Thanks for the answer. Now, only problem is to to get parameter(s) of a
given function. For gamma, I shall try with gammafit() from mhsmm
package. Also, I shall look for others appropriate parameter estimates.
Will use SuppDists too.
Best,
PM
Sunil Suchindran wrote:
#same shape
some_data -
The supposed example of a Q-Q plot is most certainly not how to make a
Q-Q plot. I don't even know where to start
First off, the two Q:s in the title of the plot stand for quantile,
not random. The answer supplied simply plots two sorted samples of
a distribution against each other. While
Eric Thompson wrote:
The supposed example of a Q-Q plot is most certainly not how to make a
Q-Q plot. I don't even know where to start
First off, the two Q:s in the title of the plot stand for quantile,
not random. The answer supplied simply plots two sorted samples of
a distribution
I think it's helpful to show the sampling variability in a QQ plot
under repeated
sampling. An example is given
in Venables, Ripley pg 86. The variance is higher at the tails. Even when the
distributions are the same, the QQ plot does not have to resemble a straight
line because of sampling. I
Thanks all! I did not want to cause any trouble and, God forbid,
offense. I thought, I asked a simple question to improve my
understanding and R-skills.
It seems that there ain't single gospel truth about QQs. :-)
Thanks, again!
Best,
PM
Juliet Hannah wrote:
I think it's helpful to show the
It seems I misunderstood Sunil's response and somewhat freaked out
because it appeared that he was giving the wrong method for making a
QQ plot, but was actually demonstrating the sampling variability. My
apologies to Sunil.
2009/9/27 Duncan Murdoch murd...@stats.uwo.ca:
Eric Thompson wrote:
#same shape
some_data - rgamma(500,shape=6,scale=2)
test_data - rgamma(500,shape=6,scale=2)
plot(sort(some_data),sort(test_data))
# You can also use qqplot(some_data,test_data)
abline(0,1)
# different shape
some_data - rgamma(500,shape=6,scale=2)
test_data - rgamma(500,shape=4,scale=2)
Hello!
I am trying with this question again:
I would like to test few distributional assumptions for some behavioral
response data. There are few theories about true distribution of those
data, like: normal, lognormal, gamma, ex-Gaussian
(exponential-Gaussian), Wald (inverse Gaussian) etc. The
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