Example 4: 2004 pid, libcon, watchtv, spatial distances; make tables; run regressions

year 2004

VARS
  VCF0301
  VCF0704
  VCF0704A
  VCF0724
  VCF0803
  VCF9088
  VCF9096
;

# get rid of unwanted value in party ID
pid <- VCF0301;
pid <- ifelse(pid == 9, NA, pid);

# get rid of unwanted value in libcon self-placement
libcon <- VCF0803;
libcon <- ifelse(libcon == 9, NA, libcon);

# presvote3 is three major party candidates only
presvote3 <- VCF0704;

# presvote is Dem and Rep candidates only
presvote <- VCF0704A - 1;

# watchtv is how much watched campaign on TV
watchtv <- VCF0724;

# compute spatial model measure of difference between Dem and Rep candidates
demlibcon <- VCF9088;
replibcon <- VCF9096;
demdist <- abs(libcon-demlibcon);
repdist <- abs(libcon-replibcon);
distdiff <- demdist-repdist;

# crosstab of libcon given pid
xtable libcon pid

# crosstab of libcon given pid and watchtv
xtable libcon pid watchtv

# crosstab of three-party vote given pid and watchtv
xtable presvote3 pid

# crosstab of two-party vote given pid and watchtv
xtable presvote pid

# one-way frequency table of libcon placements
xtable libcon
xtable demlibcon
xtable replibcon

# simple ordinary least squares regression model of two-party vote
# explanatory variables are PID dummy variables and spatial model difference
summary(lm(presvote ~ factor(pid) + distdiff, weights=wgtXXX));

# simple probit regression model of two-party vote
# explanatory variables are PID dummy variables and spatial model difference
summary(glm(presvote ~ factor(pid) + distdiff,
  family=binomial(link="probit"), weights=wgtXXX));