Dear All,
when I do :
set.seed(123)
expected_distribution<-rbinom(1000,100,.05)
#Without jitter
qqplot(jitter(expected_distribution),count1_vector, xlab="Expected
distribution",ylab="Observed values")
qqline(count1_vector,distribution = function(probs) { qbinom(probs,
size=100, prob=0.05) },co
Dear Boris,
Many thanks,
Ashim
On Tue, Apr 18, 2017 at 7:56 PM, Boris Steipe
wrote:
> As per the help pages, the data samples are expected in the second
> argument, "y".
>
> So try
> qqplot(rbinom(n=100, size=100, p=0.05), count1_vector)
>
> ... and then plot your qqline()
>
> Alternatively,
As per the help pages, the data samples are expected in the second argument,
"y".
So try
qqplot(rbinom(n=100, size=100, p=0.05), count1_vector)
... and then plot your qqline()
Alternatively, try
qqline(count1_vector,
distribution = function(probs) { qbinom(probs, size=100, prob=0.05)
Dear Boris,
Thank you for your reply.
> dput(count1_vector)
c(5, 6, 4, 4, 6, 5, 4, 5, 3, 7, 5, 5, 3, 4, 8, 6, 10, 2, 4, 6,
8, 4, 4, 6, 8, 5, 6, 3, 7, 9, 4, 7, 5, 7, 3, 4, 5, 2, 11, 7,
8, 5, 5, 6, 3, 2, 3, 5, 9, 6, 5, 6, 7, 3, 10, 7, 6, 4, 9, 5,
7, 3, 7, 3, 2, 3, 4, 5, 10, 4, 5, 5, 6, 7, 4, 8, 7,
That's not how qqline() works. The line is drawn with respect to a
_reference_distribution_ which is the normal distribution by default. For the
binomial distribution, you need to specify the distribution argument. There is
an example in the help page that shows you how this is done for qchisq()
Dear Boris,
Okay and Thanks.
Best,
Ashim
On Mon, Apr 17, 2017 at 6:45 PM, Boris Steipe
wrote:
> Moreover, setting the seed once, then evaluating two functions means you
> are sampling from the same distributions, but you do in fact have different
> values. Outliers in the rarefied tails of the
Moreover, setting the seed once, then evaluating two functions means you are
sampling from the same distributions, but you do in fact have different values.
Outliers in the rarefied tails of the distribution may lie quite considerably
off the expected diagonal. Try
set.seed(123)
qqplot(rbinom(n
Dear Spencer,
Okay. Many thanks. My next query is how do I use qqline?
When I try
> qqline(rbinom(n=100,size=100,p=.05))
I don't get the line in the right place.
Best Regards,
Ashim
On Mon, Apr 17, 2017 at 6:31 PM, Spencer Graves <
spencer.gra...@effectivedefense.org> wrote:
>
>
> On 2017-04
On 2017-04-17 7:58 AM, Ashim Kapoor wrote:
Dear All,
set.seed(123)
qqplot(rbinom(n=100,size=100,p=.05), rbinom(n=100,size=100,p=.05) )
I expect to see 1 clear line,but I don't. What am I misunderstanding?
The distribution is discrete, and points are superimposed. Try
the following:
Hi Dagmar,
I hope this code below does what you want.
I use two data.frames. One is for the tiles and one is for the lines to
show changes in state. The 'reduce_entries' function is the heart of things
and can probably be improved.
Ulrik
library(ggplot2)
library(lubridate)
library(dplyr)
librar
Have you tried fitting your data to the Pearson family of distributions? In
particular the Pearson Type IV has parameters to fit skewed and kurtotic
distributions. The Pearson library is described here:
http://cran.r-project.org/web/packages/PearsonDS/PearsonDS.pdf
The Type IV is described here
Hi
See the file http://www.mijnbestand.nl/Bestand-6ZPTBYDLBZQI.txt here .
That file contains the results of a study on breathing resistance in
children with asthma and children with cystic fibrosis to investigate wheter
there is a relationship between breathing resistance and length in each of
th
Hi,
So in my example, I can say that the data comes from a moderate normal
distribution because the points more at the right lay straight to a straight
line, then the points at the left. Please a confirmation here.
But what is the information above (that the data is from a normal
distribution) say
Dear Özgür
On Wed, Jun 20, 2012 at 7:37 AM, Özgür Asar wrote:
> Why do you prefer robust methods in the example of Noor and why you need
> exact normality here?
>
The idea is that when you do hypothesis testing to check whether a
given distribution is normal, the results are rarely informative:
>Hi,
>So in my example, I can say that the data comes from a moderate normal
distribution because the points more at the >right lay straight to a
straight line, then the points at the left. Please a confirmation here.
>But what is the information above (that the data is from a normal
distribution)
Dear Liviu ,
Why do you prefer robust methods in the example of Noor and why you need
exact normality here?
Ozgur
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_
Dear Kjetil,
Simulated point-wise confidence envelopes are available from qqPlot() only for
studentized residuals from linear and generalized linear models. For an
independent sample of observations, the confidence envelopes produced by
qqPlot() are based on the standard errors of the order sta
Or uou can try
library(car)
?qqPlot
use that with argument simulate=TRUE, which will give a simulated
envelope around the curve for
comparison.
Kjetil
On Tue, Jun 19, 2012 at 9:30 AM, Özgür Asar wrote:
> Hi,
>
> Try boxplot for outliers.
>
> To decide whether they influence significantly, try
Hi,
But what are the functions of the outliers on the left and right? Does they
influence the normal distribution?
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_
On Tue, Jun 19, 2012 at 12:42 PM, Özgür Asar wrote:
> Following a straight line indicates less evidence towards non-normality. But
> QQ-Plot is an exploratory tool.
>
> You can confirm your ideas obtained from the QQ-Plot via noramlity tests
> such as Shapiro-Wilk test.
>
Hmm, some gurus on this
Hi,
Try boxplot for outliers.
To decide whether they influence significantly, try confirmatory normality
tests.
Ozgur
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Sent from the R help mailing list archive at Nabble.com.
Hi,
Following a straight line indicates less evidence towards non-normality. But
QQ-Plot is an exploratory tool.
You can confirm your ideas obtained from the QQ-Plot via noramlity tests
such as Shapiro-Wilk test.
See shapiro.test under stats package and nortest package.
Ozgur
--
View this mes
Dear Carol,
> -Original Message-
> From: carol white [mailto:wht_...@yahoo.com]
> Sent: November-02-09 4:04 PM
> To: 'Peter Flom'; John Fox
> Cc: r-h...@stat.math.ethz.ch; 'Yihui Xie'
> Subject: RE: [R] qqplot
>
> Thanks for all you
graphical presentation like five number, mean and sd as
suggested, or boxplot would have been more suited to be used.
thanks for your advices,
--- On Mon, 11/2/09, John Fox wrote:
> From: John Fox
> Subject: RE: [R] qqplot
> To: "'Peter Flom'"
> Cc: r-h...@stat.math
Peter Ehlers wrote
>
>That's not what qqline() does and for good reason - it treats
>x and y asymmetrically.
>
>But qqline() is a very simple function, using the quartiles
>as also suggested by John. Here's a modified version that
>should work for Carol:
>
>qqline2 <- function (x, y, ...)
>{
>
John Fox wrote
>
>I assumed that Carol wanted to compare the shapes of the distributions and
>to adjust for differences in centre and spread. To put a line through the
>quartiles or to base a line on the medians and IQRs is more robust than
>using the means and sds.
>
Hi John
Indeed it is.
It al
age-
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org]
On
> Behalf Of Peter Flom
> Sent: November-02-09 11:57 AM
> To: carol white; Yihui Xie
> Cc: r-h...@stat.math.ethz.ch
> Subject: Re: [R] qqplot
>
> carol white wrote
>
> >So the c
Peter Flom wrote:
David Winsemius wrote
I always assumed that the intercept was zero and the slope = unity.
y <- rt(200, df = 5)
qqnorm(y); qqline(y, col = 2)
qqplot(y, rt(300, df = 5))
abline(0, 1, col="red")
Suppose you have the following
x <- rnorm(500)
y <- 500*(x + runif(500, 0,1)
carol white wrote
>So the conclusion is that abline(0,1) should always be used and if it doesn't
>go through the qqplot, the two distributions are not similar?
I think it depends what you mean by "similar". E.g., if you mean "are both of
these distributions (e.g.) normal?" then abline(0,1) is
David Winsemius wrote
>I always assumed that the intercept was zero and the slope = unity.
>
> y <- rt(200, df = 5)
> qqnorm(y); qqline(y, col = 2)
> qqplot(y, rt(300, df = 5))
> abline(0, 1, col="red")
>
Suppose you have the following
x <- rnorm(500)
y <- 500*(x + runif(500, 0,1))
qqplot(x,
So the conclusion is that abline(0,1) should always be used and if it doesn't
go through the qqplot, the two distributions are not similar?
Thanks
--- On Mon, 11/2/09, Yihui Xie wrote:
> From: Yihui Xie
> Subject: Re: [R] qqplot
> To: "carol white"
>
8 0.427 -0.605 -0.066 -0.283
> -0.599 0.348 -0.693 0.284 -0.436
> -0.519 0.081 -0.590 0.678 -1.095
> 0.009 -0.253 -0.940 0.526 1.623
>
>
> --- On Mon, 11/2/09, David Winsemius wrote:
>
>> From: David Winsemius
>> Subject: Re: [R]
1.623
--- On Mon, 11/2/09, David Winsemius wrote:
> From: David Winsemius
> Subject: Re: [R] qqplot
> To: "carol white"
> Cc: r-h...@stat.math.ethz.ch
> Date: Monday, November 2, 2009, 8:17 AM
>
> On Nov 2, 2009, at 10:40 AM, carol white wrote:
>
> &g
Carol,
You could run a line through the pairs of first and third quartiles of the
two distributions, i.e., c(quantile(x, .25), quantile(y, .25)) and
c(quantile(x, .75), quantile(y, .75)). (Of course, you'd want the line to
extend across the whole graph.)
I hope this helps,
John
> -Original
On Nov 2, 2009, at 10:40 AM, carol white wrote:
Hi,
We could use qqplot to see how two distributions are different from
each other. To show better how they are different (departs from the
straight line), how is it possible to plot the straight line that
goes through them? I am looking for
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