similar to: Matlab code to R code

Suppose I have the following data: tmp <- data.frame(var1 = sample(c(0:10), 3, replace = TRUE), var2 = sample(c(0:10), 3, replace = TRUE), var3 = sample(c(0:10), 3, replace = TRUE)) I can run the following double loop and yield what I want in the end (rr1) as: library(statmod) Q <- 2 b <- runif(3) qq <- gauss.quad.prob(Q, dist = 'normal', mu = 0, sigma=1) rr1 <- matrix(0,

Hi, I'm having trouble converting code from MATLAB to R; I want to find the equivalence to MATLAB's function 'fsolve'. I've tried 'nlm', on the squared argument, in R but i did not get the same results. Thankful if helped. Best regards, Martin Englund ------------------------------------------------------------------------------ This e-mail and any attachment may

Dear R-Users, I have a problem on extracting T-Stat and P-Value. I have written R-code below library("Matching") data("lalonde") attach(lalonde) names(lalonde) Y <- lalonde$re78 Tr <- lalonde$treat glm1 <- glm(Tr~age+educ+black+hisp+married+nodegr+re74+re75,family=binomial,data=lalonde) pscore.predicted <- predict(glm1) rr1 <-

Hi r-users,   I have this code here, but I just wonder how do I use 'sapply' to make it more efficient lamda_cor <- eigen(winter_cor)$values   > lamda_cor [1] 1.3459066 1.0368399 0.8958128 0.7214407   lamda_cxn <- function(dt) { n       <- length(dt)   term    <- vector(length=n, mode="numeric")     for (i in 1:n)   { term[i] <- (dt[i]/n)*log(dt[i]/n) }    

Hi R-users,  I have a data from multi skew t and would like to transform each of the data to uniform data.  I tried using 'pmst' but only got one output:   > rr1 <- as.vector(r1);rr1  [1]  0.7207582  5.2250906  1.7422237  0.5677233  0.7473555 -0.6020626 -2.1947872 -1.1128313 -0.6587316 -1.1409261     > pmst(rr1, xi=rep(0,10), Omega=diag(10), alpha=rep(1,10), df=5) [1] 3.676525e-09

I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to. The likelihood I have is (in tex below) \begin{equation} \label{eqn:marginal} L(\beta) = \prod_{s=1}^N \int

Hi R-users: [R 2.2 on OSX 10.4.3] I have a (sparse) vegetation data frame with 500 rows (sampling units) and 177 columns (plant species) where the data represent % cover. I need to summarize the cover data by returning the names of the most dominant and the second most dominant species per plot. I reduced the data frame to omit cover below 5%; this is what it looks like stacked. I have

I have the following code which produces the output below it clus1 <- svydesign(ids = ~schid, data = lower_dat) items <- as.formula(paste(" ~ ", paste(lset, collapse= "+"))) rr1 <- svymean(items, clus1, deff='replace', na.rm=TRUE) > rr1 mean SE DEff W525209 0.719748 0.015606 2.4932 W525223 0.508228 0.027570 6.2802 W525035 0.827202

Hi all, I would like to ask that is there any function in R which can solve nonlinear system equations with several variables. Thats mean some functions similar to the 'fsolve' or 'fzero' in matlab. Thanks you Jerry _________________________________________________________________ Get 10Mb extra storage for MSN Hotmail. Subscribe Now!

Hello, I have a system of quadratic equations (results of a Hamiltonian optimization) which I need to find the roots for. Is there a package and/or function which will find the roots for a quadratic system? Note that I am not opimizing, but rather solving the first order conditions which come from a Hamiltonian. I am basically looking for something in R that will do the same thing as fsolve in

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

Hey, I was wondering if there existed a R function similar to 'fsolve' or 'fzero' Matlab functions? Thanks! Francois Aucoin [[alternative HTML version deleted]]

The IMM3/IMM5 come from here X86RecognizableInstr.cpp 943 TYPE("SSECC", TYPE_IMM3) 944: TYPE("AVXCC", TYPE_IMM5) On Thu, Dec 25, 2014 at 8:22 PM, Jun Koi <junkoi2004 at gmail.com> wrote: > > > On Fri, Dec 26, 2014 at 11:54 AM, Jun Koi <junkoi2004 at gmail.com> wrote: > >> hi, >> >> some instructions

Hello, I have a problem finding a root of a function, which I define like this: tuki <- function(u, x, a, lambda){ if((lambda%%1>0) & u<0) {u<-(u+0i)} f <- Re(x-(a*(u)^lambda-(1-(u))^lambda)/lambda) f } What I want to do is to find the root, but without specifying the interval within which to search for it. I can do it easily in MATLAB with fsolve() or fzero() functions. I

Dear R-programmers, I am trying out certain methods in R, and the statistics require me to calculate n-(sample size) dimensional equations. They are not really very hard to solve - my home-brew implentation of Newton-Raphson in R succeeds most of time with simulated data. (Note that I am assured of a unique solution by theory). Problem comes in with real data, for which I should really implement

Dear R community, I want to estimate coefficients in a (non-linear) system of equations using 'mle2' from the "bbmle" package. Right now the whole data is read in as just one long time series, when it's actually 9 cross sections with 30 observations each. I would like to be able to test and correct for autocorrelation but haven't found a way to do this in this package.

for unweighted fits using `nls' I compute confidence intervals for the fitted model function by using: #------------------- se.fit <- sqrt(apply(rr$m$gradient(), 1, function(x) sum(vcov(rr)*outer(x,x)))) luconf <- yfit + outer(se.fit, qnorm(c(probex, 1 - probex))) #------------------- where `rr' contains an `nls' object, `x' is the independent variable vector, `yfit'

dear list members, I apologize in advance for posting a second time, but probably after one week chances are, the first try went down the sink.. my question concerns computation of confidence intervals in nonlinear fits with `nls' when weigthing the fit. the seemingly correct procedure does not work as expected, as is detailed in my original post below. any remarks appreciated. greetings

Hi! I have a problem with ZFS and most likely the SATA PCI-X controllers. I run opensolaris 2008.11 snv_98 and my hardware is Sun Netra x4200 M2 with 3 SIL3124 PCI-X with 4 eSATA ports each connected to 3 1U diskchassis which each hold 4 SATA disks manufactured by Seagate model ES.2 (500 and 750) for a total of 12 disks. Every disk has its own eSATA cable connected to the ports on the PCI-X

We use the tar option of smbclient (Samba 1.9.16p9, SunOS 4.1.3) to backup Windows 95 PCs: smbclient "\\${server}\backup" -U COMPUTING -d 1 -D "\" -Tc tarfile >! logfile backup is set as a share of c:\ The backups are incomplete (as shown by the logfile and gzip -tf tarfile). Typically, only very roughly 5700 of 6600 files get copied. I expect I've got a bad