title: How to plot a vglm/propodds regression?
body: <p>Update:
  When I tried "dput" to show some of my data this is all I was able to see was a long series of 0s and 1s an then:</p>
  
  <pre><code>  label = "CURRENT JOB STATUS- 1", class = c("labelled", 
  "integer"))), .Names = c("is.controlrp", "is.lowrp", "is.somerp", 
  "is.highrp", "is.substantialrp", "is.ignorerp", "Risk_Pct", "PCT_Stocks_MF_1", 
  "is.native", "is.male", "rp", "rp2", "is.lowrp.1", "is.controlrp.1", 
  "is.somerp.1", "is.highrp.1", "is.substantial2", "is.ignorerp.1", 
  "psm1", "psm2", "noStocks", "is.control2", "is.low2", "is.some2", 
  "is.high2", "is.substantial2.1", "is.ignore2", "age2", "agenew", 
  "tw", "is.controlW", "is.debt", "is.lowW", "is.medW", "is.highW", 
  "sme", "is.controlSME", "is.lowSME", "oh", "ms", "cjs"), row.names = c(NA, 
  -16000L), class = "data.frame")
  </code></pre>
  
  <p>If this helps at all, here's "head":</p>
  
  <pre><code>   &gt; head(dummydata2)
        is.controlrp is.lowrp is.somerp is.highrp is.substantialrp is.ignorerp Risk_Pct PCT_Stocks_MF_1
      1            1        1         1         0                1           1        7              50
      2            1        1         1         1                0           1       10              NA
      3            1        1         1         0                1           1        8              40
      4            1        1         1         0                1           1        8              40
      5            1        1         1         0                1           1        8              40
      6            1        1         0         1                1           1        5             998
        is.native is.male          rp rp2 is.lowrp.1 is.controlrp.1 is.somerp.1 is.highrp.1
      1         0       1        high   3          1              1           1           0
      2         0       0 substantial   4          1              1           1           1
      3         0       1        high   3          1              1           1           0
      4         0       1        high   3          1              1           1           0
      5         0       1        high   3          1              1           1           0
      6         0       1        some   2          1              1           0           1
        is.substantial2 is.ignorerp.1 psm1   psm2 noStocks is.control2 is.low2 is.some2 is.high2
      1               1             1    3   high        1           1       1        1        0
      2            &lt;NA&gt;             1   NA   &lt;NA&gt;       NA        &lt;NA&gt;    &lt;NA&gt;     &lt;NA&gt;     &lt;NA&gt;
      3               1             1    2   some        1           1       1        0        1
      4               1             1    2   some        1           1       1        0        1
      5               1             1    2   some        1           1       1        0        1
      6               1             1    5 ignore        1           1       1        1        1
        is.substantial2.1 is.ignore2 age2 agenew tw is.controlW is.debt is.lowW is.medW is.highW sme
      1                 1          1    2      1  4           1       1       1       1        0  NA
      2              &lt;NA&gt;       &lt;NA&gt;    3      1  1           1       0       1       1        1  NA
      3                 1          1    3      1 NA          NA      NA      NA      NA       NA  NA
      4                 1          1    3      1 NA          NA      NA      NA      NA       NA  NA
      5                 1          1    3      1 NA          NA      NA      NA      NA       NA  NA
      6                 1          0    3      1  4           1       1       1       1        0   4
        is.controlSME is.lowSME oh ms cjs
      1            NA      &lt;NA&gt;  0  1   1
      2            NA      &lt;NA&gt;  1  1   1
      3            NA      &lt;NA&gt;  0  0   0
      4            NA      &lt;NA&gt;  0  0   0
      5            NA      &lt;NA&gt;  0  0   0
      6             1         1  0  1   0
  </code></pre>
  
  <p>Hi I'd like some help plotting the following regression in "r": </p>
  
  <pre><code>fit1_usesrp &lt;-vglm(rp ~ is.native + is.male + oh + cjs + age2 + tw ,propodds, data = dummydata
  </code></pre>
  
  <p>Through this regression I am interested in finding if immigration status is a determinant of risk preferences. "rp" in the regression is a categorical variable that measures respondent's willingness to take risk (no risk tolerance, low risk tolerance, etc.). "is.native" is a dummy variable for immigration status (0 = native, 1 = immigrant). oh, cjs, and age2 are all dummy variables for factors that may affect risk preferences. "tw" is for total wealth and I used the actual raw values for wealth instead of trying to turn this into a dummy variable. </p>
  
  <p>the output for this regression is:</p>
  
  <pre><code>Call:
  vglm(formula = rp ~ is.native + is.male + oh + cjs + age2 + tw, 
      family = propodds, data = dummydata2)
  
  Coefficients:
  (Intercept):1 (Intercept):2 (Intercept):3 (Intercept):4 (Intercept):5     is.native       is.male 
   2.796024e+00 -4.948484e-01 -5.055242e-01 -6.460196e-01 -2.545089e+00 -7.381110e-02  1.509080e-01 
             oh           cjs          age2            tw 
  -2.070633e-02  3.643551e-03  7.149891e-02 -3.092727e-06 
  
  Degrees of Freedom: 52000 Total; 51989 Residual
  Residual deviance: 24705.39 
  Log-likelihood: -12352.69 
  &gt; summary(fit1_usesrp)
  
  Call:
  vglm(formula = rp ~ is.native + is.male + oh + cjs + age2 + tw, 
      family = propodds, data = dummydata2)
  
  Pearson residuals:
                      Min      1Q  Median      3Q     Max
  logit(P[Y&gt;=2])  -5.0292  0.1305  0.2830  0.2989  0.5201
  logit(P[Y&gt;=3])  -0.6676 -0.5986 -0.5517  0.5911 14.8594
  logit(P[Y&gt;=4]) -13.1696 -0.5270 -0.4856  0.6051  3.0437
  logit(P[Y&gt;=5])  -4.0121 -0.4082 -0.3802  1.0345  1.2412
  logit(P[Y&gt;=6])  -0.6133 -0.5764 -0.1601 -0.1548  3.5492
  
  Coefficients:
                  Estimate Std. Error z value Pr(&gt;|z|)    
  (Intercept):1  2.796e+00  6.689e-02  41.803  &lt; 2e-16 ***
  (Intercept):2 -4.948e-01  5.352e-02  -9.246  &lt; 2e-16 ***
  (Intercept):3 -5.055e-01  5.353e-02  -9.444  &lt; 2e-16 ***
  (Intercept):4 -6.460e-01  5.368e-02 -12.035  &lt; 2e-16 ***
  (Intercept):5 -2.545e+00  6.113e-02 -41.635  &lt; 2e-16 ***
  is.native     -7.381e-02  6.224e-02  -1.186   0.2357    
  is.male        1.509e-01  3.764e-02   4.010 6.08e-05 ***
  oh            -2.071e-02  4.747e-02  -0.436   0.6627    
  cjs            3.644e-03  4.365e-02   0.083   0.9335    
  age2           7.150e-02  2.449e-02   2.920   0.0035 ** 
  tw            -3.093e-06  1.479e-06  -2.092   0.0365 *  
  ---
  Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
  
  Number of linear predictors:  5 
  
  Names of linear predictors: 
  logit(P[Y&gt;=2]), logit(P[Y&gt;=3]), logit(P[Y&gt;=4]), logit(P[Y&gt;=5]), logit(P[Y&gt;=6])
  
  Dispersion Parameter for cumulative family:   1
  
  Residual deviance: 24705.39 on 51989 degrees of freedom
  
  Log-likelihood: -12352.69 on 51989 degrees of freedom
  
  Number of iterations: 4 
  
  Exponentiated coefficients:
  is.native   is.male        oh       cjs      age2        tw 
  0.9288471 1.1628897 0.9795066 1.0036502 1.0741170 0.9999969 
  </code></pre>