title: R sem function in lavaan WARNING: could not compute standard errors
body: <p>I'm having a problem with the sem() function in the 'lavaan' package in R and I was hoping someone here might have thoughts on how I can fix it.</p>
  
  <p>My model is set up as follows:</p>
  
  <pre><code>mod &lt;- 'trait ~ historical1 + historical 2 + contemporary1 + contemporary2
  abundance ~ contemporary1 + contemporary2 + trait
  bacteria ~ historical1 + historical 2 + contemporary1 + contemporary2 + trait + abundance'
  </code></pre>
  
  <p>My exogenous variables (historical1, historical 2, contemporary1, and contemporary2) are categorical variables that I manipulated for my experiment with two possible levels. I'm treating those as dummy variables with 0's and 1's. Everything else is a numeric measurement. </p>
  
  <p>I have three different variables for "bacteria" that I'm trying to look at separately. When I use the first (bacteria1), the model runs smoothly. </p>
  
  <pre><code>&gt; modA&lt;-'trait ~ historical1 + historical 2 + contemporary1 + contemporary2
  + abundance ~ contemporary1 + contemporary2 + trait
  + bacteria1 ~ historical1 + historical 2 + contemporary1 + contemporary2 + trait + abundance'
  &gt; fit&lt;-sem(modA,data=data,meanstructure=T)
  Warning message:
  In lav_data_full(data = data, group = group, group.label = group.label,  :
    lavaan WARNING: some observed variances are (at least) a factor 1000 times larger than others; use varTable(fit) to investigate
  &gt; fitmeasures(fit)
                 fmin               chisq                  df              pvalue 
                0.154              24.028               2.000               0.000 
       baseline.chisq         baseline.df     baseline.pvalue                 cfi 
              114.474              15.000               0.000               0.779 
                  tli                nnfi                 rfi                 nfi 
               -0.661              -0.661               1.000               0.790 
                 pnfi                 ifi                 rni                logl 
                0.105               0.804               0.779           -1605.897 
    unrestricted.logl                npar                 aic                 bic 
            -1593.883              19.000            3249.794            3294.572 
               ntotal                bic2               rmsea      rmsea.ci.lower 
               78.000            3234.670               0.376               0.251 
       rmsea.ci.upper        rmsea.pvalue                 rmr          rmr_nomean 
                0.517               0.000              40.988              45.826 
                 srmr        srmr_bentler srmr_bentler_nomean         srmr_bollen 
                0.080               0.080               0.089               0.080 
   srmr_bollen_nomean          srmr_mplus   srmr_mplus_nomean               cn_05 
                0.089               0.080               0.089              20.450 
                cn_01                 gfi                agfi                pgfi 
               30.899               0.997               0.946               0.057 
                  mfi                ecvi 
                0.868                  NA 
  &gt; summary(fit)
  lavaan (0.5-16) converged normally after 145 iterations
  
    Number of observations                            78
  
    Estimator                                         ML
    Minimum Function Test Statistic               24.028
    Degrees of freedom                                 2
    P-value (Chi-square)                           0.000
  
  Parameter estimates:
  
    Information                                 Expected
    Standard Errors                             Standard
  
                     Estimate  Std.err  Z-value  P(&gt;|z|)
  Regressions:
    trait ~
      historical1       8.722   39.607    0.220    0.826
      historical2      89.529   39.607    2.260    0.024
      contemporary1     6.737   39.621    0.170    0.865
      contemporary2   411.556   39.621   10.387    0.000
    abundance ~
      contemporary1   -36.390   81.354   -0.447    0.655
      contemporary2   424.993  123.301    3.447    0.001
      trait            -0.251    0.225   -1.116    0.265
    bacteria1 ~
      historical1      -2.342    2.455   -0.954    0.340
      historical2      -2.142    2.533   -0.845    0.398
      contemporary1    -0.268    2.459   -0.109    0.913
      contemporary2     3.410    4.059    0.840    0.401
      trait             0.000    0.007    0.031    0.975
      abundance        -0.008    0.003   -2.451    0.014
  
  Intercepts:
      trait          1299.789   44.285   29.351    0.000
      abundance      1438.077  311.643    4.614    0.000
      bacteria1        26.805   10.717    2.501    0.012
  
  Variances:
      trait         30569.613 4895.056
      abundance     128842.663 20631.338
      bacteria1       117.383   18.796
  </code></pre>
  
  <p>However, when I use either of the other two bacterial variables, I receive a warning "could not compute standard errors!". </p>
  
  <pre><code>    &gt; modB&lt;-'trait ~ historical1 + historical 2 + contemporary1 + contemporary2
      + abundance ~ contemporary1 + contemporary2 + trait
      + bacteria2 ~ historical1 + historical 2 + contemporary1 + contemporary2 + trait + abundance'
      &gt; fit&lt;-sem(modB,data=data,meanstructure=T)
      Warning messages:
      1: In lav_data_full(data = data, group = group, group.label = group.label,  :
        lavaan WARNING: some observed variances are (at least) a factor 1000 times larger than others; use varTable(fit) to investigate
      2: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats,  :
        lavaan WARNING: could not compute standard errors!
  &gt; fitmeasures(fit)
                 fmin               chisq                  df              pvalue      baseline.chisq 
                0.154              24.028               2.000               0.000             126.602 
          baseline.df     baseline.pvalue                 cfi                 tli                nnfi 
               15.000               0.000               0.803              -0.480              -0.480 
                  rfi                 nfi                pnfi                 ifi                 rni 
                1.000               0.810               0.108               0.823               0.803 
                 logl   unrestricted.logl                npar                 aic                 bic 
            -1277.674           -1265.660              19.000            2593.348            2638.126 
               ntotal                bic2               rmsea      rmsea.ci.lower      rmsea.ci.upper 
               78.000            2578.224               0.376               0.251               0.517 
         rmsea.pvalue                 rmr          rmr_nomean                srmr        srmr_bentler 
                0.000              15.075              16.855               0.078               0.078 
  srmr_bentler_nomean         srmr_bollen  srmr_bollen_nomean          srmr_mplus   srmr_mplus_nomean 
                0.087               0.078               0.087               0.078               0.087 
                cn_05               cn_01                 gfi                agfi                pgfi 
               20.450              30.899               0.997               0.945               0.057 
                  mfi                ecvi 
                0.868                  NA 
  &gt; summary(fit)
  lavaan (0.5-16) converged normally after 115 iterations
  
    Number of observations                            78
  
    Estimator                                         ML
    Minimum Function Test Statistic               24.028
    Degrees of freedom                                 2
    P-value (Chi-square)                           0.000
  
  Parameter estimates:
  
    Information                                 Expected
    Standard Errors                             Standard
  
                     Estimate  Std.err  Z-value  P(&gt;|z|)
  Regressions:
    trait ~
      historical1       8.723
      historical2      89.530
      contemporary1     6.737
      contemporary2   411.556
    abundance ~
      contemporary1   -36.390
      contemporary2   424.993
      trait            -0.251
    bacteria2 ~
      historical1       0.020
      historical2      -0.038
      contemporary1    -0.075
      contemporary2    -0.071
      trait            -0.000
      abundance        -0.000
  
  Intercepts:
      trait          1299.788
      abundance      1438.077
      bacteria2         0.367
  
  Variances:
      trait         30569.606
      abundance     128842.668
      bacteria2         0.026
  
      &gt; modC&lt;-'trait ~ historical1 + historical 2 + contemporary1 + contemporary2
      + abundance ~ contemporary1 + contemporary2 + trait
      + bacteria3 ~ historical1 + historical 2 + contemporary1 + contemporary2 + trait + abundance'
      &gt; fit&lt;-sem(modC,data=data,meanstructure=T)
      Warning messages:
      1: In lav_data_full(data = data, group = group, group.label = group.label,  :
        lavaan WARNING: some observed variances are (at least) a factor 1000 times larger than others; use varTable(fit) to investigate
      2: In lav_model_vcov(lavmodel = lavmodel, lavsamplestats = lavsamplestats,  :
        lavaan WARNING: could not compute standard errors!
  &gt; fitmeasures(fit)
                 fmin               chisq                  df              pvalue      baseline.chisq 
                0.154              24.028               2.000               0.000             112.875 
          baseline.df     baseline.pvalue                 cfi                 tli                nnfi 
               15.000               0.000               0.775              -0.688              -0.688 
                  rfi                 nfi                pnfi                 ifi                 rni 
                1.000               0.787               0.105               0.801               0.775 
                 logl   unrestricted.logl                npar                 aic                 bic 
            -1414.328           -1402.314              19.000            2866.656            2911.434 
               ntotal                bic2               rmsea      rmsea.ci.lower      rmsea.ci.upper 
               78.000            2851.532               0.376               0.251               0.517 
         rmsea.pvalue                 rmr          rmr_nomean                srmr        srmr_bentler 
                0.000              15.160              16.949               0.077               0.077 
  srmr_bentler_nomean         srmr_bollen  srmr_bollen_nomean          srmr_mplus   srmr_mplus_nomean 
                0.086               0.079               0.086               0.079               0.086 
                cn_05               cn_01                 gfi                agfi                pgfi 
               20.450              30.899               1.000               0.991               0.057 
                  mfi                ecvi 
                0.868                  NA 
  &gt; summary(fit)
  lavaan (0.5-16) converged normally after 119 iterations
  
    Number of observations                            78
  
    Estimator                                         ML
    Minimum Function Test Statistic               24.028
    Degrees of freedom                                 2
    P-value (Chi-square)                           0.000
  
  Parameter estimates:
  
    Information                                 Expected
    Standard Errors                             Standard
  
                     Estimate  Std.err  Z-value  P(&gt;|z|)
  Regressions:
    trait ~
      historical1       8.723
      historical2      89.530
      contemporary1     6.737
      contemporary2   411.556
    abundance ~
      contemporary1   -36.390
      contemporary2   424.993
      trait            -0.251
    bacteria3 ~
      historical1       0.098
      historical2       0.095
      contemporary1    -0.102
      contemporary2     0.204
      trait            -0.000
      abundance        -0.000
  
  Intercepts:
      trait          1299.788
      abundance      1438.077
      bacteria3        19.984
  
  Variances:
      trait         30569.585
      abundance     128842.707
      bacteria3         0.864
  </code></pre>
  
  <p>I did some research about this warning but nothing seems to help. Does the model just fit really poorly with the other two variables?</p>
  
  <p>Any ideas? Thank you in advance.</p>
  
  <pre><code>&gt; str(data)
  'data.frame':   78 obs. of  10 variables:
   $ X            : int  1 2 3 4 5 6 7 8 9 10 ...
   $ historical1  : int  0 0 1 1 0 0 1 1 1 1 ...
   $ historical2  : int  0 1 1 0 0 1 1 0 0 1 ...
   $ contemporary1: int  0 1 0 1 0 0 1 0 1 0 ...
   $ contemporary2: int  1 1 0 1 0 0 1 0 1 0 ...
   $ trait        : num  2202 1935 1371 1978 1130 ...
   $ abundance    : num  1011 1757 1347 1578 1674 ...
   $ bacteria1    : num  30.39 6.69 7.58 6.05 8.47 ...
   $ bacteria2    : num  -0.0356 0.0304 0.1344 -0.0497 0.265 ...
   $ bacteria3    : num  19.4 18.1 20 18.1 18.4 ...
  </code></pre>
  
  <p>Erica</p>