similar to: more classes to one class in one dataset

Given four known and fixed vectors, x1,x2,x3,x4, I am trying to generate a fifth vector,z, with specified known and fixed partial correlations. How can I do this? In the past I have used the following (thanks to Greg Snow) to generate a fifth vector based on zero order correlations---however I'd like to modify it so that it can generate a fifth vector with specific partial

Dear Contributors I have a problem with the collection of data from the results of a test. I need to perform a comparative test over groups of data , recall the value of the pvalue and create a table. My problem is in the way to replicate the analysis over and over again over subsets of data according to a condition. I have this database, called y: gg t1 t2 d 40 1 1

I'm pasting below a working R file featuring a function I'd like to polish up. I'm teaching regression this semester and every time I come to something that is very difficult to explain in class, I try to simplify it by writing an R function (eventually into my package "rockchalk"). Students have a difficult time with predict and newdata objects, so right now I'm

I am trying to calculate the regression for the follwing input data stored in 'data.txt' file.I am reading this and storing it in the variable i .then i am trying to get the predicted value using f1 as dependent and others f2....f10 as independent variables.It is giving the following error. Also i want that i shoul get one predicted value for each row(y). What should i do. Please help me

Hi, I am having an issue when creating a postscript file from RGL window. It seems to cut off some of the axis labels. Here is the code I am using. I created a 3D plot using RGL_0.71 with R 2.5 on Windows XP. z1<-c(5,4,1,4.5,2,3,2,1,1) z2<-c(6,8,7,7.5,5,3.5,4,1,1) z3<-c(3,2,4,7,3,4.5,6,2,3) x1<-seq(1,9) x2<-seq(1,9) x3<-seq(10,18) y1<-seq(8,0) y2<--1*y1

Hi I am running a structural equation model with R using the sem command; am getting the following error: "Error in sem.default : The model has negative degrees of freedom = -4" My model is as follows: s_model = specify.model() x1->m1, b1,NA x2->m1, b2,NA x3->m2, b3,NA x4->m2, b4,NA x5->m2, b5,NA x6->m2, b6,NA m1->y, a1,NA m2->y, a2,NA m1<->m1, v1,NA

Hello all, Probably my concepts about the data.frame and matrix and array in R are not clear, I need some clarification to help me understand them better. >M <- read.table("test1.csv",sep=",",row.names=NULL,header=T) gives me: M as M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 1 9 11 14 15 18 20 20 20 20 20 2 3 4 8 9 11 12 14 15 15 15 3 4 5 8 8 9 9 9 9 9 9 4 4

Hi, Subject: Using R for Multiple Regression I am new to statistic but am interested in applying mathematical models to solve biological problems. I have used a linear model to generate the test data. When using this data I expect R to correctly identify the model but that does not seem to be the case. I am certain that I am doing something wrong but not able to figure it out. Model: Y = m1x1 +

Hello dear R users! I know this question is not strictly R-help, yet, maybe some of the guru's in statistics can help me out. I have a sample of data all from the same "population". Say my regression equation is now this: m1 <- lm(y ~ x1 + x2 + x3) I also regress on m2 <- lm(y ~ x1 + x2 + x3 + x4) The thing is, that I want to study the effect of

I would like to perform partial mantel tests on only within group values, with "between group" values assigned to NA. This is possible in package ncf partial.mantel.test, however this sues a different permutation to that used in ecodist.ecodist will not accept data with NA values, returning a "matrix is not square error. is it possible to perform this test in ecodist? many thanks

Hi, I have a data-matrix: > PID sato hrs fim health 214 3 4.376430 6.582958 5 193 6 4.361825 3.138525 6 8441 6 4.205771 3.835886 7 7525 6 4.284489 3.245139 6 6806 7 4.168926 2.821833 7 5682 7 1.788707 1.212653 7 5225 6 1.651463 1.436980 7 4845 6 1.692710 1.267359 4 4552 5 1.686448 1.220539 6

Dear All, I am new for R and SEM. I try to fit the model with Y (ordinal outcome), X (4 categorical data), M1-M3 (continuous), and 2 covariates (Age&sex) as a diagram. library(polycor) model.ly <-specify.model() 1: x -> m1, gam11, NA 2: x -> m2, gam12, NA 3: x -> m3, gam13, NA 4: age -> m1, gam14, NA 5: age -> m2, gam15, NA 6: age -> m3, gam16, NA 7: sex -> m1,

Hi all, I have a matrix simulating migration in a spatial model. I want to be able to define movement (the values of m1, m2 and m3) as only != 0 between adjacent patches and contingent on certain conditions as defined in the function. Here is the code: WET<-function(t) {everglades$precipitation[t]} #simply reads precipitation data from a csv, value is numeric AB<-function(WET,t)

> On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > Hi Bogdan, > Kinda messy, but: > > N <- data.frame(N=c("n1","n2","n3","n4")) > M <- data.frame(M=c("m1","m2","m3","m4","m5")) > C <-

Thank you David. Using xtabs operation simplifies the code very much, many thanks ;) On Tue, Jun 6, 2017 at 7:44 AM, David Winsemius <dwinsemius at comcast.net> wrote: > > > On Jun 6, 2017, at 4:01 AM, Jim Lemon <drjimlemon at gmail.com> wrote: > > > > Hi Bogdan, > > Kinda messy, but: > > > > N <-

Hello, My name is Ick Hoon Jin and I am Ph. D. student in Texas A & M Univ.. When I run the C embedded in R in the Linux system, I confront the following error after 6,000 iteration. By googling I found this error is from the problem in C. *** caught segfault *** address (nil), cause 'memory not mapped' My C code is following:

Simple matrix indexing suffices without any fancier functionality. ## First convert M and N to character vectors -- which they should have been in the first place! M <- sort(as.character(M[,1])) N <- sort(as.character(N[,1])) ## This could be a one-liner, but I'll split it up for clarity. res <-matrix(NA, length(M),length(N),dimnames = list(M,N)) res[as.matrix(C[,2:1])] <-

Here's another approach: N <- data.frame(N=c("n1","n2","n3","n4")) M <- data.frame(M=c("m1","m2","m3","m4","m5")) C <- data.frame(n=c("n1","n2","n3"), m=c("m1","m1","m3"), I=c(100,300,400)) # Rebuild the factors using M and N C$m <-

I have a question regarding significance testing for the difference in the ratio of means. The data consists of a control and a test group, each with and without treatment. I am interested in testing if the treatment has a significantly different effect (say, in terms of fold-activation) on the test group compared to the control. The form of the data with arbitrary n and not assuming equal

Saludos, tengo un problema que no puedo resolver dentro del R Estoy creando una funcion que a partir de un objeto compuesto por diferentes matrices como el que esta a continuacion: [[1]] M1 M2 M3 M4 sp1 2 0 1 8 sp2 4 5 2 4 sp3 0 0 4 0 sp4 5 7 5 0 sp5 0 4 0 0 [[2]] M3 M2 M4 M1 sp1 1 0 8 2 sp2 2 5 4 4 sp3 4 0 0 0 sp4 5 7 0 5 sp5 0 4 0 0 [[3]]