similar to: How to write and analyze data with 3 dimensions

Hi All, The following is a snippet of my code. It works fine but it is very slow. Is it possible to speed it up by using different data structure or better solution? For 40000 runs, it takes 8 minutes now. Thanks a lot fun_activation = function(s_k, s_hat, x_k, s_hat_len) { common = intersect(s_k, s_hat); if(length(common) != 0) { index = match(common, s_k);

Dear ALL I have this function in R: func_LN <- function(data){ med_ge <- matrix(c(rep(NA,nrow(data)*ncol(data))), nrow = nrow(data), ncol=ncol(data), byrow=TRUE) T <- matrix(c(rep(NA,length(n)*ncol(data))), nrow = length(n), ncol=ncol(data), byrow=TRUE) Tdiff<- matrix(c(rep(NA,length(n)*ncol(data))), nrow = length(n), ncol=ncol(data), byrow=TRUE) T1<- c(rep(NA,ncol(data)))

Hello, I'm trying to create a line graph with a legend, but have no success controlling the legend. Since nothing I've tried seems to work, I must be doing something systematically wrong. Can anyone point this out to me? Here's my data: > weights # A tibble: 1,246 ? 3 Date J K <date> <dbl> <dbl> 1 2000-02-13 133 188 2

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

Hi, I have a vector with 4 elements, e.g., tau_i=c(100,200,300,400), but potentially tau_i[0]=0. In a "for" loop, tau_i=c(100,200,300,400) m=4 tau_i[0]=0 # <------- ? P_i=1 for(i in 2:m) { P_i = P_i*(tau_i[i-1]-tau_i[i-2]) } Error in P_i = P_i * (tau_i[k - 1] - tau_i[k - 2]): replacement has length zero Unfortunately, I can add this potential element into

Hello. I have searched the archives but have not found anything. I need to solve a constrained optimisation problem for a nonlinear function (“maximum entropy formalism”). Specifically, Optimise: -1*SUM(p_ilog(p_i)) for a vector p_i of probabilities, conditional on a series of constraints of the form: SUM(T_i*p_i)=k_i for given values of T_i and k_i (these are constraints on

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,

Dear R community, I have a fairly large file with variables in rows. Every variable (thousands) needs to be regressed on a reference variable. The file is too big to load into R (or R gets too slow having done it) and I do now read in line by line with "scan" (see below) and write the results to out. Although improved, this is still very slow... Can someone please help me and suggest

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

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 all, I have define a discrete distribution P(y_i=x_i)=p_i, which I want to plot a CDF plot. However, I can not find a function in R to draw it for me after searching R and R-archive. I only find the one for the sample CDF instead my theoretical one. I find stepfun can do it for me, however, I want to plot some different CDF with same support x in one plot. I can not manage how to do it with

Dear UseRs: I have simulated data from a zero-inflated Poisson model, and would like to use a package like zicounts to test my code of fitting the model. My question is: can I use zicounts directly with the following simulated data? Create a sample of n=1000 observations from a ZIP model with no intercept and a single covariate x_{i} which is N(0,1). The logit part is logit(p_{i})=x_{i}*beta

Hello, The question looks like simple. It's probably even stupid. But I spent several hours searching Internet, downloaded tons of papers, where deviance is mentioned and... And haven't found an answer. Well, it is clear for me the using of entropy when I split some node of a classification tree. The sense is clear, because entropy is an old good measure of how uniform is distribution.

Rui, thanks so much for your help. Your explanation and example were clear and concise. Thanks for taking the time and effort to help me. -Kevin On 5/12/23 16:06, Rui Barradas wrote: > ?s 14:24 de 12/05/2023, Kevin Zembower via R-help escreveu: >> Hello, I'm trying to create a line graph with a legend, but have no >> success controlling the legend. Since nothing I've

I'm trying to fit a nested mixed model using lmer and have some questions about the output and my model formulations. I have replicate measures on Lines which are strictly nested within Populations. (a) So if I want to fit a model where Line is a random effect and Populations are fixed and the random Line effect is constant across Populations, I have: measure_ijk = mu + P_i + L_ij +

Hi everyone, I am trying to compare proportions among groups using the logistic regression approach as follows: 1) Fit the model log(p_i/(1-p_i)) = M + G_i, where p_i is the probability of success in group i and G_i is the effect of group i, i=1,..,I. 2) Test the hypotheses: Ho: G_1 = G_2 = ... = G_I (the probability of success is the same for all groups) versus Ha: at least two

See below. On 5/16/23 10:52, Christopher Ryan wrote: > I"m more of a lattice guy than a ggplot guy, but perhaps this is part of > the problem: > > ..... > ? ? ?geom_point(aes(y = m_K, color = "red")) +? ##### >> you've > associated "K" with the color red > ? ? ?geom_smooth(aes(y = m_K, color = "red")) + >

Hi everyone, I'd like a bit of information about the stereo mode settings in psych_44.h of libvorbis. Specifically, about the adj_stereo structure: typedef struct { int pre[PACKETBLOBS]; int post[PACKETBLOBS]; float kHz[PACKETBLOBS]; float lowpasskHz[PACKETBLOBS]; } adj_stereo; What I am attempting to do is bring lossless stereo coupling down to the lower quality levels in

Hi there I am trying to learn how to compute mle in R for a multinomial negative log likelihood function. I am using for this the book by B. Bolker "Ecological models and data in R", chapter 6: "Likelihood an all that". But he has no example for multinomial functions. What I did is the following: I first defined a function for the negative log likelihood:

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