search for: epsilon

...ed. Copyright Andrew Tridgell and the Samba Team 1992-2008 [2008/11/28 20:28:24, 0] nmbd/asyncdns.c:start_async_dns(151) started asyncdns process 2531 [2008/11/28 20:28:24, 0] nmbd/nmbd_logonnames.c:add_logon_names(163) add_domain_logon_names: Attempting to become logon server for workgroup EPSILON on subnet xxx.xxx.xxx.xxx [2008/11/28 20:28:24, 0] nmbd/nmbd_logonnames.c:add_logon_names(163) add_domain_logon_names: Attempting to become logon server for workgroup EPSILON on subnet UNICAST_SUBNET [2008/11/28 20:28:24, 0] nmbd/nmbd_become_dmb.c:become_domain_master_browser_wins(335) bec...

...te the covariance matrix below. The code above is designed to create the portion of the matrix \sigma^2_{vle}\bm{J} . \begin{equation} \label{vert:cov} \bm{\Phi} = var \left [ \begin{array}{c} Y^*_{1}\\ Y^*_{2}\\ Y^*_{3}\\ Y^*_{4}\\ \end{array} \right ] = \left [ \begin{array}{cccc} \sigma^2_{\epsilon}\bm{I}& \sigma^2_{\epsilon}\rho\bm{I} & \bm{0} & \bm{0}\\ \sigma^2_{\epsilon}\rho\bm{I} & \sigma^2_{\epsilon}\bm{I}+\sigma^2_{vle}\bm{J} & \sigma^2_{\epsilon}\rho^2\bm{I} & \bm{0}\\ \bm{0} & \sigma^2_{\epsilon}\rho^2\bm{I} & \sigma^2_{\epsilon}\bm{I}+\sigma^2_{vle}\b...

In e1071 package/svm default epsilon value is set to 0.1 and not 0.5 as documentation says. R

Hi, I am trying to do run the following model saved in "C:/bugs/sus.bug" model { for (i in 1:n){ y[i] ~ dpois(lamdba[i]) log(lambda[i]) <- mu+bmale[male[i]]+bschn[schn[i]]+epsilon[i] # epsilon[i] ~ dnorm(0,tau.epsilon) } mu ~ dnorm(0,.0001) bmale ~ dnorm(0,.0001) tau.epsilon <- pow(sigma.epsilon, -2) sigma.epsilon ~ dunif(0,100) for (j in 1:n.schn){ bschn[j] ~ dnorm(0,tau.schn) } tau.schn <- pow(sigma.schn, -2) sigma.schn ~ dunif(0,...

...ining, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.04545455 coef.0: 0 epsilon: 0.1 Number of Support Vectors: 754 > svm.model <- svm(similarity ~., data = training, kernel = "polynomial", cost = 1, degree = 3, gamma = 0.04545455, coef.0 = 0, epsilon = 0.1) > pred=predict(svm.model, testing) > pred[pred > .5] = 1 > pred[pred <= .5] = 0...

With respect to calculating the epsilon index of sphericity for ANOVA, discussed on pp. 45-47 of: http://www.psych.upenn.edu/~baron/rpsych.pdf It notes that epsilon is not required for a repeated measures design with only k=2 levels, as the minimum value of epsilon (e) is given by: e = 1/(k-1) so for k=2, we have e = 1 (ie, no corr...

Dear R users, I’d like to create a function as: Y.t+1 = Y.t + (1\p)*summation(x(Y.t,Y.t-1,...)) + epsilon.t where x is a function of Y.t, Y.t-1 and so on, epsilon is a random error and p is a parameter. Do you think something of the following form might be appropriate? Y<-function(Y,p,x,epsilon){ for (i in 2:length(Y)) { Y[i]<-Y[i-1]+(1/p)*sum(x(...

...an additional fourth between subjects factor D. Everything works fine using aov(val ~ A*B*C + Error(subject/ (A*B*C) ) ) or aov(val ~ (D*A*B*C) + Error(subject/(A*B*C)) + D ) val, A, B, C, D and subject are columns in a data.frame. How can I get the estimated Greenhouse-Geisser and Huynh-Feldt epsilons? I know Peter Dalgaard described it in R-News Vol. 7/2, October 2007. However, unfortunately I am not able to apply that using my data... Furthermore, I am still confused of how SPSS calculates the epsilons since it is mentioned that perhaps there are any errors in SPSS?? I would be glad if an...

When `length( skewed.probs ) > 200' uniform samples are generated in R-devel. R-3.5.1 behaves as expected. `epsilon` can be a lot bigger than illustrated and still the uniform distribution is produced. Chuck > set.seed(123) > > epsilon <- 1e-10 > > ## uniform to 200 then small > p200 <- prop.table( rep( c(1, epsilon), c(200, 999-200))) > ## uniform to 201 then small > p201 <...

...x0 <- rnorm(30) x2 <- rnorm(30) x4 <- rnorm(30) data <- cbind(x0,x2,x4) # n is the number of 'subjects' or rows; while # k is the number of levels in the factor (columns), # which is the number of repeated measures n <- dim(data)[1] k <- dim(data)[2] # if k <= 2, the epsilon correction is not required. # When the data are perfectly spherical, epsilon = 1. # The minimum value possible for epsilon is epsi = 1 / (k - 1) # if epsi == 1 and k == 2, quit now... # --- # calculate variance-covariance matrix # diagonal entries are variance, # off-diagonal are covariance S &l...

...ors and so the installation isn't successful This is a part from the make run output.: gcc-3.3 -fpic -L/usr/lib64/gcc-lib/x86_64-suse-linux/3.3.3-hammer/ -lg2c -ffloat-store -c dlamch.f -o dlamch.o dlamch.f: In function `dlamch': dlamch.f:89: warning: INTRINSIC DIGITS, EPSILON, HUGE, MAXEXPONENT, Reference to unimplemented intrinsic `DIGITS' at (^) (assumed EXTERNAL) dlamch.f:89: INTRINSIC DIGITS, EPSILON, HUGE, MAXEXPONENT, ^ Invalid declaration of or reference to symbol `digits' at (^) [initially seen at (^)] dlamc...

.... Secondly, when I put the code given below all in one file and source it, it is unable to complete the task. But when I execute it one at a time it is able to complete it. Can someone point out any mistake that I may be making? Thanks ../Murli library(ecodist) x<-1:100 y<-corgen(x=x, r=.8, epsilon=0.01, population=FALSE)$y colnames(data)<-c("x", "y") write.csv(data, file="artDataCorrPt8.csv") rm(data) y<-corgen(x=x, r=.3, epsilon=0.01, population=FALSE)$y colnames(data)<-c("x", "y") write.csv(data, file="artDataCorrPt3.csv&quot...

...t enough to obtain a good accuracy using some classifiers( svm, rna, knn) I need to "oversampling" the number of instances of each class. I have heard that there is a method to do this. It consists in generating these new instances as follows: new_instance <---- original_instance + u(epsilon) U(epsilon) is a uniform number in the range [-epsilon,epsilon] and this number is applied to each feature of the dataset to obtain a new instance without modified the original class. Anybody has used this method to "oversampling" his data? Anybody has more information about it? Thank...

It's not a ls bug. I've stepped through the code with gdb and it looks just fine. At this point I think Epsilon (a 32-bit app) is corrupting the image of its child process in a strange way. I'm working with the author at Lugaru (who's very responsive) to track it down. He couldn't reproduce it right away. I just reproduced it on a virgin CentOS 8 image at Linode with utterly no modifications....

This is not an R question but if anyone can help me, it's much appreciated. Suppose I have a series ( stationary ) y_t and a series x_t ( stationary )and x_t has variance sigma^2_x and epsilon is normal (0, sigma^2_epsilon ) and the two series have the relation y_t = Beta*x_t + epsilon My question is if there are particular values that sigma^2_x and sigma^2_epsilon have to take in order for corr(x_t,y_t) to equal Beta ? I attempted to figure this out using two different methods...

Hi! Let's go back to this topic please! I have to replace the Windows NT 4.0 server with Samba but I still don't know how to do this. I need to allow to someone (some user group) the right to add files into the folder. They use this "feature" on current NT server ver often and to this in the following ways: 1. Rightclick on file you want to add, Copy, then go to server, for

...eventually be able to manipulate the structure of the error term to give groups of observations a random effect. Right now I am just doing a very basic set up to make sure I can recover the parameters properly. I am running into trouble with the code below. It works if you take out the object 'epsilon,' but I need that object because it is where I plan to add in the random effects eventually. Any suggestions? Thanks. set.seed(1) alpha <- numeric(100) # Vector to store coefficient estimates X1 <- numeric(100) X2 <- numeric(100) a <- 0 # True parameters B1 <- .5 B2 <- .85...

...To be more specific, here is an example: > success <- c(13,12,11,14,14,11,13,11,12) > failure <- c(0,0,0,0,0,0,0,2,2) > predictor <- c(0,80*5^(0:7)) > glm(cbind(success,failure) ~ predictor + 0, + family=binomial(link="log"), + control=glm.control(epsilon=1e-8,trace=TRUE,maxit=50)) Deviance = 3.348039 Iterations - 1 Error: subscript out of bounds In addition: Warning message: step size truncated: out of bounds > The model with intercept yields: > glm(cbind(success,failure) ~ predictor , + family=binomial(link="log"), +...

...else 843 scal <- 1 844 error(ret) <- drop((scal^2)*crossprod(fitted(ret) - y)/m) 845 } 846 } C. Finally, an example (taken from ?ksvm): require (kernlab) seed (1234) x <- seq(-20,20,0.1); x <- x[x != 0] y <- sin(x)/x + rnorm(400,sd=0.03) regm <- ksvm(x,y,epsilon=0.01,kpar=list(sigma=16),cross=3) te <- crossprod (fitted(regm)-y)/400 s <- (scaling(regm)$y.scale[["scaled:scale"]])^2 error (regm) # 0.03891344 te # 0.0008958718 te * s # 6.37252e-05 te / s # 0.01259449 These numbers can also be seen in the trace_output...

...de used for estimation is 'code1' rm(list=ls(all=TRUE)) n<-100 # Number of observations d<-4 # Dimension of X a<-0 # Lower bound b<-1 # Upper bound p<-5 # [See Bathia, Yao, Ziegelmann p. 5) ####################################### # Building the error function 'epsilon' ####################################### # Building the vector process 'Z' (which is a iiN(0,I_d) vector process) # Time varies columnwise Z<-matrix(rnorm(10*n),nrow=10,ncol=n) # Defining the deterministic base functions 'zeta' zeta<-function(u,i){ sqrt(2)+sin(pi*i*u)...