similar to: Help in R

Hello. Slope estimates in lme are shrinkage estimates which pull the OLS slope estimates towards the population estimates, the degree of which depends on the group sample size and the distance between the group-based estimate and the overall population estimate. Although these shrinkage estimates as said to be more precise with respect to the true values, they are also biased. So there is a

Dear R users, I?m a graduate students and in my master thesis I must obtain the values of the parameters x_i which maximize this Multinomial log?likelihood function log(n!)-sum_{i=1]^4 log(n_i!)+sum_ {i=1}^4 n_i log(x_i) under the following constraints: a) sum_i x_i=1, x_i>=0, b) x_1<=x_2+x_3+x_4 c)x_2<=x_3+x_4 I have been using the ?ConstrOptim? R-function with the instructions

Dear helpers in this forum, This is a clarified version of my previous questions in this forum. I really need your generous help on this issue. > Suppose I have the following data set: > > id x y > 023 1 2 > 023 2 5 > 023 4 6 > 023 5 7 > 412 2 5 > 412 3 4 > 412 4 6 > 412 7 9 > 220 5 7 > 220 4 8 > 220 9 8 > ...... > Now I want to compute the

Hi all, I have a problem using rdirichlet{gtools}. For Dir( a1, a2, ..., a_n), its mode can be found at $( a_i -1)/ ( \sum_{i}a_i - n)$; The means are $a_i / (\sum_{i} a_i ) $; I tried to study the above properties using rdirichlet from gtools. The code are: ############## library(gtools) alpha = c(1,3,9) #totoal=13 mean.expect = c(1/13, 3/13, 9/13) mode.expect = c(0, 2/10, 8/10) #

Hi all; I recently have been used 'maxLik' function for maximizing G2StNV178 function with gradient function gradlik; for receiving this goal, I write the following program; but I have been seen an error  in calling gradient  function; The maxLik function can't enter gradlik function (definition of gradient function); I guess my mistake is in line ******** ,that the vector  ‘h’ is

Hi Firstly , happy Christmas to R-Help! Secondly, I wonder if anyone can help me with the following query: I am trying to reproduce some explicit probability calculations performed in APPL (a Maple extension for computational probability). For instance, in APPL, to compute the probability that the sum of 10 iid uniform variables [0,1] will be between 4 and 6, (i..e Pr( 4 < \sum_{i=1}^{10}X_i

This has ABSOLUTELY nothing to do with R but I was hoping that someone might know because there are obviously a lot of very bright people on this list. Suppose I had a time series of data and at each point in time t, I was calculating x bar + plus minus sigma where x bar was based on a moving window of size n and so was sigma. So, if I was at time t , then x bar t plus minus sigma_t would be

Hello, I'm doing some linear regression: >lm<-lm(osas~alp,data) >summary(lm) However, the Rsquared in the output of summary() is not the same as the "standard" Rsquared calculated by spreadsheets, and outlined in statistical guidebooks, being SSR/SSTO. The output says "multiple Rsquared", but it is no multiple regression... What's the difference? Thanks,

Hi, I am trying to construct a logistic regression model from my data (104 patients and 25 events). I build a full model consisting of five predictors with the use of penalization by rms package (lrm, pentrace etc) because of events per variable issue. Then, I tried to approximate the full model by step-down technique predicting L from all of the componet variables using ordinary least squares

I have probably misunderstood something (again :-(): > CEMPLOY<-TEMPLOY[!is.na(TEMPLOY)] > sum(CEMPLOY^2/length(CEMPLOY))-mean(CEMPLOY)^2 [1] 30877.28 > var(TEMPLOY,na.rm=TRUE) [1] 30981.25 Could anybody tell me, what's going on? I suppose that var=sum(x^2/N)-xbar^2 or what did I enter wrong? Thanks Matej -- Matej Cepl, matej at ceplovi.cz, Finger: 89EF 4BC6 288A

I wonder whether the functions for skewness and kurtosis in the e1071 package are based on correct formulas. The functions in the package e1071 are: # -------------------------------------------- skewness <- function (x, na.rm = FALSE) { if (na.rm) x <- x[!is.na(x)] sum((x - mean(x))^3)/(length(x) * sd(x)^3) } # -------------------------------------------- and #

Dear Mac users, Hi, as you might have probably read the thread of "[R] Rsquared in summary(lm)" on May 10, it seems that Mac version of lm() seem to be working incorrectly. I enclose the script to produce the result both for lm() and manual calculation for a simple regression. Could you run the script and report with the version of R, so I don't have to go through every builds

Hallo, I have a question concerning SVM regression in R. I intend to use SVMs for feature selection (and knowledge discovery). For this purpose I will need to extract the weights that are associated with my features. I understand from a previous thread on SVM classification, that predictive models can be derived from SVs, coefficiants and rhos, but it is unclear for me how to transfer this

Dear list, It looks to me that R samples random integers using an intuitive but biased algorithm by going from a random number on [0,1) from the PRNG to a random integer, e.g. https://github.com/wch/r-source/blob/tags/R-3-5-1/src/main/RNG.c#L808 Many other languages use various rejection sampling approaches which provide an unbiased method for sampling, such as in Go, python, and others

I was surprised to find that the variance of a scalar, using var(), is NA. Surely this should be zero? Cheers, Jonathan. --please do not edit the information below-- Version: platform = sparc-sun-solaris2.7 arch = sparc os = solaris2.7 system = sparc, solaris2.7 status = Patched major = 0 minor = 99.0 year = 2000 month = February day = 9 language = R Search Path: .GlobalEnv,

Dear all, Does anyone knows why the results of a BIOENV (PRIMER v. 6.1.15) are diferent of the bioenv() + mantel() in vegan? Not the spearman correlation, indeed the pseudo-p value. I know that the approach bioenv() + mantel() is biased. So, how the BIOENV (PRIMER) ends with larger p values (permutated). Acctualy how the permutation test in BIOENV (PRIMER) is conducted. The user guide does not

On Feb 2, 2014, at 6:18 PM, Andrew Trick <atrick at apple.com> wrote: >> The result of such a system would produce weights for every block in the above CFG as '1.0', or equivalent to the entry block weight. This to me is a really useful metric -- it indicates that no block in the CFG is really more or less likely than any other. Only *biases* in a specific direction would

Hi Gents, I need to get the control limits from qcc function. As follows: qcc(MDI, type = "xbar.one") Call: qcc(data = MDI, type = "xbar.one") xbar.one chart for MDI Summary of group statistics: Min. 1st Qu. Median Mean 3rd Qu. Max. 0.3266 0.4249 0.4371 0.4333 0.4451 0.4858 Group sample size: 1 Number of groups: 383 Center

El mi?., 19 sept. 2018 a las 14:43, Duncan Murdoch (<murdoch.duncan at gmail.com>) escribi?: > > On 18/09/2018 5:46 PM, Carl Boettiger wrote: > > Dear list, > > > > It looks to me that R samples random integers using an intuitive but biased > > algorithm by going from a random number on [0,1) from the PRNG to a random > > integer, e.g. > >

On Wed, 19 Sep 2018 at 13:43, Duncan Murdoch <murdoch.duncan at gmail.com> wrote: > > I think the analyses are correct, but I doubt if a change to the default > is likely to be accepted as it would make it more difficult to reproduce > older results. I'm a bit alarmed by the logic here. Unbiased sampling seems basic for a statistical language. As a consumer of R I'd