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02-04-2024
dkcundiffMD
Quartz | Level 8
Member since
09-24-2019
- 77 Posts
- 33 Likes Given
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Latest posts by dkcundiffMD
Subject Views Posted 400 02-04-2024 06:24 PM 426 02-04-2024 01:30 PM 504 02-02-2024 12:18 AM 578 02-01-2024 12:21 AM 599 08-30-2021 11:25 PM 618 08-30-2021 10:29 PM 633 04-30-2021 12:20 PM 684 04-30-2021 12:06 AM 705 04-29-2021 10:53 PM 745 04-29-2021 08:04 PM -
Activity Feed for dkcundiffMD
- Posted Re: Risk factors combined variable in a multiple regression was not working on New SAS User. 02-04-2024 06:24 PM
- Liked Re: Risk factors combined variable in a multiple regression was not working for FreelanceReinh. 02-04-2024 06:23 PM
- Posted Re: Risk factors combined variable in a multiple regression was not working on New SAS User. 02-04-2024 01:30 PM
- Liked Re: Risk factors combined variable in a multiple regression was not working for FreelanceReinh. 02-04-2024 12:12 PM
- Liked Re: Risk factors combined variable in a multiple regression was not working for FreelanceReinh. 02-04-2024 12:06 PM
- Posted Re: Risk factors combined variable in a multiple regression was not working on New SAS User. 02-02-2024 12:18 AM
- Posted Risk factors combined variable in a multiple regression was not working on New SAS User. 02-01-2024 12:21 AM
- Liked Re: ERROR: Libref PRODUCTS is not assigned. for ballardw. 08-31-2021 11:56 AM
- Posted Re: ERROR: Libref PRODUCTS is not assigned. on Statistical Procedures. 08-30-2021 11:25 PM
- Liked Re: ERROR: Libref PRODUCTS is not assigned. for japelin. 08-30-2021 11:24 PM
- Posted ERROR: Libref PRODUCTS is not assigned. on Statistical Procedures. 08-30-2021 10:29 PM
- Liked Re: Lost variables between proc reg and proc corr for Reeza. 04-30-2021 01:09 PM
- Posted Re: Lost variables between proc reg and proc corr on New SAS User. 04-30-2021 12:20 PM
- Liked Re: Lost variables between proc reg and proc corr for Reeza. 04-30-2021 12:18 PM
- Posted Re: Lost variables between proc reg and proc corr on New SAS User. 04-30-2021 12:06 AM
- Posted Re: Lost variables between proc reg and proc corr on New SAS User. 04-29-2021 10:53 PM
- Posted Lost variables between proc reg and proc corr on New SAS User. 04-29-2021 08:04 PM
- Posted Re: After sub setting a dataset with a data step how do I subsequently return to the full dataset? on New SAS User. 04-29-2021 04:41 PM
- Liked Re: After sub setting a dataset with a data step how do I subsequently return to the full dataset? for Reeza. 04-29-2021 04:40 PM
- Posted After sub setting a dataset with a data step how do I subsequently return to the full dataset? on New SAS User. 04-29-2021 11:51 AM
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Posts I Liked
Subject Likes Author Latest Post 1 1 1 1 1
02-04-2024
06:24 PM
That works! I was putting in variables and getting errors. Thanks.
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02-04-2024
01:30 PM
Now I'm having problem with the proc contents syntax.
Thanks for your help.
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02-02-2024
12:18 AM
That solution worked to get the variables standardized in a new workspace (source3). Thanks.
Now I can't get proc stdize to work. My effort is attached. It would save me many hours to quickly standardize lots of variables.
Thanks again.
... View more
02-01-2024
12:21 AM
SAS on Demand for Academics
I partitioned a large analysis risk factor and health outcome database into a subset of interest. I standardized Dietary and other variables. And tried to proceed to a multiple regression with dietary variables in one combined variable and non-dietary variables with non-communicable disease early deaths as the dependent variable. With the standardized risk factors and non-communicable disease early deaths (dependent variable) data, I checked to determine that all the standardized variables were found. With proc corr, they were not found.
I have used combined dietary variables before in multiple regression equations. Why does it not work here?
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08-30-2021
11:25 PM
Thank you so much. Good eye.
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08-30-2021
10:29 PM
Using SAS on demand for academic and SAS Studio, I am deriving a multiple regression formula of dietary and other risk factors for non-communicable diseases. My library is Products. In the middle of the process, I get the error that Products is not assigned.
Also, I partitioned the dataset with the following statement: Where FSVV2 < 351.5724;
After I close this file and go to the next, the data are still partitioned by Where FSVV2 < 351.5724;
How do I end the partition?
Thanks.
David Cundiff
SAS code:
libname Projects "/home/u43393119/Projects";
proc sort data=Projects.source;
by weighted_no;
run;
quit;
*Combination dietary risk factors derived;
data Projects.source;
set Projects.source;
FSVV=pmeat17KC + rmeat17KC + fish17KC + milk17KC + poultry16KC + eggs16KC
+ aSFA16KC + aPUFA17KC + aTFA17KC;
FSVV2=pmeat17KC2 + rmeat17KC2 + fish17KC2 + milk17KC2 + poultry16KC2 + eggs16KC2
+ aSFA16KC2+ aPUFA17KC2 + aTFA17KC2;
addedFat=aSFA16KC + aPUFA17KC + aTFA17KC;
addedFat2=aSFA16KC2 + aPUFA17KC2 + aTFA17KC2;
TotalKC=pmeat17KC + rmeat17KC + fish17KC + milk17KC + poultry16KC + eggs16KC + aSFA16KC
+ aPUFA17KC + aTFA17KC + alcohol17KC + Sugarb17KC + potatoes16KC + corn16KC
+ fruits17KC + Vegetables17KC + nutsseeds17KC + wgrains17KC + legumes17KC + rice16KC + swtpot16KC;
Label
FSVV=Fat-soluble vitamin variable
FSVV2=Fat-soluble vitamin variable m/f mean
TotalKC=Total Kcal/day data available; *Standardizing the variables;
NCD17ms=NCD17m/399.68018 - 3.73754;
pmeat17KCs=pmeat17KC /0.87647 -1.77019;
rmeat17KCs=rmeat17KC /29.44548 - 1.09634;
fish17KCs=fish17KC /1.50615 - 1.85824;
milk17KCs=milk17KC /9.78892 - 1.29811;
poultry16KCs=poultry16KC /16.24909 - 1.25773;
eggs16KCs=eggs16KC /11.77554 - 1.17391;
aSFA16KCs=aSFA16KC /17.31454 - 4.36804;
aPUFA17KCs=aPUFA17KC /14.22874 - 1.56401;
aTFA17KCs=aTFA17KC /4.7895 - 1.00687;
Alcohol17KCs=Alcohol17KC /45.75517 - 1.50668;
Sugarb17KCs=Sugarb17KC /106.54402 - 2.65994;
potatoes16KCs=potatoes16KC /76.67565 - 0.98389;
corn16KCs=corn16KC /44.73077 - 0.7639;
fruits17KCs=fruits17KC /19.01693 - 1.74415;
Vegetables17KCs=Vegetables17KC /40.70559 - 1.7763;
nutsseeds17KCs=nutsseeds17KC /5.85482 - 1.09816;
wgrains17KCs=wgrains17KC /30.15109 - 1.84823;
legumes17KCs=legumes17KC /32.09786 - 1.7087;
rice16KCs=rice16KC /114.89232 - 1.51305;
swtpot16KCs=swtpot16KC /39.4746 - 0.75105;
VitAdeficms=VitAdeficm /9447 - 2.83083;
PAMETs17ms=PAMETs17m /1260 - 4.02677;
AmbientPM17ms=AmbientPM17m /23.18446 - 2.35202;
smoke17ms=smoke17m /0.19136 - 1.01969;
SLTobacco17ms=SLTobacco17m /0.10191 - 0.87387;
HHairPoll17ms=HHairPoll17m /0.23444 - 2.68993;
kidneydz17ms=kidneydz17m /0.02878 - 2.03845;
T1DM17ms=T1DM17m /10.05986 - 1.19509;
T2DM17ms=T2DM17m /14.24966 - 1.25353;
sex_IDs=sex_ID /0.50004 - 2.99976;
run;
*Table 1;
proc corr data=Projects.source fisher;
var NCD17ms FSVV FSVV2 pmeat17KC rmeat17KC fish17KC milk17KC poultry16KC eggs16KC aSFA16KC
aPUFA17KC aTFA17KC Alcohol17KC Sugarb17KC potatoes16KC corn16KC fruits17KC
Vegetables17KC nutsseeds17KC wgrains17KC legumes17KC rice16KC swtpot16KC
kcal2016m VitAdeficm sodium17m Calcium17m fiber17m PAMETs17m Childunwt17m
discbreastF17m AmbientPM17m smoke17m SHsmoke17m SLTobacco17m leadblood17m
HHairPoll17m kidneydz17m T1DM17m T2DM17m BMI17m LDLC17 FPG17m SBP17m
SDI_2016m sex_ID
pmeat17KCs rmeat17KCs fish17KCs milk17KCs poultry16KCs eggs16KCs aSFA16KCs
aPUFA17KCs aTFA17KCs Alcohol17KCs Sugarb17KCs potatoes16KCs corn16KCs fruits17KCs
Vegetables17KCs nutsseeds17KCs wgrains17KCs legumes17KCs rice16KCs swtpot16KCs
VitAdeficms PAMETs17ms AmbientPM17ms smoke17ms SLTobacco17ms HHairPoll17ms
kidneydz17ms T1DM17ms T2DM17ms sex_IDs
;
with NCD17m;
run; quit;
*Table 2: Finding the about 1-3 billion people with the lowest and highest
mean NCD male/female averaged;
data Projects.source;
set Projects.source;
Where FSVV2 < 351.5724;
NCD17f1=
- pmeat17KCs * 1.55152 * 0.006135589
- rmeat17KCs * 32.28236 * 0.013609556
- fish17KCs * 2.79879 * 0.022961341
+ milk17KCs * 12.70714 * 0.027785556
- poultry16KCs * 20.43698 * 0.080338234
- eggs16KCs * 13.8234 * 0.108609794
- aSFA16KCs * 75.63065 * 0.031915823
- aPUFA17KCs * 22.25392 * 0.069902072
- aTFA17KCs * 4.82242 * 1.45926E-08
+ Alcohol17KCs * 68.93836 * 0.123369538
+ Sugarb17KCs * 283.40041 * 0.015805518
- potatoes16KCs * 75.44008 * 0.005246105
- corn16KCs * 34.17002 * 0.003071376
- fruits17KCs * 33.1684 * 0.111389063
- Vegetables17KCs * 72.30542 * 0.010870148
- nutsseeds17KCs * 6.42952 * 0.012809712
- wgrains17KCs * 55.72616 * 0.013597892
+ legumes17KCs * 54.84575 * 0.032080392
- rice16KCs * 173.83774 * 0.008246456
- swtpot16KCs * 29.64738 * 0.059658063
;
NCD17f2=
+smoke17ms * 0.19513 * 0.1406325
+SLTobacco17ms * 0.08906 * 0.110164248
+HHairPoll17ms * 0.63063 * 0.03125824
;
NCD17f3=
+T1DM17ms *12.02246 * 0.161451276
+T2DM17ms * 17.86239 * 0.140392596
;
run; quit;
Proc reg data=Projects.source;
Where FSVV2 < 351.5724;
model NCD17ms=NCD17f1 NCD17f2 NCD17f3 PAMETs17ms AmbientPM17ms sex_ids
/ selection=STEPWISE slentry=.25 slstay=.25;
run; quit;
*NCD17f1 0.01066 0.00078537 78.74145 184.08 <.0001
NCD17f2 5.76168 0.45383 68.94733 161.18 <.0001
NCD17f3 0.11820 0.00263 864.77577 2021.63 <.0001
PAMETs17ms -0.14377 0.01079 76.00534 177.68 <.0001
AmbientPM17ms 0.19499 0.01298 96.47405 225.53 <.0001
sex_IDs -0.32929 0.01355 252.63250 590.59 <.0001
;
data Projects.source;
set Projects.source;
Where FSVV2 < 351.5724;
NCD17f4=
NCD17f1 * 0.01066
+ NCD17f2 * 5.76168
+ NCD17f3 * 0.11820
- PAMETs17ms * 0.14377
+ AmbientPM17ms * 0.19499
- sex_IDs * 0.32929
;
run;
proc corr data=Projects.source fisher;
Where FSVV2 < 351.5724;
var NCD17f1 NCD17f2 NCD17f3 NCD17f4 PAMETs17ms AmbientPM17ms sex_ids;
with NCD17ms;
RUN; quit;
*AT THIS POINT, THE LIBREF "PRODUCTS" BECOMES UNASSIGNED;
data Projects.source; set Projects.source;
Where FSVV2 < 351.5724;
NCD17f5=
- pmeat17KCs * 2.07116E-06
- rmeat17KCs * 9.55891E-05
- fish17KCs * 1.39819E-05
+ milk17KCs * 7.68185E-05
- poultry16KCs * 0.000357222
- eggs16KCs * 0.00032665
- aSFA16KCs * 0.000525174
- aPUFA17KCs * 0.000338451
- aTFA17KCs * 1.53108E-11
+ Alcohol17KCs * 0.00185041
+ Sugarb17KCs * 0.000974559
- potatoes16KCs * 8.61069E-05
- corn16KCs * 2.28338E-05
- fruits17KCs * 0.000803833
- Vegetables17KCs * 0.000171004
- nutsseeds17KCs * 1.79191E-05
- wgrains17KCs * 0.000164865
+ legumes17KCs * 0.000382809
- rice16KCs * 0.000311896
- swtpot16KCs * 0.000384817
- PAMETs17m * 0.14678
+ AmbientPM17m * 0.31418
+ smoke17m * 0.30116
+ SLTobacco17m * 0.01873
+ HHairPoll17m * 0.08264
+ T1DM17m * 0.53065
+ T2DM17m * 0.08949
- sex_id * 0.22966
;
run; quit;
proc corr data=Products.source fisher;
Where FSVV2 < 351.5724;
var NCD17f1 NCD17f2 NCD17f3 NCD17f4 NCD17f5 PAMETs17ms AmbientPM17ms sex_ids;
with NCD17ms;
run; quit;
SAS Log
Errors (1)
ERROR: Libref PRODUCTS is not assigned.
Warnings (1)
Notes (2)
1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;
68
69 proc corr data=Products.source fisher;
ERROR: Libref PRODUCTS is not assigned.
70 Where FSVV2 < 351.5724;
WARNING: No data sets qualify for WHERE processing.
71 var NCD17f1 NCD17f2 NCD17f3 NCD17f4 NCD17f5 PAMETs17ms AmbientPM17ms sex_ids;
72 with NCD17ms;
73 run;
NOTE: The SAS System stopped processing this step because of errors.
NOTE: PROCEDURE CORR used (Total process time):
real time 0.00 seconds
user cpu time 0.00 seconds
system cpu time 0.00 seconds
memory 409.68k
OS Memory 36016.00k
Timestamp 08/31/2021 02:00:00 AM
Step Count 366 Switch Count 0
Page Faults 0
Page Reclaims 16
Page Swaps 0
Voluntary Context Switches 0
Involuntary Context Switches 0
Block Input Operations 0
Block Output Operations 0
73 ! quit;
74
75 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;
85
Thanks for your help.
... View more
04-30-2021
12:20 PM
Now I understand it.
Many thanks.
David Cundiff
... View more
04-30-2021
12:06 AM
Well, using source2 and source3 did the job. However, it doesn't explain why that was necessary and when it might again be suddenly necessary.
Thanks for your help.
Result:
The CORR Procedure
1 With Variables: CVD2017m
5 Variables: CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW
Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
CVD2017m 7846 543.66067 288.00939 4265562 73.47499 1844 CVD/100k/year ages 15-69
CVDf1 7846 0 21.61400 0 -74.89858 33.74918 Combination of 20 diet risk factors
CVDf2 7846 0 0.01796 0 -0.02383 0.04322 Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3 7846 0 1.94134 0 -2.18859 20.35708 Types 1 and 2 DM
SBP17msW 7846 4.9045E-13 1.00000 3.84807E-9 -2.43011 3.23505 Systolic BP mm Hg
sex_IDsW 7846 0 1.00000 0 -0.99994 0.99994 Sex male 1 and female 2
Pearson Correlation Coefficients, N = 7846
Prob > |r| under H0: Rho=0
CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW
CVD2017m
CVD/100k/year ages 15-69
0.41439
<.0001
0.41217
<.0001
0.31755
<.0001
0.19492
<.0001
-0.39527
<.0001
Pearson Correlation Statistics (Fisher's z Transformation)
Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for
H0:Rho=0
CVDf1 CVD2017m 7846 0.41439 0.44090 0.0000264 0.41437 0.395873 0.432532 <.0001
CVDf2 CVD2017m 7846 0.41217 0.43822 0.0000263 0.41215 0.393608 0.430349 <.0001
CVDf3 CVD2017m 7846 0.31755 0.32892 0.0000202 0.31753 0.297494 0.337290 <.0001
SBP17msW CVD2017m 7846 0.19492 0.19745 0.0000124 0.19491 0.173531 0.216106 <.0001
sex_IDsW CVD2017m 7846 -0.39527 -0.41803 -0.0000252 -0.39525 -0.413755 -0.376410 <.0001
The REG Procedure
Model: MODEL1
Dependent Variable: CVD2017msW CVD/100k/year ages 15-69
Number of Observations Read 7846
Number of Observations Used 7846
Stepwise Selection: Step 1
Variable CVDf1 Entered: R-Square = 0.1717 and C(p) = 3424.469
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 1 1347.15123 1347.15123 1626.24 <.0001
Error 7844 6497.84877 0.82838
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -5.1482E-14 0.01028 2.07946E-23 0.00 1.0000
CVDf1 0.01917 0.00047543 1347.15123 1626.24 <.0001
Bounds on condition number: 1, 1
Stepwise Selection: Step 2
Variable CVDf2 Entered: R-Square = 0.3247 and C(p) = 1345.693
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 2 2547.22254 1273.61127 1885.50 <.0001
Error 7843 5297.77746 0.67548
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -4.5343E-14 0.00928 1.61312E-23 0.00 1.0000
CVDf1 0.01823 0.00042990 1214.49377 1797.98 <.0001
CVDf2 21.80856 0.51740 1200.07130 1776.62 <.0001
Bounds on condition number: 1.0027, 4.0109
Stepwise Selection: Step 3
Variable CVDf3 Entered: R-Square = 0.3877 and C(p) = 491.2699
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 3 3041.15785 1013.71928 1654.84 <.0001
Error 7842 4803.84215 0.61258
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -4.4852E-14 0.00884 1.57839E-23 0.00 1.0000
CVDf1 0.01438 0.00043120 681.70150 1112.84 <.0001
CVDf2 23.56816 0.49661 1379.71384 2252.30 <.0001
CVDf3 0.13686 0.00482 493.93531 806.32 <.0001
Bounds on condition number: 1.1212, 9.7564
Stepwise Selection: Step 4
Variable SBP17msW Entered: R-Square = 0.4126 and C(p) = 153.4894
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 4 3237.12365 809.28091 1377.11 <.0001
Error 7841 4607.87635 0.58766
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -1.2225E-13 0.00865 1.17256E-22 0.00 1.0000
CVDf1 0.01453 0.00042241 695.29876 1183.16 <.0001
CVDf2 23.97928 0.48692 1425.21520 2425.22 <.0001
CVDf3 0.11914 0.00482 359.11233 611.08 <.0001
SBP17msW 0.16191 0.00887 195.96581 333.47 <.0001
Bounds on condition number: 1.1686, 17.406
Stepwise Selection: Step 5
Variable sex_IDsW Entered: R-Square = 0.4236 and C(p) = 6.0000
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 5 3323.34051 664.66810 1152.45 <.0001
Error 7840 4521.65949 0.57674
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -1.2845E-13 0.00857 1.29454E-22 0.00 1.0000
CVDf1 0.01476 0.00041890 716.32252 1242.01 <.0001
CVDf2 16.45730 0.78178 255.58277 443.15 <.0001
CVDf3 0.11176 0.00481 311.03348 539.29 <.0001
SBP17msW 0.17041 0.00881 215.71423 374.02 <.0001
sex_IDsW -0.17070 0.01396 86.21686 149.49 <.0001
Bounds on condition number: 2.6811, 43.454
All variables left in the model are significant at the 0.2500 level.
All variables have been entered into the model.
Summary of Stepwise Selection
Step Variable
Entered Variable
Removed Label Number
Vars In Partial
R-Square Model
R-Square C(p) F Value Pr > F
1 CVDf1 Combination of 20 diet risk factors 1 0.1717 0.1717 3424.47 1626.24 <.0001
2 CVDf2 Kidney Dz, Smoking tobacco, sublingual tobacco 2 0.1530 0.3247 1345.69 1776.62 <.0001
3 CVDf3 Types 1 and 2 DM 3 0.0630 0.3877 491.270 806.32 <.0001
4 SBP17msW Systolic BP mm Hg 4 0.0250 0.4126 153.489 333.47 <.0001
5 sex_IDsW Sex male 1 and female 2 5 0.0110 0.4236 6.0000 149.49 <.0001
The REG Procedure
Model: MODEL1
Dependent Variable: CVD2017msW CVD/100k/year ages 15-69
Panel of heat maps of residuals by regressors for CVD2017msW.
The CORR Procedure
1 With Variables: CVD2017m
6 Variables: CVDf1 CVDf2 CVDf3 CVDf4 SBP17msW sex_IDsW
Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
CVD2017m 7846 543.66067 288.00939 4265562 73.47499 1844 CVD/100k/year ages 15-69
CVDf1 7846 0 21.61400 0 -74.89858 33.74918 Combination of 20 diet risk factors
CVDf2 7846 0 0.01796 0 -0.02383 0.04322 Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3 7846 0 1.94134 0 -2.18859 20.35708 Types 1 and 2 DM
CVDf4 7846 9.0822E-14 0.65083 7.1259E-10 -1.75968 2.95661 Mult reg CVDf1 CVDf2 CVDf3 SBP sex
SBP17msW 7846 4.9045E-13 1.00000 3.84807E-9 -2.43011 3.23505 Systolic BP mm Hg
sex_IDsW 7846 0 1.00000 0 -0.99994 0.99994 Sex male 1 and female 2
Pearson Correlation Coefficients, N = 7846
Prob > |r| under H0: Rho=0
CVDf1 CVDf2 CVDf3 CVDf4 SBP17msW sex_IDsW
CVD2017m
CVD/100k/year ages 15-69
0.41439
<.0001
0.41217
<.0001
0.31755
<.0001
0.65087
<.0001
0.19492
<.0001
-0.39527
<.0001
Pearson Correlation Statistics (Fisher's z Transformation)
Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for
H0:Rho=0
CVDf1 CVD2017m 7846 0.41439 0.44090 0.0000264 0.41437 0.395873 0.432532 <.0001
CVDf2 CVD2017m 7846 0.41217 0.43822 0.0000263 0.41215 0.393608 0.430349 <.0001
CVDf3 CVD2017m 7846 0.31755 0.32892 0.0000202 0.31753 0.297494 0.337290 <.0001
CVDf4 CVD2017m 7846 0.65087 0.77680 0.0000415 0.65084 0.637900 0.663415 <.0001
SBP17msW CVD2017m 7846 0.19492 0.19745 0.0000124 0.19491 0.173531 0.216106 <.0001
sex_IDsW CVD2017m 7846 -0.39527 -0.41803 -0.0000252 -0.39525 -0.413755 -0.376410 <.0001
The CORR Procedure
1 With Variables: CVD2017m
7 Variables: CVDf1 CVDf2 CVDf3 CVDf4 CVDf5 SBP17msW sex_IDsW
Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
CVD2017m 7846 543.66067 288.00939 4265562 73.47499 1844 CVD/100k/year ages 15-69
CVDf1 7846 0 21.61400 0 -74.89858 33.74918 Combination of 20 diet risk factors
CVDf2 7846 0 0.01796 0 -0.02383 0.04322 Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3 7846 0 1.94134 0 -2.18859 20.35708 Types 1 and 2 DM
CVDf4 7846 9.0822E-14 0.65083 7.1259E-10 -1.75968 2.95661 Mult reg CVDf1 CVDf2 CVDf3 SBP sex
CVDf5 7846 2.6151E-12 18.11747 2.05182E-8 -48.22646 83.18385 Final CVD risk factor formula
SBP17msW 7846 4.9045E-13 1.00000 3.84807E-9 -2.43011 3.23505 Systolic BP mm Hg
sex_IDsW 7846 0 1.00000 0 -0.99994 0.99994 Sex male 1 and female 2
Pearson Correlation Coefficients, N = 7846
Prob > |r| under H0: Rho=0
CVDf1 CVDf2 CVDf3 CVDf4 CVDf5 SBP17msW sex_IDsW
CVD2017m
CVD/100k/year ages 15-69
0.41439
<.0001
0.41217
<.0001
0.31755
<.0001
0.65087
<.0001
0.65244
<.0001
0.19492
<.0001
-0.39527
<.0001
Pearson Correlation Statistics (Fisher's z Transformation)
Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for
H0:Rho=0
CVDf1 CVD2017m 7846 0.41439 0.44090 0.0000264 0.41437 0.395873 0.432532 <.0001
CVDf2 CVD2017m 7846 0.41217 0.43822 0.0000263 0.41215 0.393608 0.430349 <.0001
CVDf3 CVD2017m 7846 0.31755 0.32892 0.0000202 0.31753 0.297494 0.337290 <.0001
CVDf4 CVD2017m 7846 0.65087 0.77680 0.0000415 0.65084 0.637900 0.663415 <.0001
CVDf5 CVD2017m 7846 0.65244 0.77953 0.0000416 0.65241 0.639517 0.664941 <.0001
SBP17msW CVD2017m 7846 0.19492 0.19745 0.0000124 0.19491 0.173531 0.216106 <.0001
sex_IDsW CVD2017m 7846 -0.39527 -0.41803 -0.0000252 -0.39525 -0.413755 -0.376410 <.0001
SAS code:
data work.source;
set projects.source;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM;
CVDf1=
- pmeat17KCsW * 5.49 * 0.0443
- rmeat17KCsW * 50.70 * 0.0560
- fish17KCsW * 10.01 * 0.0412
- milk17KCsW * 25.37 * 0.0398
- poultry16KCsW * 45.06 * 0.0867
- eggs16KCsW * 19.47 * 0.1544
- SFA16KCsW * 191.27 * 0.0603 * 0.46
- PUFA17KCsW * 82.24 * 0.1033 * 0.46
- TFA17KCsW * 13.40 * 0.0128 * 0.46
- Alcohol17KCsW * 81.71 * 0.0047
+ Sugarb17KCsW * 297.65 * 0.0136
+ potatoes16KCsW * 84.16 * 0.0024
- corn16KCsW * 34.67 * 0.0037
- fruits17KCsW * 40.39 * 0.1291
- Vegetables17KCsW * 80.14 * 0.0127
- nutsseeds17KCsW * 8.51 * 0.0797
- wgrains17KCsW * 55.65 * 0.0376
- legumes17KCsW * 51.66 * 0.0005
+ rice16KCsW * 141.23 * 0.0001
- swtpot16KCsW * 22.67 * 0.0270
;
CVDf2=
+ smoke17msW * 0.2046 * 0.08899
+ SLTobacco17msW * 0.0680 * 0.08179
+ kidneydz17msW * 0.056 * 0.037636
;
CVDf3=
+ T1DM17msW * 10.34 * 0.1169
+ T2DM17msW * 17.47 * 0.05193
;
run;quit;
proc corr data=work.source fisher;
var CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW;
with CVD2017m;
run;quit;
Proc reg data=work.source;
*label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM
;
model CVD2017msW=CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW
/ selection=STEPWISE
slentry=.25 slstay=.25;
run; quit;
*
CVDf1 0.01476 0.00041890 716.32252 1242.01 <.0001
CVDf2 16.45730 0.78178 255.58277 443.15 <.0001
CVDf3 0.11176 0.00481 311.03348 539.29 <.0001
SBP17msW 0.17041 0.00881 215.71423 374.02 <.0001
sex_IDsW -0.17070 0.01396 86.21686 149.49 <.0001
1 CVDf1 Combination of 20 diet risk factors 1 0.1717 0.1717 3424.47 1626.24 <.0001
2 CVDf2 Child wt, air pollution, Kidney Dz 2 0.1530 0.3247 1345.69 1776.62 <.0001
3 CVDf3 Types 1 and 2 DM 3 0.0630 0.3877 491.270 806.32 <.0001
4 SBP17msW Systolic BP mm Hg 4 0.0250 0.4126 153.489 333.47 <.0001
5 sex_IDsW Sex male 1 and female 2 5 0.0110 0.4236 6.0000 149.49 <.0001
;
*CVD formula R2=0.4236;
*data work.source;
* set projects.source;
data source2;
set source;
label
CVDf4=Mult reg CVDf1 CVDf2 CVDf3 SBP sex ;
CVDf4=
+ CVDf1 * 0.01476
+ CVDf2 * 16.45730
+ CVDf3 * 0.11176
+ SBP17msW * 0.17041
- sex_IDsW * 0.17070
;
run; quit;
proc corr data=source2 fisher;
var CVDf1 CVDf2 CVDf3 CVDf4 SBP17msW sex_IDsW;
with CVD2017m;
run;quit;
*CVD formula R2=0.4236;
data source3;
set source2;
label
CVDf5=Final CVD risk factor formula;
CVDf5=
- pmeat17KCsW * 0.09
- rmeat17KCsW * 1.14
- fish17KCsW * 0.17
- milk17KCsW * 0.39
- poultry16KCsW * 1.54
- eggs16kcsW * 1.23
- SFA16KCsW * 2.10
- PUFA17KCsW * 1.56
- TFA17KCsW * 0.03
- ALCOHOL17KCsW * 0.13
+ Sugarb17KCsW * 1.59
+ potatoes16KCsW * 0.09
- corn16kcsW * 0.06
- fruits17KCsW * 2.12
- vegetables17KCsW* 0.38
- nutsseeds17KCsW * 0.27
- wgrains17KCsW * 0.88
- legumes17kcsW * 0.01
+ rice16kcsW * 0.00
- swtpot16kcsW * 0.27
+ smoke17msW * 8.45
+ SLTobacco17msW * 2.57
+ kidneydz17msW * 0.98
+ T1DM17msW * 3.80
+ T2DM17msW * 2.85
+ SBP17msW * 4.83
- sex_IDsW * 4.84
;
run; quit;
proc corr data=source3 fisher;
var CVDf1 CVDf2 CVDf3 CVDf4 CVDf5 SBP17msW sex_IDsW;
with CVD2017m;
run;quit;
... View more
04-29-2021
10:53 PM
It was helpful to use work.source instead of source. However, I still lose CVDf1, CVDf2, CVDf3, and CVDf4 after a regression step. The final answer is CVDf5 and it gets that answer. That answer depends on CVDf1, CVDf2, CVDf3, and CVDf4. If I stop before the final CVDf5 data step followed by proc corr, then CVDf1, CVDf2, CVDf3, and CVDf4 are included in the work.souce. If I take it to the final step to derive CVDf5, then CVDf5 is included in the output but not CVDf1, CVDf2, CVDf3, and CVDf4. The log says: WARNING: Variable CVDF1 not found in data set WORK.SOURCE.
How does it lose CVDf1, CVDf2, CVDf3, and CVDf4 in the final proc corr? The last data step must have those variables because CVDf5, the final answer, depends on them.
Thanks
SAS code:
data work.source;
set projects.source;
CVDf1=
- pmeat17KCsW * 5.49 * 0.0443
- rmeat17KCsW * 50.70 * 0.0560
- fish17KCsW * 10.01 * 0.0412
- milk17KCsW * 25.37 * 0.0398
- poultry16KCsW * 45.06 * 0.0867
- eggs16KCsW * 19.47 * 0.1544
- SFA16KCsW * 191.27 * 0.0603 * 0.46
- PUFA17KCsW * 82.24 * 0.1033 * 0.46
- TFA17KCsW * 13.40 * 0.0128 * 0.46
- Alcohol17KCsW * 81.71 * 0.0047
+ Sugarb17KCsW * 297.65 * 0.0136
+ potatoes16KCsW * 84.16 * 0.0024
- corn16KCsW * 34.67 * 0.0037
- fruits17KCsW * 40.39 * 0.1291
- Vegetables17KCsW * 80.14 * 0.0127
- nutsseeds17KCsW * 8.51 * 0.0797
- wgrains17KCsW * 55.65 * 0.0376
- legumes17KCsW * 51.66 * 0.0005
+ rice16KCsW * 141.23 * 0.0001
- swtpot16KCsW * 22.67 * 0.0270
;
CVDf2=
+ smoke17msW * 0.2046 * 0.08899
+ SLTobacco17msW * 0.0680 * 0.08179
+ kidneydz17msW * 0.056 * 0.037636
;
CVDf3=
+ T1DM17msW * 10.34 * 0.1169
+ T2DM17msW * 17.47 * 0.05193
;
run;quit;
proc corr data=work.source fisher;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM;
var CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW;
with CVD2017m;
run;quit;
Proc reg data=work.source;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM;
;
model CVD2017msW=CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW
/ selection=STEPWISE
slentry=.25 slstay=.25;
run; quit;
*
CVDf1 0.01476 0.00041890 716.32252 1242.01 <.0001
CVDf2 16.45730 0.78178 255.58277 443.15 <.0001
CVDf3 0.11176 0.00481 311.03348 539.29 <.0001
SBP17msW 0.17041 0.00881 215.71423 374.02 <.0001
sex_IDsW -0.17070 0.01396 86.21686 149.49 <.0001
1 CVDf1 Combination of 20 diet risk factors 1 0.1717 0.1717 3424.47 1626.24 <.0001
2 CVDf2 Child wt, air pollution, Kidney Dz 2 0.1530 0.3247 1345.69 1776.62 <.0001
3 CVDf3 Types 1 and 2 DM 3 0.0630 0.3877 491.270 806.32 <.0001
4 SBP17msW Systolic BP mm Hg 4 0.0250 0.4126 153.489 333.47 <.0001
5 sex_IDsW Sex male 1 and female 2 5 0.0110 0.4236 6.0000 149.49 <.0001
;
*CVD formula R2=0.4236;
data work.source;
set projects.source;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM
CVDf4=Mult reg CVDf1 CVDf2 CVDf3 SBP sex ;
CVDf4=
+ CVDf1 * 0.01476
+ CVDf2 * 16.45730
+ CVDf3 * 0.11176
+ SBP17msW * 0.17041
- sex_IDsW * 0.17070
;
run; quit;
proc corr data=work.source fisher;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM
CVDf4=Mult reg CVDf1 CVDf2 CVDf3 SBP sex ;
var CVDf1 CVDf2 CVDf3 CVDf4 SBP17msW sex_IDsW;
with CVD2017m;
run;quit;
*CVD formula R2=0.4236;
data work.source;
set projects.source;
CVDf5=
- pmeat17KCsW * 0.09
- rmeat17KCsW * 1.14
- fish17KCsW * 0.17
- milk17KCsW * 0.39
- poultry16KCsW * 1.54
- eggs16kcsW * 1.23
- SFA16KCsW * 2.10
- PUFA17KCsW * 1.56
- TFA17KCsW * 0.03
- ALCOHOL17KCsW * 0.13
+ Sugarb17KCsW * 1.59
+ potatoes16KCsW * 0.09
- corn16kcsW * 0.06
- fruits17KCsW * 2.12
- vegetables17KCsW* 0.38
- nutsseeds17KCsW * 0.27
- wgrains17KCsW * 0.88
- legumes17kcsW * 0.01
+ rice16kcsW * 0.00
- swtpot16kcsW * 0.27
+ smoke17msW * 8.45
+ SLTobacco17msW * 2.57
+ kidneydz17msW * 0.98
+ T1DM17msW * 3.80
+ T2DM17msW * 2.85
+ SBP17msW * 4.83
- sex_IDsW * 4.84
;
run; quit;
proc corr data=work.source fisher;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM
CVDf4=Mult reg CVDf1 CVDf2 CVDf3 SBP sex
CVDf5=Final CVD risk factor formula;
var CVDf5 SBP17msW sex_IDsW;
with CVD2017m;
run;quit;
results:
The REG Procedure
Model: MODEL1
Dependent Variable: CVD2017msW CVD/100k/year ages 15-69
Number of Observations Read 7846
Number of Observations Used 7846
Stepwise Selection: Step 1
Variable CVDf1 Entered: R-Square = 0.1717 and C(p) = 3424.469
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 1 1347.15123 1347.15123 1626.24 <.0001
Error 7844 6497.84877 0.82838
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -5.1482E-14 0.01028 2.07946E-23 0.00 1.0000
CVDf1 0.01917 0.00047543 1347.15123 1626.24 <.0001
Bounds on condition number: 1, 1
Stepwise Selection: Step 2
Variable CVDf2 Entered: R-Square = 0.3247 and C(p) = 1345.693
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 2 2547.22254 1273.61127 1885.50 <.0001
Error 7843 5297.77746 0.67548
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -4.5343E-14 0.00928 1.61312E-23 0.00 1.0000
CVDf1 0.01823 0.00042990 1214.49377 1797.98 <.0001
CVDf2 21.80856 0.51740 1200.07130 1776.62 <.0001
Bounds on condition number: 1.0027, 4.0109
Stepwise Selection: Step 3
Variable CVDf3 Entered: R-Square = 0.3877 and C(p) = 491.2699
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 3 3041.15785 1013.71928 1654.84 <.0001
Error 7842 4803.84215 0.61258
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -4.4852E-14 0.00884 1.57839E-23 0.00 1.0000
CVDf1 0.01438 0.00043120 681.70150 1112.84 <.0001
CVDf2 23.56816 0.49661 1379.71384 2252.30 <.0001
CVDf3 0.13686 0.00482 493.93531 806.32 <.0001
Bounds on condition number: 1.1212, 9.7564
Stepwise Selection: Step 4
Variable SBP17msW Entered: R-Square = 0.4126 and C(p) = 153.4894
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 4 3237.12365 809.28091 1377.11 <.0001
Error 7841 4607.87635 0.58766
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -1.2225E-13 0.00865 1.17256E-22 0.00 1.0000
CVDf1 0.01453 0.00042241 695.29876 1183.16 <.0001
CVDf2 23.97928 0.48692 1425.21520 2425.22 <.0001
CVDf3 0.11914 0.00482 359.11233 611.08 <.0001
SBP17msW 0.16191 0.00887 195.96581 333.47 <.0001
Bounds on condition number: 1.1686, 17.406
Stepwise Selection: Step 5
Variable sex_IDsW Entered: R-Square = 0.4236 and C(p) = 6.0000
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 5 3323.34051 664.66810 1152.45 <.0001
Error 7840 4521.65949 0.57674
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -1.2845E-13 0.00857 1.29454E-22 0.00 1.0000
CVDf1 0.01476 0.00041890 716.32252 1242.01 <.0001
CVDf2 16.45730 0.78178 255.58277 443.15 <.0001
CVDf3 0.11176 0.00481 311.03348 539.29 <.0001
SBP17msW 0.17041 0.00881 215.71423 374.02 <.0001
sex_IDsW -0.17070 0.01396 86.21686 149.49 <.0001
Bounds on condition number: 2.6811, 43.454
All variables left in the model are significant at the 0.2500 level.
All variables have been entered into the model.
Summary of Stepwise Selection
Step Variable
Entered Variable
Removed Label Number
Vars In Partial
R-Square Model
R-Square C(p) F Value Pr > F
1 CVDf1 Combination of 20 diet risk factors 1 0.1717 0.1717 3424.47 1626.24 <.0001
2 CVDf2 Kidney Dz, Smoking tobacco, sublingual tobacco 2 0.1530 0.3247 1345.69 1776.62 <.0001
3 CVDf3 Types 1 and 2 DM 3 0.0630 0.3877 491.270 806.32 <.0001
4 SBP17msW Systolic BP mm Hg 4 0.0250 0.4126 153.489 333.47 <.0001
5 sex_IDsW Sex male 1 and female 2 5 0.0110 0.4236 6.0000 149.49 <.0001
The REG Procedure
Model: MODEL1
Dependent Variable: CVD2017msW CVD/100k/year ages 15-69
Panel of heat maps of residuals by regressors for CVD2017msW.
The CORR Procedure
1 With Variables: CVD2017m
6 Variables: CVDf1 CVDf2 CVDf3 CVDf4 SBP17msW sex_IDsW
Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
CVD2017m 7846 543.66067 288.00939 4265562 73.47499 1844 CVD/100k/year ages 15-69
CVDf1 0 . . . . . Combination of 20 diet risk factors
CVDf2 0 . . . . . Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3 0 . . . . . Types 1 and 2 DM
CVDf4 0 . . . . . Mult reg CVDf1 CVDf2 CVDf3 SBP sex
SBP17msW 7846 4.9045E-13 1.00000 3.84807E-9 -2.43011 3.23505 Systolic BP mm Hg
sex_IDsW 7846 0 1.00000 0 -0.99994 0.99994 Sex male 1 and female 2
Pearson Correlation Coefficients
Prob > |r| under H0: Rho=0
Number of Observations
CVDf1 CVDf2 CVDf3 CVDf4 SBP17msW sex_IDsW
CVD2017m
CVD/100k/year ages 15-69
.
.
0
.
.
0
.
.
0
.
.
0
0.19492
<.0001
7846
-0.39527
<.0001
7846
Pearson Correlation Statistics (Fisher's z Transformation)
Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for
H0:Rho=0
CVDf1 CVD2017m 0 . . . . . . .
CVDf2 CVD2017m 0 . . . . . . .
CVDf3 CVD2017m 0 . . . . . . .
CVDf4 CVD2017m 0 . . . . . . .
SBP17msW CVD2017m 7846 0.19492 0.19745 0.0000124 0.19491 0.173531 0.216106 <.0001
sex_IDsW CVD2017m 7846 -0.39527 -0.41803 -0.0000252 -0.39525 -0.413755 -0.376410 <.0001
The CORR Procedure
1 With Variables: CVD2017m
3 Variables: CVDf5 SBP17msW sex_IDsW
Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
CVD2017m 7846 543.66067 288.00939 4265562 73.47499 1844 CVD/100k/year ages 15-69
CVDf5 7846 2.6151E-12 18.11747 2.05182E-8 -48.22646 83.18385 Final CVD risk factor formula
SBP17msW 7846 4.9045E-13 1.00000 3.84807E-9 -2.43011 3.23505 Systolic BP mm Hg
sex_IDsW 7846 0 1.00000 0 -0.99994 0.99994 Sex male 1 and female 2
Pearson Correlation Coefficients, N = 7846
Prob > |r| under H0: Rho=0
CVDf5 SBP17msW sex_IDsW
CVD2017m
CVD/100k/year ages 15-69
0.65244
<.0001
0.19492
<.0001
-0.39527
<.0001
Pearson Correlation Statistics (Fisher's z Transformation)
Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for
H0:Rho=0
CVDf5 CVD2017m 7846 0.65244 0.77953 0.0000416 0.65241 0.639517 0.664941 <.0001
SBP17msW CVD2017m 7846 0.19492 0.19745 0.0000124 0.19491 0.173531 0.216106 <.0001
sex_IDsW CVD2017m 7846 -0.39527 -0.41803 -0.0000252 -0.39525 -0.413755 -0.376410 <.0001
Log;
217 *CVD formula R2=0.4236;
218 data work.source;
219 set projects.source;
220 CVDf5=
221 -pmeat17KCsW*0.09
222 -rmeat17KCsW*1.14
223 -fish17KCsW*0.17
224 -milk17KCsW*0.39
225 -poultry16KCsW*1.54
226 -eggs16kcsW*1.23
227 -SFA16KCsW*2.10
228 -PUFA17KCsW*1.56
229 -TFA17KCsW*0.03
230 -ALCOHOL17KCsW*0.13
231 +Sugarb17KCsW*1.59
232 +potatoes16KCsW*0.09
233 -corn16kcsW*0.06
234 -fruits17KCsW*2.12
235 -vegetables17KCsW*0.38
236 -nutsseeds17KCsW*0.27
237 -wgrains17KCsW*0.88
238 -legumes17kcsW*0.01
239 +rice16kcsW*0.00
240 -swtpot16kcsW*0.27
241 +smoke17msW*8.45
242 +SLTobacco17msW*2.57
243 +kidneydz17msW*0.98
244 +T1DM17msW*3.80
245 +T2DM17msW*2.85
246 +SBP17msW*4.83
247 -sex_IDsW*4.84
248
249 ;
250 run;
NOTE: There were 7846 observations read from the data set PROJECTS.SOURCE.
NOTE: The data set WORK.SOURCE has 7846 observations and 1273 variables.
NOTE: DATA statement used (Total process time):
real time 0.11 seconds
user cpu time 0.02 seconds
system cpu time 0.10 seconds
memory 5043.34k
OS Memory 68032.00k
Timestamp 04/30/2021 02:16:01 AM
Step Count 299 Switch Count 15
Page Faults 0
Page Reclaims 743
Page Swaps 0
Voluntary Context Switches 51
Involuntary Context Switches 0
Block Input Operations 0
Block Output Operations 167952
250 ! quit;
251
252 proc corr data=work.source fisher;
253 label
254 CVDf1=Combination of 20 diet risk factors
255 CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
WARNING: Variable CVDF1 not found in data set WORK.SOURCE.
256 CVDf3=Types 1 and 2 DM
WARNING: Variable CVDF2 not found in data set WORK.SOURCE.
257 CVDf4=Mult reg CVDf1 CVDf2 CVDf3 SBP sex
WARNING: Variable CVDF3 not found in data set WORK.SOURCE.
258 CVDf5=Final CVD risk factor formula;
WARNING: Variable CVDF4 not found in data set WORK.SOURCE.
259 var CVDf5 SBP17msW sex_IDsW;
260 with CVD2017m;
261 run;
NOTE: PROCEDURE CORR used (Total process time):
real time 0.08 seconds
user cpu time 0.06 seconds
system cpu time 0.02 seconds
memory 2373.78k
OS Memory 64952.00k
Timestamp 04/30/2021 02:16:01 AM
Step Count 300 Switch Count 13
Page Faults 0
Page Reclaims 246
Page Swaps 0
Voluntary Context Switches 36
Involuntary Context Switches 0
Block Input Operations 0
Block Output Operations 8
261 ! quit;
262
263 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;
275
... View more
04-29-2021
08:04 PM
This analysis uses complex variables and multiple regression. I'll show the code and the log. CVDf1, CVDf2, CVDf3, CVDf4 got lost at the end.
data source;
set projects.source;
CVDf1=
- pmeat17KCsW * 5.49 * 0.0443
- rmeat17KCsW * 50.70 * 0.0560
- fish17KCsW * 10.01 * 0.0412
- milk17KCsW * 25.37 * 0.0398
- poultry16KCsW * 45.06 * 0.0867
- eggs16KCsW * 19.47 * 0.1544
- SFA16KCsW * 191.27 * 0.0603 * 0.46
- PUFA17KCsW * 82.24 * 0.1033 * 0.46
- TFA17KCsW * 13.40 * 0.0128 * 0.46
- Alcohol17KCsW * 81.71 * 0.0047
+ Sugarb17KCsW * 297.65 * 0.0136
+ potatoes16KCsW * 84.16 * 0.0024
- corn16KCsW * 34.67 * 0.0037
- fruits17KCsW * 40.39 * 0.1291
- Vegetables17KCsW * 80.14 * 0.0127
- nutsseeds17KCsW * 8.51 * 0.0797
- wgrains17KCsW * 55.65 * 0.0376
- legumes17KCsW * 51.66 * 0.0005
+ rice16KCsW * 141.23 * 0.0001
- swtpot16KCsW * 22.67 * 0.0270
;
CVDf2=
+ smoke17msW * 0.2046 * 0.08899
+ SLTobacco17msW * 0.0680 * 0.08179
+ kidneydz17msW * 0.056 * 0.037636
;
CVDf3=
+ T1DM17msW * 10.34 * 0.1169
+ T2DM17msW * 17.47 * 0.05193
;
run;quit;
proc corr data=source fisher;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM;
var CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW;
with CVD2017m;
run;quit;
* Works ok to here;
Proc reg data=source;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM;
;
model CVD2017msW=CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW
/ selection=STEPWISE
slentry=.25 slstay=.25;
run; quit;
* Works ok to here;
*CVD formula R2=0.4236;
data source;
set projects.source;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM
CVDf4=Mult reg CVDf1 CVDf2 CVDf3 SBP sex ;
CVDf4=
+ CVDf1 * 0.01476
+ CVDf2 * 16.45730
+ CVDf3 * 0.11176
+ SBP17msW * 0.17041
- sex_IDsW * 0.17070
;
run; quit;
proc corr data=source fisher;
label
CVDf1=Combination of 20 diet risk factors
CVDf2= Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3=Types 1 and 2 DM
CVDf4=Mult reg CVDf1 CVDf2 CVDf3 SBP sex ;
var CVDf1 CVDf2 CVDf3 CVDf4 SBP17msW sex_IDsW;
with CVD2017m;
run;quit; *Here SAS can't find CVDf1 CVDf2 CVDf3 CVDf4;
Here is the log;
The CORR Procedure
1 With Variables: CVD2017m
5 Variables: CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW
Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
CVD2017m 7846 543.66067 288.00939 4265562 73.47499 1844 CVD/100k/year ages 15-69
CVDf1 7846 0 21.61400 0 -74.89858 33.74918 Combination of 20 diet risk factors
CVDf2 7846 0 0.01796 0 -0.02383 0.04322 Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3 7846 0 1.94134 0 -2.18859 20.35708 Types 1 and 2 DM
SBP17msW 7846 4.9045E-13 1.00000 3.84807E-9 -2.43011 3.23505 Systolic BP mm Hg
sex_IDsW 7846 0 1.00000 0 -0.99994 0.99994 Sex male 1 and female 2
Pearson Correlation Coefficients, N = 7846
Prob > |r| under H0: Rho=0
CVDf1 CVDf2 CVDf3 SBP17msW sex_IDsW
CVD2017m
CVD/100k/year ages 15-69
0.41439
<.0001
0.41217
<.0001
0.31755
<.0001
0.19492
<.0001
-0.39527
<.0001
Pearson Correlation Statistics (Fisher's z Transformation)
Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for
H0:Rho=0
CVDf1 CVD2017m 7846 0.41439 0.44090 0.0000264 0.41437 0.395873 0.432532 <.0001
CVDf2 CVD2017m 7846 0.41217 0.43822 0.0000263 0.41215 0.393608 0.430349 <.0001
CVDf3 CVD2017m 7846 0.31755 0.32892 0.0000202 0.31753 0.297494 0.337290 <.0001
SBP17msW CVD2017m 7846 0.19492 0.19745 0.0000124 0.19491 0.173531 0.216106 <.0001
sex_IDsW CVD2017m 7846 -0.39527 -0.41803 -0.0000252 -0.39525 -0.413755 -0.376410 <.0001
The REG Procedure
Model: MODEL1
Dependent Variable: CVD2017msW CVD/100k/year ages 15-69
Number of Observations Read 7846
Number of Observations Used 7846
Stepwise Selection: Step 1
Variable CVDf1 Entered: R-Square = 0.1717 and C(p) = 3424.469
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 1 1347.15123 1347.15123 1626.24 <.0001
Error 7844 6497.84877 0.82838
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -5.1482E-14 0.01028 2.07946E-23 0.00 1.0000
CVDf1 0.01917 0.00047543 1347.15123 1626.24 <.0001
Bounds on condition number: 1, 1
Stepwise Selection: Step 2
Variable CVDf2 Entered: R-Square = 0.3247 and C(p) = 1345.693
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 2 2547.22254 1273.61127 1885.50 <.0001
Error 7843 5297.77746 0.67548
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -4.5343E-14 0.00928 1.61312E-23 0.00 1.0000
CVDf1 0.01823 0.00042990 1214.49377 1797.98 <.0001
CVDf2 21.80856 0.51740 1200.07130 1776.62 <.0001
Bounds on condition number: 1.0027, 4.0109
Stepwise Selection: Step 3
Variable CVDf3 Entered: R-Square = 0.3877 and C(p) = 491.2699
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 3 3041.15785 1013.71928 1654.84 <.0001
Error 7842 4803.84215 0.61258
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -4.4852E-14 0.00884 1.57839E-23 0.00 1.0000
CVDf1 0.01438 0.00043120 681.70150 1112.84 <.0001
CVDf2 23.56816 0.49661 1379.71384 2252.30 <.0001
CVDf3 0.13686 0.00482 493.93531 806.32 <.0001
Bounds on condition number: 1.1212, 9.7564
Stepwise Selection: Step 4
Variable SBP17msW Entered: R-Square = 0.4126 and C(p) = 153.4894
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 4 3237.12365 809.28091 1377.11 <.0001
Error 7841 4607.87635 0.58766
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -1.2225E-13 0.00865 1.17256E-22 0.00 1.0000
CVDf1 0.01453 0.00042241 695.29876 1183.16 <.0001
CVDf2 23.97928 0.48692 1425.21520 2425.22 <.0001
CVDf3 0.11914 0.00482 359.11233 611.08 <.0001
SBP17msW 0.16191 0.00887 195.96581 333.47 <.0001
Bounds on condition number: 1.1686, 17.406
Stepwise Selection: Step 5
Variable sex_IDsW Entered: R-Square = 0.4236 and C(p) = 6.0000
Analysis of Variance
Source DF Sum of
Squares Mean
Square F Value Pr > F
Model 5 3323.34051 664.66810 1152.45 <.0001
Error 7840 4521.65949 0.57674
Corrected Total 7845 7845.00000
Variable Parameter
Estimate Standard
Error Type II SS F Value Pr > F
Intercept -1.2845E-13 0.00857 1.29454E-22 0.00 1.0000
CVDf1 0.01476 0.00041890 716.32252 1242.01 <.0001
CVDf2 16.45730 0.78178 255.58277 443.15 <.0001
CVDf3 0.11176 0.00481 311.03348 539.29 <.0001
SBP17msW 0.17041 0.00881 215.71423 374.02 <.0001
sex_IDsW -0.17070 0.01396 86.21686 149.49 <.0001
Bounds on condition number: 2.6811, 43.454
All variables left in the model are significant at the 0.2500 level.
All variables have been entered into the model.
Summary of Stepwise Selection
Step Variable
Entered Variable
Removed Label Number
Vars In Partial
R-Square Model
R-Square C(p) F Value Pr > F
1 CVDf1 Combination of 20 diet risk factors 1 0.1717 0.1717 3424.47 1626.24 <.0001
2 CVDf2 Kidney Dz, Smoking tobacco, sublingual tobacco 2 0.1530 0.3247 1345.69 1776.62 <.0001
3 CVDf3 Types 1 and 2 DM 3 0.0630 0.3877 491.270 806.32 <.0001
4 SBP17msW Systolic BP mm Hg 4 0.0250 0.4126 153.489 333.47 <.0001
5 sex_IDsW Sex male 1 and female 2 5 0.0110 0.4236 6.0000 149.49 <.0001
The REG Procedure
Model: MODEL1
Dependent Variable: CVD2017msW CVD/100k/year ages 15-69
Panel of heat maps of residuals by regressors for CVD2017msW
.
The CORR Procedure
1 With Variables: CVD2017m
6 Variables: CVDf1 CVDf2 CVDf3 CVDf4 SBP17msW sex_IDsW
Simple Statistics
Variable N Mean Std Dev Sum Minimum Maximum Label
CVD2017m 7846 543.66067 288.00939 4265562 73.47499 1844 CVD/100k/year ages 15-69
CVDf1 0 . . . . . Combination of 20 diet risk factors
CVDf2 0 . . . . . Kidney Dz, Smoking tobacco, sublingual tobacco
CVDf3 0 . . . . . Types 1 and 2 DM
CVDf4 0 . . . . . Mult reg CVDf1 CVDf2 CVDf3 SBP sex
SBP17msW 7846 4.9045E-13 1.00000 3.84807E-9 -2.43011 3.23505 Systolic BP mm Hg
sex_IDsW 7846 0 1.00000 0 -0.99994 0.99994 Sex male 1 and female 2
Pearson Correlation Coefficients
Prob > |r| under H0: Rho=0
Number of Observations
CVDf1 CVDf2 CVDf3 CVDf4 SBP17msW sex_IDsW
CVD2017m
CVD/100k/year ages 15-69
.
.
0
.
.
0
.
.
0
.
.
0
0.19492
<.0001
7846
-0.39527
<.0001
7846
Pearson Correlation Statistics (Fisher's z Transformation)
Variable With Variable N Sample Correlation Fisher's z Bias Adjustment Correlation Estimate 95% Confidence Limits p Value for
H0:Rho=0
CVDf1 CVD2017m 0 . . . . . . .
CVDf2 CVD2017m 0 . . . . . . .
CVDf3 CVD2017m 0 . . . . . . .
CVDf4 CVD2017m 0 . . . . . . .
SBP17msW CVD2017m 7846 0.19492 0.19745 0.0000124 0.19491 0.173531 0.216106 <.0001
sex_IDsW CVD2017m 7846 -0.39527 -0.41803 -0.0000252 -0.39525 -0.413755 -0.376410 <.0001
I use SAS on Demand for Academics SAS9.4
Where did CVDf1, CVDf2, CVDf3, CVDf4 go?
Thanks.
... View more
04-29-2021
04:41 PM
That was it! Thanks very much for your help.
... View more
04-29-2021
11:51 AM
At times I use data steps to subset my full database. When I then try to return to the full database with a proc corr, the database is still a subset even though there is no where statement to subset it. This happened even when I pasted the un subsetted code into a new program without the preceding data step.
*Standardizing the CVD and risk factors;
*Low FSVV cohort: FSVV2 < 567.2738 for multiple regression;
data Projects.source;
set Projects.source;
Where FSVV2 < 567.2738;
CVD2017ms=CVD2017m/282.8077 - 2.05765;
. . .
run; quit;
Subsequently a new program:
libname Projects "/home/u43393119/Projects";
proc sort data=Projects.source;
by weighted_no;
run; quit;
*Table 1 Low FSVV multiple regression: subsetting code removed;
proc corr data=Projects.source fisher ;
*Where FSVV2 < 567.2738;
*Where FSVV2 < 567.2738 or CVD2017m2 le 292.96526;
var CVD2017m2 LDLC17 FSVV FSVV2 pmeat17KC
.
.
.
with CVD2017m;
run; quit;
I use SAS on Demand for Academics. SAS 9.4.
What is the fix?
Thanks.
... View more
03-09-2021
10:55 PM
My dataset is Projects.source. My variables of interest are LDLc17, sex_ID and weighted_no. I don't know how these data go with %let work.path=%sysfunc(pathname(work));
I got it to export the variables of interest only in the following way:
libname x xlsx 'C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS codes/CVDoutput.xlsx';
options validvarname=v7;
data x;
set Projects.source;
Keep sex_ID weighted_no LDLC17
run;
proc print data=x;
run;
proc export data=x
outfile='C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS codes\CVDoutput.xlsx'
dbms=xlsx replace;
run;
log:
73 libname x xlsx 'C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS
73 ! codes/CVDoutput.xlsx';
NOTE: Libref X was successfully assigned as follows:
Engine: XLSX
Physical Name: C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS
codes/CVDoutput.xlsx
74 options validvarname=v7;
75
76 data x;
77 set Projects.source;
78 Keep sex_ID weighted_no LDLC17
79 run;
80
WARNING: The variable run in the DROP, KEEP, or RENAME list has never been referenced.
NOTE: There were 7886 observations read from the data set PROJECTS.SOURCE.
NOTE: The data set WORK.X has 7886 observations and 3 variables.
NOTE: DATA statement used (Total process time):
real time 0.02 seconds
user cpu time 0.01 seconds
system cpu time 0.01 seconds
memory 3249.15k
OS Memory 42168.00k
Timestamp 03/10/2021 03:35:32 AM
Step Count 270 Switch Count 5
Page Faults 0
Page Reclaims 815
Page Swaps 0
Voluntary Context Switches 26
Involuntary Context Switches 0
Block Input Operations 0
Block Output Operations 520
81 proc print data=x;
82 run;
NOTE: There were 7886 observations read from the data set WORK.X.
NOTE: PROCEDURE PRINT used (Total process time):
real time 8.39 seconds
user cpu time 8.33 seconds
system cpu time 0.07 seconds
memory 45520.65k
OS Memory 88500.00k
Timestamp 03/10/2021 03:35:41 AM
Step Count 271 Switch Count 0
Page Faults 0
Page Reclaims 25218
Page Swaps 0
Voluntary Context Switches 3
Involuntary Context Switches 14
Block Input Operations 0
Block Output Operations 4320
83
84 proc export data=x
85 outfile='C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS
85 ! codes\CVDoutput.xlsx'
86 dbms=xlsx replace;
87
88 run;
ERROR: Temporary file for XLSX file can not be created -> /pbr/biconfig/940/Lev1/SASApp/C:\Users\user\Documents\MDM\Health outcomes
formulas manuscripts\CVD Statistical tables and SAS codes\/CVDoutput.$$1. Make sure the path name is correct and that you have
write permission.
NOTE: The SAS System stopped processing this step because of errors.
NOTE: PROCEDURE EXPORT used (Total process time):
real time 0.00 seconds
user cpu time 0.00 seconds
system cpu time 0.01 seconds
memory 2075.40k
OS Memory 44652.00k
Timestamp 03/10/2021 03:35:41 AM
Step Count 272 Switch Count 0
Page Faults 0
Page Reclaims 550
Page Swaps 0
Voluntary Context Switches 1
Involuntary Context Switches 0
Block Input Operations 0
Block Output Operations 8
Results:
Obs sex_id weighted_no LDLc17
1 1 1 2.43536
2 2 2 2.43617
3 1 3 2.43536
4 2 4 2.43617
5 1 5 2.43536
6 2 6 2.43617
7 1 7 2.43536
8 2 8 2.43617
9 1 9 2.43536
10 2 10 2.4361
Then I went to Libraries and found library x with the 3 variables. I exported it back to Projects. Then I found it in Projects and downloaded it.
... View more
03-08-2021
01:35 PM
It didn't work because of something about the physical location of the output file. It was the same physical location that was ok before.
libname x xlsx 'C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS codes/CVD output.xlsx';
data x;
set Projects.source;
keep sex_ID weighted_no pmeat17KC rmeat17KC fish17KC milk17KC poultry16KC
eggs16KC SFA16KC PUFA17KC TFA17KC;
run;
proc print data=x;
run;
Ods excel file="<C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS codes/CVD output.xlsx";
proc print data=x;
run;
ods excel close; /*import, this finishes the output*/
Log
1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;
NOTE: ODS statements in the SAS Studio environment may disable some output features.
73
74 libname x xlsx 'C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS codes/CVD
74 ! output.xlsx';
NOTE: Libref X was successfully assigned as follows:
Engine: XLSX
Physical Name: C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS codes/CVD
output.xlsx
75 data x;
76 set Projects.source;
77 keep sex_ID weighted_no pmeat17KC rmeat17KC fish17KC milk17KC poultry16KC
78 eggs16KC SFA16KC PUFA17KC TFA17KC;
79 run;
NOTE: There were 7886 observations read from the data set PROJECTS.SOURCE.
NOTE: The data set WORK.X has 7886 observations and 11 variables.
NOTE: DATA statement used (Total process time):
real time 0.02 seconds
user cpu time 0.01 seconds
system cpu time 0.02 seconds
memory 3783.03k
OS Memory 46952.00k
Timestamp 03/08/2021 06:23:04 PM
Step Count 433 Switch Count 5
Page Faults 0
Page Reclaims 1027
Page Swaps 0
Voluntary Context Switches 23
Involuntary Context Switches 0
Block Input Operations 0
Block Output Operations 1544
80
81 proc print data=x;
82 run;
NOTE: There were 7886 observations read from the data set WORK.X.
NOTE: PROCEDURE PRINT used (Total process time):
real time 21.95 seconds
user cpu time 21.77 seconds
system cpu time 0.17 seconds
memory 123808.12k
OS Memory 180068.00k
Timestamp 03/08/2021 06:23:26 PM
Step Count 434 Switch Count 0
Page Faults 0
Page Reclaims 72519
Page Swaps 0
Voluntary Context Switches 4
Involuntary Context Switches 35
Block Input Operations 0
Block Output Operations 12128
83
84 Ods excel file="<C:\Users\user\Documents\MDM\Health outcomes formulas manuscripts\CVD Statistical tables and SAS
84 ! codes/CVD output.xlsx";
85
86 proc print data=x;
87 run;
NOTE: There were 7886 observations read from the data set WORK.X.
NOTE: PROCEDURE PRINT used (Total process time):
real time 47.88 seconds
user cpu time 47.23 seconds
system cpu time 0.66 seconds
memory 523083.93k
OS Memory 649096.00k
Timestamp 03/08/2021 06:24:14 PM
Step Count 435 Switch Count 0
Page Faults 0
Page Reclaims 351747
Page Swaps 0
Voluntary Context Switches 5
Involuntary Context Switches 71
Block Input Operations 0
Block Output Operations 21248
88 ods excel close; /*import, this finishes the output*/
ERROR: Physical file does not exist, /pbr/biconfig/940/Lev1/SASApp/<C:\Users\user\Documents\MDM\Health outcomes formulas
manuscripts\CVD Statistical tables and SAS codes/CVD output.xlsx.
89
... View more
03-05-2021
10:49 PM
It didn't work. Using data x.source, gave me an error message:
libname x xlsx
'C:\Users\user\OneDrive\IHME GBD work\CVD v diet RFs paper tables and SAS codes\CVD ouput.xlsx';
%put My version of SAS is &sysvlong;
options validvarname=v7;
data x;
set Projects.source;
keep weighted_no
sex_ID
pmeat17KC
rmeat17KC
fish17KC
milk17KC
poultry16KC
eggs16KC
SFA16KC
PUFA17KC
TFA17KC
;
run;
Ods excel file="C:\Users\user\OneDrive\IHME GBD work\CVD v diet RFs paper tables and SAS codes\CVD ouput.xlsx';
/procprintoutput.xlsx";
proc print data=x;
run;
ods excel close; /*import, this finishes the output*/
log
Errors (1)
Warnings
Notes (7)
1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;
NOTE: ODS statements in the SAS Studio environment may disable some output features.
73
74 libname x xlsx
75 'C:\Users\user\OneDrive\IHME GBD work\CVD v diet RFs paper tables and SAS codes\CVD ouput.xlsx';
NOTE: Libref X was successfully assigned as follows:
Engine: XLSX
Physical Name: C:\Users\user\OneDrive\IHME GBD work\CVD v diet RFs paper tables and SAS codes\CVD ouput.xlsx
76 %put My version of SAS is &sysvlong;
My version of SAS is 9.04.01M6P110718
77 options validvarname=v7;
78
79 data x;
80 set Projects.source;
81 keep weighted_no
82 sex_ID
83 pmeat17KC
84 rmeat17KC
85 fish17KC
86 milk17KC
87 poultry16KC
88 eggs16KC
89 SFA16KC
90 PUFA17KC
91 TFA17KC
92 ;
93 run;
NOTE: There were 7886 observations read from the data set PROJECTS.SOURCE.
NOTE: The data set WORK.X has 7886 observations and 11 variables.
NOTE: DATA statement used (Total process time):
real time 0.11 seconds
user cpu time 0.03 seconds
system cpu time 0.03 seconds
memory 3559.46k
OS Memory 43688.00k
Timestamp 03/06/2021 03:42:43 AM
Step Count 322 Switch Count 5
Page Faults 0
Page Reclaims 538
Page Swaps 0
Voluntary Context Switches 747
Involuntary Context Switches 0
Block Input Operations 115904
Block Output Operations 1544
94
95 Ods excel file="C:\Users\user\OneDrive\IHME GBD work\CVD v diet RFs paper tables and SAS codes\CVD ouput.xlsx';
96 /procprintoutput.xlsx";
97
98 proc print data=x;
99 run;
NOTE: There were 7886 observations read from the data set WORK.X.
NOTE: PROCEDURE PRINT used (Total process time):
real time 47.97 seconds
user cpu time 47.24 seconds
system cpu time 0.72 seconds
memory 527794.81k
OS Memory 646344.00k
Timestamp 03/06/2021 03:43:31 AM
Step Count 323 Switch Count 0
Page Faults 0
Page Reclaims 499265
Page Swaps 0
Voluntary Context Switches 14
Involuntary Context Switches 78
Block Input Operations 0
Block Output Operations 21264
100 ods excel close; /*import, this finishes the output*/
ERROR: Physical file does not exist, /pbr/biconfig/940/Lev1/SASApp/C:\Users\user\OneDrive\IHME GBD work\CVD v diet RFs paper tables
and SAS codes\CVD ouput.xlsx';/procprintoutput.xlsx.
101
102 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK;
Results:
Obs sex_id weighted_no Pmeat17KC Rmeat17KC Fish17KC Milk17KC Poultry16KC Eggs16KC SFA16KC PUFA17KC TFA17KC
1 1 1 2.4652 93.121 3.628 8.226 30.343 31.9542 182.576 72.481 5.5570
2 2 2 1.8631 61.095 3.102 7.806 30.343 31.9542 183.016 70.471 6.7463
3 1 3 2.4652 93.121 3.628 8.226 30.343 31.9542 182.576 72.481 5.5570
4 2 4 1.8631 61.095 3.102 7.806 30.343 31.9542 183.0
... View more
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