diff --git a/README.html b/README.html index 757b7c0..b91e238 100644 --- a/README.html +++ b/README.html @@ -366,6 +366,18 @@

Set up

library(devtools) install_github("livioivil/flipscores") +
+

References

+

J Hemerik, JJ Goeman and L Finos (2019) Robust testing in generalized +linear models by sign-flipping score contributions. Journal of the Royal +Statistical Society Series B: Statistical Methodology, Volume 82, Issue +3, July 2020, Pages 841–864.
+https://doi.org/10.1111/rssb.12369

+

R De Santis, J Goeman, J Hemerik, L Finos (2022) Inference in +generalized linear models with robustness to misspecified variances +arXiv: 2209.13918.
+https://arxiv.org/abs/2209.13918

+

Some examples

library(flipscores)
@@ -386,10 +398,10 @@ 
Some examples

 #> 
 #> Coefficients:
 #>             Estimate   Score Std. Error z value Part. Cor Pr(>|z|)   
-#> (Intercept)  -0.1026 -0.7229     2.7127 -0.2665    -0.088    0.757   
-#> ZB           -0.1501 -0.7125     2.1789 -0.3270    -0.104    0.641   
-#> ZC            0.1633  0.8106     2.2232  0.3646     0.117    0.689   
-#> X             0.9439 16.2062     4.7272  3.4283     0.671    0.009 **
+#> (Intercept)  -0.1026 -0.7229     2.7127 -0.2665    -0.088   0.7460   
+#> ZB           -0.1501 -0.7125     2.1789 -0.3270    -0.104   0.6564   
+#> ZC            0.1633  0.8106     2.2232  0.3646     0.117   0.6924   
+#> X             0.9439 16.2062     4.7272  3.4283     0.671   0.0098 **
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 #> 
@@ -411,8 +423,8 @@ 
Some examples

 #> 
 #> Model: Y ~ Z + X
 #>   Df   Score Pr(>Score)   
-#> Z  2 0.73408     0.7022   
-#> X  1 0.02957     0.0090 **
+#> Z  2 0.77184     0.6984   
+#> X  1 0.02977     0.0098 **
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 # or
@@ -427,7 +439,7 @@ 
Some examples

 #> Model 1: Y ~ Z
 #> Model 2: Y ~ Z + X
 #>                    Df    Score Pr(>Score)  
-#> Model 2 vs Model 1  1 0.029235     0.0122 *
+#> Model 2 vs Model 1  1 0.029586     0.0108 *
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 # and
@@ -442,7 +454,7 @@ 
Some examples

 #> Model 1: Y ~ X
 #> Model 2: Y ~ Z + X
 #>                    Df  Score Pr(>Score)
-#> Model 2 vs Model 1  2 1.4179     0.5172
+#> Model 2 vs Model 1 2 1.4245 0.5244

Negative Binomial

set.seed(1)
@@ -492,10 +504,10 @@ 
Negative Binomial

 #> -2.0746  -0.7748  -0.1086   0.4617   2.0435  
 #> 
 #> Coefficients:
-#>             Estimate    Score Std. Error  z value Part. Cor Pr(>|t|)    
-#> (Intercept) -0.15365 -1.84162    3.42399 -0.53786    -0.087   0.5674    
-#> x            0.92089 14.93128    4.42610  3.37346     0.547   0.0008 ***
-#> z           -0.01282 -0.72457    7.24491 -0.10001    -0.016   0.9580    
+#>             Estimate    Score Std. Error  z value Part. Cor Pr(>|t|)   
+#> (Intercept) -0.15365 -1.84162    3.42399 -0.53786    -0.087   0.5808   
+#> x            0.92089 14.93128    4.42610  3.37346     0.547   0.0014 **
+#> z           -0.01282 -0.72457    7.24491 -0.10001    -0.016   0.9612   
 #> ---
 #> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
 #> 
@@ -508,18 +520,6 @@ 
Negative Binomial

 #> Number of Fisher Scoring iterations: 1
-
-

References

-

J Hemerik, JJ Goeman and L Finos (2019) Robust testing in generalized -linear models by sign-flipping score contributions. Journal of the Royal -Statistical Society Series B: Statistical Methodology, Volume 82, Issue -3, July 2020, Pages 841–864.
-https://doi.org/10.1111/rssb.12369

-

R De Santis, J Goeman, J Hemerik, L Finos (2022) Inference in -generalized linear models with robustness to misspecified variances -arXiv: 2209.13918.
-https://arxiv.org/abs/2209.13918

-

Bug reports

If you encounter a bug, please file a reprex (minimal diff --git a/README.md b/README.md index 629a516..3a8b9ac 100644 --- a/README.md +++ b/README.md @@ -49,11 +49,11 @@ summary(mod) #> -1.6910 -0.5792 0.1012 0.4900 1.0440 #> #> Coefficients: -#> Estimate Score Std. Error z value Part. Cor Pr(>|z|) -#> (Intercept) -0.1026 -0.7229 2.7127 -0.2665 -0.088 0.7578 -#> ZB -0.1501 -0.7125 2.1789 -0.3270 -0.104 0.6700 -#> ZC 0.1633 0.8106 2.2232 0.3646 0.117 0.6928 -#> X 0.9439 16.2062 4.7272 3.4283 0.671 0.0118 * +#> Estimate Score Std. Error z value Part. Cor Pr(>|z|) +#> (Intercept) -0.1026 -0.7229 2.7127 -0.2665 -0.088 0.7460 +#> ZB -0.1501 -0.7125 2.1789 -0.3270 -0.104 0.6564 +#> ZC 0.1633 0.8106 2.2232 0.3646 0.117 0.6924 +#> X 0.9439 16.2062 4.7272 3.4283 0.671 0.0098 ** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #> @@ -74,9 +74,9 @@ anova(mod) #> Inference is provided by FlipScores approach (5000 sign flips). #> #> Model: Y ~ Z + X -#> Df Score Pr(>Score) -#> Z 2 0.76757 0.6946 -#> X 1 0.02942 0.0118 * +#> Df Score Pr(>Score) +#> Z 2 0.77184 0.6984 +#> X 1 0.02977 0.0098 ** #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # or @@ -91,7 +91,7 @@ anova(mod0,mod) #> Model 1: Y ~ Z #> Model 2: Y ~ Z + X #> Df Score Pr(>Score) -#> Model 2 vs Model 1 1 0.029502 0.0114 * +#> Model 2 vs Model 1 1 0.029586 0.0108 * #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # and @@ -106,7 +106,7 @@ anova(mod0,mod) #> Model 1: Y ~ X #> Model 2: Y ~ Z + X #> Df Score Pr(>Score) -#> Model 2 vs Model 1 2 1.4314 0.5128 +#> Model 2 vs Model 1 2 1.4245 0.5244 ``` ### Negative Binomial @@ -159,10 +159,10 @@ summary(mod) #> -2.0746 -0.7748 -0.1086 0.4617 2.0435 #> #> Coefficients: -#> Estimate Score Std. Error z value Part. Cor Pr(>|t|) -#> (Intercept) -0.15365 -1.84162 3.42399 -0.53786 -0.087 0.5766 -#> x 0.92089 14.93128 4.42610 3.37346 0.547 0.0008 *** -#> z -0.01282 -0.72457 7.24491 -0.10001 -0.016 0.9610 +#> Estimate Score Std. Error z value Part. Cor Pr(>|t|) +#> (Intercept) -0.15365 -1.84162 3.42399 -0.53786 -0.087 0.5808 +#> x 0.92089 14.93128 4.42610 3.37346 0.547 0.0014 ** +#> z -0.01282 -0.72457 7.24491 -0.10001 -0.016 0.9612 #> --- #> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 #>