Hi there,
I am relatively new user of R. I need to generate random number following
Gamma distribution with mean 14 und st.dev 3. I read the help-text but I can
not understand it well.
Regards,
Azizi
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Hello Hadi,
See ?rgamma
The Gamma distribution usually takes two parameters, shape and scale,
not the mean and st. deviation. If you have data, you can estimate
those parameters using MLE methods, which are nicely provided in MASS:
library(MASS)
fitdistr(yourdata,Gamma)
Once you have your
Hi list,
are there any functions or ideas to compute random numbers with a specific
population mean and standard deviation from symmetric (but not normal) and
asymmetric distributions? My first idea was to use e.g. rf() (and other
R-functions for random number generation) and then scale the
Garbade, Sven a écrit :
Hi list,
are there any functions or ideas to compute random numbers with a specific
population mean and standard deviation from symmetric (but not normal) and
asymmetric distributions? My first idea was to use e.g. rf() (and other
R-functions for random number
Hi All.
(This is probably an incredibly stupid question :))
I have just noticed that every time I start an R2.3 session and request
a random number (say with rnorm) that I receive the same number. AFAIK I
have not set a seed at any point. Returning to an older version of
R(v1.8) I get the
On 6/1/2006 3:13 PM, Carl wrote:
Hi All.
(This is probably an incredibly stupid question :))
I have just noticed that every time I start an R2.3 session and request
a random number (say with rnorm) that I receive the same number. AFAIK I
have not set a seed at any point. Returning to an
according to ?.Random.seed, `Note' section, different R sessions give
differen simulation results; maybe each time you start R you load a
previously saved workspace, with an existing .Random.seed, i.e., check
if the .Random.seed value is the same each time you start R.
I hope it helps.
Best,
That certainly sounds like the behaviour you would get if you
had a .Random.seed in your work space. If you do not save your
workspace at the end of the session then the random seed will
be in exactly the same state every time you start a new R session,
and you will get identical simulations from
Hi every one,
I am trying to generate a normally distributed random variable with the
following descriptive statistics,
min=1, max=99, variance=125, mean=38.32, 1st quartile=38, median=40, 3rd
quartile=40, skewness=-0.274.
I know the rnorm will allow me to simulate random numbers with mean
Hi every one,
I am trying to generate a random variable with the following descriptive
statistics,
min=1, max=99, variance=125, mean=38.32, 1st quartile=38, median=40, 3rd
quartile=40, skewness=-0.274.
I tried with rgamma and as I cannot use rnorm, can any one please suggest me
what
You need to know the exact distribution of the random numbers you want
to generate. For rnorm, in fact, you do not just specify the mean and
the variance, but implicitely also that the data is normally
distributed. Likewise, it is not sufficient to give min, max, skewness
etc, you also need to
Is this a student exercise? If not, please enlighten us as to the
real-world problem from which this is extracted.
Given that 50% of the probability mass lies between 38 and 40, and the
median and 3rd quartile are both 40, this cannot be a continuous
distribution. I would design a discrete
These conditions are mutually exclusive for a lot of reasons, therefore,
there's no way to generate such data. Briefly, the normal distribution
is fully specified by the mean and variance, the other conditions are
superfluous, and, in some cases, impossible
Please tell us what you are actually
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