similar to: if else with 4 conditions problem

Hello everyone, I have a question regarding simulating based on runif.? Let say I have generated matrix A and B based on runif. Then I find mean for each matrix A and matrix B.? I want this process to be done let say 10 times. Anyone can help me.? Actually I want make the function that I can play around with the number of simulation process that I want. Thanks. Eg: a <- matrix(runif(5,1, 10))

Hi, Let say I have data by two columns A and B, and I have fit each column using the gamma distribution by 'fitdist' . I just want the result show only the shape and rate only. Eg: library(fitdistrplus) A <-c(1,2,3,4,5) B<-c(6,7,8,9,10) C <-cbind(A,B) apply(C, 2, fitdist, "gamma") Output show like this: $A Fitting of the distribution ' gamma ' by maximum

On 1/29/2018 9:03 PM, smart hendsome via R-help wrote: > Hello everyone, > I have a question regarding simulating based on runif.? Let say I have generated matrix A and B based on runif. Then I find mean for each matrix A and matrix B.? I want this process to be done let say 10 times. Anyone can help me.? Actually I want make the function that I can play around with the number of simulation

Capture the results of the apply command into an object and then work with that. Here is one way to do it: > res <- apply(C, 2, fitdist, "gamma") > out <- c( res$A$estimate["shape"], res$B$estimate["shape"], res$A$estimate["rate"], res$B$estimate["rate"]) > names(out) <- c("A shape","B shape","A

Hello, Another way would be to use ?replicate and ?colMeans. set.seed(2511) # Make the results reproducible fun <- function(n){ f <- function(){ a <- runif(5, 1, 10) b <- runif(5, 10, 20) colMeans(cbind(a, b)) } replicate(n, f()) } fun(10) Hope this helps, Rui Barradas On 1/30/2018 8:58 AM, Daniel Nordlund wrote: > On 1/29/2018

Hi, I'm trying to conduct sensitivity analysis in R using the 'sensitivity' package. Although the description of functions seem straightforward, I can?t succeed. The definition of input factors can be the problem. library(sensitivity) #A simple model with 4 input factor to test the morris function: model01=function(a1,a2,a3,a4) { Z<-numeric(10) Z[1]<-runif(1)

Hello, I'm new in R and I want to do one thing that is very easy in excel, however, I cant do it in R. Suppose we have the data frame: data<- data.frame(A=c("a1","a2","a3","a4","a5")) I need to obtain another column in the same data frame (lets say B=c(b1,b2,b3,b4,b5) in the following way: b1=a1/(a1+a2+a3+a4+a5)

Dear R users, Basically, from the following arbitrary data set: a <- data.frame(id=c(c("A1","A2","A3","A4","A5"),c("A3","A2","A3","A4","A5")),loc=c("B1","B2","B3","B4","B5"),clm=c(rep(("General"),6),rep("Life",4))) > a

Dear User, thank for the attention. I have a data.frame with 5 columns (ex:ID, a1,a2,a3,a4) and 1000 rows. I wish to find the absolute max value for all data.frame and save a new data.frame with the row where is that value. Ex: ID: 1,2,3,4,5,6,7,8,9,10 a1:1,2,3,4,5,6,7,8,9,10 a2:11,12,13,14,15,16,17,18,19,20 a3:21,22,23,24,25,26,27,28,29,30 a4:31,32,33,34,35,36,37,38,39,40 The

Dear All, Here is a hypothetical sample (sorry for the clumsy code): A1 <- matrix(1:5, nrow=5, ncol=1) A2 <- matrix(6:10, nrow=5, ncol=1) A3 <- matrix(11:15, nrow=5, ncol=1) A4 <- matrix(16:20, nrow=5, ncol=1) A5 <- matrix(21:25, nrow=5, ncol=1) A6 <- matrix(26:30, nrow=5, ncol=1) B1 <- matrix(c(A1, A2, A3), nrow=5, ncol=3) B2 <- matrix(c(A2, A3, A4), nrow=5, ncol=3) B3

Dear R users, I have a data where I desire to subset according to certain conditions. However, the script is very messy as there are about 30 distinct conditions. (i.e. same script but with different conditions) I would like to make a user defined function so that I can input the desired conditions and just get the results accordingly. Below is an arbitrary data set & sample statements

Dear colleagues, I'd like to reshape a datafame in a long format to a wide format, but I do not quite get what I want. Here is an example of the data I've have (dat): sp <- c("a", "a", "a", "a", "b", "b", "b", "c", "d", "d", "d", "d") tr <- c("A",

Hello, I am using the mda package and in particular the fda routine to classify/predict in terms of color to a set of 20 samples for which i don?t know the color. I preformed a flexible discriminant analysis (FDA) using a set of 147 samples for which i know all the information. My script and data follow in attachment. A total of 23 predictors were considered. 20 of the predictors are

On Mon, 15 Dec 2003, jw schultz <jw@pegasys.ws> wrote: > OK, first pass on TODO complete. .... This hardlink bug report is nearly 21 months old... So I took a look at it using 2.5.7. See below. > BUGS --------------------------------------------------------------- > > Fix hardlink reporting 2002/03/25 > (was: There seems

Dear all, I'm struggling to find the best (set of?) function(s) to do repeated measures logistic regression on some data from a psychology experiment. An artificial version of the data I've got is as follows. Firstly, each participant filled in a questionnaire, the result of which is a score. > questionnaire ID Score 1 1 6 2 2 5 3 3 6 4 4 2 ...

Buenas, Tengo dos data.frames de la siguiente manera library(data.table) id<-c("a1","a2","a3","a4") id2<-c("a2","a3","a1","a4") y<-c(1,2,3,4) z<-c(3,5,6,7) k<-c(1,3,8,7) df1<-data.table(id,y,z) id<-c("a2","a3","a1","a4") df2<-data.table(id,x,y) Quiero

Hello I have a data set like below: plate.id well.id Group HYB rlt1 1 P1 A1 Control SKOV3hyb 0.190 2 P1 A2 Control SKOV3hyb 0.210 3 P1 A3 Control SKOV3hyb 0.205 4 P1 A4 Control SKOV3hyb 0.206 5 P1 A5 Control SKOV3hyb 0.184 385 P1 A1 ovca SKOV3hyb 0.184 386 P1 A2 ovca SKOV3hyb 0.229 387

OK, this is very elementary, but I need help. I have looked in Verzani, past postings etc. Problem: I need to subtract the "length" date between "h4" and "a3" #which would be 4-1 I would rather not convert the two columns into four columns (with headings being "a3","a4","c4","h4"). DF <- data.frame(length=c(1,2,3,4),

I would like to compare two data sets saved as text files (example below) to determine if both sets are identical(or if dat2 is missing information that is included in dat1) and if they are not identical list what information is different between the two sets(ie output "a1", "a3" as the differing information). The overall purpose would be to remove "a1" and

I have the following array: 3 dimensional object, one dimension being year. Object is 3*3*3 library(plyr, reshape) a1<-rep(c(2007,2008,2009),9) a2<-c(rep("a",9),rep("b",9),rep("c",9)) a3<-c(rep(c(rep(1,3),rep(2,3),rep(3,3)),3)) a4 <- rnorm(27) A<-data.frame(cbind(comp=a2,val=a3, year=a1, a4)) A1<-melt(A, id=c("year", "comp",