similar to: New var

Displaying 20 results from an estimated 10000 matches similar to: "New var"

2017 Jun 03
0
New var
Ii is difficult to provide useful help, because you have failed to read and follow the posting guide. In particular: 1. Plain text, not HTML. 2. Use dput() or provide code to create your example. Text printouts such as that which you gave require some work to wrangle into into an example that we can test. Specifically: 3. Have you gone through any R tutorials?-- it sure doesn't look like
2017 Jun 03
2
New var
Thank you all for the useful suggestion. I did some of my homework. library(data.table) DFM <- read.table(header=TRUE, text='obs start end 1 2/1/2015 1/1/2017 2 4/11/2010 1/1/2011 3 1/4/2006 5/3/2007 4 10/1/2007 1/1/2008 5 6/1/2011 1/1/2012 6 10/5/2004 12/1/2004',stringsAsFactors = FALSE) DFM DFM$D =as.numeric(difftime(as.Date(DFM$end,format="%m/%d/%Y"),
2017 Jun 04
2
New var
Thank you Jeff and All, Within a given time period (say 700 days, from the start day), I am expecting measurements taken at each time interval;. In this case "0" means measurement taken, "1" not taken (stopped or opted out and " -1" don't consider that time period for that individual. This will be compared with the actual measurements taken (Observed-
2017 Jun 04
0
New var
# read.table is NOT part of the data.table package #library(data.table) DFM <- read.table( text= 'obs start end 1 2/1/2015 1/1/2017 2 4/11/2010 1/1/2011 3 1/4/2006 5/3/2007 4 10/1/2007 1/1/2008 5 6/1/2011 1/1/2012 6 10/5/2004 12/1/2004 ',header = TRUE, stringsAsFactors = FALSE) # cleaner way to compute D DFM$start <- as.Date( DFM$start, format="%m/%d/%Y" ) DFM$end
2017 Jun 04
0
New var
Since the number of choices is small (6), how about this? Starting with Jeff's initial DFM: DFM <- structure(list(obs = 1:6, start = structure(c(16467, 14710, 13152, 13787, 15126, 12696), class = "Date"), end = structure(c(17167, 14975, 13636, 13879, 15340, 12753), class = "Date"), D = c(700, 265, 484, 92, 214, 57), bin = structure(c(6L, 3L, 5L, 1L, 3L, 1L), .Label
2010 Jun 02
1
compute the associate vector of distances between leaves in a binary non-rooted tree
Hello. I'd like to compute the associate vector of distances between leaves in a binary non-rooted tree. The definition of a distance between two leaves in a binary non-rooted tree is the number of edges in the path joining the two leaves. I've tried the ape package but I'm unable to find this vector. For example, using rtree(5,rooted=F) I've obtained the following tree: $edge
2010 Mar 05
2
Selecting rows of a matrix based on some condition on the columns
The data set consists of two sets of matrices, as labelled by the columns, T's and C's. > xy x T1 T2 T3 T4 T5 C1 C2 C3 C4 C5 [1,] 50 0.00 0.00 33.75 0.00 0.00 0.00 36.76 0.00 35.26 0.00 [2,] 13 34.41 0.00 0.00 36.64 32.86 34.11 35.80 37.74 0.00 0.00 [3,] 14 35.85 0.00 33.88 36.68 34.88 34.58 0.00 32.75 37.45 0.00 [4,] 33 34.56
2004 Jul 07
3
KalmanSmooth problem
Hello, In R I am trying to use Kalman filtering to find a solution for an hydrological problem. With Kalman Filtering I want to estimate the discharge comming from three storage bassins. I have programmed a function in R which can run KalmanSmooth. When I'm asking for the function and putting in values, R detects the following error: "Error in as.vector(data) : Argument "S1" is
2009 Nov 09
1
Models
Hi all, I hope that there might be some statistician out there to help me for a possible explanation for the following simple question. Y1~ lm(y~ t1 + t2 + t3 + t4 + t5,data=temp) # oridnary linear model library(gam) Y2~ gam(y~ lo(t1) +lo(t2) +lo(t3) +lo(t4) +lo(t5),data=temp) # additive model In the first model t1, t2 and t3 found to be significant,. However, in the second model (using
2015 Jun 03
3
[LLVMdev] [lld] TBSS wrong size
Hi, Yes, ldd is generating wrong tbss size. It is just considering one tbss section and not calculating all sections from all objects. The following example on x86_64 shows the issue: --- t0.c --- #include <stdio.h> extern __thread int t0; extern __thread int t1; extern __thread int t2; extern __thread int t3; __thread int t4; __thread int t5; __thread int t6; __thread int t7; int
2005 Jun 27
2
simplifying the code
dear list: I need help to achieve a simpler code to complete a task I'm performing. here is an example: dat<-expand.grid(a=seq(1,5),b=seq(1000,1005)) I want to add a new column dat$c in that: t1<-ifelse(dat$a==1&dat$b==1001,1001,0) t2<-ifelse(dat$a==2&dat$b==1002,1001,0) t3<-ifelse(dat$a==3&dat$b==1003,1001,0) t4<-ifelse(dat$a==1&dat$b==1002,1002,0)
2012 May 02
3
strange differences in vector operation versus manual calculation
Hi, I'm running a calculation in two ways. The first way is to employ vectors and evaluate a function in one go. The second way is to break down the function into pieces and combine the pieces to the final answer. Algebraically, they should give me the same result. But the final vector differs significantly. I must be missing something very obvious, but I just cannot see it xx <-
2007 Nov 01
2
computing the mean of a few variables
hello, I have a simple question: I want to compute the average of 6 variables, my data looks like this: t0 t1 t2 t3 t4 t5 1 32 34 36 40 38 40 2 35 37 38 36 33 39 . . . I want the mean of t0-t5 for each row (observation). thank you, sigalit. [[alternative HTML version deleted]]
2009 Mar 18
1
Reading a file line by line - separating lines VS separating columns
Hello all. I wish to read a large data set into R. My current issue is in getting the data so that R would be able to access it. Using read.table won't work since the data is over 1GB in size (and I am using windows XP), so my plan was to read the file chunk by chunk and each time move it into bigmemory (I'll play with that when the time will come, maybe ff is better ?!). I encountered
2011 Dec 08
1
optimize()
Hi! I have a difficulty in the use of function optimize(). Could you help me? I want to maximize this function: ##### Logaritmo da distribui??o condicional de alpha[i] lp_alphai <- function(alphai, i, beta, tau, N){ t1 <- (N[i+1] - N[i])*log(alphai) t2 <- - (N[i+1] - N[i])*alphai*log(beta[i]) t3 <- (alphai - 1)*sum(log(times[(N[i] + 1):N[i+1]])) t4 <- -
2012 Sep 17
1
How to filter information from a big .csv table into a new table
Hi, I have big .csv file. I would like to filter that file into a new table. For example, I have .csv file as below: f1 f2 f3 f4 f5 f6 f7 f9 f10 f11 t1 1 0 1 0 1 0 0 0 0 1 t2 1 0 0 0 0 1 1 1 1 1 t3 0 0 0 0 0 0 0 0 0 0 t4 1 0 0 0 1 0 0 0 0 0 t5 0 0 0 0 0 0 0 0 0 0 t6 0 0 0 0 0 0 0 0 0 0
2007 May 17
2
How to analyse simple study: Placebo-controlled (2 groups) repeated measurements (ANOVA, ANCOA???)
Hallo! I have two groups (placebo/verum), every subject is measured at 5 times, the first time t0 is the baseline measurement, t1 to t4 are the measurements after applying the medication (placebo or verum). The question is, if there is a significant difference in the two groups and how large the differnce is (95% confidence intervals). Let me give sample data # Data
2010 Jan 12
1
parsing protocol of states
Dear R-users, actually i try to parse some state protocols for my work. i an easy stetting the code below works fine, if states are reached only once. in harder settings it could be possible that one state gets visited more times. in this case for me its interesting to see how much waiting time lies between to states on the whole. by the way i didn't use R as a parsing tool so far, so
2007 Sep 20
1
help with making a function of scatter plot with multiple variables
Dear list, I have done a scatter plot of multiple variables in the same graph, with different col and pch. I managed to do it with the following code but not know how to make a function of these so that next time if I want to do similar graph but with new variables, I dont have to copy the code and then change the old variables with the new ones but just call a function with the new
2012 Sep 18
1
chunk row to new table/file
I have big .csv file. I would like to filter that file into a new table. For example, I have .csv file as below: f1 f2 f3 f4 f5 f6 f7 f9 f10 f11 t1 1 0 1 0 1 0 0 0 0 1 t2 1 0 0 0 0 1 1 1 1 1 t3 0 0 0 0 0 0 0 0 0 0 t4 1 0 0 0 1 0 0 0 0 0 t5 0 0 0 0 0 0 0 0 0 0 t6 0 0 0 0 0 0