I have a data set containing 980 paired observations of fish length and age. I would like to use a bootstrapping approach to examine how sample size affects the precision of model parameter estimates.
I have used the following program to successfully create a dataset where 3 fish per cm length class are selected 1000 times from the original dataset:
PROC SURVEYSELECT DATA = AGE OUT = SAMPLE
METHOD = URS
N = 3
SEED = 9876
OUTHITS
REP = 1000;
STRATA TLINT;
RUN;
Next I fit model parameters to each replicate in this dataset:
PROC SORT DATA = SAMPLE;
BY REPLICATE;
RUN;
PROC NLIN DATA = SAMPLE;
PARMS LINF = 550 K = .25 t0 = 0.1;
MODEL TL = LINF*(1-EXP(-K*(AGE-t0)));
BY REPLICATE;
OUTPUT OUT = FITTED;
RUN;
I would like to create a dataset that contains parameter estimates for each replicate. The dataset would contain the following variables:
REPLICATE LINF K t0
and would look like this, for example:
1 568 .25 .02
2 520 .27 .14
3 491 .32 .01
etc.
I have also attached the SAS file.
Any help would be appreciated.
Try adding the statement:
ODS OUTPUT ParameterEstimates=samplePE;
to your NLIN procedure.
PG
PG,
That was helpful in summarizing the parameter estimates by replicate. However, is there a way to get them into a format that is easier to work with? Ideally this would be a SAS data set.
Actually I may be ok working with it by exporting to Excel.
In the example I gave, samplePE would be a SAS dataset containing the parameter estimates. That seemed to be what you wanted.
Got it. Thanks!
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