jncaskey published a site update

Author: jncaskey

bayesian.html

@@ -737,7 +737,7 @@ <h2><span class="header-section-number">4.4</span> Robust linear
 seed : int(0) </code></pre>
 <pre class="r fold-show"><code>confint(robustLinearRegression)</code></pre>
 <pre><code>                             2.5 %    97.5 %
-(Intercept)             0.83801565 1.0499981
+(Intercept)             0.83801566 1.0499981
 bpi_antisocialT1Sum     0.44749135 0.5352118
 bpi_anxiousDepressedSum 0.09683728 0.2184779</code></pre>
 </div>
@@ -758,19 +758,19 @@ <h2><span class="header-section-number">4.5</span> Least trimmed squares
 
 Residuals (from reweighted LS):
     Min      1Q  Median      3Q     Max 
--3.9225 -0.8914  0.0000  1.0133  3.9185 
+-3.9305 -0.9091  0.0000  0.9997  3.9036 
 
 Coefficients:
                         Estimate Std. Error t value Pr(&gt;|t|)    
-Intercept                0.74662    0.04796  15.568  &lt; 2e-16 ***
-bpi_antisocialT1Sum      0.54341    0.01525  35.644  &lt; 2e-16 ***
-bpi_anxiousDepressedSum  0.13275    0.02343   5.665 1.62e-08 ***
+Intercept                0.74932    0.04795  15.626  &lt; 2e-16 ***
+bpi_antisocialT1Sum      0.54338    0.01524  35.647  &lt; 2e-16 ***
+bpi_anxiousDepressedSum  0.13826    0.02343   5.901 4.06e-09 ***
 ---
 Signif. codes:  0 &#39;***&#39; 0.001 &#39;**&#39; 0.01 &#39;*&#39; 0.05 &#39;.&#39; 0.1 &#39; &#39; 1
 
-Residual standard error: 1.535 on 2710 degrees of freedom
-Multiple R-Squared: 0.4103, Adjusted R-squared: 0.4099 
-F-statistic: 942.9 on 2 and 2710 DF,  p-value: &lt; 2.2e-16 </code></pre>
+Residual standard error: 1.534 on 2710 degrees of freedom
+Multiple R-Squared: 0.4121, Adjusted R-squared: 0.4116 
+F-statistic: 949.7 on 2 and 2710 DF,  p-value: &lt; 2.2e-16 </code></pre>
 </div>
 <div id="bayesian-linear-regression" class="section level2"
 number="4.6">
@@ -1852,7 +1852,7 @@ <h2><span class="header-section-number">9.4</span> Multiple
 summary(imputedData_pooledEstimates)</code></pre>
 <div data-pagedtable="false">
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@@ -1918,18 +1918,18 @@ <h2><span class="header-section-number">11.1</span> Linear
 summary(multipleRegressionModelBootstrapped)</code></pre>
 <div data-pagedtable="false">
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 </div>
 <pre class="r fold-show"><code>confint(multipleRegressionModelBootstrapped, level = .95, type = &quot;bca&quot;)</code></pre>
 <pre><code>Warning in confint.boot(multipleRegressionModelBootstrapped, level = 0.95, :
 BCa method fails for this problem.  Using &#39;perc&#39; instead</code></pre>
 <pre><code>Bootstrap percent confidence intervals
 
-                             2.5 %    97.5 %
-(Intercept)             1.07349576 1.3257446
-bpi_antisocialT1Sum     0.42064620 0.5100082
-bpi_anxiousDepressedSum 0.09548154 0.2289897</code></pre>
+                            2.5 %    97.5 %
+(Intercept)             1.0699677 1.3253791
+bpi_antisocialT1Sum     0.4167496 0.5120914
+bpi_anxiousDepressedSum 0.0918046 0.2305352</code></pre>
 <pre class="r fold-show"><code>hist(multipleRegressionModelBootstrapped)</code></pre>
 <pre><code>Warning in confint.boot(x, type = ci, level = level): BCa method fails for this
 problem.  Using &#39;perc&#39; instead</code></pre>
@@ -1942,7 +1942,7 @@ <h2><span class="header-section-number">11.2</span> Generalized
 summary(generalizedRegressionModelBootstrapped)</code></pre>
 <div data-pagedtable="false">
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+{"columns":[{"label":[""],"name":["_rn_"],"type":[""],"align":["left"]},{"label":["R"],"name":[1],"type":["dbl"],"align":["right"]},{"label":["original"],"name":[2],"type":["dbl"],"align":["right"]},{"label":["bootBias"],"name":[3],"type":["dbl"],"align":["right"]},{"label":["bootSE"],"name":[4],"type":["dbl"],"align":["right"]},{"label":["bootMed"],"name":[5],"type":["dbl"],"align":["right"]}],"data":[{"1":"1000","2":"1.1983004","3":"-7.667005e-05","4":"0.06222045","5":"1.1986654","_rn_":"(Intercept)"},{"1":"1000","2":"0.4655286","3":"4.961828e-04","4":"0.02301143","5":"0.4663421","_rn_":"bpi_antisocialT1Sum"},{"1":"1000","2":"0.1607541","3":"-1.325790e-03","4":"0.03235557","5":"0.1599957","_rn_":"bpi_anxiousDepressedSum"}],"options":{"columns":{"min":{},"max":[10]},"rows":{"min":[10],"max":[10]},"pages":{}}}
   </script>
 </div>
 <pre class="r fold-show"><code>confint(generalizedRegressionModelBootstrapped, level = .95, type = &quot;bca&quot;)</code></pre>
@@ -1951,9 +1951,9 @@ <h2><span class="header-section-number">11.2</span> Generalized
 <pre><code>Bootstrap percent confidence intervals
 
                              2.5 %    97.5 %
-(Intercept)             1.07498282 1.3214226
-bpi_antisocialT1Sum     0.41859059 0.5075609
-bpi_anxiousDepressedSum 0.09722923 0.2213951</code></pre>
+(Intercept)             1.07299901 1.3194043
+bpi_antisocialT1Sum     0.41889417 0.5117848
+bpi_anxiousDepressedSum 0.09540227 0.2229572</code></pre>
 <pre class="r fold-show"><code>hist(generalizedRegressionModelBootstrapped)</code></pre>
 <pre><code>Warning in confint.boot(x, type = ci, level = level): BCa method fails for this
 problem.  Using &#39;perc&#39; instead</code></pre>
@@ -2015,9 +2015,9 @@ <h2><span class="header-section-number">12.1</span> K-fold cross
 <pre><code>
 10-fold CV results:
   Fit       CV
-1 OLS 1.980613
-2  MM 1.026745
-3 LTS 1.007103
+1 OLS 1.980924
+2  MM 1.026443
+3 LTS 1.007390
 
 Best model:
    CV 
@@ -3152,7 +3152,7 @@ <h6><span class="header-section-number">14.1.6.2.3.1</span> Robust
 bpi_antisocialT2Sum            1.493955            3.122528
 
 $msg
-[1] &quot;155 singular samples of size 3 out of 1500&quot;
+[1] &quot;149 singular samples of size 3 out of 1500&quot;
 
 $crit
 [1] 3.737972
@@ -3289,10 +3289,10 @@ <h6><span class="header-section-number">14.1.6.2.3.1</span> Robust
 bpi_antisocialT2Sum            1.096437            2.482409
 
 $msg
-[1] &quot;149 singular samples of size 3 out of 1500&quot;
+[1] &quot;126 singular samples of size 3 out of 1500&quot;
 
 $crit
-[1] -0.3839279
+[1] -0.3817849
 
 $best
    [1]    1    3    8    9   10   12   13   14   15   16   17   18   19   21