similar to: Merge problem

Hi, I am trying to write a loop to recode my data from -999 to NA in R. What's the most efficient way to do this? Below is what I'm presently doing, which is inefficient. Thanks, Chris dat0 <- read.table("time1.dat") colnames(dat0) <- c("e1dq", "e1arcp", "e1dev", "s1prcp", "s1nrcp", "s1ints",

I'm trying to use mars{mda} to model functions that look fairly close to a sequence of straight line segments. Unfortunately, 'mars' seems to totally miss the obvious places for the knots in the apparent first order spline model, and I wonder if someone can suggest a better way to do this. The following example consists of a slight downward trend followed by a jump up after

A data set(dat),has 2 variables: x and a, and 100 rows. I wanna add 2 variables,and call the new data set dat1: var1:f = a/median(a) var2:x_new = x*f My solution: dat1<-transform(dat,f = a/median(a),x_new = x*f) But gets error reply which says that "f" is not exits since dat has no variables called "f". So I have to do through 2 steps:

Hi, I am trying to merge data similar to the example data below > dat0 id var1 var2 var3 2 1 0 1 3 1 0 1 4 0 1 1 5 0 1 1 > dat1 id var4 var5 var6 2 1 0 1 3 1 0 1 6 0 1 1 7 0 1 1 > dat2 id

Dear All, I've been searching relevant topics about replacing values, none seemed to be applicable to me... I have a file with many many varieties, and want to replace some of them into different names. I tried various of ways, still don't know how to do that most efficiently.. Here is part of the example data: Gen Rep A_1 1 A_1 2 A_2 1 A_2 2 B_1 1 B_1

Is there an easy way to change to color of the :highlight effect? :complete => visual_effect(:highlight, "targetid", :duration => 1) thanks -- Posted via http://www.ruby-forum.com/.

I have a longitudinal competing risk data of the form: ID COMPL SEX HEREDITY 1 0 1 2 1 0 1 2 1 3 1 2 2 0 0 1 2 1 0 1 2 2 0 1 2 2 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 0 0 1 3 2 0 1 4 0 1 2 4 0 1

Hi, I am wondering if anyone can propose a simple/best way to do the following: Let's say I have a data frame dat <- cbind(expand.grid(mode=c('right','left'),time=0:3,id=c('p1','p2','p3')),y=c(3,5,rep(4,6),6,2,rep(3,6),4,4,rep(2,6))) dat mode time id y 1 right 0 p1 3 2 left 0 p1 5 3 right 1 p1 4 4 left 1 p1 4 5 right 2

In the first line, use the dist function, found in library mva, to get the distance between each pair of rows. From this calculate an incidence matrix for which element i,j is true if row i in dat equals row j in dat (and false elsewhere). In the second line, for each row calculate the indices of the matching rows and take the minimum of those as the key. incid <-

From: Hank Janssen <hjanssen at microsoft.com> Convert camel case struct fields in vstorage.h to lowercase Signed-off-by: Abhishek Kane <v-abkane at microsoft.com> Signed-off-by: Haiyang Zhang <haiyangz at microsoft.com> Signed-off-by: Hank Janssen <hjanssen at microsoft.com> --- drivers/staging/hv/storvsc.c | 133 +++++++++++++++++++++--------------------

From: Hank Janssen <hjanssen at microsoft.com> Convert camel case struct fields in vstorage.h to lowercase Signed-off-by: Abhishek Kane <v-abkane at microsoft.com> Signed-off-by: Haiyang Zhang <haiyangz at microsoft.com> Signed-off-by: Hank Janssen <hjanssen at microsoft.com> --- drivers/staging/hv/storvsc.c | 133 +++++++++++++++++++++--------------------

Hi I'm doing a regression analysis with dummy variables and I'm getting NA for some coefficients. I've inspected residuals, leverage effects and Cook's distance and it seems ok. Can someone explains what can cause this problem ? Thanks EJ

Hi, For anyone who is interested; I created some basic scripts based on my current iSCSI block script. http://kinkrsoftware.nl/contrib/xen/block-netapp http://kinkrsoftware.nl/contrib/xen/netapp-lun.py Basically these two scripts allow you to start a vfiler with a qtree of customers. You log in the NetApp upon boot, and can use: netapp://customer1/disk1 It is work in progress... because I

Hi, For anyone who is interested; I created some basic scripts based on my current iSCSI block script. http://kinkrsoftware.nl/contrib/xen/block-netapp http://kinkrsoftware.nl/contrib/xen/netapp-lun.py Basically these two scripts allow you to start a vfiler with a qtree of customers. You log in the NetApp upon boot, and can use: netapp://customer1/disk1 It is work in progress... because I

Why are the results not reliable? ________________________________ From: ESCHEN Rene [mailto:rene.eschen@unifr.ch] Sent: Wednesday, August 23, 2006 3:48 AM To: Spencer Graves; r-help@stat.math.ethz.ch Cc: Doran, Harold Subject: RE: [R] Random structure of nested design in lme The output of the suggested lmer model looks very similar to the output of aov, also when I ran the model

Hola a todos, me ha gustado mucho la solución de Carlos, muy eficiente y muy ingeniosa al utilizar la funcion col() que o no la conocia o no me acordaba de ella. La parte mas "lenta" sigue siendo el apply que en el fondo no es mas que un ciclo for a traves de las filas, asi que inspirado por el metodo de Carlos pense que podria ser mas rapido si iteramos a traves de las columnas por lo

Hello all, So I am having a puzzling problem. I am working with a 534 x 1065 data frame entitled LocalMaxExpBlue.COR which is completely full of logical values "TRUE" and "FALSE." However, when I attempt to manipulate this data frame, R does not recognize it as logical. Strangely enough, it will identify individual columns (but not rows) as logical. It will also

Por último, utilizando la indexación lineal de matriz que propusó luisfo en su momento: > t <- Sys.time() > M=as.matrix(dat) > index <- which(!is.na(M)) - 1 > meses<-colnames(M) > M2<- data.table(columna=index %/% nrow(M) +1L, jugador=index %% nrow(M) +1L , valor=M[index+1L]) > setkey(M2,jugador,columna) >

https://bugzilla.mindrot.org/show_bug.cgi?id=3733 Bug ID: 3733 Summary: "forced command options do not match" after key error Product: Portable OpenSSH Version: 9.8p1 Hardware: amd64 OS: Linux Status: NEW Severity: normal Priority: P5 Component: sshd Assignee:

Otra solución algo más rapida: > t <- Sys.time() > dat[,jugador:=1:.N] > dat2=melt(dat,id.vars="jugador") > setkey(dat2,jugador) > dat2[,index:=min(which(!is.na(value))),by=jugador] > dat2[,.(First_month=variable[index[1]],Value_First_month=value[index[1]]),by=jugador] jugador First_month Value_First_month 1: 1 Uno 0.93520715 2: