search for: m_i

...rep(NA,ncol(data)*ncol(data))), nrow = ncol(data), ncol = ncol(data) , byrow=TRUE) med <- c(rep(NA,ncol(data))) mean_ge <- c(rep(NA,ncol(data))) n<-c(NA,2) if (ncol(data)>1){ for(m_j in 1:ncol(data)){ med[m_j]<-median(data[,m_j])} for(m_j in 1:ncol(data)) for(m_i in 1:nrow(data)) { if(data[m_i,m_j]>med[m_j]) med_ge[m_i,m_j]=0 else med_ge[m_i,m_j]=1 } y=c(1,1,1,1,1,1,0,0,0,0) n<-c(sum(y == 1),sum(y==0)) touse3 <- y==1 T1<- apply(med_ge[touse3,], 2, mean) T0<- apply(med_ge[...

Dear all - We perform some measurements with a machine that needs to be recalibrated. The best calibration we get with polynomial regression. The data might look like follows: > true_y <- c(1:50)*.8 > # the real values > m_y <- c((1:21)*1.1, 21.1, 22.2, 23.3 ,c(25:50)*.9)/0.3-5.2 > # the measured data > x <- c(1:50) > # and the x-axes > > # Now I do the following:

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 algebraic...

...of matrices form. However when using xtabs function it orders my rows alphabetically and apparently doesn't take "reorder = FALSE" option or anything like it. Is there anything I can do to stop it from doing so? Relevant parts of code: matrices.m <- melt(combined_list) matrices.m_i[is.na(matrices.m_i$value),]$value <- predictions matrices <- xtabs(value ~ location + variable + week, data = matrices.m_i) -- while(!succeed) { try(); }

Hello, I have a concrete statistical question: I have a sample of an univariate mixture of an unknown number (k) of normal distributions, each time with an unknown mean `m_i' and a standard deviation `k * m_i', where k is known factor constant for all the normal distributions. (The `i' is a subscript.) Is there a function in R that can estimate the number of normal distributions k and the means `m_i' for the different normal distributions from a sam...

>>>>> Serguei Sokol <sokol at insa-toulouse.fr> >>>>> on Mon, 29 May 2017 15:28:12 +0200 writes: > Sorry, I have seen it too late that we had different tab > width in the original file and my editor. Here is the > patch with all white spaces instead of mixing tabs and > white spaces. thank you - it still gives quite a few

...package in R called mlogit. I am not sure whether I have already found the correct package or software. May I ask am I correct? ? Basically, let's say ? I have observations of n outcomes, for each outcome 1<=i<=n, they were selected by a choice from a set S_i = { H(i,j) | 1 <= j <= m_i } with utility U(i,j) ??Assuming each outcome was chosen by maximizing?the utility. ? Let U*(i) = max{ U(i,j) | 1 <= j <= m_i } ? Therefore, each outcome was chosen among m_i items by finding the index j such that U(i,j) = U*(i). ? For the utility function U(i,j), there are some independent...

...statistics matrix 1000 x 4: qnt <- c(0.01, 0.05) cmp_fun <- function(x) { LAST <- length(x) smpls <- x[1:(LAST-1)] real <- x[LAST] ret <- vector(length=length(qnt)*2) for (i in 1:length(qnt)) { q_i <- quantile(smpls, qnt[i]) # the quantile i m_i <- mean(smpls[smpls<q_i ] ) # mean of obs less than q_i ret[i] <- ifelse(real < q_i, 1, 0) ret[length(qnt)+i] <- ifelse(real < q_i, real - m_i, 0) } ret } hcvx <- apply(testm, 1, cmp_fun) The code is functioning well, but seems to take forever to calculate the...

Greetings, Sorry, the last message was sent by mistake! Here it is again: I encounter a strange problem computing some numerical integrals on [0,oo). Define $$ M_j(x)=exp(-jax) $$ where $a=0.08$. We want to compute the $L^2([0,\infty))$-inner products $$ A_{ij}:=(M_i,M_j)=\int_0^\infty M_i(x)M_j(x)dx $$ Analytically we have $$ A_{ij}=1/(a(i+j)). $$ In the code below we compute the matrix $A_{i,j}$, $1\leq i,j\leq 5$, numerically and check against the known analytic values. When I run this code most components of A are correct, but some are zero. I get the foll...

...ht-line growth model (intercept and slope both have fixed and random effects), the degrees of freedom seem to be N*T-N-1, where N is total sample size and T is the number of timepoints (at least when data are balanced). In the Pinheiro and Bates book (p. 91), the degrees of freedom are given as m_i-(m_1-1+pi), where m_i is the number of groups at the ith level, m_0=1 if an intercept is included and p_i is the sum of the degrees of freedom corresponding to the terms estimated. I'm not sure how the N*T-N-1 matches up with the formula given on page 91. It seems to me the number of "...

Here is an attached patch. Best, Serguei. Le 29/05/2017 ? 12:21, Serguei Sokol a ?crit : > The problem or actual R implementation relies on an assumption > that median(x[i] | x[i] <= quantile(x, 1/3)) == quantile(x, 1/6) > which reveals not to be true despite very trustful appearance. > > If we continue with the example of x=y=1:9 > then quantile(x, 1/6)=2.5 (here quantile()

...me # Generating data which are right truncated library(DTDA) library(splines) library(survival) n<-25 X<-runif(n,0,1) V<-runif(n,0.75,1) for (i in 1:n){ while (X[i]>V[i]){ X[i]<-runif(1,0,1) V[i]<-runif(1,0.75,1) }} res<-lynden(X=X,U=NA, V=V, boot=TRUE) attach(res) temps = time M_i = n.event L_t = res F_t=1-L_t??????????????????????????????????? ? par(mfrow=c(1,1)) plot(L_t$time,L_t$survival,type="s",lty=2:3,lwd=2,las=1,cex.lab=1.1,font.lab=2,col="red",xlab="temps",ylab="L(t)",main="Esitmation de la Fonction de Survie L(t)")...

Greetings,   I encounter a strange problem computing some numerical integrals on [0,oo). Define $$ M_j(x)=exp(-jax) $$ where $a=0.08$. We want to compute the $L^2([0,\infty))$-inner products $$ A_{ij}:=(M_i,M_j)=\int_0^\infty M_i(x)M_j(x)dx $$ Analytically we have $$ A_{ij}=1/(a(i+j)). $$ In the code below we compute the matrix $A_{i,j}$, $1\leq i,j\leq 5$, numerically and check against the known analytic values.   When I run this code most components of A are correct, but some are zero. I get the fol...

Hello Rprofessionals, The SOM-Obj works very well, when i normalize my data and the plot-function, too ! But i miss or didn't find the possibility , extract the information from the SOMplot "clusterSize" and "mean" for every cluster as quantitative information ( i.e. the DataFrame with an additional column which define the calculate clusters from SOM)? My intention -

Dear R-help group, I would like to model directly following random effect model: Y_ik = M_ik + E_ik where M_ik ~ N(Mew_k,tau_k^2) E_ik ~ N(0,s_ik^2) i = number of study k = number of treatment --------------------------------------------------------------------------- I have practiced using the command from 'Mixed -Effects models in S and S-plus&...

...100 simulations, only 53 are good simulations because we don't have enough data for nls or chol2inv to work correctly. monte {x: ########################################################################################### ## case I ## EQUAL SAMPLE SIZES and design points nsim = 100; N_i = M_i = 10; ## also try (10, 30, 50, 100, 200) r = M_i / N_i; X.start = 170; # 6 design points, at 170,180,190, etc. where each point has N_i elements X.increment = 10; X.points = 6; X.end = 260; Xval = seq(X.start,length.out=X.points,by=X.increment ); Xval = seq(X.start,X.end,length.out=X.points); L =...

Hi, I would like to know whether there exist algorithms to compute the coefficients or, at least, the degree of the minimal polynomial of a square matrix A (over the field of complex numbers)? I don't know whether this would require symbolic computation. If not, has any of the algorithms been implemented in R? Thanks very much, Ravi. P.S. Just for the sake of completeness, a

...ary(splines) > library(survival) > n<-25 > X<-runif(n,0,1) > V<-runif(n,0.75,1) > for (i in 1:n){ > while (X[i]>V[i]){ > X[i]<-runif(1,0,1) > V[i]<-runif(1,0.75,1) > }} > res<-lynden(X=X,U=NA, V=V, boot=TRUE) > attach(res) > temps = time > M_i = n.event > L_t = res > F_t=1-L_t > F_t=1-L_t Error in 1 - L_t : non-numeric argument to binary operator L_t is a list. You cannot subtract a list (at least in R). I'm not sure what you think F_t is supposed to be and you don't seem to use i...

Dear list, I've got a question concerning difference between loess and locpoly. I have to use a plug-in method to chose a bandwith so I take locpoly method to fit a curve. My problem is:I know how to get predicted values in loess: m=loess(y~x) y_fitted=predict(m). But how to get the same in locpoly? I computed like this: bw=dpill(x, y, blockmax = 5, divisor = 20,trim = 0.01, proptrun =

We need some idea of the problem. http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example http://adv-r.had.co.nz/Reproducibility.html On Thursday, May 10, 2018, 11:07:30 a.m. EDT, malika yassa via R-help <r-help at r-project.org> wrote: Hello Do You help me, i have the problem in the package DTDA for ?find the probability of truncation