dear r users
I have one question that how we add one or more outliers in the data set.
For example if we generate data set from Weibull distribution using function
n=10
k<-rweibull(n, shape=2.5, scale = 1.3)
k
the output is
> k
[1] 0.6507619 0.6229385 1.6838931 1.1661324 0.4907947 1.341
Dear Ellison
yes its working but if i want to replaced the any value in the the output . e.g
i want to replace 0.65 with 10. then what i do
From: S Ellison
Sent: Monday, September 19, 2016 5:15:20 PM
To: Muhammad Kashif; r-help@r-project.org
Subject: RE: add
Dear i optimized the gama and beta value using simmulation. if i run this code
it generate very small value of beta. Could any one help me in this regard. i
use gbs package to generate data.
gama=1.0
beta=1.3
n=25
iterCount=1000
for(i in 1:iterCount){
x<-rgbs(n,gama,beta)
P<-function(theta,
Dear i optimized the gama and beta value using MLE via simmulation of birnbaum
saunders distribution. if i run this code it generate very small value of beta.
Could any one help me in this regard. i use gbs package to generate data.
gama=1.0
beta=1.3
n=25
iterCount=1000
for(i in 1:iterCount){
x<
Thank you Professor and sorry for question on personal email.
From: peter dalgaard
Sent: Monday, January 4, 2016 2:15 AM
To: Muhammad Kashif
Cc: Group R-help
Subject: Re: Thanks and further question
Please keep on-list (cc'ed), for various good re
Dear
Is there any function which calculate time for one simulation when we use "
asim=2000" under any simulation study. I run one simulation code on R and i
have core i 5 laptop. Each simulation take about 30 -40 minutes.
Is there any function which calculate time of each output.
thanks
Dear respected group members
who we calculate trimmed and Winsorized mean of data. can we calculate directly
of any latest package is available for their calculation.
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Dear Group members
Can any one help to code this situation. Suppose we have a population with some
mean and a standard deviation. Then , there are n1 observations out of n
which are less than or equal to n. Also, there are n2 observations out of n
which are greater than . We divide the wh
Dears
Can anyone help me to solve the issue.
By using" boot" and "boot.ci" package in R we can construct bootstrap
confidence intervals. How we calculate the coverage probability of these
intervals.
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