Hello,
I've added comments to the code below to try and explain the problem
I'm having. Basically, I have found that the average standard error
increased from case 1 to case 2, yet a covariate was included in the
second case.
I can provide the input datafile (kind of surprised I can't attach it
to this post) if one would like to run the code. I always like being
able to reproduce the problem myself.
My question is this: At worst, wouldn't the error term
in case 2 (code below "title 4") be less than or equal to the error
term in case 1 (code below title 1)? I wouldn't expect a covariate
to increase the error? Average errors are provided in the commented
code below.
Thanks,
Eric
/* Some preliminary code - Read in data and some additional */
/* processing */
data morway; infile 'c:\temp
\All_Soil_Salinity_Data_For_AOV_Update_With_Covariants.txt'
expandtabs;
input loc $ field $ season $ year Ece logECe ECE_sd WTD ECgw SM Sand
Silt Clay Theta_resid Theta_sat Ks;
if season='Early' then time = 2*(year-1999)+1;
if season='Late' then time = 2*(year-1999)+2;
if loc ne 'loc';
*if year ge 2002;
proc print data=morway(obs=21);run;
proc sort data=morway; by loc;run;
/* Original analysis with odd behavoir in standard error between */
/* analyses titled 1 & 4 for the 'loc=DS' case. */
/* Avg stderr in title 1, loc=DS = 0.02118134 */
/* Avg stderr in title 4, loc=DS = 0.021399158 */
title '1) reduced sample size - no cov - remove rows with missing
Sand, Silt, Clay, wtd, ECgw, & SM';
proc mixed data=morway; class field year loc season;by loc;
model logece = season|year /ddfm=kr; where not missing(Sand) and not
missing(Silt) and not missing(Clay) and not missing(wtd) and not
missing(ECgw) and not missing(SM) and not missing(Theta_resid) and not
missing(Theta_sat) and not missing(Ks);
repeated year*season/subject=field(loc) r rcorr type=sp(pow)(time);
lsmeans season*year/ adjust=tukey;
ods output lsmeans = lsmeans;
run;
title '4) reduced sample size - with cov SM - remove rows with
missing Sand, Silt, Clay, wtd, ECgw, & SM';
proc mixed data=morway; class field year loc season;by loc;
model logece = season|year SM /ddfm=kr; where not missing(Sand) and
not missing(Silt) and not missing(Clay) and not missing(wtd) and not
missing(ECgw) and not missing(SM) and not missing(Theta_resid) and not
missing(Theta_sat) and not missing(Ks);
repeated year*season/subject=field(loc) r rcorr type=sp(pow)(time);
lsmeans season*year/ adjust=tukey;
ods output lsmeans = lsmeans;
run;
/* Modified analysis with odd behavoir persisting in standard */
/* error between analyses titled 1 & 4 for the 'loc=DS' case. */
/* ddfm=kr (Kenwood-Rodgers) argument removed */
/* 'repeated' line commented out */
/* Avg stderr in title 1, loc=DS = 0.023698976 */
/* Avg stderr in title 4, loc=DS = 0.023820179 */
title '1) reduced sample size - no cov - remove rows with missing
Sand, Silt, Clay, wtd, ECgw, & SM';
proc mixed data=morway; class field year loc season;by loc;
model logece = season|year /; where not missing(Sand) and not
missing(Silt) and not missing(Clay) and not missing(wtd) and not
missing(ECgw) and not missing(SM) and not missing(Theta_resid) and not
missing(Theta_sat) and not missing(Ks);
*repeated year*season/subject=field(loc) r rcorr type=sp(pow)(time);
lsmeans season*year/ adjust=tukey;
ods output lsmeans = lsmeans;
run;
title '4) reduced sample size - with cov - SM';
proc mixed data=morway; class field year loc season;by loc;
model logece = season|year SM /; where not missing(Sand) and not
missing(Silt) and not missing(Clay) and not missing(wtd) and not
missing(ECgw) and not missing(SM) and not missing(Theta_resid) and not
missing(Theta_sat) and not missing(Ks);
*repeated year*season/subject=field(loc) r rcorr type=sp(pow)(time);
lsmeans season*year/ adjust=tukey;
ods output lsmeans = lsmeans;
run;
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