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  • #16
    Hello,
    I did the U test. How shall I interpret? P value is insignificant. Does that mean that there is no shape? my model is linear without the squared term?



    . utest agemo sq_age

    Specification: f(x)=x^2
    Extreme point: 42.57625

    Test:
    H1: U shape
    vs. H0: Monotone or Inverse U shape

    -------------------------------------------------
    | Lower bound Upper bound
    -----------------+-------------------------------
    Interval | .00274 64.56667
    Slope | -.0673668 .0347968
    t-value | -15.02906 8.016549
    P>|t| | 1.65e-50 6.40e-16
    -------------------------------------------------

    Overall test of presence of a U shape:
    t-value = 8.02
    P>|t| = 6.40e-16




    Thanks so much

    Comment


    • #17
      Hi Clyde,
      I am sorry. Please disregard my #12.
      Warm regards,
      Aye Aye Khaine

      Comment


      • #18
        Hello,

        I did the margins plot on the regression model I presented earlier with the squared of age. My age ranges are from zero to sixty months. I got the following graph. I don't think it is actually U shaped. It is just a liner declining function as children ages and their height are not growing. Am I right? Is the graph below showing?

        margins, at (agemo= (2 (4) 65))
        marginsplot





        Last edited by Aye Aye Khaine; 03 Apr 2019, 17:14.

        Comment


        • #19
          No graph is showing in #18.

          Comment


          • #20
            Hi,
            How do I get the graph to show? I just did the simple cut and paste and it is not showing.

            Comment


            • #21
              Click image for larger version

Name:	Picture1.png
Views:	3
Size:	29.8 KB
ID:	1491738 Hello, here is the graph on the regression I posted with age and age squared. U shape test's p value was way above p>.10. so, not sure I need to do age squared??? please advise and hope to hear from you soon.
              Best regards,
              Aye Aye
              Attached Files

              Comment


              • #22
                In #16 you show results of the -utest- command, and the pvalue is 6.40e-16. In ordinary scientific notation that's 6.4x10-16. So why do you say p > .10? You know, I'm one of those people who doesn't like the concept of statistical significance at all, and am often dismissive of p-values, but even I would be loathe to go against a pvalue of 6.4x10-16. Even though it is absurd to pretend that p-values have anything approaching that level of accuracy, you don't get a pvalue that small without something going on that's worth talking about.

                Even ignoring p-values, the U-shapedness of the graph is unmistakeable. Even if the p-value really were > .10, I would call this a definitely U-shaped relationship.

                Comment


                • #23
                  Hi Clyde,
                  Please let me try the following in the graph and please correct me if I am not making sense of the graph. the dependent variable was height for age z score (standard deviation)unit. Children <-2 z score are considered stunted.

                  The graph means that

                  at about 2 months of age, his or her height for age z score (or their height growth according to that age) is growing between .-5 (fish) and .-6 height for age z score. as a child reaches to 6 months old, their height for age z score between -8 and -9. Older children in my sample average height for age z score is low in general. However, all children are above -2 z score.


                  Is that how I should be making sense?

                  Thanks so much for your previous reply.

                  Hope to hear again soon. Please help and I got to get over this.

                  Comment


                  • #24
                    You are reading the graph accurately, but you are misinterpreting its meaning. You cannot, from this graph, say anything about the height for age z-score of any one child, nor can you make statements such as "all children are above -2 z score." The predicted values that are plotted in that graph are the mean height-for-age z-scores at that age, adjusted for other variables in your model. So the average height for age is always above -2, but there may be many children whose height for age dips below that value. And you can't say anything about any single child from this graph. It is a graph of the average values (adjusted) only.

                    Comment


                    • #25
                      Thank you so much, Professor Clyde!
                      Much appreciated!

                      Comment


                      • #26
                        Dear,

                        I estimated a random effects ordered probit model:
                        Code:
                         xtoprobit shealth c.age##c.age i.wave, vce(cluster mergeid_n)
                        To see whether the turning point was included in my data i ran as was suggested in this thread.

                        Code:
                        nlcom -_b[age]/(2*_b[c.age#c.age])
                        The data in my sample runs from 65-95 but I receive

                        Code:
                        _nl_1:  -_b[age]/(2*_b[c.age#c.age])
                        
                        ------------------------------------------------------------------------------
                             shealth |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
                        -------------+----------------------------------------------------------------
                               _nl_1 |   1427.445   5472.045     0.26   0.794    -9297.566    12152.46
                        ------------------------------------------------------------------------------
                        The age 1427.445 simply does not exists. Any idea what is going on?

                        When I run:

                        Code:
                         margins, at (age=(65(5)95)) nose
                        marginsplot
                        I get this margins plot:



                        Not sure of this margins plot proves that I should use age squared or not.

                        Thank you in advance!
                        Attached Files
                        Last edited by sophie maene; 30 Apr 2020, 13:48.

                        Comment


                        • #27
                          Sophie:
                          why not sharing -xtoprobit- outcome table, too? Thanks.
                          Kind regards,
                          Carlo
                          (Stata 17.0 SE)

                          Comment


                          • #28
                            Hi, yes sorry here it is!

                            Code:
                             xtoprobit shealth c.age##c.age i.wave, vce(cluster mergeid_n)
                            
                            Fitting comparison model:
                            
                            Iteration 0:   log likelihood = -220787.09  
                            Iteration 1:   log likelihood = -216727.71  
                            Iteration 2:   log likelihood = -216726.89  
                            Iteration 3:   log likelihood = -216726.89  
                            
                            Refining starting values:
                            
                            Grid node 0:   log likelihood = -209010.45
                            
                            Fitting full model:
                            
                            Iteration 0:   log pseudolikelihood = -209010.45  
                            Iteration 1:   log pseudolikelihood = -200663.47  
                            Iteration 2:   log pseudolikelihood =  -196345.3  
                            Iteration 3:   log pseudolikelihood = -195566.04  
                            Iteration 4:   log pseudolikelihood = -195532.62  
                            Iteration 5:   log pseudolikelihood = -195532.47  
                            Iteration 6:   log pseudolikelihood = -195532.47  
                            
                            Random-effects ordered probit regression        Number of obs     =    154,711
                            Group variable: mergeid_n                       Number of groups  =     62,399
                            
                            Random effects u_i ~ Gaussian                   Obs per group:
                                                                                          min =          1
                                                                                          avg =        2.5
                                                                                          max =          7
                            
                            Integration method: mvaghermite                 Integration pts.  =         12
                            
                                                                            Wald chi2(8)      =    6777.25
                            Log pseudolikelihood  = -195532.47              Prob > chi2       =     0.0000
                            
                                                     (Std. Err. adjusted for 62,399 clusters in mergeid_n)
                            ------------------------------------------------------------------------------
                                         |               Robust
                                 shealth |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
                            -------------+----------------------------------------------------------------
                                     age |  -.0634849   .0137425    -4.62   0.000    -.0904197   -.0365502
                                         |
                             c.age#c.age |   .0000222   .0000901     0.25   0.805    -.0001543    .0001987
                                         |
                                    wave |
                                      2  |  -.1946302    .014936   -13.03   0.000    -.2239043   -.1653562
                                      3  |  -.3675597   .0161868   -22.71   0.000    -.3992852   -.3358342
                                      4  |   -.243044   .0154814   -15.70   0.000     -.273387    -.212701
                                      5  |  -.1574564   .0153565   -10.25   0.000    -.1875545   -.1273583
                                      6  |  -.1856671   .0154187   -12.04   0.000    -.2158873    -.155447
                                      7  |  -.2027917   .0157322   -12.89   0.000    -.2336263   -.1719571
                            -------------+----------------------------------------------------------------
                                   /cut1 |  -6.501648   .5206987                     -7.522199   -5.481098
                                   /cut2 |  -4.913978   .5207371                     -5.934604   -3.893352
                                   /cut3 |  -3.246265   .5207259                     -4.266869   -2.225661
                                   /cut4 |  -2.076403   .5207411                     -3.097037    -1.05577
                            -------------+----------------------------------------------------------------
                               /sigma2_u |   1.575051   .0200396                       1.53626    1.614822
                            ------------------------------------------------------------------------------

                            Comment


                            • #29
                              Sophie:
                              your results do not show evidence of a squared relationship for -age-.
                              That said, only the time dimension of your panel seems to play a role in explaining variations in the regressand.
                              That said, what strikes me is that you have -age- only as other predictors.
                              Are you sure that you gave a fair and true view of the data generating process?
                              Kind regards,
                              Carlo
                              (Stata 17.0 SE)

                              Comment


                              • #30
                                I actually have a lot more variables in my model but thought that it would become a too long post so I just used the age squared: this is my full model

                                Code:
                                toprobit shealth c.age##c.age##i.LTCsystem female bmi i.co007_ eduyears_mod chronic_mod i.childhoodhealth eurod ever_smoked smoking i.br010_mod i.sportsoractivities ch001_ ch021_mod partnerinhh gdp i.wave, vce(cluster mergeid_n)
                                Code:
                                 xtoprobit shealth c.age##c.age##i.LTCsystem female bmi i.co007_ eduyears_mod chronic_mod i.childhoodhealth eurod ever_smoked smoking i
                                > .br010_mod i.sportsoractivities ch001_ ch021_mod partnerinhh gdp i.wave, vce(cluster mergeid_n)
                                
                                Fitting comparison model:
                                
                                Iteration 0:   log likelihood = -68026.187  
                                Iteration 1:   log likelihood =  -57366.91  
                                Iteration 2:   log likelihood = -57291.882  
                                Iteration 3:   log likelihood = -57291.846  
                                Iteration 4:   log likelihood = -57291.846  
                                
                                Refining starting values:
                                
                                Grid node 0:   log likelihood = -58448.777
                                
                                Fitting full model:
                                
                                Iteration 0:   log pseudolikelihood = -58448.777  (not concave)
                                Iteration 1:   log pseudolikelihood = -56606.722  
                                Iteration 2:   log pseudolikelihood =   -55660.1  
                                Iteration 3:   log pseudolikelihood = -55633.542  
                                Iteration 4:   log pseudolikelihood = -55633.321  
                                Iteration 5:   log pseudolikelihood = -55633.321  
                                
                                Random-effects ordered probit regression        Number of obs     =     47,521
                                Group variable: mergeid_n                       Number of groups  =     26,564
                                
                                Random effects u_i ~ Gaussian                   Obs per group:
                                                                                              min =          1
                                                                                              avg =        1.8
                                                                                              max =          4
                                
                                Integration method: mvaghermite                 Integration pts.  =         12
                                
                                                                                Wald chi2(42)     =   11773.70
                                Log pseudolikelihood  = -55633.321              Prob > chi2       =     0.0000
                                
                                                                            (Std. Err. adjusted for 26,564 clusters in mergeid_n)
                                -------------------------------------------------------------------------------------------------
                                                                |               Robust
                                                        shealth |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
                                --------------------------------+----------------------------------------------------------------
                                                            age |  -.0300641   .0482202    -0.62   0.533     -.124574    .0644458
                                                                |
                                                    c.age#c.age |   .0000321   .0003173     0.10   0.919    -.0005899    .0006541
                                                                |
                                                      LTCsystem |
                                                     Cluster 2  |   2.331213    2.42739     0.96   0.337    -2.426384    7.088811
                                                     Cluster 3  |   1.362363   2.425582     0.56   0.574     -3.39169    6.116417
                                                     Cluster 4  |   2.824523   2.755899     1.02   0.305    -2.576939    8.225986
                                                                |
                                                LTCsystem#c.age |
                                                     Cluster 2  |  -.0797573    .064341    -1.24   0.215    -.2058634    .0463488
                                                     Cluster 3  |  -.0385814   .0640747    -0.60   0.547    -.1641655    .0870027
                                                     Cluster 4  |  -.0881586   .0731019    -1.21   0.228    -.2314357    .0551185
                                                                |
                                          LTCsystem#c.age#c.age |
                                                     Cluster 2  |   .0005722   .0004242     1.35   0.177    -.0002592    .0014035
                                                     Cluster 3  |   .0002343   .0004209     0.56   0.578    -.0005906    .0010593
                                                     Cluster 4  |   .0006058   .0004824     1.26   0.209    -.0003397    .0015513
                                                                |
                                                         female |   .1099243   .0180642     6.09   0.000      .074519    .1453295
                                                            bmi |   -.028748   .0018042   -15.93   0.000    -.0322842   -.0252117
                                                                |
                                                         co007_ |
                                       2. With some difficulty  |   .1223905   .0272272     4.50   0.000     .0690263    .1757548
                                              3. Fairly easily  |   .2568748   .0284901     9.02   0.000     .2010352    .3127144
                                                     4. Easily  |   .3786829   .0299674    12.64   0.000     .3199479    .4374179
                                                                |
                                                   eduyears_mod |   .0350989   .0020617    17.02   0.000      .031058    .0391399
                                                    chronic_mod |  -.5988879   .0161005   -37.20   0.000    -.6304442   -.5673315
                                                                |
                                                childhoodhealth |
                                                        2.Poor  |  -.4891291   .1292088    -3.79   0.000    -.7423737   -.2358845
                                                        3.Fair  |  -.3603825   .1221198    -2.95   0.003    -.5997329    -.121032
                                                        4.Good  |  -.2329024   .1201075    -1.94   0.052    -.4683088     .002504
                                                   5.Very good  |  -.0536764   .1199848    -0.45   0.655    -.2888422    .1814895
                                                   6.Excellent  |   .1493303   .1202162     1.24   0.214    -.0862892    .3849499
                                                                |
                                                          eurod |  -.2254837   .0036085   -62.49   0.000    -.2325562   -.2184111
                                                    ever_smoked |  -.0774778    .018341    -4.22   0.000    -.1134255   -.0415302
                                                        smoking |  -.0065085   .0257136    -0.25   0.800    -.0569063    .0438893
                                                                |
                                                      br010_mod |
                                     2. less than once a month  |   .1802149   .0240414     7.50   0.000     .1330947    .2273352
                                      3. once or twice a month  |   .2578486   .0239887    10.75   0.000     .2108316    .3048655
                                       4. once or twice a week  |   .3625201   .0219147    16.54   0.000     .3195681    .4054721
                                  5. three or four days a week  |   .4214576    .029104    14.48   0.000     .3644148    .4785005
                                    6. five or six days a week  |   .4273772   .0399496    10.70   0.000     .3490775    .5056769
                                           7. almost every day  |   .3480736   .0209166    16.64   0.000     .3070778    .3890693
                                                                |
                                             sportsoractivities |
                                 2. One to three times a month  |   .4058166   .0225476    18.00   0.000     .3616241    .4500091
                                                3. Once a week  |    .451309   .0194354    23.22   0.000     .4132163    .4894018
                                       4.More than once a week  |   .5949294   .0168049    35.40   0.000     .5619923    .6278664
                                                                |
                                                         ch001_ |   .0028423   .0079224     0.36   0.720    -.0126853    .0183699
                                                      ch021_mod |   .0025406   .0031504     0.81   0.420     -.003634    .0087152
                                                    partnerinhh |  -.1024684   .0179812    -5.70   0.000    -.1377109    -.067226
                                                            gdp |   .0000116   1.24e-06     9.37   0.000     9.20e-06    .0000141
                                                                |
                                                           wave |
                                                             2  |  -.2832655   .0204394   -13.86   0.000     -.323326    -.243205
                                                             4  |  -.3138069   .0218063   -14.39   0.000    -.3565466   -.2710673
                                                             5  |  -.3327459   .0217811   -15.28   0.000    -.3754361   -.2900557
                                --------------------------------+----------------------------------------------------------------
                                                          /cut1 |  -5.117054   1.827863                       -8.6996   -1.534509
                                                          /cut2 |  -3.394676   1.828006                     -6.977502    .1881492
                                                          /cut3 |  -1.677042   1.827917                     -5.259693    1.905609
                                                          /cut4 |  -.5206001   1.827813                     -4.103048    3.061848
                                --------------------------------+----------------------------------------------------------------
                                                      /sigma2_u |   .7327147   .0234679                      .6881324    .7801854
                                -------------------------------------------------------------------------------------------------
                                Which gives the following marginsplot

                                Code:
                                 
                                  margins, at (age=(65(5)95)) nose marginsplot
                                Click image for larger version

Name:	Graph.png
Views:	1
Size:	88.8 KB
ID:	1550428 So I guess no age square necessary!

                                Comment

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