similar to: Cannot calculate mean() for double vector

For a given linear regression, I wish to find the 2-tailed t-dist probability that Y-hat <= newly observed values. I generate prediction intervals in predict() for plotting, but when I calculate my t-dist probabilities, they don't agree. I have researched the issues with variance of individual predictions and been advised to use the variance formula below (in the code). I presume my

Hi, I am trying to replicate the R's result for VCV matrix of estimated coefficients from linear model as below data(mtcars) model <- lm(mpg~disp+hp, data=mtcars) model_summ <-summary(model) MSE = mean(model_summ$residuals^2) vcov(model) Now I want to calculate the same thing manually, library(dplyr) X = as.matrix(mtcars[, c('disp', 'hp')] %>% mutate(Intercept =

Simple question. For a simple linear regression, I obtained the "standard error of predicted means", for both a confidence and prediction interval: x<-1:15 y<-x + rnorm(n=15) model<-lm(y~x) predict.lm(model,newdata=data.frame(x=c(10,20)),se.fit=T,interval="confidence")$se.fit 1 2 0.2708064 0.7254615

sigma(model)^2 will give the correct MSE. Also note that your model matrix has intercept at the end whereas vcov will have it at the beginning so you will need to permute the rows and columns to get them to be the same/ On Wed, Sep 4, 2024 at 3:34?PM Daniel Lobo <danielobo9976 at gmail.com> wrote: > > Hi, > > I am trying to replicate the R's result for VCV matrix of

Hi, I am working with CART regression now to predict a probability; the response is binary. Could anyone tell me in which cases it is better to use mean square error for splitting nodes and when mean absolute error should be preferred. I am now using the default (MSE) version and I can see that the obtained optimal tree is very different from the tree with the least mean absolute error. Thanks in

I am trying to understand what is the difference between linear and polynomial kernel: linear: u'*v polynomial: (gamma*u'*v + coef0)^degree It would seem that polynomial kernel with gamma = 1; coef0 = 0 and degree = 1 should be identical to linear kernel, however it gives me significantly different results for very simple data set, with linear kernel

Sure: https://www.reub.net/files/dovecot/lightning-dovecot.conf https://www.reub.net/files/dovecot/thunderstorm-dovecot.conf Updated nightly. Reuben On 1/09/2018 12:26 am, Aki Tuomi wrote: > Can you provide doveconf -n? > > > > --- > Aki Tuomi > Dovecot oy > > -------- Original message -------- > From: Reuben Farrelly <reuben-dovecot at reub.net> >

This is bit of a longshot, but can you try applying https://github.com/dovecot/core/commit/63a74b9.patch and see if this helps? Aki On 19.09.2018 10:33, Reuben Farrelly wrote: > glibc-2.28. > > There was a posting by Michael Marley on 31/08 saying he saw something > similar to this problem after upgrading to 2.28 as well with 'doveadm > purge'. > > Reuben > >

Can you provide your /etc/nssswitch.conf? Aki On 19.09.2018 10:40, Reuben Farrelly wrote: > I've been running with this almost identical patch already for some time: > > diff -up dovecot-2.3.0.1/src/auth/mycrypt.c.libxcrypt > dovecot-2.3.0.1/src/auth/mycrypt.c > --- dovecot-2.3.0.1/src/auth/mycrypt.c.libxcrypt?????? 2018-02-28 > 15:28:58.000000000 +0100 > +++

Which glibc version are you using? Aki On 19.09.2018 09:39, Reuben Farrelly wrote: > Hi Aki, > > How did you go with this in the end?? Do you need anything more from > me or were you able to reproduce this? > > Thanks, > Reuben > > On 1/09/2018 12:41 am, Reuben Farrelly wrote: >> Sure: >> >> https://www.reub.net/files/dovecot/lightning-dovecot.conf

I have the following function, a MSE calc based on some Multivariate normals: MV.MSE<-function(n,EP,X,S){ (dmvnorm(X,mean=rep(0,2),I+S+EP)-dmvnorm(X,mean=rep(0,2),I+S))^2 + 1/n*(dmvnorm(X,mean=rep(0,2),1+S+EP/2)*det(4*pi*EP)^-.5- (dmvnorm(X,mean=rep(0,2),I+S+EP ))^2)} I can get the MV.MSE for given values of the function e.g

Hi, I am trying to integrate a function which is approximately constant over the range of the integration. The function is as follows: > my.fcn = function(mu){ + m = 1000 + z = 0 + z.mse = 0 + for(i in 1:m){ + z[i] = rnorm(1, mu, 1) + z.mse = z.mse + (z[i] - mu)^2 + } + return(z.mse/m) + } > my.fcn(-10) [1] 1.021711 > my.fcn(10) [1] 0.9995235 > my.fcn(-5) [1] 1.012727 > my.fcn(5)

Dear Random Forests gurus, I have a question about R^2 provided by randomForest (for regression). I don't succeed in finding this information. In the help file for randomForest under "Value" it says: rsq: (regression only) - "pseudo R-squared'': 1 - mse / Var(y). Could someone please explain in somewhat more detail how exactly R^2 is calculated? Is "mse"

I have no idea why it's returning invalid argument, maybe you need to raise this as issue with glibc? Aki On 19.09.2018 10:43, Reuben Farrelly wrote: > Sure: > > thunderstorm ~ # cat /etc/nsswitch.conf > # /etc/nsswitch.conf: > # $Header: > /var/cvsroot/gentoo/src/patchsets/glibc/extra/etc/nsswitch.conf,v 1.2 > 2017/08/12 16:21:44 slyfox Exp $ > > passwd:?????

Dear friends, I have written the following lines in R console wich already exist in pdf file systemfit: data( "GrunfeldGreene" ) library( "plm" ) GGPanel <- plm.data( GrunfeldGreene, c( "firm", "year" ) ) greeneSur <- systemfit( invest ~ value + capital, method = "SUR", + data = GGPanel ) greenSur I have obtained the following incomplete

Hi Aki, On 30/10/2017 12:43 AM, Aki Tuomi wrote: >> On October 29, 2017 at 1:55 PM Reuben Farrelly <reuben-dovecot at reub.net> wrote: >> >> >> Hi again, >> >> Chasing down one last problem which seems to have been missed from my >> last email: >> >> On 20/10/2017 9:22 PM, Stephan Bosch wrote: >>> >>> Op 20-10-2017 om

Hi again, Chasing down one last problem which seems to have been missed from my last email: On 20/10/2017 9:22 PM, Stephan Bosch wrote: > > > Op 20-10-2017 om 4:23 schreef Reuben Farrelly: >> On 18/10/2017 11:40 PM, Timo Sirainen wrote: >>> On 18 Oct 2017, at 6.34, Reuben Farrelly <reuben-dovecot at reub.net> >>> wrote: This problem below is still

Dear R Users, My question is overall and not necessarily related to R. Suppose we face to a situation in which MSE( Mean Squared Error) shows desired results but Bias shows undesired ones, Or in advers. How can we evaluate the results. And suppose, Both MSE and Bias are important for us. The ecact question is that, whether there is any combined measure of two above metrics. Thank you so

I think this might be caused by glibc 2.28. I saw a similar error with the "doveadm purge" command after upgrading my system to that version. Michael Marley On 2018-08-31 13:10, Aki Tuomi wrote: > I'll see if this is reproducible > > --- > Aki Tuomi > Dovecot oy > > -------- Original message -------- > From: Reuben Farrelly <reuben-dovecot at

Hi, I am using nnet to learn transfer functions. For each transfer function I can estimate the best possible Mean Squared Error (MSE). So, rather than trying to grind the MSE to 0, I would like to use abstol to stop training once the best MSE is reached. Can anyone confirm that the abstol parameter in the nnet function is the MSE, or is it the Sum-of-Squares (SSE)? Best regards, Sam.