similar to: Quick recode of -999 to NA in R

Can anybody give me a neat example of interval censored data analysis codes in R? Given that suvreg(Surv(c(1,1,NA,3),c(2,NA,2,3),type="interval2")~1) works why does survreg(Surv(data[,1],data[,2],type="interval2")~1) not work where data is : T.1 T.2 Status 1 0.0000000 0.62873036 1 2 0.0000000 2.07039068 1 3 0.0000000

Hello all, I have read as many merge issues as I possibly could tonight and although I presume this is a small error, I have not found the solution to my problem. I'm trying to merge two data sets: dat0 and TransTable. As you can see below, dat0 has 8000 rows, whereas TransTable has 47296 rows. I would expect when I merge the two data sets, with all.x=F, and all.y=F, that the

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

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

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 <-

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

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

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:

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) >

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:

I R-helpers #I have a data panel of thousands of firms, by year and industry and #one dummy variable that separates the firms in two categories: 1 if the firm have an auditor; 0 if not #and another variable the represents the firm dimension (total assets in thousand of euros) #I need to create two separated samples with the same number os firms where #one firm in the first have a corresponding

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

Greetings, I am trying the get the standard errors of multiple comparisons using Tukey's HSD. These are not reported by the function TukeyHSD. When I apply the following code to the data, which I store as PROLE4.TXT, several unexpected things happen. First, the function TukeyHSD works for all the comparisons but the function simint doesn't. Second, after the application of na.omit