(cross post, but I think this area gets more traffic) Hello--
I have a data set that shows a nonlinear response. I was trying to model it to the generic Y=AB**X algebraic curve. Since I need to reflect and translate it I think the form is Y = [AB**(-X+H)] + K
/* where: Y is the response */ /* -X reflects Y=AB**X */ /* A and B affect the shape the curve */ /* H translates the curve on the X axis */ /* K translates the curve on the Y axis */ However, when I run PROC NLIN, the procedure doesn't try to optimize a value for H. It just accepts the value I set in the PARM statement. The model it creates fits okay, but I am forced to wonder if I am doing this right since I haven't done my own non-linear model in quite some time. I am not opposed to using another function either, but I am rusty on my algebra. Comments on my choice of function would also be welcome. I am aware of the observation at line 106 7.044 28.2205 ----------code------------
data a; input X csfc;
cards; 19.894 2.1695 14.357 3.1134 14.634 2.8468 16.849 2.8502 20.73 2.0644 21.313 1.885 21.078 2.0202 22.711 1.828 18.571 2.2236 17.324 5.1571 16.833 2.3027 7.787 5.2283 13.571 3.2182 16.709 2.4278 103.36 1.9032 14.626 2.7794 11.961 4.0494 16.649 2.4224 11.9 3.1598 12.037 3.4 15.27 2.7087 15.71 3.1774 16.64 2.4366 22.985 1.8892 13.589 3.0338 13.231 3.5303 9.916 2.3978 16.875 2.6956 15.502 2.6239 62.418 1.8696 67.368 1.9096 12.707 4.5418 13.646 3.5238 12.347 3.7747 10.492 5.9589 12.92 5.6311 15.703 4.804 15.945 3.7153 17.812 2.906 19.456 2.3938 16.081 2.8086 15.193 2.8184 12.06 3.7871 11.997 3.464 13.977 3.0224 17.165 2.3578 9.955 4.2154 10.078 3.8764 11.896 3.5085 10.837 3.8571 8.536 4.7339 8.312 4.9184 9.301 4.9311 10.705 3.8221 9.366 5.0422 22.31 2.5539 21.365 2.1591 21.251 2.1821 19.524 2.2874 19.934 2.1676 13.886 3.8416 15.718 2.8793 14.531 3.3531 14.332 3.3009 17.202 2.7417 16.869 2.4636 14.337 3.108 16.054 2.7407 10.45 6.3506 34.281 1.8753 40.231 1.6295 34.453 1.8897 38.428 1.7759 40.005 1.6519 90.734 0.9022 36.63 1.726 35.402 1.7388 76.791 0.8395 35.249 1.6582 48.56 1.2963 39.266 1.6639 44.37 1.452 41.683 1.4932 45.59 1.5308 74.01 0.8218 45.272 1.376 44.352 1.5411 37.685 1.8526 42.117 1.7654 38.659 1.7523 45.305 1.537 46.625 1.4801 45.971 1.435 42.783 1.4562 39.755 1.6883 47.272 1.4124 43.601 1.5922 49.839 1.8895 49.527 1.5169 46.418 1.5731 55.454 1.3089 7.044 28.2205 48.519 1.5486 48.658 1.4955 54.508 1.5457 46.313 1.7545 9.346 5.3788 9.147 5.1461 10.428 5.0431 18.505 2.7788 16.651 3.2919 22.862 2.5291 25.334 2.0102 12.182 4.1494 10.23 5.3308 9.134 5.0723 8.594 5.6128 10.669 4.3122 9.316 5.1614 16.287 3.1224 12.83 3.883 63.214 1.586 58.959 1.3681 55.911 1.6491 65.894 1.8837 56.965 1.3704 56.958 1.3552 62.972 1.3213 78.47 1.2049 64.248 1.348 72.855 1.1896 63.487 1.41 57.215 1.3024 56.201 1.4495 60.765 1.3704 65.9 1.3719 62.118 1.3137 26.976 1.7391 26.521 1.5494 29.442 1.6611 26.807 1.5139 26.365 1.5992 30.367 1.5658 21.368 2.3015 14.443 3.1963 19.454 3.3426 21.448 2.1596 14.986 5.916 20.085 2.3998 27.787 1.7884 26.624 1.809 32.641 1.5445 23.44 2.1039 65.804 1.8539 21.747 1.873 28.834 1.4601 25.411 1.6552 21.637 1.9228 37.783 1.3651 32.867 1.9354 34.038 1.0684 29.043 1.5156 38.605 1.7499 42.605 1.4879 38.399 0.8986 37.153 1.3436 18.009 1.5775 19.849 1.7135 19.305 1.9004 20.067 1.6874 20.61 1.6846 23.473 1.5383 16.409 2.246 32.881 1.0103 20.855 1.6756 22.072 1.5011 22.399 1.5077 22.422 1.6045 28.705 1.5148 21.491 2.0123 24.138 1.6838 23.237 1.5113 18.886 1.7919 23.216 1.4259 23.471 1.4605 23.227 1.5253 25.337 1.3194 19.049 1.7622 19.681 1.8024 20.113 1.6858 19.748 1.8308 26.875 1.2624 31.016 1.1567 16.783 2.9245 21.099 1.6738 20.545 1.6725 18.909 2.8377 32.773 1.1761 26.978 1.2892 36.644 1.7227 23.856 1.4219 22.931 1.4248 22.589 1.6277 13.077 2.6969 19.172 1.9055 12.508 2.0986 24.206 1.4886 ;; run;
/* CSFC by X appears to follow a modified version of AB**X */ /* */ /* Y = AB**(-X + H) + K */ /* */ /* where: Y is the response */ /* -X reflects Y=AB**X */ /* A and B affect the shape the curve */ /* H translates the curve on the X axis */ /* K translates the curve on the Y axis */
proc nlin data=a; parameters A = 1 B = 1.0125 H = 17 K = 2;
model CSFC = A * B**(-X + H) + K;
* output out=pred p=Yhat l95m=low_conf u95m=up_conf sse=sse; run;
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Hello--
I have a data set that shows a nonlinear response. I was trying to model it to the generic Y=AB**X algebraic curve. Since I need to reflect and translate it I think the form is Y = [AB**(-X+H)] + K
/* where: Y is the response */ /* -X reflects Y=AB**X */ /* A and B affect the shape the curve */ /* H translates the curve on the X axis */ /* K translates the curve on the Y axis */ However, when I run PROC NLIN, the procedure doesn't try to optimize a value for H. It just accepts the value I set in the PARM statement. The model it creates fits okay, but I am forced to wonder if I am doing this right since I haven't done my own non-linear model in quite some time. I am not opposed to using another function either, but I am rusty on my algebra. Comments on my choice of function would also be welcome. I am aware of the observation at line 106 7.044 28.2205 ----------code------------
data a; input X csfc;
cards; 19.894 2.1695 14.357 3.1134 14.634 2.8468 16.849 2.8502 20.73 2.0644 21.313 1.885 21.078 2.0202 22.711 1.828 18.571 2.2236 17.324 5.1571 16.833 2.3027 7.787 5.2283 13.571 3.2182 16.709 2.4278 103.36 1.9032 14.626 2.7794 11.961 4.0494 16.649 2.4224 11.9 3.1598 12.037 3.4 15.27 2.7087 15.71 3.1774 16.64 2.4366 22.985 1.8892 13.589 3.0338 13.231 3.5303 9.916 2.3978 16.875 2.6956 15.502 2.6239 62.418 1.8696 67.368 1.9096 12.707 4.5418 13.646 3.5238 12.347 3.7747 10.492 5.9589 12.92 5.6311 15.703 4.804 15.945 3.7153 17.812 2.906 19.456 2.3938 16.081 2.8086 15.193 2.8184 12.06 3.7871 11.997 3.464 13.977 3.0224 17.165 2.3578 9.955 4.2154 10.078 3.8764 11.896 3.5085 10.837 3.8571 8.536 4.7339 8.312 4.9184 9.301 4.9311 10.705 3.8221 9.366 5.0422 22.31 2.5539 21.365 2.1591 21.251 2.1821 19.524 2.2874 19.934 2.1676 13.886 3.8416 15.718 2.8793 14.531 3.3531 14.332 3.3009 17.202 2.7417 16.869 2.4636 14.337 3.108 16.054 2.7407 10.45 6.3506 34.281 1.8753 40.231 1.6295 34.453 1.8897 38.428 1.7759 40.005 1.6519 90.734 0.9022 36.63 1.726 35.402 1.7388 76.791 0.8395 35.249 1.6582 48.56 1.2963 39.266 1.6639 44.37 1.452 41.683 1.4932 45.59 1.5308 74.01 0.8218 45.272 1.376 44.352 1.5411 37.685 1.8526 42.117 1.7654 38.659 1.7523 45.305 1.537 46.625 1.4801 45.971 1.435 42.783 1.4562 39.755 1.6883 47.272 1.4124 43.601 1.5922 49.839 1.8895 49.527 1.5169 46.418 1.5731 55.454 1.3089 7.044 28.2205 48.519 1.5486 48.658 1.4955 54.508 1.5457 46.313 1.7545 9.346 5.3788 9.147 5.1461 10.428 5.0431 18.505 2.7788 16.651 3.2919 22.862 2.5291 25.334 2.0102 12.182 4.1494 10.23 5.3308 9.134 5.0723 8.594 5.6128 10.669 4.3122 9.316 5.1614 16.287 3.1224 12.83 3.883 63.214 1.586 58.959 1.3681 55.911 1.6491 65.894 1.8837 56.965 1.3704 56.958 1.3552 62.972 1.3213 78.47 1.2049 64.248 1.348 72.855 1.1896 63.487 1.41 57.215 1.3024 56.201 1.4495 60.765 1.3704 65.9 1.3719 62.118 1.3137 26.976 1.7391 26.521 1.5494 29.442 1.6611 26.807 1.5139 26.365 1.5992 30.367 1.5658 21.368 2.3015 14.443 3.1963 19.454 3.3426 21.448 2.1596 14.986 5.916 20.085 2.3998 27.787 1.7884 26.624 1.809 32.641 1.5445 23.44 2.1039 65.804 1.8539 21.747 1.873 28.834 1.4601 25.411 1.6552 21.637 1.9228 37.783 1.3651 32.867 1.9354 34.038 1.0684 29.043 1.5156 38.605 1.7499 42.605 1.4879 38.399 0.8986 37.153 1.3436 18.009 1.5775 19.849 1.7135 19.305 1.9004 20.067 1.6874 20.61 1.6846 23.473 1.5383 16.409 2.246 32.881 1.0103 20.855 1.6756 22.072 1.5011 22.399 1.5077 22.422 1.6045 28.705 1.5148 21.491 2.0123 24.138 1.6838 23.237 1.5113 18.886 1.7919 23.216 1.4259 23.471 1.4605 23.227 1.5253 25.337 1.3194 19.049 1.7622 19.681 1.8024 20.113 1.6858 19.748 1.8308 26.875 1.2624 31.016 1.1567 16.783 2.9245 21.099 1.6738 20.545 1.6725 18.909 2.8377 32.773 1.1761 26.978 1.2892 36.644 1.7227 23.856 1.4219 22.931 1.4248 22.589 1.6277 13.077 2.6969 19.172 1.9055 12.508 2.0986 24.206 1.4886 ;; run;
/* CSFC by X appears to follow a modified version of AB**X */ /* */ /* Y = AB**(-X + H) + K */ /* */ /* where: Y is the response */ /* -X reflects Y=AB**X */ /* A and B affect the shape the curve */ /* H translates the curve on the X axis */ /* K translates the curve on the Y axis */
proc nlin data=a; parameters A = 1 B = 1.0125 H = 17 K = 2;
model CSFC = A * B**(-X + H) + K;
* output out=pred p=Yhat l95m=low_conf u95m=up_conf sse=sse; run;
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Is there a definition that can be put in the KEYS<DMKEYS> that will erase everything in the work library?
Also, is there a sas programming line that empties the work library?
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Yeah... mostly just fun to think about. In this forum it's always better to have actual data to go with the question. I am not a heavy user of SAS and what problems I get are usually one-offs so I don't make as good of use of things like do loops because I can often do something in EXCEL first by hand in a few minutes rather than have to code and debug code for unexpected results. This problem I was given though, manual was going to be very tedious and I couldn't insist the people giving it to us have it perfect beforehand because they're doing us the favor. Thanks again!
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What's funny is that I successfully filtered some of the formatting peculiarities imputing the data only to be stymied by this. Is there a way I could make this more generic... like if there were missing values? Could I write the code where the count might vary from group to group? Not my current problem, but I get data with missing observations all the time.
Thanks
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Okay. This is partway there. The problem is I have a few dozen files of differing lengths 40,000 or more lines. So labeling each group is still 2500 assignments I have to make to my species. So it seems that am still roughly in the same place as labeling each diameter group manually because groups 1,5,9,13,17 etc are species A, 2,6,10,14,18 etc are species B, 3,7,11,15,19 etc are species C, and 4,8,12,16,20 etc. are total. So it solves the grouping problem, but not the naming problem.
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I have a dataset that was generated by an old FORTRAN model. The output are time steps, diameter groups, and some variables calculated for a model that goes with those groups.
There are 16 lines giving model outputs for 16 diameter classes. The first 16 lines is species A for time X, the second 16 lines for species B for time X, the third 16 lines for species C for time X, the fourth 16 lines the combined total of the three species. Then the pattern repeats.
Any suggestions for automating the labeling? I have a few dozen of these to go through and the data sets for each time X are 40K lines each. Attached is one set of data. and below is what I need it to look like with a new species column. I think with this model output it's always 16 lines per species or total.
Species
time
group
numtrees
BA
STMV
SVTS
A
0
1.00-4.99
112
0.1
0
0
A
0
5.00-9.99
56
0.2
0
0
A
0
10.00-14.99
49
0.6
0
0
A
0
15.00-19.99
41
0.9
4.74
0
A
0
20.00-24.99
30
1.1
7.49
0
A
0
25.00-29.99
27
1.5
12.26
0
A
0
30.00-34.99
18
1.4
12.72
8.46
A
0
35.00-39.99
8
0.9
8.06
6.35
A
0
40.00-44.99
10
1.4
14.42
11.87
A
0
45.00-49.99
7
1.3
13.28
10.76
A
0
50.00-54.99
5
1.1
11.62
9.13
A
0
55.00-59.99
1
0.3
3.04
2.26
A
0
60.00-64.99
0
0
0
0
A
0
65.00-69.99
0
0
0
0
A
0
70.00-74.99
0
0
0
0
A
0
75.00+
0
0
0
0
B
0
1.00-4.99
46
0.1
0
0
B
0
5.00-9.99
21
0.1
0
0
B
0
10.00-14.99
17
0.2
0
0
B
0
15.00-19.99
17
0.4
2.09
0
B
0
20.00-24.99
8
0.3
2
0
B
0
25.00-29.99
7
0.4
2.88
0
B
0
30.00-34.99
6
0.5
4.46
3.08
B
0
35.00-39.99
5
0.5
5.05
3.99
B
0
40.00-44.99
2
0.3
2.78
2.28
B
0
45.00-49.99
1
0.2
1.9
1.54
B
0
50.00-54.99
1
0.2
2.18
1.73
B
0
55.00-59.99
0
0
0
0
B
0
60.00-64.99
0
0
0
0
B
0
65.00-69.99
0
0
0
0
B
0
70.00-74.99
0
0
0
0
B
0
75.00+
0
0
0
0
C
0
1.00-4.99
42
0.1
0
0
C
0
5.00-9.99
23
0.1
0
0
C
0
10.00-14.99
14
0.2
0
0
C
0
15.00-19.99
12
0.3
1.51
0
C
0
20.00-24.99
17
0.6
4.16
0
C
0
25.00-29.99
6
0.3
2.73
0
C
0
30.00-34.99
6
0.5
4.24
2.82
C
0
35.00-39.99
7
0.7
6.63
5.15
C
0
40.00-44.99
3
0.4
4.01
3.29
C
0
45.00-49.99
2
0.3
3.35
2.74
C
0
50.00-54.99
2
0.4
4.27
3.4
C
0
55.00-59.99
3
0.7
8
6.13
C
0
60.00-64.99
0
0
0
0
C
0
65.00-69.99
0
0
0
0
C
0
70.00-74.99
0
0
0
0
C
0
75.00+
0
0
0
0
T
0
1.00-4.99
200
0.3
0
0
T
0
5.00-9.99
100
0.4
0
0
T
0
10.00-14.99
80
0.9
0
0
T
0
15.00-19.99
70
1.5
8.35
0
T
0
20.00-24.99
55
2
13.64
0
T
0
25.00-29.99
40
2.2
17.88
0
T
0
30.00-34.99
30
2.4
21.43
14.36
T
0
35.00-39.99
20
2.1
19.74
15.49
T
0
40.00-44.99
15
2.1
21.21
17.44
T
0
45.00-49.99
10
1.8
18.53
15.04
T
0
50.00-54.99
8
1.7
18.06
14.27
T
0
55.00-59.99
4
1
11.04
8.39
T
0
60.00-64.99
0
0
0
0
T
0
65.00-69.99
0
0
0
0
T
0
70.00-74.99
0
0
0
0
T
0
75.00+
0
0
0
0
A
5
1.00-4.99
1063
0.2
0
0
A
5
5.00-9.99
116
0.4
0
0
A
5
10.00-14.99
46
0.5
0
0
A
5
15.00-19.99
41
1
5.66
0
A
5
20.00-24.99
36
1.4
9.83
0
A
5
25.00-29.99
23
1.4
10.92
0
A
5
30.00-34.99
20
1.6
14.67
10
A
5
35.00-39.99
13
1.4
13.6
10.8
A
5
40.00-44.99
7
1
10.46
8.6
A
5
45.00-49.99
6
1.1
10.99
8.93
A
5
50.00-54.99
7
1.5
15.47
12.27
A
5
55.00-59.99
3
0.8
8.32
6.32
A
5
60.00-64.99
1
0.3
3.19
2.35
A
5
65.00-69.99
0
0
0
0
A
5
70.00-74.99
0
0
0
0
A
5
75.00+
0
0
0
0
B
5
1.00-4.99
392
0.1
0
0
B
5
5.00-9.99
48
0.2
0
0
B
5
10.00-14.99
15
0.2
0
0
etc.
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Those are the column attribute designations when you right click on the column when the table is open. Lists it as numeric, displays it as a date. This seems to be a recent thing because whenever I've used datetime. in the past it has always been an integer. At any rate, the problem is solved well enough for now. If there is a more elegant solution, I'd be happy to hear it.
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I have two data sets I want to merge based on a variable sas_dt. Both were imported from CSV files at one point. On the first dataset the variable sas_dt has the column attributes Format - BEST12., informat 12., numeric, and if you look at the table in the library it lists sas_dt as an integer, that is ultimately the number of seconds.
On the other dataset the variable sas_dt has the column attributes Format - datetime16. Informat 16., numeric, and if you look at the table in the library it lists sas_dt formated as the date.
When I print
proc print;
format sas_dt datetime.; Example of the integer I want to use as a commonality between all tables would look like be 1820225812, 18202225813, etc.
They list the same dates and times.
I need to merge these datasets, but this seems to be the hangup. I also wish to work with the integer format. How to I convert sas_dt to the integer format so that it is stored in my library as an integer (i.e. I click on the table and it is displayed/stored as an integer)?
And just for the sake of asking, how do I convert an integer so it is stored in my library as a datetime (i.e. I click on the table and it is displayed/stored as a date and time)?
Thank you
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http://support.sas.com/kb/56/673.html The advice listed in the link does not apply to my Windows system. The shortcut is missing, but also the entire directory with renewsas.exe as indicated in the link.
I've done this many times witn 9.2 but the 9.4 renewal program just isn't there.... or anywhere.
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I instlled SAS 9.4 for the first time last year. I just got my renewal materials from our ITS, but SASrenew is missing. It's not even in the SAS directory.
What is the best and least disruptive way to fix this problem?
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