Arguments p. numeric vector of p-values (possibly with NAs). Any other R object is coerced by as.numeric.. method. correction method. Can be abbreviated. n. number of comparisons, must be at least length(p); only set this (to non-default) when you know what you are doing!, p: numeric vector of p-values (possibly with NAs). Any other R object is coerced by as.numeric.. method: correction method, a character string. Can be abbreviated. n: number of comparisons, must be at least length(p); only set this (to non-default) when you know what you are doing!, Details. This function is a wrapper around the standard p.adjust function from the stats package. It takes the p.value metadata column from the AssocTestResultRanges object p, applies the multiple testing correction method specified as method argument. The method returns a copy of p with an additional metadata column p.value.adj that contains the adjusted p-values.
p: numeric vector of p-values (possibly with NAs).Any other R object is coerced by as.numeric.. method: correction method, a character string. Can be abbreviated. n: number of comparisons, must be at least length(p); only set this (to non-default) when you know what you are doing!, Show activity on this post. The first argument of p.adjust should be a vector, see. ?p.adjust. In your specific case, you need to select the values from your data frame and pass them to the function, so something like: p.adjust(pvalues$p.values, fdr) if the column name was p.values.
Section 6: p-value adjustment for multiple comparisons, Section 6: p-value adjustment for multiple comparisons, Bonferroni adjusted p-values | R, 8/3/2016 · For studies with multiple outcomes, p-values can be adjusted to account for the multiple comparisons issue. The ‘ p.adjust( ) ‘ command in R calculates adjusted p-values from a set of un-adjusted p-values, using a number of adjustment procedures.
Suppose you have a p-value of 0.005 and there are eight pairwise comparisons. Use the p.adjust() function while applying the Bonferroni method to calculate the adjusted p-values.Be sure to specify the method and n arguments necessary to adjust the .005 value. Assign the result to bonferroni_ex.; Print the result to see how much the p-values are deflated to correct for the inflated type I …
I just performed a factorial ANOVA, followed by the TukeyHSD post-test. Some of my adjusted P values from the TukeyHSD output are 0.0000000.Can these P values really be zero? Or is this a rounding situation, and my true P value might be something like 1e-17, that is rounded to 0.0000000.. Are there any options for the TukeyHSD() function in R that will give output P-values that contain exponents?, R .V. Craiu and L. Sun. Choosing the lesser evil: trade-off between false discovery rate and non-discovery rate. Statistica Sinica, 18:861-879, 2008. K.S. Pollard, S. Dudoit and M.J. van der Laan. Multiple Testing Procedures: R multtest Package and Applications to Genomics, in Bioinformatics and Computational Biology Solutions Using R and …
R functions to calculate critical constants and R functions to calculate adjusted P-values of test statistics. In addition, the package also contains functions to evaluate testing powers and hence the necessary sample sizes for the classic statistical problem of comparing multiple treatments with a
