About cb23_york

cb23_york · ‎04-15-2015

Ok that's clear. No, there are no observations with missing data on the day. But thank you for the amended code which would work under such circumstances.

cb23_york · ‎04-14-2015

Thanks Xia, I do indeed not want to count the missing days, but the first code works fine as well. In the example Bob has missing days 3 and 4, but the original code does fine as counting that he has 3 days of observed data.

cb23_york · ‎04-14-2015

That's fantastic Xia, much appreciated. In answer to your question there indeed some missing days, but never any duplication of days for a name.

cb23_york · ‎04-14-2015

To clarify, the table should contain each individuals name, a column counting the number of distinct days that we have an observation for that individual and the following columns should summarise the proportion of times that individual consumed that fruit or veg. Bob has 3 data entries ( days 1,2 and 5) and consumed a banana on 66.6% of those days a carrot on 33.3% of those days and an apple on 0% of those days. Hope that is clearer, thanks.

cb23_york · ‎04-14-2015

I'm sure this is very basic, but I'm having a mind block and would appreciate some help. Suppose I had data like this which is a list of names, dates and fruit and veg consumption recorded by dummy variablese Name Day Banana Carrot Apple Bob 1 0 0 0 Bob 2 1 0 0 Bob 5 1 1 0 Claire 1 0 0 1 Claire 2 0 0 1 Claire 3 1 0 1 Claire 4 1 0 1 And I wished to produce a summary table like this Name Days Banana Carrot Apple Bob 3 0.67 0.33 0 Claire 4 0.5 0 1 What would be the best way about it (and imagine I had 50 more types of fruit and veg consumption and don't want to type in all their names). Many thanks, Chris

cb23_york · ‎11-07-2014

many thanks slchen and Xia I'm very grateful you spent time to consider and answer the question. It was the vname function that is key!

cb23_york · ‎11-06-2014

Hello All, hoping someone can help with this little problem. I have a dataset which looks something like this age_1 age_2 gender Mean 1 0 0 15 0 1 0 32 0 0 1 23 created from a PROC MEANS command and I would like to convert it to a dataset like this Var Mean age_1 15 age_2 32 gender 23 I had though maybe an ARRAY approach would work, but can't quite figure out how to pass the variable name from the array into a new variable. Perhaps someone could help? Many thanks, Chris

cb23_york · ‎11-03-2014

Many thanks for taking the time to answer Anca and Sanjay, it is much appreciated (and forgive the late reply ... was away on holiday last week). My eyes have been opened to PROC TEMPLATE and SGRENDER so I am well on the way to learning some new tools. However, your answers don't quite solve my original problem. This is my fault for not being specific enough in my original question, but these solutions overlay two histograms (per disease) split on age. What I really want is one histogram (per disease) with different shading beyond a certain point. I cannot treat them as two different histograms because the percentage values get messed up. The first image shows the correct histogram, but I would like the area to the right of 75 in each graph to be red and the second image shows the problem with the histograms themselves if I adopt the solutions above.

cb23_york · ‎10-24-2014

Dear all, Thought this might be doable via attribute maps, but can't quite seem to do it. I am using SGPANEL to draw histograms of patient ages across four disease areas and what I would really like to be able to do is have the area/proportion of patients who are aged 75 or over have a different fill color such that it is very easy to see at a glance which disease area has the highest proportion of patients above the specified threshold. Any ideas? many thanks, Chris

cb23_york · ‎08-01-2013

The compress statement has worked a treat, many thanks Sarath.

cb23_york · ‎08-01-2013

I should add that the closest I have got to a solution is varname = COMPRESS(varname, ' ',"s") which does indeed get rid of the leading CR, but also unfortunately gets rid of all blanks in the text which is not quite what I want to do.

cb23_york · ‎08-01-2013

Dear all, would appreciate your help on this. I have an excel imported data sheet which for the most part looks fine. Within this imported data I have one character variable which for some cases shows ... Now if I try and select variables on the basis that this variable contains ... it selects none. Furthermore when I hover my cursor over the the relevant box on the data screen the pop-up shows a blank line followed by some meaningful text underneath the blank line. I'm guessing then that the character variable in this case is a carriage return followed by text. I want to get rid off the CR and just have the text but I can't seem to get compress or trim to do the trick. Does anyone have any suggestions? Hope my question is clear enough. Best wishes, Chris

cb23_york · ‎03-16-2012

Thanks Ivm, I can now see how the Std Err Preds are produced. I am still struggling a little with the intuition as to why they are produced this way rather than the standard way that, say, the SE of the Beta estimates are produced. Searching on the terms you provided and looking at the Stata manuals on how it calculates SEs it would seem that the motivation underpinning the SAS approach is that we cannot appeal to the Central Limit Theorem and make a safe (ish) assumption regarding the distribution of the parameter (i.e. that its Normal). Is this understanding correct?

cb23_york · ‎03-15-2012

Dear SAS community, I have a fundamental question regarding the calculation of the standard errors (or rather the standard error predictions) of cluster specific random effects in the GLIMMIX procedure. I note the following from the manual that states "The numbers displayed in the Std Err Pred column of the "Solution for Random Effects" table are not the standard errors of the gamma-hat displayed in the Estimate column; rather, they are the square roots of the prediction errors gamma-hat-i - gamma-i, where gamma-hat-i is the predictor of the th random effect and gamma-i is the ith random effect." Unfortunately I'm missing something in my understanding. Firstly, I'm interested in the standard errors of the random effects, so why give me the square roots of the prediction errors? Secondly how on earth are these calculated? We produce an estimate, gamma-hat-i of gamm_i, but how is it possible to know what gamma-i is to obtain the difference? I'm also a little surprised that there seems to be so little documentation about this calculation. Any help about the motivation or intuition for this would be greatly appreciated. best wishes, Chris

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Re: Creating a summary table from long list of variables

Re: Creating a summary table from long list of variables

Re: Creating a summary table from long list of variables

Re: Creating a summary table from long list of variables

Creating a summary table from long list of variables

Re: Converting variable names to variable values via an Array

Converting variable names to variable values via an Array

Re: Histogram with different fill colors beyond a specified point

Histogram with different fill colors beyond a specified point

Re: Dealing with carriage returns in a SAS string

Re: Dealing with carriage returns in a SAS string

Re: Converting variable names to variable values via an Array

Re: Dealing with carriage returns in a SAS string

Re: Creating a summary table from long list of variables

Re: Creating a summary table from long list of variables

Re: Creating a summary table from long list of variables

Re: Creating a summary table from long list of variables

Creating a summary table from long list of variables

Re: Converting variable names to variable values via an Array

Converting variable names to variable values via an Array

Re: Histogram with different fill colors beyond a specified point

Histogram with different fill colors beyond a specified point

Re: Dealing with carriage returns in a SAS string

Re: Dealing with carriage returns in a SAS string

Dealing with carriage returns in a SAS string

Standard errors of random effects in GLIMMIX

Standard errors of random effects in GLIMMIX