I am examining the effect of passing vs running plays on injuries across a few football seasons. The way the data was collected, all injuries were recorded as well as information about the play in which the injury occurred (ie position, quarter, play type), game info (ie weather conditions, playing surface, etc), and team info (ie number of pass vs run plays in the game).
I would like to use one play as the primary exposure with the outcome as injury vs no injury with analysis using logistic regression, but to do so I would need to create all the records with no injury. There is a range from 0 to around 6-7 injuries in a game for a team, and the total passing and running plays are recorded so I would need to find a way to add X (total passing plays minus injuries on passing plays) and Y (total running plays - injuries on running plays) records that share all the details for that particular game but have no injury as the outcome. I imagine there is a way in proc sql to do this, but I could not find it online. How would I go about coding this?
I have attached an example of the relevant data. An example of what I would need to do is for game 1 add 30 records for passing plays and 38 records for running plays with outcome of no injury and otherwise the same data (team A, dry weather, game plays).
You can use the freq statement to prevent having to de-aggregate it.
The FREQ statement identifies a variable that contains the frequency
of occurrence of each observation. PROC LOGISTIC treats each
observation as if it appears n times, where n is the value of the FREQ
variable for the observation. If it is not an integer, the frequency
value is truncated to an integer.
SAS Documentation
De-aggregating the data would require the data step and a do loop. It's not recommended to do this.
Related
I'm not sure if it's right to ask this here but any help greatly appreciated. I'm working on sas forecast studio.
This is my time series dataset (quarterly data):
Date e.g. 1-Jan-80, 1-Apr-80, 1-Jul-80
DateQ e.g. 1980Q1, 1980Q2, 1980Q3
Year e.g. 1980, 1981, 1982
GDP (dependable variable) e.g. 2650.1
T e.g. 1, 2, 3
Which of this variable, or should I create a new quarterly variable, to use as an independent variable for a linear regression to evaluate if there is any seasonal effect?
Seasonal effects should not be identified using simple linear regression on the time variable when analyzing time-series data. But, to answer your question, use date with the intnx() function to convert it to quarter.
data want;
format quarter yyq.;
set have;
quarter = intnx('quarter', date, 0, 'B');
run;
Seasonal effects can be identified a number of ways:
1. Graphing it
If a time series has a seasonal effect, it will tend to be clear. Simply looking at a graph of the data will let you know whether it is seasonal by your chosen interval.
In sashelp.air, it's very clear that there is a 12-month season.
2. Spectral Density Analysis
proc timeseries will give you a spectrum analysis to help identify significant seasons within the data. Peaks indicate possible cycles or seasons. You will need to do some filtering to a reasonable seasonal amount since the density may increase significantly after a certain point, and it is not representative of the true season.
Forecast Studio and Time Series Studio will do this for you and can give you similar output to the below.
proc timeseries data=sashelp.air
outspectra=outspectra;
id date interval=month;
var air;
spectra;
run;
proc sgplot data=outspectra;
where period BETWEEN 1 AND 24;
scatter x=period y=p;
series x=period y=p;
run;
We can see a strong indicator for a seasonality of 12. We also see some potential 3-month and 6-month cycles that could be tested within a model for significance.
3. ACF/PACF/IACF plots
Your ACF/PACF/IACF plots in Forecast Studio will also help you identify clear seasons.
The classic decaying suspension-bridge look is indicative of a seasonal effect. Note that the season increases around 12 and then decreases again. Additionally, the significant negative spike at 12 in the PACF and IACF plots are other indicators of a significant seasonal effect at 12.
Model Building and Testing
Tools like the seasonal augmented dickey fuller test that are available Forecast Studio can help you identify if you've captured seasonality and achieved stationarity after differencing.
The selection boxes in the Series view allow you to quickly add simple or seasonal differencing. Selecting (1) for simple differencing will add one simple difference. i.e:
y = y - lag(y)
Selecting (1) for seasonal differencing will add 1 seasonal difference. Note that when you create a project in Forecast Studio, the season is automatically diagnosed and assumed. This should be done after doing our diagnostics above for our best guess as to what the true season is. In our case, we've assumed our season is 12. This would be equivalent to:
y = y - lag12(y)
We can then use stationarity tests to ensure we've achieved stationarity. In our case, we'll add 1 simple and seasonal difference.
Notice how our white noise plot has improved and our spikes at 12 have decreased to non-significance. Additionally, our stationarity tests are looking good and significant - that is, there is no unit root present.
Adding Seasonal or Cyclical Effects
Your model choice will dictate how seasonal or cyclical effects are added. Differencing in an ARIMA model will take care of seasonality. Dummy variables can be used for additional cyclical effects in the ARIMA model. For example:
data want;
set have;
q1 = (qtr(date) = 1);
q2 = (qtr(date) = 2);
q3 = (qtr(date) = 3);
run;
UCMs can take care of all of these by adding both seasonal and cyclical effects. Holt-Winters ESMs take care of trend and seasonality without requiring dummy variables. Your modeling goals and performance considerations for each type of model will dictate which model you choose.
How do you take an average of the coefficients across all months?
Please refer to this question earlier
How do I perform regression by month on the same SAS data set?
The comments in the linked question provide the code to get the estimates in a data set. Then you would run a PROC MEANS on the saved data set to get the averages. But you could also run the model without which a variable to get the monthly estimates alone. In general, it isn't common to average parameter estimates this way, except in a bootstrapping process.
I'm interested in seeing how sedentary behaviors change throughout time (Time 1, 2, 3) and see in a second step how it relates to mental health.
Thus, I would like to obtain an estimate (slope/intercept) for each subject to allow me to do the 2nd step. I can't find online how to do it (not sure what to search for).
Here's my code so far, which gives me 2 estimates (boys and girls); I would rather have an estimate for every participant.
ods output LSMeans=Means1;
proc mixed data=sb.LFcomplete method=ml covtest;
class SexeF time;
model CompDay = Time SexeF Time*SexeF;
repeated time;
lsmeans time*sexeF;
run;
Thank you in advance!
Please check this website for a similar example:
https://www.stat.ncsu.edu/people/davidian/courses/st732/examples/ex10_1.sas
The professor was using HLM for longitudinal data analysis. He used gender like your SexeF, age like your Time, and child as ID. The tricky part is when he was organizing the random effect file, he sorted ID and created Gender (Group or your SexeF) for subsequent merging with the fixed effects file. If your current ID variable is not aligned with your SexeF, you may sort your SexeF and create a new ID variable in SPSS before you import your data in SPSS.
I'm new to SAS EG, I usually use BASE SAS when I actually need the program, but my company is moving heavily toward EG. I'm helping some areas with some code to get data they need on an ad-hoc basis (the code won't change though).
However, during processing, we create many temporary files that are just iterations across months. I.E. if the user wants data from 2002 - 2016, we have to pull all those libraries and then concatenate them with our results. This is due to high transactional volume, the final dataset is limited to a small number of observations. Whenever I run this program though, SAS outputs all 183 of the datasteps created in the macro, making it very ugly, and sometimes the "Output Data" that appears isn't even output from the last datastep, but from an intermediary step, making it annoying to search through for the 'final output dataset'.
Is there a way to limit the datasets written to "Output Data" so that it only shows the final dataset - so that our end user doesn't need to worry about being confused?
Above is an example - There's a ton of output data sets that I don't care to see. I just want the final, which is located (somewhere) in that list...
Version is SAS E.G. 7.1
EG will always automatically show every dataset that was created after the program ends. If you don't want it to show any intermediate tables, delete them at the very last step in your process.
In your case, it looks as if your temporary tables all share the name TRN. You can clean it up as such:
/* Start of process flow */
<program statements>;
/* End of process flow*/
proc datasets lib=work nolist nowarn nodetails;
delete TRN:;
quit;
Be careful if you do this. Make sure that all of your temporary tables follow the same prefix naming scheme, otherwise you may accidentally delete tables that you need.
Another solution is to limit the number of datasets generated, and have a user-created link to the final dataset. There's an article about it here.
The alternate solution here is to add the output dataset explicitly as an entry on your process flow, and disregard the OUTPUT window unless you need to investigate something from the intermediary datasets.
This has the advantage that it lets you look at the intermediary datasets if something goes wrong, but also lets you not have to look through all of them to see the final dataset.
You should be able to add the final output dataset to the process flow once it's created once easily, and then after that one time it will be there for you to select to look at.
I have a big data set like in the
and I want to create a time series for every cont_no. It means I want to have something like
I tried with
Proc timeseries data=contracts
out=timeseries;
by cont_no;
id date interval=MS_ID accumulate=total;
var exp_amount;
run
It is running for more hours without results.
How can I correct the code? Or probably I am not using the correct commands and "tool" of SAS!?
You're not creating a time series - you already have a time series. You want to transpose the time series (though I wonder if you really do want to do that, but anyway).
If you first create a variable year_month with the year/month catted together, and start it with a A-Z character or underscore please, and then sort by cont_no, then this will work:
proc transpose data=contracts out=timeseries;
by cont_no;
var exp_amount;
id year_month;
run;
But I'd encourage you to do time series analysis on the data as it is currently structured - this is a more useful structure in most cases for time series analysis than the horizontal structure.