I am testing the SAS REG procedure, in the Student version. I set up the table below:
data work.house;
input houseSize lotSize bedrooms granite bathroom sellingPrice;
cards;
3529 9191 6 0 0 205000
3247 10061 5 1 1 224900
4032 10150 5 0 1 197900
2397 14156 4 1 0 189900
2200 9600 4 0 1 195000
3536 19994 6 1 1 325000
2983 9365 5 0 1 230000
;
run;
And I performed the regression analysis with the various variables, as follows:
proc reg data=work.HOUSE;
model sellingPrice = houseSize lotSize bedrooms granite bathroom;
run;
SAS Student shows the results in a well organized and complete visual form, including many graphics.
However, I need to use the estimated parameters to forecast other inputs.
Is there any way to access these parameters?
Or is there a way to save the results (in particular the Parameter Estimates table) into a SAS dataset?
Use the procedure option OUTEST= to save the parameter estimates.
Use OUTPUT statement to save the original data with predicted and residual values.
Example:
* output data sets highlighted with ^^^^;
proc reg noprint data=work.HOUSE outest=parameters;
* ^^^^^^^^^^ ;
model sellingPrice = houseSize lotSize bedrooms granite bathroom;
output out=predicted p=fitprice r=fitresidual;
* ^^^^^^^^^;
run;
quit;
Parameters
Predicted
Related
I am new to sas and are trying to handle some customer data, and I'm not really sure how to do this.
What I have:
data transactions;
input ID $ Week Segment $ Average Freq;
datalines;
1 1 Sports 500 2
1 1 PC 400 3
1 2 Sports 350 3
1 2 PC 550 3
2 1 Sports 650 2
2 1 PC 700 3
2 2 Sports 720 3
2 2 PC 250 3
;
run;
What I want:
data transactions2;
input ID Week1_Sports_Average Week1_PC_Average Week1_Sports_Freq
Week1_PC_Freq
Week2_Sports_Average Week2_PC_Average Week2_Sports_Freq Week2_PC_Freq;
datalines;
1 500 400 2 3 350 550 3 3
2 650 700 2 3 720 250 3 3
;
run;
The only thing I got so far is this:
Data transactions3;
SET transactions;
if week=1 and Segment="Sports" then DO;
Week1_Sports_Freq=Freq;
Week1_Sports_Average=Average;
END;
else DO;
Week1_Sports_Freq=0;
Week1_Sports_Average=0;
END;
run;
This will be way too much work as I have a lot of weeks and more variables than just freq/avg.
Really hoping for some tips are, as I'm stucked.
You can use PROC TRANSPOSE to create that structure. But you need to use it twice since your original dataset is not fully normalized.
The first PROC TRANSPOSE will get the AVERAGE and FREQ readings onto separate rows.
proc transpose data=transactions out=tall ;
by id week segment notsorted;
var average freq ;
run;
If you don't mind having the variables named slightly differently than in your proposed solution you can just use another proc transpose to create one observation per ID.
proc transpose data=tall out=want delim=_;
by id;
id segment _name_ week ;
var col1 ;
run;
If you want the exact names you had before you could add data step to first create a variable you could use in the ID statement of the PROC transpose.
data tall ;
set tall ;
length new_name $32 ;
new_name = catx('_',cats('WEEK',week),segment,_name_);
run;
proc transpose data=tall out=want ;
by id;
id new_name;
var col1 ;
run;
Note that it is easier in SAS when you have a numbered series of variable if the number appears at the end of the name. Then you can use a variable list. So instead of WEEK1_AVERAGE, WEEK2_AVERAGE, ... you would use WEEK_AVERAGE_1, WEEK_AVERAGE_2, ... So that you could use a variable list like WEEK_AVERAGE_1 - WEEK_AVERAGE_5 in your SAS code.
I have data that's tracking a certain eye phenomena. Some patients have it in both eyes, and some patients have it in a single eye. This is what some of the data looks like:
EyeID PatientID STATUS Gender
1 1 1 M
2 1 0 M
3 2 1 M
4 3 0 M
5 3 1 M
6 4 1 M
7 4 0 M
8 5 1 F
9 6 1 F
10 6 0 F
11 7 1 F
12 8 1 F
13 8 0 F
14 9 1 F
As you can see from the data above, there are 9 patients total and all of them have the particular phenomena in one eye.
I need the count the number of patients with this eye phenomena.
To get the number of total patients in the dataset, I used:
PROC FREQ data=new nlevels;
tables PatientID;
run;
To count the number of patients with this eye phenomena, I used:
PROC SORT data=new out=new1 nodupkey;
by Patientid Status;
run;
proc freq data=new1 nlevels;
tables Status;
run;
However, it gave the correct number of patients with the phenomena (9), but not the correct number without (0).
I now need to calculate the gender distribution of this phenomena. I used:
proc freq data=new1;
tables gender*Status/chisq;
run;
However, in the cross table, it has the correct number of patients who have the phenomena (9), but not the correct number without (0). Does anyone have any thoughts on how to do this chi-square, where if the has this phenomena in at least 1 eye, then they are positive for this phenomena?
Thanks!
PROC FREQ is doing what you told it to: counting the status=0 cases.
In general here you are using sort of blunt tools to accomplish what you're trying to accomplish, when you probably should use a more precise tool. PROC SORT NODUPKEY is sort of overkill for example, and it doesn't really do what you want anyway.
To set up a dataset of has/doesn't have, for example, let's do a few things. First I add one more row - someone who actually doesn't have - so we see that working.
data have;
input eyeID patientID status gender $;
datalines;
1 1 1 M
2 1 0 M
3 2 1 M
4 3 0 M
5 3 1 M
6 4 1 M
7 4 0 M
8 5 1 F
9 6 1 F
10 6 0 F
11 7 1 F
12 8 1 F
13 8 0 F
14 9 1 F
15 10 0 M
;;;;
run;
Now we use the data step. We want a patient-level dataset at the end, where we have eye-level now. So we create a new patient-level status.
data patient_level;
set have;
by patientID;
retain patient_status;
if first.patientID then patient_status =0;
patient_status = (patient_Status or status);
if last.patientID then output;
keep patientID patient_Status gender;
run;
Now, we can run your second proc freq. Also note you have a nice dataset of patients.
title "Patients with/without condition in any eye";
proc freq data=patient_level;
tables patient_status;
run;
title;
You also may be able to do your chi-square analysis, though I'm not a statistician and won't dip my toe into whether this is an appropriate analysis. It's likely better than your first, anyway - as it correctly identifies has/doesn't have status in at least one eye. You may need a different indicator, if you need to know number of eyes.
title "Crosstab of gender by patient having/not having condition";
proc freq data=patient_level;
tables gender*patient_Status/chisq;
run;
title;
If your actual data has every single patient having the condition, of course, it's unlikely a chi-square analysis is appropriate.
I am trying to create a two way transposed table. The original table I have looks like
id cc
1 2
1 5
1 40
2 55
2 2
2 130
2 177
3 20
3 55
3 40
4 30
4 100
I am trying to create a table that looks like
CC CC1 CC2… …CC177
1 264 5 0
2 0 132 6
…
…
177 2 1 692
In other words, how many id have cc1 also have cc2..cc177..etc
The number under ID is not count; an ID could range from 3 digits to 5 digits ID or with numbers such as 122345ab78
Is it possible to have percentage display next to each other?
CC CC1 % CC2 %… …CC177
1 264 100% 5 1.9% 0
2 0 132 6
…
…
177 2 1 692
If I want to change the CC1 CC2 to characters, how do I modify the arrays?
Eventually, I would like my table looks like
CC Dell Lenovo HP Sony
Dell
Lenovo
HP
Sony
The order of the names must match the CC number I provided above. CC1=Dell CC2=Lenovo, etc. I would also want to add percentage to the matrice. If Dell X Dell = 100 and Dell X Lenovo = 25, then Dell X Lenovo = 25%.
This changes your data structure to a wide format with an indicator for each value of CC and then uses proc corr (correlation) to create the summary table.
Proc Corr will generate the SCCP - which is the uncorrected sum of squares and crossproducts. It's something that's related to correlation, but the gist is it creates the table you're looking for. The table is output in the SAS results window and the ODS OUTPUT statement will capture the table in a dataset called coocs.
data temp;
set have;
by ID;
retain CC1-CC177;
array CC_List(177) CC1-CC177;
if first.ID then do i=1 to 177;
CC_LIST(i)=0;
end;
CC_List(CC)=1;
if last.ID then output;
run;
ods output sscp=coocs;
ods select sscp;
proc corr data=temp sscp;
var CC1-CC177;
run;
proc print data=coocs;
run;
Here's another answer, but it's inefficient and has it's issues. For one, if a value is not anywhere in the list it will not show up in the results, i.e. if there is no 20 in the dataset there will be no 20 in the final data. Also, the variables are out of order in the final dataset.
proc sql;
create table bigger as
select a.id, catt("CC", a.cc) as cc1, catt("CC", b.cc) as cc2
from have as a
cross join have as b
where a.id=b.id;
quit;
proc freq data=bigger noprint;
table cc1*cc2/ list out=bigger2;
run;
proc transpose data=bigger2 out=want2;
by cc1;
var count;
id cc2;
run;
I inherited a poorly documented person-month dataset that does not have a matching person-level dataset. I want to determine which of the variables in the person-month dataset are actually person-level variables (constant for all observations with a particular id), such as you would expect for date of birth. Simplistic example:
id month dob race tx weight
1 1 4058 1 1 105
1 2 4058 1 1 107
1 3 4058 1 2 108
2 1 1622 2 1 153
2 2 1622 2 3 153
2 3 1622 2 2 153
In this example, dob and race are fixed within an individual but tx and weight vary by month within an individual.
I have come up with a clumsy solution: use proc means to calculate the standard deviation of all numeric variables BY id, and then take the maximum of those standard deviations. If the maximum of the std of a variable is 0, there is no variance of that column within any individual, and I can flag that variable as being fixed (or person-level).
I feel like I'm missing a simpler statistical test to determine which of my hundreds of variables are fixed within each individuals and which vary within an individual's observations. Any suggestions?
pT
I would use the NLEVELS option in PROC FREQ. This gives you the number of unique values for each variable, so you're looking for variables with a unique value (nlevels) of 1.
Here's the code, you'll need to sort the data by id beforehand if not done already.
data have;
input id month dob race tx weight;
cards;
1 1 4058 1 1 105
1 2 4058 1 1 107
1 3 4058 1 2 108
2 1 1622 2 1 153
2 2 1622 2 3 153
2 3 1622 2 2 153
;
run;
ods select nlevels;
ods output nlevels=want;
ods noresults;
proc freq data=have nlevels;
by id;
run;
ods results;
I don't think there's a 'simple statistical test' beyond what you have worked out - standard deviation, or even MIN/MAX (which is about the same). I'd probably just do it in PROC SQL, unless there are a huge number of variables; this allows you to use character variables also.
%macro comparetype(var);
max(&var.) = min(&var.) as &var.
%mend comparetype;
proc sql;
select min(origin) as origin, min(type) as type, min(drivetrain) as drivetrain,
min(msrp) as msrp,min(invoice) as invoice,min(enginesize) as enginesize from (
select make,
%comparetype(origin),
%comparetype(type),
%comparetype(drivetrain),
%comparetype(msrp),
%comparetype(invoice),
%comparetype(enginesize)
from sashelp.cars
group by make
);
quit;
Using PROC REPORT in SAS, if a certain ACROSS variable has 5 different value possibilities (for example, 1 2 3 4 5), but in my data set there are no observations where that variable is equal to, say, 5, how can I get the report to show the column for 5 and display 0 for the # of observations having that value?
Currently my PROC REPORT output is just not displaying those value columns that have no observations.
When push comes to shove, you can do some hacks like this. Notice that there are no missing on SEX variable of the SASHELP.CLASS:
proc format;
value $sex 'F' = 'female' 'M' = 'male' 'X' = 'other';
run;
options missing=0;
proc report data=sashelp.class nowd ;
column age sex;
define age/ group;
define sex/ across format=$sex. preloadfmt;
run;
options missing=.;
/*
Sex
Age female male other
11 1 1 0
12 2 3 0
13 2 1 0
14 2 2 0
15 2 2 0
16 0 1 0
*/