About Kurt

Kurt · ‎03-19-2014

Steve, Thank you for your help! However, I've ran into two (at least) additional problems. First, as you mentioned, it is important to understand the distribution of your data. My data appear normally distributed when examining the histograms in PROC UNIVARIATE, however, based on the Kolmogorov-Smirnov test (< 0.01) they are not normally distributed. I attempted a test to determine the distribution (below), ODS SELECT PARAMETER ESTIMATES GOODNESSOFFIT FITQUANTILES MYHIST; PROC UNIVARIATE DATA=KURT; VAR MOISTURE; HISTOGRAM / MIDPOINTS=0.2 TO 1 BY 0.2 LOGNORMAL WEIBULL GAMMA VAXIS=AXIS1 NAME='MYHIST'; INSET N MEAN (5.3) STD='STD DEV' (5.3) SKEWNESS (5.3)/POSITION=NE HEADER='SUMMARY STATISTICS'; AXIS1 LABEL=(A=90 R=0); RUN; however, it seems the dataset also does not qualify under the lognormal, Weibull, or gamma distributions either (p < 0.01 for all). Is it possible this dataset is normally distributed (as the residual plots suggest) but due to the large size of the dataset the K-S test is incorrect? I also tried a second method to determine distribution but SAS didn't recognize the LOSS statement (it appears in red instead of blue). proc severity data=KURT crit=aicc; loss MOISTURE; dist _predefined_; run; And secondly, when I try to run the model you specified I get an error message stating "The initial estimates did not yield a vaid objective function". I read up on this and tried to address this problem by adding an "INITITER=" option, but whatever number I put, the program will not properly run. Do you have any further ideas for addressing the distribution of the data or getting the model to perform correctly? Your help is greatly appreciated!

Kurt · ‎03-18-2014

Hello, I am working with a large dataset (over 12,000 lines of data) of measured values for soil moisture. The experimental design is somewhat complex: measurements collected at four distances in relation to a central tree row (DIST), within five silvicultural treatments (TRT), over three years (YEAR), at two sites (SITE). Each site contains four blocks (BLOCK). Essentially, DIST is nested within TRT which is nested within YEAR within SITE. The premise of the experiment is to determine how soil moisture varies in relation to distance from trees due to silvicultural treatment. Soil moisture measurements were collected at each site every two weeks, during each of the three growing seasons. After examining residuals and running the Kolmogorov-Smirnov test, I have determined the data is not normally distributed, even when it is log transformed. Therefore, I've decided testing with Proc Glimmix is necessary. The SAS code I'm using runs properly and converges, but I am unsure I've specified the SUBJECT correctly and after running it several ways it seems specifying the correct subject can drastically affect the output. The code is as follows: PROC GLIMMIX DATA=KURT; CLASS BLOCK SITE YEAR TRT DIST; MODEL MOISTURE = SITE YEAR TRT DIST SITE*TRT YEAR*TRT TRT*DIST SITE*TRT*DIST YEAR*TRT*DIST SITE*YEAR*TRT*LOCATION; RANDOM _RESIDUAL_/ TYPE=CS SUBJECT=REP(TRT*DIST); LSMEANS SITE*YEAR*TRT*DIST/ PDIFF=ALL ADJUST=TUKEY LINES SLICE=SITE; NLOPTIONS TECH=NRRIDG; RUN; When I run this, I get a significant SITE*YEAR*TRT*LOCATION effect. However, my other inclination was to run the model with SUBJECT=REP(SITE*YEAR*TRT*DIST), and running this model I do not get the significant four way interaction....so I'm curious if anyone has an idea which is the proper way to conduct this analysis? This will be the first of my heiarchial testing for treatment effects. If site interacts with treatment, I will be analyzing the data seperately sites. At that point, if year interacts with treatment I will analyze each year within each site seperately by sampling date. Any advise concerning this question would be greatly appreciated! Thanks, Kurt

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Re: Proc Glimmix subject identifier

Proc Glimmix subject identifier

Re: Proc Glimmix subject identifier

Proc Glimmix subject identifier