loop (looping over columns) but perhaps and apply function would do the trick. I have unsuccessfully tried both. A scaled down version of my situation is as follows:

I would suggest the method of Sokal and Rholf (1995) S. 498, using the F test. Below I repeat the analysis by Spencer Graves: Spencer: > df1 <- data.frame(x=1:3, y=1:3+rnorm(3)) > df2 <- data.frame(x=1:3, y=1:3+rnorm(3)) > fit1 <- lm(y~x, df1) > s1 <- summary(fit1)$coefficients > fit2 <- lm(y~x, df2) > s2 <- summary(fit2)$coefficients > db <-

Hi, I'm trying to compare two linear regressions. I'm using the following approach: ################## xx<-1:100 df1 <- data.frame(x = xx, y = xx * 2 + 30 + rnorm(n=length(xx),sd=10), g = 1) df2 <- data.frame(x = xx, y = xx * 4 + 9 + rnorm(n=length(xx),sd=10), g = 2) dta <- rbind(df1, df2) dta$g <- factor(dta$g)

Philip: I think there are two separate issues: 1) Handling GC pointers stored in stack locations -- tracking their liveness, reporting them to the GC. etc. 2) Treating pointers to stack locations as GC-pointers. I think the two options that you presented here are for solving (1). I'm trying to solve issue (2) using alloca in addrspace(1). Basically, we want to create a pointer to a stack

Currently, the alloca instruction always allocates in the generic address space (0). This proposal is to extend the semantics of alloca instruction to allow allocation in any specified address space. Proposed Syntax <result> = alloca [inalloca] <type> [, <ty> <NumElements>] [, align <alignment>] [, addrspace <num>] ; yields type addrspace(num)*:result

Dear R helpers, I know R language at a preliminary level. This is my first post to this R forum. I have recently learned the use of function and have been successful in writing few on my own. However I am not able to figure out how to apply the function to multiple sets of data. # MY QUERY Suppose I am having following data.frame df = data.frame(k = c(1:8), ratings = c("A",

> On Jul 9, 2015, at 3:33 PM, Swaroop Sridhar <Swaroop.Sridhar at microsoft.com> wrote: > > Regarding Call-site size specification: > > CoreCLR (https://github.com/dotnet/coreclr <https://github.com/dotnet/coreclr>) requires the size of the Call-instruction to be reported in the GCInfo encoding. > > The runtime performs querries for StackMap records using

Hello R user, I have several data frames with >100 columns and I did a linear regression over time of each column df1.lm <- lapply(df1, function(x) lm(x~year)$coeff[2]) that worked fine and I get slope of each column oder time - until I divided df1 by df2 df3 <- df1/df2 > df3.lm <- lapply(df3, function(x) lm(x~year)$coeff[2]) Error in lm.fit(x, y, offset = offset, ...) :

Please see inline. From: Chandler Carruth [mailto:chandlerc at google.com] Sent: Wednesday, August 26, 2015 7:03 PM To: Swaroop Sridhar <Swaroop.Sridhar at microsoft.com>; llvm-dev <llvm-dev at lists.llvm.org>; Philip Reames <listmail at philipreames.com>; Sanjoy Das <sanjoy at playingwithpointers.com> Subject: Re: [llvm-dev] RFC: alloca -- specify address space for

I've written a simple function to draw a regression line in a plot and annotate the line showing the slope with a label. It works, as I'm using it, when the horizontal variable is 'x', but gives incorrect results otherwise. What's wrong? # simple function to show the slope of a line show.beta <- function(model, x="x", x1, x2, label, col="black", ...)

Hi there, I am stuck trying to solve what should be a fairly easy problem. I have a data frame that essentially consists of (ID, time as seqMonth, variable, value) and i want to find the regression coefficient of value vs time for each combination of ID and Variable. I have tried several approaches and none of them seems to work as i expected. For example, i have tried:

Inline. From: Philip Reames [mailto:listmail at philipreames.com] Sent: Thursday, August 27, 2015 11:01 AM To: Swaroop Sridhar <Swaroop.Sridhar at microsoft.com>; llvm-dev <llvm-dev at lists.llvm.org>; Sanjoy Das <sanjoy at playingwithpointers.com> Cc: Joseph Tremoulet <jotrem at microsoft.com>; Andy Ayers <andya at microsoft.com>; Russell Hadley <rhadley at

> -----Original Message----- > From: Philip Reames [mailto:listmail at philipreames.com] > Sent: Friday, August 28, 2015 9:38 AM > To: Swaroop Sridhar <Swaroop.Sridhar at microsoft.com>; llvm-dev <llvm- > dev at lists.llvm.org>; Sanjoy Das <sanjoy at playingwithpointers.com> > Cc: Joseph Tremoulet <jotrem at microsoft.com>; Andy Ayers > <andya at

On 08/26/2015 07:02 PM, Chandler Carruth wrote: > On Wed, Aug 26, 2015 at 6:46 PM Swaroop Sridhar via llvm-dev > <llvm-dev at lists.llvm.org <mailto:llvm-dev at lists.llvm.org>> wrote: > > Currently, the alloca instruction always allocates in the generic > address space (0). > > This proposal is to extend the semantics of alloca instruction to >