similar to: Reducing a matrix

I have a matrix, called data. I used the code below to rearrange the data such that the first column remains the same, but the y value falls under either columns 2, 3 or 4, depending on the value of z. If z=1 for example, then the value of y will fall under column 2, if z=2, the value of y falls under column 3, and so on. data x y z [1,] 50 13 1 [2,] 14 8 2 [3,] 3 7 3 [4,] 4 16 1 [5,] 6

Hi all, I'm extracting the name of the term in a regression model that dropterm specifies as the least significant one, and I'm assigning this name to an object. However, when I use update(), it ignores this object. Is there a way I can make it not ignore it? A reproducible example is below: > lm(x1~1+y1*y2+y3+y4,data=anscombe)->my.lm >

Hi R-experts, suppose I have a list with containing data frame elements: [[1]] (Intercept) y1 y2 y3 y4 -6.64 0.761 0.383 0.775 0.163 [[2]] (Intercept) y2 y3 -3.858 0.854 0.834 Now I want to put them into ONE dataframe like this: (Intercept) y1

Dear R-users; Previously I posted a question about the problem of rank deficiency in summary.manova. As somebody suggested, I'm attaching a small part of the data set. #*************************************************** "test" <- structure(.Data = list(structure(.Data = c(rep(1,3),rep(2,18),rep(3,10)), levels = c("1", "2", "3"), class =

RNG in R and Splus 3.4 Prof. Ripley asked the details of the example. We were doing parametric bootstrap, so it is similar to simulation. Anyway here is the details. We start with a sample of 19 positive numbers. We know the sample is from truncated exp(0.3)...only the truncation point, theta, is unknown. In other words, the sample can be generated from something like x1 <- rexp(100,

RNG in R and Splus 3.4 Prof. Ripley asked the details of the example. We were doing parametric bootstrap, so it is similar to simulation. Anyway here is the details. We start with a sample of 19 positive numbers. We know the sample is from truncated exp(0.3)...only the truncation point, theta, is unknown. In other words, the sample can be generated from something like x1 <- rexp(100,

Hello all! I want to estimate parameters in a MIMIC model. I have one latent variable (ksi), four reflexive indicators (y1, y2, y3 and y4) and four formative indicators (x1, x2, x3, x4). Is there a way to do it in R? I know there is the SEM library, but it seems not to be possible to specify formative indicators, that is, observed exogenous variables which causes the latent variable. Thanks,

Hi, I would like to create a plot of y1,y2,y3,y4 against x for several subjects such that y1 and y2 are plotted against x in one panel and y3 and y4 against x in another panel. Thus if there are 3 subjects I should end up with 6 panels. Is there a simple way of doing so (i.e. without calling xyplot() several times, and then padding the results together)?? Regards S?ren

Hi   I have four groups   y1=c(1.214,1.180,1.199) y2=c(1.614,1.710,1.867,1.479) y3=c(1.361,1.270,1.375,1.299) y4=c(1.459,1.335) Is there a function that can give me the length for each, like the made up example below?   >function(length(y1:y2) [1] 3 4 4 2 [[alternative HTML version deleted]]

Dear members, I want to create 8 graphs and write it into one page using mfrow=c(4,2). How to make all graphs (including the titles, legends, line types) to be scale down (resized proportionally). As an illustration, below is the code: pdf("testmfrow.pdf") par(mfrow=c(4,2)) x<-seq(1:10) y1<-rnorm(10) y2<-rnorm(10,mean=2,sd=1) y3<-rnorm(10,mean=3,sd=1)

Hi Yves lavaan looks like a very nice package. From the tutorial introduction I see you create path diagrams for some of the models you describe. How did you do this? I don't see a function for this in the package. I know there is a path.diagram function in the sem package that uses dot to draw the diagram, but I've always found the layouts from dot somewhat strange for path diagrams

Dear R- and Omega-list-members, I am trying to make statistical inference about the following design: A dependent variable y has been measured multiple times, i.e. 4 times (y1,y2, y3, y4), unfortunately suffering from some successive dropouts (i.e. the sample sizes varies for y1, y2, y3, and y4). For every y, two other variables (covariates) were also measured: x & z, and both do presumably

Dear list, I'm trying to do a set of generic functions do make contingency tables from data.frames. It is just running "nice" (I'm learning R), but I think it can be better. I would like to filter the data.frame, i.e, eliminate all not numeric variables. And I don't know how to make it: please, help me. Below one of the my functions ('er' is a mention to EasieR,

Hi After melt you can change levels of your factor variable. Again with the toy example. > levels(temp$variable) [1] "y1" "y2" "y3" "y4" > levels(temp$variable) <- levels(temp$variable)[c(2,4,1,3)] > levels(temp$variable) [1] "y2" "y4" "y1" "y3" > And you will get graphs with this new levels ordering.

I am relatively new to R and object oriented programming. I have relied on SAS for most of my data analysis. I teach an introductory undergraduate forecasting course using the Diebold text and I am considering using R in addition to SAS and Eviews in the course. I work primarily with univariate or multivariate time series data. I am having a great deal of difficulty understanding and working with

On Sat, 15 Jul 2017, Michael Reed via R-help wrote: > Dear All, > > I need some help arranging data that was imported. It would be helpful if you were to use dput to give us the sample data since you say you have already imported it. > The imported data frame looks something like this (the actual file is > huge, so this is example data) > > DF: > IDKey X1 Y1 X2 Y2

Dear R-users I have 5 dependent variables (y1 to y5) and one independent variable (x) and 3 conditioning variables (m, n, and 0). Each of the conditioning variables has 2 levels. I created 2*4 panel plots. xyplot(y1+y2+y3+y4+y5 ~ x | m*n*o,layout = c(4,2)) I would like to reorder the 8 panels. I tried to use index.cond (e.g., index.cond = list(c(1,3,2,4,5,7,6,8)) but it didn't work out.

Hello, I have a data frame with the following variables: ID, X1,X2,X3,X4,X5,Y1,Y2,Y3,Y4,Y5 and some other that do not matter, some of the X and Y can be missing (NA). I want to compute the slope of the linear regression Y ~ X for each subject, so using apply(DF,1,FUN,ra.rm=TRUE) now How do I define FUN? The X are different for each subject. Thanks for any help R.Heberto Ghezzo Ph.D. Montreal -

Complete newbie to R -- struggling with something which should be pretty basic. Trying to create a simple data set (which I gather R refers to as a data.frame). So > a <- c(1,2,3,4,5); > b <- c(0.3,0.4,0.5,0,6,0.7); Stick the two together into a data frame (call test) using cbind > test <- data.frame(cbind(a,b)) Seems to do the trick: > test a b 1 1 0.3 2 2 0.4 3 3

Petr, there was a thinko in your response. tmp <- data.frame(m=factor(letters[1:4]), n=1:4) tmp tmp$m <- factor(tmp$m, levels=c("c","b","a","d")) ## right tmp[order(tmp$m),] tmp <- data.frame(m=factor(letters[1:4]), n=1:4) levels(tmp$m) <- c("c","b","a","d") ## wrong tmp[order(tmp$m),] changing levels