similar to: Bootstrapping in R

Hi all, I have 4 matrices, each having 5 columns and 4 rows .....denoted by B1,B2,B3,B4. I have generated a vector of 7 indices, say (1,2,4,3,2,3,1} which refers to the index of the matrices to be chosen and then appended one on the top of the next: like, in this case, I wish to have the following mega matrix: B1over B2 over B4 over B3 over B2 over B3 over B1. 1> How can I achieve this?

Hi all, In my data analysis, I have created a random matrix M ( of order 500 X 7). I want to use the same matrix when I start a new session, or suppose I want to send this matrix to one of my friends (because this matrix is randomly generated, and I dont want to use any other 500X7 matrix randomly generated by R). How can I save and call this matrix in the later sessions as well? Appreciate

Hi, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] How do I find the estimates of the coefficients in R? And also I would like to know what technique R employs to find the estimates? Any help is appreciated. Thanks,

Hi all, I have a data set containing variables LOSS, GDP, HPI and UE. (I have attached it in case it is required). Having renamed the variables as l,g,h and u, I wish to run a Lasso Regression with l as the dependent variable and all the other 3 as the independent variables. data=read.table("data.txt", header=T) l=data$LOSS h=data$HPI u=data$UE g=data$GDP matrix=data.frame(l,g,h,u)

Hi guys, I am running glm(y~., data = history,family=binomial)-essentially, logistic regression for credit scoring (y = 0 or 1). The dataset 'history' has 14 variables, a few examples: history <- read.csv("history.csv". header = TRUE) 1> 'income = 100,200,300 (these are numbers in my dataset; however interpretation is that these are just tags or labels,for every

Hi all, I have run a ridge regression as follows: reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+final$u, lambda=seq(0,10,0.01)) Then I enter : select(reg) and it returns: modified HKB estimator is 19.3409 modified L-W estimator is 36.18617 smallest value of GCV at 10 I think it means that it is advisable to

Hi all, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] So, I run the following code: for (i in 1:rep) { index=sample(4,15,replace=T) final<-do.call(rbind,lapply(index,function(i)

Hi all, I was running the adf test in R. CODE 1: adf.test(data$LOSS) Augmented Dickey-Fuller Test data: data$LOSS Dickey-Fuller = -1.9864, Lag order = 2, p-value = 0.5775 alternative hypothesis: stationary CODE 2: adf.test(diff(diff(data$LOSS))) Augmented Dickey-Fuller Test data: diff(diff(data$LOSS)) Dickey-Fuller = -6.9287, Lag order = 2, p-value = 0.01 alternative

Hi all, I have run a ridge regression as follows: reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+final$g+final$u, lambda=seq(0,10,0.01)) Then I enter : select(reg) and it returns: modified HKB estimator is 19.3409 modified L-W estimator is 36.18617 smallest value of GCV at 10 I think it means that it is

Hi all, Suppose I am fitting an arma(p,q) model to a time series y_t. So, my model should contain (q+1) white noise variables. As far as I know, each of them should have the same variance. How do I get the estimate of this variance by running the arma(y) function (or is there any other way)? Appreciate your help. Thanks, Preetam -- Preetam Pal (+91)-9432212774 M-Stat 2nd Year,

I have the following time series model for prediction purposes *Loss_t = b1* Loss_(t-1) + b2*GDP_t + b3*W_(t-1)* where W_t is the usual white noise variable. So this is similar to ARMA(1,1) except that it also contains an extra predictor, GDP at time t. I have only 20 observations on each variable except GDP for which I know till 100 values. And most importantly,I have also calculated

Hi all, I have run a ridge regression on a data set 'final' as follows: reg=lm.ridge(final$l~final$lag1+final$lag2+final$g+final$u, lambda=seq(0,10,0.01)) Then I enter : select(reg) and it returns: modified HKB estimator is 19.3409 modified L-W estimator is 36.18617 smallest value of GCV at 10 I think it

Hi all, I have run a regression and want to calculate the likelihood of obtaining the sample. Is there a way in which I can use R to get this likelihood value? Appreciate your help on this. The following are the details: raw_ols1=lm(data$LOSS~data$GDP+data$HPI+data$UE) summary(raw_ols1) Call: lm(formula = data$LOSS ~ data$GDP + data$HPI + data$UE) Residuals: Min 1Q

Dear Sir/Madam, I'm getting a problem with a R-code which converts a data frame to a matrix. It first generate a (m^(n-m) * m) matrix A and then regenerate another matrix B having less dimension than A which satisfy some condition. Now I wish to assign each row of B to a vector as individual. My problem is when I set any choice of (n,m) except m=1 it works fine but setting m=1 I got the

Dear R-listers, I am giving part of my R code : ########################################################### n=15 m=1 library("partitions") library("gregmisc") library("combinat") x = t(restrictedparts(n-m,m)) l = length(x[,1]) for(u in 1:l){ A= unique(matrix( unlist(permn(x[u,])), ncol=m, byrow=TRUE )) }

Sorry for misinterpreting, but what is the basis for the simple fact you mentioned? David On Wed, Jun 24, 2015 at 2:43 PM, Robinson, Paul <Paul_Robinson at playstation.sony.com> wrote: >> -----Original Message----- >> From: Xinliang David Li [mailto:davidxl at google.com] >> Sent: Wednesday, June 24, 2015 2:17 PM >> To: Robinson, Paul >> Cc: Easwaran Raman;

Hi, I want asterisk to call a person on the phone for monitoring the refrigerator storing vaccines. I am clueless where to look. Can someone clue me in ? Thanks, balu raman -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.digium.com/pipermail/asterisk-users/attachments/20071017/923a7b49/attachment.htm

Hi, I am currently analysis Raman spectroscopic data with the hyperSpec package. I consulted the documentation on this package and I found an example work-flow dedicated to Raman spectroscopy (see the address : http://hyperspec.r-forge.r-project.org/chondro.pdf) I am currently trying to remove outliers thanks to PCA just as they did in the documentation, but I get a message error I can't

On Wed, Jun 24, 2015 at 2:10 PM, Robinson, Paul <Paul_Robinson at playstation.sony.com> wrote: >> -----Original Message----- >> From: Easwaran Raman [mailto:eraman at google.com] >> Sent: Wednesday, June 24, 2015 1:27 PM >> To: Xinliang David Li >> Cc: Robinson, Paul; Xinliang David Li; <llvmdev at cs.uiuc.edu> List >> Subject: Re: [LLVMdev] Inline

On Wed, Jun 24, 2015 at 2:35 PM, David Blaikie <dblaikie at gmail.com> wrote: > > > On Wed, Jun 24, 2015 at 2:20 PM, Easwaran Raman <eraman at google.com> wrote: >> >> On Wed, Jun 24, 2015 at 2:10 PM, Robinson, Paul >> <Paul_Robinson at playstation.sony.com> wrote: >> >> -----Original Message----- >> >> From: Easwaran Raman