On Fri, Sep 29, 2017 at 2:32 PM, peter dalgaard wrote:
>
>> On 29 Sep 2017, at 22:43 , MacQueen, Don wrote:
>>
>> I used to use SAS a lot, but I don't know what the line
>> *Yield Champagin;
>> does.
>
> Nothing. It's a comment...
Fortune nomination!
Peter
> On 29 Sep 2017, at 22:43 , MacQueen, Don wrote:
>
> I used to use SAS a lot, but I don't know what the line
> *Yield Champagin;
> does.
Nothing. It's a comment...
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
P
For the initial data step, assuming a data frame named stress already exists,
and using base R, you can start with something like this:
barcodes.to.delete <- c('16187DD4015', '16187DD6002', {complete the
comma-delimited vector of barcodes you don't want} )
yield <- subset(stress, !(barcode
On 30/09/17 07:45, jlu...@ria.buffalo.edu wrote:
The conceptual paradigm for R is only marginally commensurate with that of
standard statistical software.
You must immerse yourself in R to become proficient.
Fortune nomination.
cheers,
Rolf
--
Technical Editor ANZJS
Department of Statist
Hi all
I am not an experienced user of R.
I am trying to perform DIF analysis using Lordif and I get the follow error:
> GroupDIF <- lordif(resp.data=Resp, group=Group, criterion="R2",
> pseudo.R2="McFadden", R2.change=0.02)
Iteration: 500, Log-Lik: -137340.437, Max-Change: 0.00119
EM cycles ter
Hi,
I would echo Bert's comments below.
The last thing that you want to try to do is to convert SAS code to R code on a
"line for line" basis. The programming paradigm of R, which is built upon
vectorized operations, takes a "whole object" approach for efficiency. SAS does
not, since it is gen
All HTML emails have a plain text part along with the HTML part... but it is
usually invisible to the author and is automatically generated by the email
composing software and some software is better than others at that job (by a
lot). However, without a doubt, sending the email in text form at
I wish to second this approach to learning R.
I tried for several years to translate other stat programs or provide
parallel analyses with R.
This dabbling-in-R approach did not work
.
When a transferred to a research unit that could ill afford commercial
software, I devoted my entire time
I will offer an opinion, with which others may fairly take issue.
If you are coming from SAS and wish to learn R, you should forget about SAS
entirely; it is ancient and convoluted. But more to the point, as others
have already suggested, you will only confuse and hamstring yourself trying
to conv
Regarding point 3, as a moderator I have been helping Andrew get this
post out to the list over the past week. His previous attempts were
encoded in some way that the listserv rejected. He sent me the post via
his gmail account and viewing the source I saw it had at least both
plain test and HT
Hello,
in my experience the most direct path of converting SAS code to R is by
using dplyr. dplyr provides the filter function, the first part of your
code could look like this, assuming your datasets are stored as
data.frames:
library(dplyr)
yield <- filter(stress,
field != "YV",
field !=
You might get better answers if you
1 - break this down into separate issues
2 - tell us what you want to achieve in words rather than SAS, we all
read English but few of us speak SAS
3 - post in plain text not HTML as HTML mangles your post
On 29/09/2017 13:47, Andrew Harmon wrote:
Hello all
Hello all,
My statistical analysis training up until this point has been entirely done
in SAS. The code I frequently used was:
*Yield Champagin;
data yield;
set stress;
if field='YV' then delete;
if field='HB' then delete;
if barcode='16187DD4015' then delete;
if barcode='16187DD6002' then
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