dear friends
can any body give me a simple idea about Logstic regression and log-
lenear models

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dear friends
can any body give me a simple idea about Logstic regression and log-
lenear models

Hello All,

 

Can anyone tell me as to how the SQL SMALLINT & VARCHAR datatype is used in SAS? In my case I have the definition of a field in a file as SMALLINT & the content of the file is in EBCDIC format. I tried using S370FZD, S370FF, etc. But it doesnt seem to work.

 

Thanks a ton.

 

Nisha.

---

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ya I agree Matlab has some in-built function for GARCH. But it is only
for Univariate GARCH, not multivariate. However some people already
wrote algo. for Multivariate GARCH, based on BEKK implementation,
flexM, etc, but I dont think they are much reliable. I will prefer R
for this type of analysis. R has "GARCH" function for Univariate case,
and "mgarchBEKK" for bivariate case. However general multivariate case
yet to be come.

as a bit of knitpicking ..I don't think that the distribution of your
random variable is ever known..it is just assumed..

People use nonparametrics for a more robust and flexible solution to
cover a wide set of data sets.
for further discussion reference:
http://sunsite.univie.ac.at/textbooks/statistics/stnonpar.html

--- In Statisticians_group@..., karthik karthik
<g_s_karthik@y...> wrote:
>
> Hi
>   When we go for non parametric tests???
>
>   Case (1) The sample size is very small (<30)
>   Case(2) The distribution of the random variable is not known.
>
>   Is there anyother case???
>
>   Even in the second case, we can use central limit theorem and can
proeceed with parametric tests. (Then why non-parametric..?? Any
specific reasons??)
>
>   Thanks in advance.
>   Regards
>   Karthik.S
>
>
> ---------------------------------
> Yahoo! Messenger with Voice. Make PC-to-Phone Calls to the US (and
30+ countries) for 2¢/min or less.
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Hi friends,

Recently I am working on Multivariate Time series model like VARMA
(Vector autoregressive moving average) and also models like GARCH and
Multivariate GARCH.Would you give me some references of free
Statistical packages for calculation regarding this models?

Dear Madan



I would suggest you can use command "Leave"



For example



do until (l > h);

    m = floor ((l+h)*.5);

    if      k > a(m) then l = m+1;

    else if k < a then h = m-1;

    else do;

       put a(m)=;

       leave;

    end;

end;



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--- In Statisticians_group@..., "Madan Gopal Kundu"
<Madan.Kundu@r...> wrote:
>
>
> Dear all,
> We know that in C language we can use BREAK statement to get out
of a
> loop. What is the analogous statement in SAS that can meet my
purpose as
> BREAK dose in C language.
> Thanks in advance.
> Regards,
> MADAN
>
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Dear all,
We know that in C language we can use BREAK statement to get out of a
loop. What is the analogous statement in SAS that can meet my purpose as
BREAK dose in C language.
Thanks in advance.
Regards,
MADAN

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Use R

www.r-project.org

I think investigating R (http://www.r-project.org)
would be a good idea.  I don't know if there are
implementations of multivariate GARCH, but there
is a working paper on the Burns Statistics website
detailing how to get a multivariate GARCH estimate
when only univariate GARCH estimators are available.

Patrick Burns
patrick@...
+44 (0)20 8525 0696
http://www.burns-stat.com


maitytanmay wrote:

> Hi friends,
>
> Recently I am working on Multivariate Time series model like VARMA
> (Vector autoregressive moving average) and also models like GARCH and
> Multivariate GARCH.Would you give me some references of free
> Statistical packages for calculation regarding this models?
>
>
>
>
>
>
>

Hi friends,

Recently I am working on Multivariate Time series model like VARMA
(Vector autoregressive moving average) and also models like GARCH and
Multivariate GARCH.Would you give me some references of free
Statistical packages for calculation regarding this models?

I would request you all to give me suggestion where to learn SAS
package in Delhi

I am new to stats and have a couple basic questions regarding
sampling , the central limit theorem and probability.

If the sample size is 30 or greater then the sample means follow a
normal distribution.I wanted to know the minimum size of the
observations in each sample and its limits. Can I take 30 samples
each having 2 observations with the knowledge that the means of
these samples would follow a normal distribution?

Also I would like to get an intuition about probability
distributions. What is the fundamental difference between
probalility distruibutions and frequency distributions?

Can anyone please let me know about these as I am not able to
progress much without getting my basics clear :-)

Thanks

Viv

I have little experience with binary data or logistic regression.

I am interested in doing a simple analysis of an interlaboratory study of a
rapid presence-absence test kit vs. a reference method.

There are say 8 laboratories, each of whom analyze the same sample in
replicate (say 10 times). Each replicate corresponds to a pair of test
results (Test method +/- and reference method +/-). Obviously each
replicate is a matched pair, or repeated measure. There are a total of 8 x
10 x (2) = 160 data or 80 pairs, each of the form ++,+-,-+ or --.

Sensitivity is defined as the % of the time the two methods agree on a +
result, and specificity is defined as the % of the time the two methods
agree on a - result.

There would appear to be a number of questions arising from such a study:

Q1. Is there a significant difference between laboratories? I.e.,
heterogeneity.

Q2. Is there a significant difference between the two methods? (Based on
the difference of sensitivity of the two methods?)

Q3. What is the best point and interval estimates for the sensitivity and
specificity of the test method?

Q4. Is there a convenient manual method for answering the above questions?

Q5. What is the likelihood function for this experiment?

Q6. What logistic regression model should be used? What dependent variable
should be fit?

Q7. How would the logistic regression be implemented in SAS? (I.e., PROC
LOGISTIC lines.)

If anyone is willing to answer one or more of these questions, I would be
very grateful.

Thanks.
================================================================
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral@...
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
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Virginia Beach, VA 23464-3239            Fax: 757-467-2947

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At 04:41 AM 9/12/2006, you wrote:
>Hi all,
>
>Can any one tell me what is log regression? I know
>about logistic regression, log-linear regression etc,
>but I have no idea about log regression.

E(Y) =  a + b ln X

================================================================
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS  e-mail: ral@...
Least Cost Formulations, Ltd.            URL: http://lcfltd.com/
824 Timberlake Drive                     Tel: 757-467-0954
Virginia Beach, VA 23464-3239            Fax: 757-467-2947

"Vere scire est per causas scire"
================================================================

Dear Robert,

Thanks a lot for your kind response. It is quiet informative for me.

Thanks and Regards,
MADAN

-----Original Message-----
From: Robert Newcombe [mailto:newcombe@...]
Sent: 11 September 2006 20:29
To: Madan Gopal Kundu
Subject: Re: [Statisticians_group] McNemar test in SAS

Dear Madan,

I'm not familiar with what SAS does here. But it's very easy to compute
a McNemar test manually, simply calculate (b-c)^2 / (b+c) where b and c
are the two doscordant cell counts, this has asymptotically a chi-square
dist. with 1 df. For your data, chi-sq = 88, with p << 0.001. Or you
could use an exact binomial p-value which is 2 ^ -88 one-sided, or
preferably 2 ^ -87 two-sided. All of these are of course astronomically
statistically significant (2 ^ -87 is approx 10 ^ -26).

Given the 'overkill' statistical significance, a confidence interval
may be of greater interest. If an odds ratio is of interest, for example
in  retrospective casr-control study, this is simply estimated as b/c
i.e. 0/88 or 0. Then use your preferred method to get a CI for the
binomial proportion b/(b+c), then get the corresponding CI for b/c. If
the confidence limits for b/(b+c) are L and U, the CLs for b/c are
L/(1-L) and U/(1-U). The first block of the attached Excel file (which
is freely available either from my website or the statisticians' group
website) calculates a CI for the single proportion using the Wilson
score method. For b/(b+c) = 0/88, the 95% CI is 0 to 0.0418.   So the
95% CI for the odds ratio b/c is from 0 to 0.0418/0.9582 i.e. 0 to
0.0437.

On the other hand, if the data come from a crossover or longitudinal
study, the difference of proportions is of greater interest. Let a, b, c
and d be the four cells, with a+b+c+d=n. For your data, a=256, b=0,
c=88, d=0, n=344. Then the difference is (a+b)/n - (a+c)/n = (b-c)/n
which is -88/256 = -0.2558. The 3rd block of the Excel spreadsheet
calculates a CI for this using a good method, this is -0.3044 to -0.2112
here.  I'm attaching a pdf of the paper that presents this method.

There is the usual correspondence (approximately at least) between CI
and p-value. Here, the p-value is extreme, the CI for the odds ratio is
well clear of the null hypothesis value of 1, and the CI for the paired
difference is well clear of the null hypothesis value of 0.

Hope this helps.


Robert G. Newcombe PhD CStat FFPH
Professor of Medical Statistics
Department of Epidemiology, Statistics and Public Health
Centre for Health Sciences Research
Cardiff University
4th floor, Neuadd Meirionnydd
Heath Park, Cardiff CF14 4YS
Tel: 029 2068 7247
Fax: 029 2068 7236

Home page
http://www.cardiff.ac.uk/medicine/epidemiology_statistics/research/stati
stics/newcombe
For location see
http://www.cardiff.ac.uk/locations/maps/heathpark/index.html


>>> "Madan Gopal Kundu" <Madan.Kundu@...> 11/09/06 05:39 >>>

I want to compute McNemar test with the following data:

data abc;
input x $ y $ count;
cards;
Absent Absent 256
Absent Present 0
Present Absent 88
Present Present 0
;
run;
proc freq data=abc;
table x*y/agree;
weight count;
run;

But log shows that:

NOTE: No statistics are computed for x * y since y has less than 2
nonmissing levels.

However, with the formula given in book it can be computed manually.
So
I am wonder why SAS does not produce the McNemar test statistic. Is
this
a software limitation???? ..... or anything else...



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