epistasis {happy} | R Documentation |
epistasis() will test for a statistical interaction between two sets of markers within the happy framework. The markers should be sufficiently far apart that they are unlinked (in practice 10cM for a 30 generation HS is sufficient). A partial F-test is performed to test if a model allowing for interactions fits better than a model in which each marker's contribution is additive between loci. Note that the effect of each marker within a locus can be either additive or full. Merging of strain is permitted.
epistasispair() is the same as epistasis() except that only one pair of markers is tested.
epistasis( h, markers1, markers2, merge1=NULL, merge2=NULL, model='additive', verbose=FALSE, family='gaussian' ) epistasispair( h, marker1, marker2, merge1=NULL, merge2=NULL, model='additive', verbose=FALSE, d1=NULL, d2=NULL, main1=0, main2=0, family='gaussian' )
h |
an object returned by a previous call to happy() |
markers1 |
an array of marker names or indices |
markers2 |
an array of marker names or indices |
marker1 |
a single marker name or index |
marker2 |
a single marker name or index |
merge1 |
an optional merge object (returned by mergematrices()) determining how the strains should be merged together for the markers listed in marker1 |
merge2 |
an optional merge object (returned by mergematrices()) determining how the strains should be merged together for the markers listed in marker2 |
model |
the type of model fitted at each locus. Either 'additive' or 'full' |
verbose |
switch controlling output to screen |
d1 |
optional design matrix for the main effect of the first marker (saves computation time) |
main1 |
optional log-P-value for the main effect of the first marker. NOTE: If d1 is not NULL then main1 must be set |
d2 |
optional design matrix for the main effect of the second marker (saves computation time). |
main2 |
optional log-P-value for the main effect of the second marker. NOTE: If d2 is not NULL then main2 must be set |
family |
The distribution of errors in the data. The default is 'gaussian'. This variable controls the type of model fitting. In the gauusian case a standard linear model is fitted using lm(). Otherwise the data are fitted as a generalised linear model using glm(), when the value of family must be one of the distributions hangled by glm(), such as 'binomial', 'gamma'. See family() for the full range of models. |
epistasis() returns a matrix with columns named 'marker1', 'marker2', 'main1', 'main2', 'main1+main2', 'main1*main2', 'main1.main2'. marker1 and marker2 are the names of the markers being compared in a given row, the remaining values are the ANOVA log-P-values of the main effects (main1 and main2), the combined additive effect (main1+main2), the addtive plus interaction (main1*main2) and the partial F of the interaction (main1.main2) after allowing for main1+main2. epistasispair() returns a list with the same fields.
Richard Mott