search for: dat0

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", "a1gpar", "a1pias", "a1devt") dat0[dat0$e1dq==-999.00000000,"e1dq"] <- NA...

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 intersection would yield 8000 rows, considering dat0 is a subset of TransTable. However, I get a neat little sur...

...tally 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 t1=4, following by a more marked downward trend after t1=5: Dat0 <- cbind(t1=1:10, x=c(1, 0, 0, 90, 99, 95, 90, 87, 80, 77)) library(mda) fit0 <- mars(Dat0[, 1, drop=FALSE], Dat0[, 2], penalty=.001) plot(Dat0, type="l") lines(Dat0[, 1], fit0$fitted.values, lty=2, col="red") Are there 'mars' options I...

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

...dd 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: dat0<-transform(dat,f=a/median(a)) dat1<-transform(dat0,x_new=x*f) How to do it through 1 step? Many thanks! [[alternative HTML version deleted]]

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...

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

...o a great extend. However, one of my response variables is survival of my plants, which is a binary variable (alive = 1; dead = 0). To analyze this case, I added family = "binomial" to the command line: fit.lme4 <- lmer(binary.response~soiltype*habitat+(1|destination)+(1|origin), Dat0, family = "binomial") > anova(fit.lme4) Analysis of Variance Table Df Sum Sq Mean Sq Denom F value Pr(>F) soiltype 1 0.029 0.029 32.000 0.0238 0.8784 habitat 1 0.029 0.029 32.000 0.0238 0.8784 soiltype:habitat 1 0.062 0.062 32...

...1:.N] dat2=melt(dat,id.vars="jugador") setkey(dat2,jugador) dat2[,index:=min(which(!is.na(value))),by=jugador] dat3 <- dat2[,list(First_month_Olivier =variable[index[1]],Value_First_month_Olivier =value[index[1]]),by=jugador] setkey(x = dat, jugador) dat0 <- merge( x = dat, y = dat3, all.x = TRUE, all.y = FALSE) }, times = N, unit = "s") tabla %>% print beepr::beep(3) # Unit: seconds # expr min lq mean median uq max neval # JVG 0.6479002 0.7518933 0.8673007 0.8216553 0.9137195...

...,id.vars="jugador") setkey(dat2,jugador) dat2[,index:=min(which(!is.na(value))),by=jugador] dat3 <- dat2[,list(First_month_Olivier =variable[index[1]],Value_First_month_Olivier =value[index[1]]),by=jugador] setkey(x = dat, jugador) dat0 <- merge( x = dat, y = dat3, all.x = TRUE, all.y = FALSE) }, Adolfo = { dat <- data.table( Uno = sample( c(runif(numero) , rep(NA , numero /2e0 )) , size = numero ) , dos = sample( c(runif(numero) , rep(NA , numero /1e1 )) , size = numero...

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

...pected things happen. First, the function TukeyHSD works for all the comparisons but the function simint doesn't. Second, after the application of na.omit def2$EL and def4$EL report factor values which have been omitted. Please inform me what I am doing wrong. Thank you very much. Peter B. dat0 <- read.table("prole4.txt",header=T) nrow(dat0) attach(dat0) dat <- data.frame(PRO,GRA,SOL,CEN,LAC,factor(NP),factor(TP),factor(SC),factor(PD),factor(EL)) names(dat) <- c("PRO","GRA","SOL","CEN","LAC","NP","TP&quot...

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