similar to: try (nls stops unexpectedly because of chol2inv error

Dear R-users, The following question bothered me for the whole afternoon: how can one pass a string as the conditioning argument to subset? I tried plain mystr, eval(mystr), expression(mystr), etc... I don't to be able to find the correct syntax > foo <- data.frame(a=1:10,b=10:1,c=rep(1:2,5)) > mystr<-"c==1" > subset(foo,c==1) a b c 1 1 10 1 3 3 8 1 5 5 6

Dear R-users, I need to dynamically recognize the index of a given string myStr in a vector of string. The problem is that myStr might contain parenthesis, causing grep not to recognize it the way I want (see below). The help mentions that the pattern used by grep should be in the POSIX format... I guess the problem is here; unfortunately, I am not familiar with the subtleties of the POSIX

Dear R-users, I am trying to use the grep function to test whether a particular string is of the form "n.../mydir/myfile.mytype.myext". Anything between n and mytype could vary, and anything after mytype could vary. I tried to proceed by steps to build my regular expression... but I do not really understand why the last call of the following code do not work. Any help would be

Given the string > mystr <- "(Preconsuntivo del giorno gas 10 maggio 2007)Tj" I'm trying to detect and eliminate the string ")Tj" at the very end of mystr by means of gsub(rx2,"",mystr) BUT preparing the matching regexp string a warning pops up > rx2 <- "\)Tj$" Warning messages: 1: '\)' is an unrecognized escape in a

Hi: I've been reading "Computational Methods for Multilevel Modeling" by Pinheiro and Bates, the idea of embedding the technique in my own c-level code. The basic idea is to rewrite the joint density in a form to mimic a single least squares problem conditional upon the variance parameters. The paper is fairly clear except that some important level of detail is missing. For

Dear All, Apologies if this is too simple for this list. Let us assume that you have an instrument measuring particle distributions. The output is a set of counts {n_i} corresponding to a set of average sizes {d_i}. The set of {d_i} ranges from d_i_min to d_i_max either linearly of logarithmically. There is no access to further detailed information about the distribution of the measured sizes, but

Hi Folks, I am dealing with data which have been presented as at each x_i, mean m_i of the y-values at x_i, sd s_i of the y-values at x_i number n_i of the y-values at x_i and I want to linearly regress y on x. There does not seem to be an option to 'lm' which can deal with such data directly, though the regression problem could be algebraically

Hi: I've been reading "Computational Methods for Multilevel Modeling" by Pinheiro and Bates, the idea of embedding the technique in my own c-level code. The basic idea is to rewrite the joint density in a form to mimic a single least squares problem conditional upon the variance parameters. The paper is fairly clear except that some important level of detail is missing. For

Hi, I am drawing several plots and want to have italics in a main title; this is easy with expression(). However, I want also to add a value to it, say n_i, that depends on an ith plot. For this I am using paste(). An example: n_i = 10, 20, 30; I want to draw a plot for each i with the title: "Relative efficiency for sample size n = n_i", where n should be in italics, and of course n_i

Hello, I'm very new to using R, but I was told it could do what I want. I'm not sure how best to enter the information but here goes... I'm trying to transfer the following integral into R to solve for ln(gamma_1), on the left, for multiple instances of gamma_i and variable N_i. gamma_i is, for example, (0, 0.03012048, 0.05000000, 0.19200000, 0.44000000, 0.62566845) N_i (N_1 or

I am running a 3-parameter nonlinear fit using the default Gauss-Newton method of nls. initialValues.L = list(b=4,d=0.04,t=180); fit.nls.L = nls( myModel.nlm , fData.L, start = initialValues.L, control = nls.control(warnOnly = TRUE), trace=T ); summary.nls.L = summary(fit.nls.L); I run the same analysis in SAS proc NLIN. proc nlin data=apples outest=a; parms b=4 d=.04 t=180; model Y =

Buenas tardes, Estoy interesado en generar observaciones de una distribucion binomial bivariada en la que hay _cierto_ grado de correlacion (denotemoslo rho). Podria por favor alguien indicarme como hacerlo en R? Este es el contexto. Supongamos que se tienen dos experimentos en los que la variable respuesta _sigue_ una distribucion binomial, i.e., X_i ~Binomial(n_i, p_i), i=1,2 y que, por ahora,

This is for BPF output. BPF program output bytes to perf through a tracepoint. For decoding such data, we need a way to describe the format of the buffer. This patch is a try which gives each variable a unique number by introducing a new intrinsic 'llvm.typeid.for'. At the bottom is an example of using that intrinsic and the result of $ clang -target bpf -O2 -c -S ./test_typeid.c

Can someone clarify what I cannot make out from the documentation? The function 'clogit' in the 'survival' package is described as performing a "conditional logistic regression". Its return value is stated to be "an object of class clogit which is a wrapper for a coxph object." This suggests that its usefulness is confined to the sort of data which arise in

R Experts: I am conducting a meta-analysis where the effect measures to be pooled are simple proportions. For example, consider this data from Fleiss/Levin/Paik's Statistical methods for rates and proportions (2003, p189) on smokers: Study N Event P(Event) 1 86 83 0.965 2 93 90 0.968 3 136 129 0.949 4 82 70 0.854 Total

Dear helpers in this forum, This is a clarified version of my previous questions in this forum. I really need your generous help on this issue. > Suppose I have the following data set: > > id x y > 023 1 2 > 023 2 5 > 023 4 6 > 023 5 7 > 412 2 5 > 412 3 4 > 412 4 6 > 412 7 9 > 220 5 7 > 220 4 8 > 220 9 8 > ...... > Now I want to compute the

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

Dear R users, I am writing to you because I have a few question on how to fix the error term variances in lme in the hope that you could help me. To my knowledge, the closest possibility is to fix the var-cov structure, but not the whole var-cov matrix. I found an old thread (a few years ago) about this, and it seems that the only alternative is to write the likelihood down and use optim or a

Hi, ? I have a relatively big dataset and I want to construct some histograms using the histogram function in lattice. One thing I am interested in is to look at differences between density and percent. I know I can use the hist function but it seems that this function gives sometimes some wrong answers and the density is actually a percent since it is calculated as counts in the bin divided by

Dear All, In the past I have often used minpack (http://bit.ly/zXVls3) relying on the Levenberg-Marquardt algorithm to perform non-linear fittings. However, I have always dealt with a function of a single variable. Is there any difference if the function depends on two variables? To fix the ideas, please consider the function f(R,N)=(a/(log(2*N))+b)*R+c*N^d, where a,b,c,d are fit parameters. For