I am running the following simple linear regression:
proc reg data=Nationalnew;
model PE_PCHNG = HWOL_CHANGE;
run;
My dataset starts with may of 2005m5. I am wondering if there is anyway that instead of going to my dataset and deleting the observations I don't need, that I can start the regression with 2006m12?
PROC REG supports a WHERE statement (as nearly every PROC does). In your case, assuming date is a true date, you could run:
proc reg data=Nationalnew;
where date > '01DEC2006'd;
model PE_PCHNG = HWOL_CHANGE;
run;
If the variable is date format but character, you use input to convert to date.
Related
I am handing over some code to a colleague, which is to be run daily to generate reports.
Once every month a new cycle starts, and we have to update the code for cycle_start_date
data mtd_table;
set ytd_table;
where entry_date> '10Mar2021'd; /*different every month*/
run;
Since he'll be running them from now on, along with other reports from other teams, I don't want to bother him every month to tweak the code. So I devised this:
i run(once a month)
data shared1.cycle_start_date;
cycle_start_date='10Mar2021'd;
run;
he runs(everyday)
data mtd_table;
set ytd_table;
where entry_date>/*(select cycle_start_date from shared1.cycle_start_date)*/;
run;
I'm not sure how to correctly implement this (select cycle_start_date from shared1.cycle_start_date) part, since it is from proc sql. Would appreciate help.
When you store program parameters in a data set (called control data) one use case is having later code extract the values into macro variables, at which point other code can resolve the macro variable for replacement at (automatic) step compile and run time. Two ways to extract values into macro variables are:
Proc SQL, SELECT ... INTO :<macro-variable>, and
DATA _NULL_, CALL SYMPUT(<macro-variable>, <data step expression>);
Don't forget, macro resolution replaces the macro variable as source code text. Dates in macro variables can be either the SAS data value (the text representation of a SAS date integer) or part of a date literal (the text <dd-mon-yyyy>) that would be resolved as source date literal "&<macro-variable>"D when to be utilized as a date value. The date literal part is used when you want to show the date value as human readable in when output; for example: TITLE "cycle start: &cycle_start_date";
Control data (you)
Rebuild or edit values in data set (name it parameters to be more useful)
data shared1.parameters;
cycle_start_date = '10Mar2021'd; * stored as a SAS date value (integer);
run;
Note: Some control data layouts use a name/value organization and has one row per parameter.
Other
Extract date value as SAS date value text, and as date literal text portion and use.
proc sql noprint;
select
cycle_start_date
, cycle_start_date format=date11.
into
:cycle_start_date_value trimmed
, :cycle_start_date_literal trimmed
from
shared1.parameters
;
%put &=cycle_start_date_value;
%put &=cycle_start_date_literal;
/*
* will log the macro variable value as follows:
* CYCLE_START_DATE_VALUE=22349 and
* CYCLE_START_DATE_LITERAL=10-MAR2021
*/
data ...
set ...;
where date >= &cycle_start_date; *resolve parameter as text representation of a SAS date value (integer);
...
title "Cycle starts: &cycle_start_date_literal";
proc print data=...; * title in output shows human readable part of date;
run;
Another approach is to use a common source code file that is %included by others. You would edit or recreate the parameters file by whatever process you want.
parameters.sas
%let cycle_start_date = 10-Mar-2021;
use
%include 'parameters.sas';
data ...
set ...;
where date >= "&cycle_start_date"D; *resolve parameter as part of date literal;
...
title "Cycle starts: &cycle_start_date";
proc print data=...; * title in output shows human readable part of date literal;
run;
One possible solution would be to put the date from the cycle_start_date table that is in the shared library shared1 into a macro-variable date that will be used in your data step to filter the ytd_table table based on the entry_date variable.
proc sql noprint;
select cycle_start_date into :date
from shared1.cycle_start_date;
quit;
data mtd_table;
set ytd_table;
where entry_date > &date.;
run;
How do i start this??
I have two data sets.
For the output you will deliver:
It should be an excel or XML format
Each query logic/programmed check should be on each tab
Columns should be
Subject #,
Visit Date (You will need the Visit Date Listing also attached)
Visit Name (Visit date from the file_34422 must match Visit name in the Blood Pressure File)
Date of Assessment (From the BP Log), VSBPDT_RAW, VSTPT, BP results.
A column for SYBP1. SYBP2, SYBP3, DIABP1, DIABP2, DIABP3
Findings/query text.
Below are Specification for BP:
For same SUBJECT and same FOLDERNAME, where VSTPT is Blood Pressure 1.
if VSBPYN is No, then all must be null or =0 (VSBPDT_RAW, VSBPTM1, SYSBP1, DIABP1, VSBPND2, VSBPTM2, SYSBP2, DIABP2, VSBPND3, VSBPTM3, SYSBP3, DIABP3)
This is what i have started with and
proc sql;
select
f.subject,
f.SVSTDT_RAW, f.FolderName,
b.FolderName,
VSBPDT_RAW, VSTPT,
SYSBP1, SYSBP2, SYSBP3,
DIABP1, DIABP2, DIABP3
FROM first_data as f, bp_data as b
group by subject, foldername
where f.subject = b.subject
having VSTPT is Blood Pressure set 1,
VSBPYN is No;
quit;
I just need to be pointed towards the right direction. I know this can't be right.
I do not know the exact structure of your data, so the solution below may need to be modified by you to select the right columns.
From the descritpion, this looks like it might be a good situation for SQL and a data step. You have a lot of columns to merge with the bp table. It will be easy to do merge all of these columns with first_data in SQL.
When you have lots of by-row conditionals, a data step will be easier to work with and read than many CASE statements in SQL. We'll do a two-stage approach in which we use SQL and a data step.
Step 1: Merge the data
proc sql noprint;
create table stage as
select t1.*
, t2.VSBPYN
from bp_data as t1
INNER JOIN
first_data as t2
ON t1.subject = t2.subject
AND foldername = t2.foldername
where t1.VSTPT = 1
;
quit;
Step 2: Conditionally set values to missing
Next, we'll do a data step for our conditional logic. call missing() is a useful function that will let you set the value of many variables to missing all in a single statement.
data want;
set stage;
if(upcase(VSBPYN) = 'NO') then call missing(VSBPDT_RAW, VSBPTM1, SYSBP1, DIABP1,
VSBPND2, VSBPTM2, SYSBP2, DIABP2,
VSBPND3, VSBPTM3, SYSBP3, DIABP3
);
run;
Step 3: Output to Excel
Finally, we sent the output to Excel.
proc export
data=want
file='/my/location/want.xlsx'
dbms=xlsx
replace;
run;
I have a dataset with visitors and weather variables. I'm trying to forecast visitors based on the weather variables. Since the dataset only consists of visitors in season there is missing values and gaps for every year. When running proc reg in sas it's all okay but the issue comes when i'm using proc VARMAX. I cannot run the regression due to missing values. How can i tackle this?
proc varmax data=tivoli4 printall plots=forecast(all);
id obs interval=day;
model lvisitors = rain sunshine averagetemp
dfebruary dmarch dmay djune djuly daugust doctober dnovember ddecember
dwednesday dthursday dfriday dsaturday dsunday
d_24Dec2016 d_05Dec2013 d_24Dec2017 d_24Dec2014 d_24Dec2015 d_24Dec2019
d_24Dec2018 d_24Sep2012 d_06Jul2015
d_08feb2019 d_16oct2014 d_15oct2019 d_20oct2016 d_15oct2015 d_22sep2017 d_08jul2015
d_20Sep2019 d_08jul2016 d_16oct2013 d_01aug2012 d_18oct2012 d_23dec2012 d_30nov2013 d_20sep2014 d_17oct2012 d_17jun2014
dFrock2012 dFrock2013 dFrock2014 dFrock2015 dFrock2016 dFrock2017 dFrock2018 dFrock2019
dYear2015 dYear2016 dYear2017
/p=7 q=2 Method=ml dftest;
garch p=1 q=1 form=ccc OUTHT=CONDITIONAL;
restrict
ar(3,1,1)=0, ar(4,1,1)=0, ar(5,1,1)=0,
XL(0,1,13)=0, XL(0,1,14)=0, XL(0,1,13)=0, XL(0,1,27)=0, XL(0,1,38)=0, XL(0,1,42)=0;
output lead=10 out=forecast;
run;
As with any forecast, you will first need to prepare your time-series. You should first run through your data through PROC TIMESERIES to fill-in or impute missing values. The impute choice that is most appropriate is dependent on your variables. The below code will:
Sum lvisitors by day and set missing values to 0
Set missing values of averagetemp to average
Set missing values of rain, sunshine, and your variables starting with d to 0 (assuming these are indicators)
Code:
proc timeseries data=have out=want;
id obs interval = day
setmissing = 0
notsorted
;
var lvisitors / accumulate=total;
crossvar averagetemp / accumulate=none setmissing=average;
crossvar rain sunshine d: / accumulate=none;
run;
Important Time Interval Consideration
Depending on your data, this could bias your error rate and estimates since you always know no one will be around in the off-season. If you have many missing values for off-season data, you will want to remove those rows.
Since PROC VARMAX does not support custom time intervals, you can instead create a simple time identifier. You can alternatively turn this into a format for proc format and converttime_id at the end.
data want;
set have;
time_id+1;
run;
proc varmax data=want;
id time_id interval=day;
...
output lead=10 out=myforecast;
run;
data myforecast;
merge myforecast
want(keep=time_id date)
;
by time_id;
run;
Or, if you made a format:
data myforecast;
set myforecast;
date = put(time_id, timeid.);
drop time_id;
run;
I have been working on a SAS problem on the University Edition where it is given that:
Separate out the data only for passenger vehicle launched after 1-October-2014;
data passenger;
set avik1.clean;
informat Latest_Launch ddmmyy10.;
if Vehicle_type = "Passenger" and Latest_Launch > "01-10-2014";
run;
proc print data=passenger;
run;
I am able to separate only the passenger vehicles however my date has no effect as it doesn't separate out the dates after 01/10/2014.
I ran the Proc Contents Command just in case you would like to have a look on my data attributes
Proc Contents Print Output
I am new to SAS and I am facing some issues whenever there is a date problem.
In SAS date constants are written 'DDMONYYYY'D date9 format followed by D.
for you '01OCT2014'd
Is it possible to score a data set with a model created by PROC ARIMA in SAS?
This is the code I have that is not working:
proc arima data=work.data;
identify var=x crosscorr=(y(7) y(30));
estimate outest=work.arima;
run;
proc score data=work.data score=work.arima type=parms predict out=pred;
var x;
run;
When I run this code I get an error from the PROC SCORE portion that says "ERROR: Variable x not found." The x column is in the data set work.data.
proc score does not support autocorrelated variables. The simplest way to get an out-of-sample score is to combine both proc arima and a data step. Here's an example using sashelp.air.
Step 1: Generate historical data
We leave out the year 1960 as our score dataset.
data have;
set sashelp.air;
where year(date) < 1960;
run;
Step 2: Generate a model and forecast
The nooutall option tells proc arima to only produce the 12 future forecasts.
proc arima data=have;
identify var=air(12);
estimate p=1 q=(2) method=ml;
forecast lead=12 id=date interval=month out=forecast nooutall;
run;
Step 3: Score
Merge together your forecast and full historical dataset to see how well the model did. I personally like the update statement because it will not replace anything with missing values.
data want;
update forecast(in=fcst)
sashelp.air(in=historical);
by Date;
/* Generate fit statistics */
Error = Forecast-Air;
PctError = Error/Air;
AbsPctError = abs(PctError);
/* Helpful for bookkeeping */
if(fcst) then Type = 'Score';
else if(historical) then Type = 'Est';
format PctError AbsPctError percent8.2;
run;
You can take this code and convert it into a generalized macro for yourself. That way in the future, if you wanted to score something, you could simply call a macro program to get what you need.