Posted by
keltre19 on
May 21, 2019; 10:44am
URL: http://spssx-discussion.165.s1.nabble.com/negative-binomial-regression-tp5737913p5737925.html
I hope these answers help as I tried to keep them as brief as possible,
Thank you all for the kind suggestions, Kelly
Hello Bruce,
yes, I have seen the example on UCLA website, it was the most informative
website of all I found on the net, however they used an older version of
SPSS (ver. 19) I am running version 25, the syntax doesn’t work the way I
want it unfortunately, it must be missing something small within it (im
thinking due to software updates the syntax was modified) - I want to look
at deprivation (NIMDM) held at 1 (its most deprived ranking, and then at
other rankings for comparisons) and see what the predicted deaths are in
wards and its
influence this has on the other IVs.
GENLIN deaths11_fig BY farms11code avSOfarm_abovBEL grass_NEW crops_NEW
catttle11code sheep11code
pigs11code poultry11code (ORDER=DESCENDING) WITH pcnt65to100A
SEXmalprcnt11 tot_livalone11 Qbeldeg
FT31plushrs NIMDMrank2010 yesUNPcar LLTIyesTOT OCCprcnt51 LFAfarms11
FARMEDpcnt_NEW
/MODEL farms11code avSOfarm_abovBEL grass_NEW crops_NEW catttle11code
sheep11code pigs11code
poultry11code pcnt65to100A SEXmalprcnt11 tot_livalone11 Qbeldeg
FT31plushrs NIMDMrank2010 yesUNPcar
LLTIyesTOT OCCprcnt51 LFAfarms11 FARMEDpcnt_NEW INTERCEPT=YES
OFFSET=NL_popul11
DISTRIBUTION=NEGBIN(MLE) LINK=LOG
/CRITERIA METHOD=FISHER(1) SCALE=1 COVB=ROBUST MAXITERATIONS=1000
MAXSTEPHALVING=50
PCONVERGE=1E-006(ABSOLUTE) SINGULAR=1E-012 ANALYSISTYPE=3(WALD)
CILEVEL=95 CITYPE=WALD
LIKELIHOOD=FULL
/EMMEANS CONTROL=NIMDMrank2010(1)
/MISSING CLASSMISSING=EXCLUDE
/PRINT CPS DESCRIPTIVES MODELINFO FIT SUMMARY SOLUTION (EXPONENTIATED)
/SAVE MEANPRED XBPRED COOK DEVIANCERESID.
The rest of the code seems to work for me, however it is when I include the
line
/EMMEANS CONTROL=NIMDMrank2010(1)
it doesn’t seem to do what I need it to,
Hello Eugene and David,
Yes, it does seem different to the norm but I am using the 582 ward
geographical areas and treating them the same as an individual or ID (I just
included this line in the initial description to show the layout of my
data),
So I did transpose this dataset (wards = rows; variables = columns) it will
give me the characteristics of each of the 582 areas within each ward (for
descriptives) but this will not allow me then to carry out the negative
binomial regression as I only have the ward as variable names in rows
therefore I don’t think transposing is a viable option, keeping it in its
original format does overlap with the dataset layout on UCLA website,
--
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