similar to: nls not solving

hi, I would like to find the x value (independent variable) for a certain dependent value using the fitted model with nls. with (predict) I can find y that corresponds to a list of x. I need the other way around. can it be done? thanks, afadda

hi all, i am using a dcc model for my senior thesis, it looks at stock returns during times of market uncertainty. my current rfile is below. library(SparseM) library(quantreg) library(zoo) library(nortest) library(MASS) library(fEcofin) library(mvtnorm) library(ccgarch) library(stats) library(foreign) #dataset<-read.csv(file="xxxx",header=FALSE) attach(dataset);

Unfortunately there's no sure-fire way to distinguish a sched "op" from regular ones. They happen purely based on position. So for disassembly, use the byte position in the file to determine whether the current op should be decoded as sched or as regular. For assembly, we go back to using a shared table. Ideally we would pick out which one to use based on the position in the

Hi! Consider d <- data.frame(x=1:10,y=5+1:10, yf=rnorm(10,5+1:10)) x y yf 1 1 6 5.268621 2 2 7 8.623896 3 3 8 8.114830 4 4 9 10.125955 5 5 10 9.977261 ... I plot y and yf against x with xyplot(y+yf~x,data=d,col=c('red','green'),pch=c("a","b")) BUT - I would like that the plot of y against x is with type='l' and the

Folks: In the following mixed effect model, the "x" and residual variance components are nonidentifiable. Yet lme() (or the equivalent in lmer()) happily produces an answer in which the sum of the variance components is the correct estimate of the single variance parameter. Why? -- i.e. why doesn't lme complain? x <- 1:50 y <- rnorm(50) m1 <- lme( y ~ 1, rand = ~1|x)

Hi, I have a set of matrix data named “invest” consists of 450 observations (75 countries, 6 years) with 7 variables (set as I, pop, inv, gov, c, life, d; which each is “numeric[450]”). The procedure is modify from code provided by B.E. Hansen at http://www.ssc.wisc.edu/~bhansen/progs/ecnmt_00.html. *Then the variable is being transformed to* y <- lag_v(i,0) cf <-

Hi Sherri, There are examples of topographic maps which you have been pointed to, however, I suspect that you want to know where you can obtain topographic data from rather than a canned example. There are quite a few intricacies to the process so I will go through them for you. (1) Topography files can be found in the geomapdata library. You will probably want to use the maps package too (if

I believe there is a memory leak in isoreg in the current version of R, as I believe the following shows > gc() used (Mb) gc trigger (Mb) max used (Mb) Ncells 120405 3.3 350000 9.4 350000 9.4 Vcells 78639 0.6 786432 6.0 392463 3.0 > for(k in 1:100) { + + y <- runif(10000) + isoreg(x,y) + } > rm(x) > rm(y) > gc() used (Mb) gc

Dear R-List, I?m new in R, so I hope my question is not to primitive, but I haven?t found a solution in the R-help. I?ve got a datatframe with 3 factors, called xf (9 levels), yf (9 levels), zf (3 levels) and one variable (rt): > xf yf zf rt > 1 1 1 67 > 1 1 1 56 > 1 1 1 60 [...] > 1 1 2 58 > 1 1 2 61 [...] > 9 1

Hi R forum, Each row of my data (below) show a new contact event between animals. In order to ultimately look at the patterns of intervals between contacts, I need to calculate a contact end time. The contact starts at the date and time shown in V4 and V5, and lasts for the duration shown IN SECONDS in V6: > data2<- read.csv(file=file.choose(), header=F, sep=" ") >

I'm having trouble creating a looping variable and i can't see wher ethe problem arises from any hep gratfully appreciated First create a table x<-table(SURVEY$n_0,exposed) > x exposed False True Under 16 24 1 16-19 68 9 20-24 190 37 25-34 555 204 35-44 330 87 45-54 198 65 55-64 67 35 65+

I am having trouble understanding results I'm getting back from coxph doing a recurrent event analysis. I've included the model below and the summary. In some cases, with minor variations, the Robust variance and Wald tests are significant, but the individual covariates may or may not be significant. My main question is: If Wald and robust tests both take into account the

Hello R-users: I am using "yags" for fitting GEE which is giving me the same result as "Proc GENMOD". Now I have couple of questions related to yags output. (By the way, someone told me to run the geeglm for the same analysis and I did run but did not get the same result as of genmod and don't know how to correct the geeglm codes so that all three will be same!)

Dear all, Further to my previous message I now made a one-line convencience function to export your currently active graphics window/plot to either Word or Powerpoint in Office-native vector-based DrawingML format using either export2ppt(file="plot.pptx") or export2doc(file="plot.docx") : see

Hi, when I apply a glm() model in two ways, first with the response in a two column matrix specification with successes and failures y <- matrix(c( 5, 1, 3, 3, 2, 2, 0, 4), ncol=2, byrow=TRUE) X <- data.frame(x1 = factor(c(1,1,0,0)), x2 = factor(c(0,1,0,1))) glm(y ~ x1 + x2, data = X, family="binomial") second with a model matrix that

G'day I'm currently using NUT on Debian squeeze box in the same subnet with an IBM 3000 HV (branded Eaton 5125 with Web/SNMP card), now I wanted to get our storage appliance running NexentaStor 3.1 set up to be using NUT too. Nexenta- Stor is an OpenSolaris / illumos-based appliance OS currently using NUT packages from ubuntu hardy. My question here is whether I'm running into a -

Dear Researchers, I wrote two function to fit a circle using noisy data. 1- the fitCircle() is derived from MATLAB code of * zhak Bucher* from the link http://www.mathworks.com/matlabcentral/fileexchange/5557-circle-fit/content/circfit.m 2- the CircleFitByPratt() from MATLAB code of *Nikolai Chernov *from the link

I'm trying to specify a model using lmer with a binary response and interaction term, but I get an error I can't parse (see below). Here is some sample data: Subject Concord Age Disc SVC999MX148SU-F yes u int TOU999JU030S1 yes u int TOU999JU030S1 yes u int TOU999JU030S1 yes u int TUT578MX037S2 yes g int COL140MX114S2 yes yf

Hi everybody, I'm a beginner R user and I'm trying to use the package "sapa" to estimate the spectral density function of several time series using the SDF function. For each time series, I want to calculate the density function at two temporal resolutions (daily and monthly). The monthly values calculated as a mean of the daily values. I first create a ts object for both series

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