similar to: Index (which) of last non-NA data in dataframe

Hello, This is not a problem with R, the calculated results are mathematically correct. This a matrix stability problem. Because of measuring errors, my matrix solution is a bit off. Here is what my equations look like: A11 x11+A12 x12 +A13 x13 = b1 A21 x21+A22 x21 +A23 x23 = b2 A31 x31+A32 x31 +A33 x33 = b3 A is a reading, X is a measured weight, and b is total. The 3 experiments give

Hello, I need to remove columns in a matrix. The number of columns varies from 0 to n. I can't figure out how to specify the zero case. aa <-matrix(runif(5^2),5,5) #remove column 3 aa[,-3] #remove no column aa[,-NULL] Error in -NULL : invalid argument to unary operator I know I could use ifelse, but it would complicate my model a lot. Is there a direct way to

Dear UseR I have a dataset, for instance x1(A, B, C), x2(M,F), x3(X1,X2,X3,X4) and x4(W,F,P). I want to make a small dataset with the random tuple. I know package e1071 can handle a random discrete, > library(e1071) > x1 <- rdiscrete(6, c(2,2,2), c("A","B","C")) > x2 <- rdiscrete(6, c(3,3), c("M","F")) > x3 <-

Hello all, I have an optimization routine that is giving me good results, but the results are not in the nice "model" format like "lm". How can I get optim results into a model so that I can use the clever 'fitted', 'residuals', and 'summary' functions? Using optim is the only way that I was able to make a model that 1) sums the betas to 1, 2)

Hi, Using S=1000 and simdata <- replicate(S, generate(3000)) #If you want both "m1" and "m0" #here the missing values are 0 res1<-sapply(seq_len(ncol(simdata.psm1)),function(i) {x1<-merge(simdata.psm0[,i],simdata.psm1[,i],all=TRUE); x1[is.na(x1)]<-0; x1}) res1[,997:1000] #????? [,1]???????? [,2]???????? [,3]???????? [,4]??????? #x1??? Numeric,3000 Numeric,3000

Hi: Suppose that I have a data frame as below x1 x2 x3 ... x10 wk1 wk2 ... Wk208 (these are the column names) For each record, x1, x2, x3 ... x10 are attributes. and wk1, wk2, ..., wk208 are the sales recoreded for this attribute combination. Suppose that now, that I want to do the following 1. Merge the data frame so that I have a new data frame grouped by values of x2 and x3 (for example).

Dear all, I'd appreciate any hints how to arrange some plots. I have three plots. I would like to arrange them in the following order: - Plot 1 and Plot 2 should be in the upper row - Plot 3 should be in the lower row but centered in the middle. I hope the following sketch will help understanding my problem ================ | === === | || P1| |P2 | | || | | | | | === === |

There's something peculiar that I do not understand here. However, did you realize that the thing you are assigning into parts of `a' is NULL? Check you're my.test.boot.ci.1: It's NULL. Be that as it may, I get: > a <- data.frame(matrix(1:4, nrow=2), X3=NA, X4=NA) > a X1 X2 X3 X4 1 1 3 NA NA 2 2 4 NA NA > a[a$X1 == 1,]$X3 <- NULL > a X1 X2 X3 X4 1 1

Hi All, I'm trying to do the following constrained optimization example. Maximize x1*(1-x1) + x2*(1-x2) + x3*(1-x3) s.t. x1 + x2 + x3 = 1 x1 >= 0 and x1 <= 1 x2 >= 0 and x2 <= 1 x3 >= 0 and x3 <= 1 which are the constraints. I'm expecting the answer x1=x2=x3 = 1/3. I tried the "constrOptim" function in R and I'm running into some issues. I first start off

The following dataframe will illustrate the problem DF<-data.frame(name=rep(1:5,each=2),x1=rep("A",10),x2=seq(10,19,by=1),x3=rep(NA,10),x4=seq(20,29,by=1)) DF$x3[5]<-50 # we have a data frame. we are interested in the columns x2,x3,x4 which contain sparse # values and many NA. DF name x1 x2 x3 x4 1 1 A 10 NA 20 2 1 A 11 NA 21 3 2 A 12 NA 22 4 2 A 13 NA

Hello All, I need help with my dataframe, it is big but here I am using a small table as an example. My dataframe df looks like: X1 X2 X3 1 2011-02 0.00 96.00 2 2011-02 0.00 2.11 3 2011-02 2.00 3.08 4 2011-02 0.06 2.79 5 2011-02 0.00 96.00 6 2011-02 0.00 97.00 7 2011-02 0.08 2.23 I want values in columns X2 and X3 to be checked if they are greater than

Hi, I'm using a loop to evaluate several models by taking adjacent variables from my dataframe. When i try to get predictions for new values, i get an error message about a missing variable in my new dataframe. Below is an example adapted from ?gam in mgcv package library(mgcv) set.seed(0) n<-400 sig<-2 x0 <- runif(n, 0, 1) x1 <- runif(n, 0, 1) x2 <- runif(n, 0, 1) x3 <-

Hi All, I'm using the subset function to select a list of variables, some of which are contiguous in the data frame, and others of which are not. It works fine when I use the form: subset(mydata,select=c(x1,x3:x5,x7) ) In reality, my list is far more complex. So I would like to store it in a variable to substitute in for c(x1,x3:x5,x7) but cannot get it to work. That use of the c function

hi all: Here's a dataframe(dat) and a vector(z): dat: x1 x2 x3 0.2 1.2 2.5 0.5 2 5 0.8 3 6.2 > z [1] 10 100 100 I wanna do the following: 10*x1,100*x2,1000*x3 My solution is using the loop for z and dat(since the length of z is the same as ncol of dat),which is tedious. I wanna an efficient solution to do it . Any help? Many thanks! My best

Hi r-helpers! I need to save the output of summary() function that I?ve runned like this: z<- lmList(y~x1+x2| x3, na.action=na.omit,data1,subset=year==1999) w<-summary(z) The output (w) is something like this: Call: Model: y ~ x1 + x2 | x3 Data: data1 Coefficients: (Intercept) Estimate Std. Error t value Pr(>|t|) 1 0.081110514 1.141352e-01

hi netters, i have a dataframe TEST like this: Y1 Y2 Y3 X1 4 7 8 X2 6 2 Z X3 8 0 1 i would like to change it to a numeric matrix, replacing "Z" with NA Y1 Y2 Y3 X1 4 7 8 X2 6 2 NA X3 8 0 1 i've tried the function data.matrix but it didn't work. is there any easy way to do this? thanks a lot!

Dear R-helpers, I have a dataframe like this: ID X1 X2 X3 X4 X5 X6 49 1 1 1 0 NA NA 50 1 1 1 1 NA 1 I would like to convert a missing value (NA) that follows a 0 (zero) or another missing value (NA) into a 0 (zero). So, the above lines would be converted to: ID X1 X2 X3 X4 X5 X6 49 1 1 1 0 0 0 50 1 1 1 1 NA 1 I have been struggling with

First, thanks to everyone who helped me get to grips with R in (x)emacs (I get confused easily). Special thanks to Stephen Eglen for continued support. My question is about non-linear binning, or density functions over distributions governed by a power law ... y ~ mu*x**lambda # In one of its forms # (can't find Pareto in the online help) Looking at the following

Dear list members, How can I replace the nested for loops at then end of the script below with more efficient code? # Begin script__________________________________________________ # Dichotomous scores for 100 respondents on 3 items with # probabilities of a correct response = .6, .4, and .7, # respectively x1 <- rbinom(100,1,.6) x2 <- rbinom(100,1,.4) x3 <- rbinom(100,1,.7) #

Hi Has anyone used R to conduct confirmatory factor analysis? This email pertains to use of SEM. For context consider an example: the basic idea is that there are a bunch of observables variables (say study habbits, amount of time reading in the bus, doing homework, helping other do homework, doing follow-up on errors etc.) and one believes that all these variables maybe measured by two or