/*-------------------------------------------

ex5.do

You won't need the first line if you have read this 
in by clicking on file
log using ex5_res

---------------------------------------------*/

clear
use "C:\Documents and Settings\gillian raab\My Documents\aprojects\peas\web\exemp5\data\ex5.dta"

/*-----------------------------------------------------------
first define the design - only need weighting
-------------------------------------------------------------*/
svyset [pweight=weight]
/*-----------------------------------------------------------
now get proportions of various categories
compared with unweighted tables
-------------------------------------------------------------*/
svyprop q85a 
tabulate q85a
svyprop q85b
tabulate q85b
svyprop living
tabulate living
svyprop genhelf
tabulate genhelf
svyprop empl
tabulate empl
/*----------------------------------------------------------------
some code to recode drug use into scores so that they make 
ordered categories
------------------------------------------------------------------*/
recode q85a (1=0) (2=0) (3=1) (4=1) (5=1) (6=0.5) ,gen (canscore)
recode q85b (3=6) (1=0) (2=0) (3=1) (4=1) (5=1) (6=0.5) ,gen (ampscore)
/*-------------------------------------------------------------
now some mean scores to check design effects
----------------------------------------------------------------*/
svymean  genhelf sinc sacc
/*------------------------------------------------------------
now some regressions to predict general health score
although this is categorical it is quite legitimate to
use it in a regression to look for simple associations
----------------------------------------------------------*/
svyregress genhelf sinc canscore ampscore
regress genhelf sinc canscore ampscore

/*---------------------------------------------------
next bit of code gets some Stata commands that
enable you to generate nice output to paste into reports

the findit command gives access to some regression formatiing 
commands available from a submission to the Stata journal
 FOLLOW THE WEB LINK IF YOU WANT TO INSTALL IT

http://www.ats.ucla.edu/stat/Stata/faq/outreg.htm

Results are sent to an external file output.doc
------------------------------------------------------*/
findit outreg
net from http://www.Stata.com 

svyregress genhelf canscore
   outreg using output.doc, nolabel replace
svyregress genhelf ampscore
   outreg using output.doc , nolabel append
svyregress genhelf sinc
   outreg using output.doc , nolabel append
svyregress genhelf canscore  sinc
    outreg using output.doc , nolabel append
svyregress genhelf  ampscore sinc
    outreg using output.doc , nolabel append
svyregress genhelf canscore ampscore
     outreg using output.doc , nolabel append
svyregress genhelf canscore ampscore sinc
     outreg using output.doc , nolabel append

/*-----------------------------------------------------
now the same thing for an unweighted regression
------------------------------------------------------*/

regress genhelf canscore
   outreg using output2.doc, nolabel replace
regress genhelf ampscore
   outreg using output2.doc , nolabel append
regress genhelf sinc
   outreg using output2.doc , nolabel append
regress genhelf canscore  sinc
    outreg using output2.doc , nolabel append
regress genhelf  ampscore sinc
    outreg using output2.doc , nolabel append
regress genhelf canscore ampscore
     outreg using output2.doc , nolabel append
regress genhelf canscore ampscore sinc
     outreg using output2.doc , nolabel append
/*----------------------------------------------
now look at the proportions in health groups
by the original categories of cannabis use
and a survey-corrected chi square
----------------------------------------------*/ 

svytab genhelf q85a,column percent

/*------------------------------------------
now test out the effect of the finite population correction
The number of women of this age group in the
population is 29457. This will be set as the population
size for all units since there is no startification here
--------------------------------------------------*/
generate popsize=29457
/*---------------------------------------------------
now redo the survey set up putting in the FPC
this can be either the pop size if > number in strata
(as here) or the sampling fraction
GWEIGHT needs to be used since 
we need to have weights that add to population totals
-------------------------------------------------*/
svyset [pweight=gweight],fpc(popsize)
/*---------------------------------------------
now rerun one svy mean from above 
it makes little difference
Though the first run was wrong because it needed
to have weights that add to population totals
-----------------------------------------------------------*/
svymean  genhelf sinc sacc