similar to: Conditional merging in R & if then statement

I have a dataset w/ 184K obs & 16 variables. In SAS I proc sort nodupkey it in seconds by 11 variables. I tried to do the same thing in R using both the unique & then the !duplicated functions but it just hangs there & I get no output. Does anyone know how to solve this? This is how I tried to do it in R: detail3 <-

I am trying to fit a nonlinear model using nlm(). My application of nlm() is a bit complicated. Here is the story behind the model being fit: The observer is trying to detect a signal corrupted by noise. On each trial, the observer gets stim=signal+rnorm(). In the simulation below I have 500 trials. Each row of stim is a new trial. On each trial, if the cross-correlation between the stim and the

Dear all, I am having quite a hard time in trying to figure out how to correctly spell out a model in R (a repeated-measures anova with a within-subject covariate, I guess). Even though I have read in the posting guide that statistical advice may or may not get an answer on this list, I decided to try it anyway, hoping not to incur in somebody's ire for misusing the tool. For the sake of

Hi I'm reading a CSV file using read.table, and it keeps importing a text variable as a factor. To overcome this, I've used the as.is command referring to the variable in question (called "stim") data<-read.table(file.choose(), header=T, sep=",", as.is = "stim") However, "stim" is still imported as a factor. I notice there are other read.table

Hi, I would have thought that these two constructions would produce the same result but they do not. Resp <- rbinom(10, 1, 0.5) Stim <- rep(0:1, 5) mm <- model.matrix(~ Stim) Xb <- mm %*% c(0, 1) ifelse(Resp, log(pnorm(Xb)), log(1 - pnorm(Xb))) pnorm(as.vector(Xb), lower.tail = Resp, log.p = TRUE) > ifelse(Resp, log(pnorm(Xb)), log(1 - pnorm(Xb))) [1] -0.6931472 -1.8410216

Hello, I am trying to re-code all my programs from SAS into R. In SAS I use the following code: proc sort data=upper; by tdate stock_symbol expire strike; run; data upper1; set upper; by tdate stock_symbol expire strike; if first.expire then output; rename strike=astrike; run; on the following data set: tdate stock_symbol expiration strike 9/11/2012 C 9/16/2012

Hello, I have a dataframe w/ 3 variables of interest: transaction,date(tdate) & time(event_tim). How could I create a 4th variable (last_trans) that would flag the last transaction of the day for each day? In SAS I use: proc sort data=all6; by tdate event_tim; run; /*Create last transaction flag per day*/ data all6; set all6; by tdate event_tim; last_trans=last.tdate; Thanks

Hi I'm an R newbie. I can't seem to add new variables to data frames. Here are the stages (1) I import the data using read.csv. (2) I fix it using fix(data) (3) I create a new variable using spos<-tagPOS(stim,language="en",model=NULL,tagdict=NULL). (tagPOS is a function in the OpenNLP toolkit, which tags a string for part of speech. "stim" is a variable in the

Hi all .., I have a list of 7000 dataframes with similar column headers and I wanted to add a new column to each dataframe based on a certain condition which is the same for all dataframes. When I extract one dataframe and apply my code it works very well as follows :- First suppose this is my first dataframe in the list > OneDF <- Mylist[[1]] > OneDF ID Pdate

Hello all: I wrote a function: my.bastimeToSynoptic <- function(x) { f<-unlist(strsplit(as.character(x), " ")) hr<-unlist(strsplit(f[2], ":")) if(as.numeric(hr[1])<6) { synoptic<-"00" } else { synoptic<-as.integer(as.numeric(hr[1])/6)*6 } tdate<-paste(c(f[1],"

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Hola a tod en s, sigo intentando representar una variable en función de meses. En la columna Mesos tengo los meses de la siguiente manera: 01/08/1996, 01/09/1996 etc. He probado con el siguiente comando: plot(Mesos, Serie01) obteniendo y tendría que obtener: [image: Imágenes integradas 2] Pero donde pone Observation Index, me gustaria tener los meses (para esta grafica he usado

I have a 45 x 16 data frame consisting of dissimilarities among 10 colors, giving in each column the 45 = 10*9/2 pairwise judgments for one of 16 subjects. The rownames identify each pair of colors, e.g, "AC" = ("A","C"), and the pairs are ordered by columns in the lower triangle of each distance matrix. > helm.raw <-

> On Apr 15, 2018, at 4:08 AM, Allaisone 1 <Allaisone1 at hotmail.com> wrote: > > > Hi all .., > > > I have a list of 7000 dataframes with similar column headers and I wanted to add a new column to each dataframe based on a certain condition which is the same for all dataframes. > > > When I extract one dataframe and apply my code it works very well as

Hello, I have a matrix (class matrix) composed of GridCell (row and column). The matrix value is the beta diversity index value between two grids. Now I would like to get the average value of each GridCell. Please kindly advise how to make the calculation. Thank you. Elaine The matrix looks like (cited from Michael Friendly) I would like to get the average value of each color. Obs

Dear All, I´m using the following code: all1<-gg2[[1]][[1]]; for(i in 1:48){ all1 <- rbind(all1,gg2[[i]][[1]]) } to create a new matrix that contains all the matrices in a list called gg2. gg2 is a list that looks like >> gg2 [[1]] [[1]][[1]] <matrix one> [[2]] [[2]][[1]] <matrix two> . . . [[48]] [[48]][[1]] <matrix 48> Is there a faster way to do the rbind?

I'm attempting to create an array of treatment comparisons for modelling data generation. This involves comparison of one treatment (c2) with another (c3), relative to a common comparator (c1). Attached code gives me the correct array but need to remove duplicates. Duplicates relate only to c2 and c3 such that I need to remove r3 because c2 and c3 are same as r1 with c2 and c3 swapped r5

Dear R list, I would like to perform an ANOVA in a set of measurements, but I have problems formatting the data. The data is a two dimensional array containing two columns: - "Stim" : the type of stimulation (string) - "Ratio" : a ratio of two numeric values Now, because some values are missing in the data (defaulting to zero), part of this array will be populated with NA

I will have data in the following form: Time resp type stim type 300 a A 200 b A 155 a B 250 b B 80 c A 1000 d B ... c is left censored observation; d is right censored This sort of problem is discussed in Chap 9 of Cox & Oakes Analysis of Survival Data under the name

I am trying to replicate the SAS proc univariate in R. I got most of the stats I needed for a by grouping in a data frame using: all1 <- ddply(all,"ACT_NAME", summarise, mean=mean(COUNTS), sd=sd(COUNTS), q25=quantile(COUNTS,.25),median=quantile(COUNTS,.50), q75=quantile(COUNTS,.75), q90=quantile(COUNTS,.90), q95=quantile(COUNTS,.95), q99=quantile(COUNTS,.99) )