search for: sigma_a

...3/252 i.e. an ANNUAL 3% rate converted to a DAILY rate, expressed in decimal. That means that the other argument to this function, x, should be DAILY returns, expressed in decimal. Suppose he wanted to create random data from a distribution of returns with ANNUAL mean MU_A and ANNUAL std deviation SIGMA_A, both stated in decimal. The equivalent DAILY returns would have mean MU_D = MU_A / 252 and standard deviation SIGMA_D = SIGMA_A/SQRT(252). He calls MU_D by the name mu_base and SIGMA_D by the name sigma_base. His loop now converts the random numbers in his matrix so that each column has mean...

...converted > to a DAILY rate, expressed in decimal. > That means that the other argument to this function, x, should be DAILY > returns, expressed in decimal. > > Suppose he wanted to create random data from a distribution of returns > with ANNUAL mean MU_A and ANNUAL std deviation SIGMA_A, both stated in > decimal. > The equivalent DAILY returns would have mean MU_D = MU_A / 252 and > standard deviation SIGMA_D = SIGMA_A/SQRT(252). > > He calls MU_D by the name mu_base and SIGMA_D by the name sigma_base. > > His loop now converts the random numbers in his mat...

...e converted to a DAILY rate, expressed in decimal. >> That means that the other argument to this function, x, should be DAILY returns, expressed in decimal. >> >> Suppose he wanted to create random data from a distribution of returns with ANNUAL mean MU_A and ANNUAL std deviation SIGMA_A, both stated in decimal. >> The equivalent DAILY returns would have mean MU_D = MU_A / 252 and standard deviation SIGMA_D = SIGMA_A/SQRT(252). >> >> He calls MU_D by the name mu_base and SIGMA_D by the name sigma_base. >> >> His loop now converts the random numbe...

I would like to fit the following model: logit(p_{ij}) = \mu + a_i + b_j where a_i ~ N(0, \sigma_a^2) , b_j ~ N(0, \sigma_b^2) and \sigma_a = \sigma_b. Is it possible to fit a model with such a constraint on the variance components in glmer? -- View this message in context: http://r.789695.n4.nabble.com/glmer-question-tp4351829p4351829.html Sent from the R help mailing list archive at Na...

...3/252 i.e. an ANNUAL 3% rate converted to a DAILY rate, expressed in decimal. That means that the other argument to this function, x, should be DAILY returns, expressed in decimal. Suppose he wanted to create random data from a distribution of returns with ANNUAL mean MU_A and ANNUAL std deviation SIGMA_A, both stated in decimal. The equivalent DAILY Then he does two steps: (1) generate a matrix of random values from the N(0,1) distribution. (2) convert them to DAILY After initializing the matrix with random values (from N(0,1)), he now wants to create a series of DAILY sr_base <- 0 mu_base <...

...But the few references I've been able to track down (Arvesen, Biometrcs, 1970 is one), seem to say that I should just sample with replacement the i units. Plus they seem to indicate that a log transform is needed. The Arvesen reference used something like using log(v_a/v_e) as an estimator for sigma_a^2/sigma_e^2 and getting an interval and then transforming to get to an interval for the ICC (although it's not clear to me how to get the other ICC in a two-level nested design). Any insights would be appreciated. Rick B.

Wrong list. Post on r-sig-finance instead. Cheers, Bert On Nov 20, 2017 11:25 PM, "Joe O" <joerodonnell at gmail.com> wrote: Hello, I'm trying to understand how to use the pbo package by looking at a vignette. I'm curious about a part of the vignette that creates simulated returns data. The package author transforms his simulated returns in a way that I'm

Hi all: I am trying to do PCA on the following matrix. N1 N2 A1 A2 B1 B2 gene_a 90 110 190 210 290 310 gene_b 190 210 390 410 590 610 gene_c 90 110 110 90 120 80 gene_d 200 100 400 90 600 200 >dataf<-read.table("matrix") >

HI, I would like to extract the variance components estimation in lme function like a.fit<-lme(distance~age, data=aaa, random=~day/subject) There should be three variances \sigma_day, \sigma_{day %in% subject } and \sigma_e. I can extract the \sigma_e using something like a.fit$var. However, I cannot manage to extract the first two variance components. I can only see the results in