If you wanted to do a t.test
res1-do.call(cbind,lapply(seq_len(nrow(m)),function(i)
do.call(rbind,lapply(split(rbind(m[i,-1],n),1:nrow(rbind(m[i,-1],n))),
function(x) {x1- rbind(x,m[i,-1]); t.test(x1[1,],x1[2,])$p.value}
res2-do.call(cbind,lapply(seq_len(ncol(res1)),function(i)
On Wed, 27 Jun 2012 16:58:29 +1200
Rolf Turner rolf.tur...@xtra.co.nz wrote:
On 27/06/12 08:54, arun wrote:
Hi,
The error is due to less than 5 observations in some cells.
NO, NO, NO It's not the observations that matter, it is
the ***EXPECTED COUNTS***. These must
On 2012-06-26 23:02, John wrote:
On Wed, 27 Jun 2012 16:58:29 +1200
Rolf Turnerrolf.tur...@xtra.co.nz wrote:
On 27/06/12 08:54, arun wrote:
Hi,
The error is due to less than 5 observations in some cells.
NO, NO, NO It's not the observations that matter, it is
the
Dear all!
Thanks for clarification.
OV
To: Rolf Turner rolf.tur...@xtra.co.nz
Sent: Wednesday, June 27, 2012 1:33 PM
Subject: Re: [R] chisq.test
Hi Rolf,
Thanks for spotting the mistake.
A.K.
- Original Message -
From: Rolf Turner rolf.tur
Dear list!
I would like to calculate chisq.test on simple data set with 70 observations,
but the output is ''Warning message:''
Warning message:
In chisq.test(tabele) : Chi-squared approximation may be incorrect
Here is an example:
tabele - matrix(c(11, 3, 3, 18, 3, 6, 5, 21), ncol
On 2012-06-26 11:27, Omphalodes Verna wrote:
Dear list!
I would like to calculate chisq.test on simple data set with 70 observations,
but the output is ''Warning message:''
Warning message:
In chisq.test(tabele) : Chi-squared approximation may be incorrect
Here is an example:
Sent: Tuesday, June 26, 2012 1:28 PM
To: r-help@r-project.org
Subject: [R] chisq.test
Dear list!
I would like to calculate chisq.test on simple data set with 70
observations, but the output is ''Warning message:''
Warning message:
In chisq.test(tabele) : Chi-squared approximation may
On Jun 26, 2012, at 2:27 PM, Omphalodes Verna wrote:
Dear list!
I would like to calculate chisq.test on simple data set with 70
observations, but the output is ''Warning message:''
Warning message:
In chisq.test(tabele) : Chi-squared approximation may be incorrect
Here is an example:
To: r-help@r-project.org r-help@r-project.org
Cc:
Sent: Tuesday, June 26, 2012 2:27 PM
Subject: [R] chisq.test
Dear list!
I would like to calculate chisq.test on simple data set with 70 observations,
but the output is ''Warning message:''
Warning message:
In chisq.test(tabele) : Chi-squared
omphalodes.ve...@yahoo.com
To: r-help@r-project.org r-help@r-project.org
Cc:
Sent: Tuesday, June 26, 2012 2:27 PM
Subject: [R] chisq.test
Dear list!
I would like to calculate chisq.test on simple data set with 70 observations,
but the output is ''Warning message:''
Warning message:
In chisq.test
Hello,
I am trying to fit gamma, negative exponential and inverse power functions
to a dataset, and then test whether the fit of each curve is good. To do
this I have been advised to calculate predicted values for bins of data (I
have grouped a continuous range of distances into 1km bins), and
On Feb 20, 2012, at 5:57 AM, Louise Mair wrote:
Hello,
I am trying to fit gamma, negative exponential and inverse power
functions
to a dataset, and then test whether the fit of each curve is good.
To do
this I have been advised to calculate predicted values for bins of
data (I
have
On Aug 19, 2011, at 20:40 , David Winsemius wrote:
On Aug 19, 2011, at 1:28 PM, Stephen Davies wrote:
I'm using chisq.test() on a matrix of categorical data, and I see that the
residuals attribute of the returned object will give me the Pearson
residuals.
That's cool. However, what I'd
On Aug 20, 2011, at 3:43 AM, peter dalgaard wrote:
On Aug 19, 2011, at 20:40 , David Winsemius wrote:
On Aug 19, 2011, at 1:28 PM, Stephen Davies wrote:
I'm using chisq.test() on a matrix of categorical data, and I see
that the
residuals attribute of the returned object will give me the
I'm using chisq.test() on a matrix of categorical data, and I see
that the
residuals attribute of the returned object will give me the
Pearson residuals.
Actually they are not an attribute in the R sense, but rather a list
value.
Oh. I was just going by:
On Aug 20, 2011, at 12:04 PM, Stephen Davies wrote:
I'm using chisq.test() on a matrix of categorical data, and I see
that the
residuals attribute of the returned object will give me the
Pearson residuals.
Actually they are not an attribute in the R sense, but rather a list
value.
Oh.
On Aug 20, 2011, at 18:04 , Stephen Davies wrote:
As for $stdres, that would be wonderful, but
as you can see from the above list of attributes, it's not one of the 8
returned. What am I missing?
An upgrade, most likely.
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen
On Aug 20, 2011, at 12:57 PM, peter dalgaard wrote:
On Aug 20, 2011, at 18:04 , Stephen Davies wrote:
As for $stdres, that would be wonderful, but
as you can see from the above list of attributes, it's not one of
the 8
returned. What am I missing?
An upgrade, most likely.
Whoosh.
I'm using chisq.test() on a matrix of categorical data, and I see that the
residuals attribute of the returned object will give me the Pearson residuals.
That's cool. However, what I'd really like is the standardized (adjusted)
Pearson residuals, which have a N(0,1) distribution. Is there a way to
On Aug 19, 2011, at 1:28 PM, Stephen Davies wrote:
I'm using chisq.test() on a matrix of categorical data, and I see
that the
residuals attribute of the returned object will give me the
Pearson residuals.
That's cool. However, what I'd really like is the standardized
(adjusted)
Pearson
Hi,
This is a mixed conceptual/methodological issue.
I have 3 years and 2 localities, I want to compare the Sex Ratio series between
the two localities.
I can do it year by year, for instance:
SR2010-data.frame(FAO=c(96,52),JUNC=c(60,42))
SR2010
FAO JUNC
1 96 60
2 52 42
Hello,
I am trying to see whether there has been a significant difference in whether
people experienced damages from wildlife in two different years. I therefore
have two columns:
year 1:
yes
no
no
no
yes
yes
no
year 2:
no
yes
no
yes
I wanted to do a chisq.test, but if I enter it this way:
On Aug 24, 2010, at 10:12 AM, Marino Taussig De Bodonia, Agnese wrote:
Hello,
I am trying to see whether there has been a significant difference
in whether people experienced damages from wildlife in two different
years. I therefore have two columns:
year 1:
yes
no
no
no
yes
yes
no
On Aug 24, 2010, at 4:12 PM, Marino Taussig De Bodonia, Agnese wrote:
Hello,
I am trying to see whether there has been a significant difference in whether
people experienced damages from wildlife in two different years. I therefore
have two columns:
year 1:
yes
no
no
no
yes
yes
A Likert scale may have produced counts of answers per category.
According to theory I may expect equality over the categories. A
statistical test shall reveal the actual equality in my sample.
When applying a chi square test with increasing number of repetitions
(simulate.p.value) over a
soeren.vo...@eawag.ch wrote:
A Likert scale may have produced counts of answers per category.
According to theory I may expect equality over the categories. A
statistical test shall reveal the actual equality in my sample.
When applying a chi square test with increasing number of repetitions
On Mar 11, 2009, at 6:36 AM, soeren.vo...@eawag.ch wrote:
A Likert scale may have produced counts of answers per category.
According to theory I may expect equality over the categories. A
statistical test shall reveal the actual equality in my sample.
When applying a chi square test with
Thanks to Peter Dalgaard for the correct answer. I misinterpreted what
R was returning.
On Mar 11, 2009, at 7:32 AM, David Winsemius wrote:
On Mar 11, 2009, at 6:36 AM, soeren.vo...@eawag.ch wrote:
A Likert scale may have produced counts of answers per category.
According to theory I
Thanks to Peter, David, and Michael! After having corrected the coding
error, the p values converge to particular value, not necessarily
zero. The whole story is, 634 respondents in 6 different areas marked
their answer on a 7-step Likert scale (very bad, bad, ..., very good
-- later
Dear all,
I would like to compute hundreds of chisq.test ´s, and for each test output I
would like to extract only the p-values. So my question is: how can I make this
without making it manually?
Example:
# Test nº1
chisq.test(c(220,240))
Chi-squared test for given
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