This would almost certainly fit better on the r-sig-mixed-models
list,rather than here. You are more likely to get authoritative responses
about this specialized statistical topic there.
Also -- these are **plain text** mailing lists. Please do not post in html.
Cheers,
Bert
Bert Gunter
"The t
d
Sent: Monday, June 29, 2015 9:21 AM
To: PIKAL Petr
Cc: r-help
Subject: Re: [R] Simulating data
ok. ill just paste the data here ... hope it helps
y
x
5721
20175
4285
1
4327
59426
4964
75536
7899
79432
11140
125735
11843
89411
18146
124805
24712
110859
31993
178038
ok. ill just paste the data here ... hope it helps
y x 5721 20175 4285 1 4327 59426 4964 75536 7899 79432 11140
125735 11843 89411 18146 124805 24712 110859 31993 178038 41217 164212
96 1823 129 3440 151 3860 243 4630 292 4550 336 4775 517 5326 617
6030 1572 8038 1628
Hi
Attachments are usually discarded.
maybe ?sample
Petr
> -Original Message-
> From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of deva d
> Sent: Monday, June 29, 2015 8:36 AM
> To: r-help
> Subject: [R] Simulating data
>
> i wish to simulate data to generate twice the sa
The examples on the help page for the function "simfun" in the
TeachingDemos package have some examples of simulating data from
nested designs with some terms fixed and some random. I don't think
any of the examples match your conditions exactly, but could be
modified to do so (changing a random e
Hi,
I am trying to 'create' a nested design with A, B nested in A and C nested
in B. C is random and the others are fixed.
Does anyone have any idea how to do this?
I would also like to try the other nested designs with all random and all
effects fixed.
--
Thanks,
Jim.
[[alternative HTM
Hi Teresa
Try this:
Median <- 4.3
Mean <- 4.2
SD <- 1.8
RangeLower <- 0
RangeUpper <- 8
par(mfcol = c(1,2))
(bpstats <- boxplot(rnorm(100, mean = Mean, sd = SD), outl = FALSE))
## assuming normality of original data since we don't know the distribution
## get the 25% and 75% quantile
q25 <- qn
Hi Sarah,
There is one thing you need to think about: how do you choose which
values should not be removed if you have more than 20 and which should
be if you have less than 20. In my code, I've just done it with
sample(), which might not be what you need.
Here is what I have:
if (length(whi
Dirty hack, but it's working.
library(MASS)
mu <- aic.mv$best.mo...@expected.value
sigma <- aic.mv$best.mo...@variance
mvrnorm(100,mu,sigma)
If you'd like to follow the rules, look for the functions to extract
the expected value and the variance of the best model out of the
stepAIC.ghyp object.
Hello
Your attachement didn't seem to get through.
You can simulate data using rnorm() or any of the r*() functions [1].
You can also use it to add noise to a custom function that you use to
generate your specific data.
Liviu
[1] http://www.statmethods.net/management/functions.html
On 8/25/09,
Dear All
I know that you do not have to help me but please do, i am new to R as a CPI
compiler, i just need to do a sample to see which sampling method best works in
different situations, therefore since this is for practice purposes nobody will
finance a real project thats why i need you to h
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