similar to: matching pairs in a Dataframe?

Hi everyone, I'm having some trouble with my ifelse statements. I'm trying to put 12 conditions within 3 groups. Here is the code I have so far: dat$cond <- ifelse(test = dat$cond == "cond1" | dat$cond == "cond2" | dat$cond == "cond3" dat$cond == "cond4" yes = "Uniform" no = ifelse(test =

Not a reproducible example, so a bit of guessing here, but a) don't try to assign results to variables inside the ifelse. That is, remove all the single-equals signs and "test" variables. If you really need to conditionally assign variables then use "if"... but chances are good you don't need that. b) "closure" is effectively another word

Dear R users, I have a matrix a and a classification vector b such that > str(a) num [1:50, 1:800000] and > str(b) Factor w/ 3 levels "cond1","cond2","cond3" I'd like to do an anova on all 800000 columns and record the F statistic for each test; I currently do this using f.stat.vec <- numeric(length(a[1,]) for (i in 1:length(a[1,]) { f.test.frame

Hi all, suppose there is a vector y with rownames: > y cond1 cond2 cond3 cond4 78.952 87.308 86.490 74.040 how can I easily plot this vector using the rownames? plot(y) gives me a plot with a x-axis from 1 to 4 in 0.5 steps, also plot(rownames(y), y) and plot(y ~ rownames(y) don't work. I know I can build a x-axis with axis(1, ...), but in this case I need a character string like

Hi: I have a large dataset mydata, of 1000 rows and 1000 columns. The rows have gene names and columns have condition names (cond1, cond2, cond3, etc). mydata<- read.table(file="c:/file1.mtx", header=TRUE, sep="") I applied PCA as follows: data_after_pca<- prcomp(mydata, retx=TRUE, center=TRUE, scale.=TRUE); Now i get 1000 PCs and i choose first three PCs and make a

Dear R users, I have a data where I desire to subset according to certain conditions. However, the script is very messy as there are about 30 distinct conditions. (i.e. same script but with different conditions) I would like to make a user defined function so that I can input the desired conditions and just get the results accordingly. Below is an arbitrary data set & sample statements

Hi Sebpop, Thanks for your explanation. I noticed that Polly would finally run the SROA pass to transform these load/store instructions into scalar operations. Is it possible to run such a pass before polly-dependence analysis? Star Tan At 2013-08-15 21:12:53,"Sebastian Pop" <sebpop at gmail.com> wrote: >Codeprepare and independent blocks are introducing these loads and

Codeprepare and independent blocks are introducing these loads and stores. These are prepasses that polly runs prior to building the dependence graph to transform scalar dependences into data dependences. Ether was working on eliminating the rewrite of scalar dependences. On Thu, Aug 15, 2013 at 5:32 AM, Star Tan <tanmx_star at yeah.net> wrote: > Hi all, > > I have investigated the

Dear Rxperts! I was wondering if it is possible to write a function which can take in argument of a subset or condition.. Of course, I am aware of the alternate methods like coplot, par.plot, xyplot etc... I am specifically interested in using conditions/subsets with "plot".. A simple fragmented example is shown here.. pltit <- function(y,x,dat,dat1,dat2,sbst) { plot(y~x, data=dat,

I do not think that running SROA before polly is a good idea: it would defeat the purpose of the code preparation passes that polly intentionally schedules for the data dependence analysis. If you remove the data references before polly runs, you would miss them in the dependence graph: that could lead to incorrect transforms. On Thu, Aug 15, 2013 at 7:28 PM, Star Tan <tanmx_star at

Hi all, I have investigated the 6X extra compile-time overhead when Polly compiles the simple nestedloop benchmark in LLVM-testsuite. (http://188.40.87.11:8000/db_default/v4/nts/31?compare_to=28&baseline=28). Preliminary results show that such compile-time overhead is resulted by the complicated polly-dependence analysis. However, the key seems to be the polly-prepare pass, which introduces

Hi, I have a general question on LoopUnswtich pass. Consider the following IR snippet: define i32 @test(i1 %cond) { br label %loop_begin loop_begin: br i1 %cond, label %loop_body, label %loop_exit loop_body: br label %do_something do_something: call void @some_func() noreturn nounwind br label %loop_begin loop_exit: ret i32 0 } declare void @some_func() noreturn After running

Dear R-Users, I would like to know whether it is possible to draw several stacked barplots (i.e. side by side on the same sheet)... my data look like : Cond1 Cond1' Cond2 Cond2' Compartment 1 11,81 2,05 12,49 0,70 Compartment 2 10,51 1,98 13,56 0,85 Compartment 3 1,95 0,63 2,81 0,22 Compartment 4 2,08 0,17

Hi friends, I have a matrix with following format. group var1 var2 .......varN c1 group1 1.2399 1.4990....-1.4829 c2 group4 0.8989 0.7849.....1.8933 ... ... c100 group10 ..... I want to draw a profile plot of each condition c1 to c100, which rows in above matrix and each line representing a row should be uniquely colored according to the group(1 to 10). I think this is simple task

Hello everyone, I am running a mixed effects model where I have two fixed factors, one with 2 levels and one with 4, and their interaction. Let's say these are my factors and their levels: FirstFactor: 1, 2 SecondFactor: A, B, C, D For the interaction, I am interested in the four two-way comparisons, not the two four-way comparisons. In other words, I want to test whether 1A is

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Estimada comunidad, tengo el siguiente problema: 1. tengo un data.frame con varias columnas, algunas numericas, otras son variables categoricas 2. necesito hacer un calculo simple sobre este data.frame (un promedio ponderado de los valores de la columna 7), pero solo para las filas que cumplan ciertos criterios 3. en ocasiones ninguna de las filas

I know how to convert a simple dataframe from wide to long format with one varying factor. However, for a dataset with two factors like the following, Subj T1_Cond1 T1_Cond2 T2_Cond1 T2_Cond2 1 0.125869 4.108232 1.099392 5.556614 2 1.427940 2.170026 0.120748 1.176353 How to elegantly convert to a long form as Subj Time Cond Value 1 1 1 0.125869 1

Hello everyone, I've conducted a Type III repeated-measures ANOVA using Anova() from the car package, based on the suggestions at http://blog.gribblelab.org/2009/03/09/repeated-measures-anova-using-r/(option 3) and http://languagescience.umd.edu/wiki/EEG#ERP_ANOVA_in_R. My ANOVA has two factors: Condition (3 levels) and Region (6 levels) and their interaction. Below is code to run the Anova

I've developed a working back-end for a custom architecture, based on LLVM 2.6. I'm now trying to cover more of the unique features of this architecture. To make use of one such feature, I'm trying something cunning/crazy with the stack - implementing it in a type of memory that can only be addressed via immediates. I've got this mostly working. However, I came across a problem

On Jan 17, 2010, at 2:56 AM, Mark Muir wrote: > I've developed a working back-end for a custom architecture, based on LLVM 2.6. I'm now trying to cover more of the unique features of this architecture. > > To make use of one such feature, I'm trying something cunning/crazy with the stack - implementing it in a type of memory that can only be addressed via immediates. >