Dear Professor Lumley

I am relatively new to using R and also to logistic regression.  We have
analysed our Dudley Health Survey using the survey package.  I am now
trying to look at associations using svyglm but I am unsure of how to
interpret the output and present the resulting model or whether there
are any other things I should do to check the validity of the model.
Below is an example of what I have completed so far.  All of my factors
are numbered 1,2,3 etc instead of 0,1,2,etc - is this a problem?

 

Q14sum is Eating >= 5 portions fruit & veg per day vs eating less

Q16-q19 are dietary behaviours categorised into good, ok, bad

 

summary(svyglm(q14sum~q16x+q17ax+q17bx+q17cx+q17dx+q17ex+q17fx+q18x+q19x
+heavy+binge+smoksum+q41sum+q46+exsum+q53+q55+q61+ethnicgrp+pcs_sum+mcs_
sum,design=dudleyls1design,family=binomial))

 

Call:

svyglm(q14sum ~ q16x + q17ax + q17bx + q17cx + q17dx + q17ex + 

    q17fx + q18x + q19x + heavy + binge + smoksum + q41sum + 

    q46 + exsum + q53 + q55 + q61 + ethnicgrp + pcs_sum + mcs_sum, 

    design = dudleyls1design, family = binomial)

 

Survey design:

svydesign(id = ~1, probs = ~prob1, strata = ~strat1, fpc = ~pop1, 

    data = dudleyls1)

 

Coefficients:

                                     Estimate Std. Error t value
Pr(>|t|)    

(Intercept)                         -0.929963   0.286259  -3.249
0.001170 ** 

q16xOK                               0.433945   0.086506   5.016
5.51e-07 ***

q16xBad                              0.615792   0.227680   2.705
0.006868 ** 

q17axOK                             -0.754625   0.205562  -3.671
0.000245 ***

q17axBad                             0.308037   0.085643   3.597
0.000326 ***

q17bxOK                              0.065208   0.084367   0.773
0.439627    

q17bxBad                             0.038653   0.105495   0.366
0.714093    

q17cxOK                              0.554372   0.097740   5.672
1.52e-08 ***

q17cxBad                             0.755530   0.158943   4.753
2.07e-06 ***

q17dxOK                              0.196032   0.089092   2.200
0.027844 *  

q17dxBad                            -0.006027   0.346917  -0.017
0.986140    

q17exBad                             0.649370   0.075907   8.555  <
2e-16 ***

q17fxOK                              0.764552   0.092198   8.293  <
2e-16 ***

q17fxBad                             1.345113   0.181593   7.407
1.58e-13 ***

q18xOK                               0.074950   0.089852   0.834
0.404252    

q19xOK                               0.080513   0.091597   0.879
0.379461    

q19xBad                              0.213237   0.097400   2.189
0.028637 *  

heavyHeavy drinkers (>3 / >4)        0.324062   0.111947   2.895
0.003816 ** 

bingeBinge drinkers (6+ / 8+)        0.104460   0.136534   0.765
0.444270    

smoksumNon-smokers                  -0.562577   0.111547  -5.043
4.79e-07 ***

q41sumNo accident in past 12 months  0.120413   0.087097   1.383
0.166895    

q46Very good                         0.485598   0.174487   2.783
0.005412 ** 

q46Good                              0.708913   0.182242   3.890
0.000102 ***

q46Fair                              0.996615   0.215941   4.615
4.06e-06 ***

q46Poor                              0.861350   0.282816   3.046
0.002338 ** 

exsumNot enough exercise             0.248127   0.076451   3.246
0.001182 ** 

q53No                                0.233167   0.200720   1.162
0.245450    

q53Don't know                        0.297236   0.309616   0.960
0.337109    

q55No                                0.171200   0.200076   0.856
0.392231    

q55Don't know/It depends             0.274091   0.199006   1.377
0.168500    

q61No                                0.155685   0.113807   1.368
0.171400    

ethnicgrpOther                       1.101213   0.281635   3.910
9.39e-05 ***

pcs_sumMiddle two quartiles          0.074597   0.124994   0.597
0.550672    

pcs_sumTop quartile score            0.109348   0.161871   0.676
0.499384    

mcs_sumMiddle two quartiles         -0.077917   0.107135  -0.727
0.467100    

mcs_sumTop quartile score           -0.245545   0.129996  -1.889
0.058987 .  

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

 

(Dispersion parameter for binomial family taken to be 0.5965213)

 

Number of Fisher Scoring iterations: 5

 

There were 24 warnings (use warnings() to see them)

>
summary(svyglm(q14sum~q16x+q17ax+q17cx+q17dx+q17ex+q17fx+q19x+heavy+smok
sum+q46+exsum+ethnicgrp,design=dudleyls1design,family=binomial))

 

Call:

svyglm(q14sum ~ q16x + q17ax + q17cx + q17dx + q17ex + q17fx + 

    q19x + heavy + smoksum + q46 + exsum + ethnicgrp, design =
dudleyls1design, 

    family = binomial)

 

Survey design:

svydesign(id = ~1, probs = ~prob1, strata = ~strat1, fpc = ~pop1, 

    data = dudleyls1)

 

Coefficients:

                              Estimate Std. Error t value Pr(>|t|)    

(Intercept)                   -0.70031    0.19458  -3.599 0.000323 ***

q16xOK                         0.49963    0.07974   6.265 4.06e-10 ***

q16xBad                        0.57798    0.19554   2.956 0.003134 ** 

q17axOK                       -0.64888    0.18319  -3.542 0.000401 ***

q17axBad                       0.30817    0.07937   3.883 0.000105 ***

q17cxOK                        0.50542    0.08794   5.747 9.66e-09 ***

q17cxBad                       0.77768    0.14523   5.355 8.99e-08 ***

q17dxOK                        0.17925    0.08222   2.180 0.029313 *  

q17dxBad                       0.08186    0.33318   0.246 0.805924    

q17exBad                       0.70146    0.07022   9.989  < 2e-16 ***

q17fxOK                        0.73539    0.08525   8.626  < 2e-16 ***

q17fxBad                       1.35306    0.16878   8.017 1.37e-15 ***

q19xOK                         0.13074    0.08431   1.551 0.121030    

q19xBad                        0.33539    0.08956   3.745 0.000183 ***

heavyHeavy drinkers (>3 / >4)  0.40715    0.07698   5.289 1.29e-07 ***

smoksumNon-smokers            -0.61383    0.10289  -5.966 2.62e-09 ***

q46Very good                   0.49491    0.16297   3.037 0.002404 ** 

q46Good                        0.67543    0.15889   4.251 2.17e-05 ***

q46Fair                        0.93525    0.17103   5.468 4.79e-08 ***

q46Poor                        0.82608    0.21400   3.860 0.000115 ***

exsumNot enough exercise       0.27654    0.07021   3.939 8.31e-05 ***

ethnicgrpOther                 1.00241    0.23850   4.203 2.69e-05 ***

---

Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

 

(Dispersion parameter for binomial family taken to be 0.7067328)

 

Number of Fisher Scoring iterations: 5

 

I am aware that if independent factors are highly correlated that this
could have a large impact on the validity of the model - so how can I
test for this?.  How can I identify the accuracy of the model should I
look at hit rates and if so how?  Can you present the outcome of the
model graphically? 

 

I would appreciate any help you could provide.

Many thanks

Yours faithfully

Angela Moss

Dr Angela Moss 
Public Health Information Analyst 
Dudley PCT 
St. John's House 
Union Street 
Dudley 
DY2 8PP 

Tel: 01384 366091 
Fax: 01384 366485 

 


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