search for: familywise

I am using VEGAN ordistep function for stepwise model selection. By default the Pin and Pout values are set to .05 and .1 Is it appropriate to use a family wise correction ( such as bonferroni or one of the alternatives) to adjust these values where there are several (5-10), potentially correlated variables in the model selection process? -- Nevil Amos Molecular Ecology Research Group

Dear List, We are planning a genotyping study to be analyzed using false discovery rates (FDRs) (See Storey and Tibshirani PNAS 2003; 100:9440-5). I am interested in learning if there is any consensus as to how many features (ie. how many P values) need to be studied before reasonably reliable FDRs can be derived. Does anyone know of a citation where this is discussed? Bill Dupont William D.

...with a large number of response variables, these results are questionable since we will expect a lot of type I errors ("incorrect significance"). Therefore the p-values need to be adjusted. By using rotation testing it is possible to adjust the single response p-values according to the familywise error rate criterion in an exact and non-conservative (unlike Bonferroni) way. It is also possible to adjust p-values according to a false discovery rate criterion. Our method is based on rotation testing and allows any kind of dependence among the responses (Moen et al., 2005). Note that rotati...

...with a large number of response variables, these results are questionable since we will expect a lot of type I errors ("incorrect significance"). Therefore the p-values need to be adjusted. By using rotation testing it is possible to adjust the single response p-values according to the familywise error rate criterion in an exact and non-conservative (unlike Bonferroni) way. It is also possible to adjust p-values according to a false discovery rate criterion. Our method is based on rotation testing and allows any kind of dependence among the responses (Moen et al., 2005). Note that rotati...

The Partek package (www.partek.com) allows only two selections for Multiple Test Correction: Bonferroni and Dunn-Sidak. Can anyone suggest why Partek implemented Dunn-Sidak and not the other methods that R has? Is there any particular advantage to the Dunn-Sidak method? R knows about these methods (in R 2.1.1): > p.adjust.methods [1] "holm" "hochberg" "hommel"