diagnosticTest {biclust}  R Documentation 
Testing Procedure for Bicluster Diagnostics
Description
Calculate the statistical value of the row, column and multiplicative effect based on discovered biclusters in the data. Additionally multiple sampling methods are available to compute the statistical significance through pvalues.
Usage
diagnosticTest(BCresult, data, number = 1:BCresult@Number, verbose = TRUE,
statistics = c("F", "Tukey"), sampling = TRUE, samplingtypes = NULL,
nSim = 1000, alpha = 0.05, save_F = FALSE)
Arguments
BCresult 
An object of class 
data 
data matrix, which 
number 
Vector of bicluster numbers of which the diagnostics should be calculated. (default = all available biclusters) 
verbose 
Boolean value to print progression of computed statistics. 
statistics 
Vector select which statistics to compute. (default =

sampling 
Boolean value to apply sampling methods to compute statistical significance (default= 
samplingtypes 
Vector of sampling methods for
See Details for more info. 
nSim 
Number of permutations/bootstraps. 
alpha 
Significance level (default=0.05) 
save_F 
Option to save the permuted/bootstraped statistics. This is necessary for 
Details
Due to the uncertainty of discovering the true bicluster(s) in the data, it's often advisable to not rely on the theoretical pvalues but instead retrieve the pvalues through a sampling procedure.
Available pvalues/sampling types for each statistical method:

"F"
:"Theoretical"
and"Permutation"
for both row and column effect. 
"Tukey"
:"Theoretical"
,"SemiparPerm"
and"SemiparBoot"
. 
"ModTukey"
:"Theoretical"
,"SemiparPerm"
,"SemiparBoot"
,"PermutationCor"
and"SamplingCor"
. 
"Tusell"
:"SemiparPerm"
,"SemiparBoot"
and"NormSim"
. 
"Mandel"
:"Theoretical"
,"SemiparPerm"
and"SemiparBoot"
. 
"LBI"
:"SemiparPerm"
,"SemiparBoot"
and"NormSim"
. 
"JandG"
:"SemiparPerm"
,"SemiparBoot"
and"NormSim"
.
More info on the sampling types can be found in the secion below.
If available, the "Theoretical"
will always be computed.
By default when sampling=TRUE
, a sampling method without replacement is chosen, namely "Permutation"
and "SemiparPerm"
.
When save_F=TRUE
, the null distributions of the statistics can be visualised with diagnosticPlot2
.
Disclaimer: While their functionality did not change, some functions of the additivityTests
package were altered in order to be able to return the permuted/bootstrapped statistics and pvalues.
Value
Returns a list with length(number)
elements.
Each element corresponds with the requested biclusters and is a list containing:

table
: a data frame where each row isstatistics
andsamplingtypes
(including Theoretical) combination. The data frame contains theMethod
,Type
(pvalue type),StatVal
(statistical value),CritVal
(critical value),pVal
andSign
(0/1 significance indicator based onalpha
). 
save_F
: ifsave_F=TRUE
, a (nSim
x number of permuted/bootstrapped pvalues) matrix contained the sampled statistics.
Sampling Types
For each sampling type a permuted/bootstrapped BC is created as following:

"Permutation"
: Sample a BC from the entire dataset with replacement. 
"SemiparPerm"
: A semiparametric permutation procedure. Twoway ANOVA is applied on the original BC and the residual matrix extracted. A new residual matrix is created by sampling without replacement from the original residual matrix. The sampled BC is then generated by adding this sampled residual matrix on top the mean, row and column effect of the ANOVA procedure of the original BC. 
"SemiparBoot"
: A semiparametric bootstrapping procedure. Twoway ANOVA is applied on the original BC and the residual matrix extracted. A new residual matrix is created by sampling with replacement from the original residual matrix. The sampled BC is then generated by adding this sampled residual matrix on top the mean, row and column effect of the ANOVA procedure of the original BC. 
"PermutationCor"
: Seecorrection=1
parameter ofmtukey.test
. More info in Simecek and Simeckova (2012). 
"SamplingCor"
: Seecorrection=2
parameter ofmtukey.test
. More info in Simecek and Simeckova (2012). 
"NormSim"
: Sample a BC from a standard normal distribution. This sampling procedure is used for some methods in theadditivityTests
package.
Author(s)
Ewoud De Troyer
References
Tukey, J.W.: One Degree of Freedom for Nonadditivity, Biometrics 5, pp. 232242, 1949.
Simecek, Petr, and Simeckova, Marie. "Modification of Tukey's additivity test." Journal of Statistical Planning and Inference, 2012.
Examples
## Not run:
#Random matrix with embedded bicluster (with multiplicative effect)
test < matrix(rnorm(5000),100,50)
roweff < sample(1:5,10,replace=TRUE)
coleff < sample(1:5,10,replace=TRUE)
test[11:20,11:20] < test[11:20,11:20] +
matrix(coleff,nrow=10,ncol=10,byrow=TRUE) +
matrix(roweff,nrow=10,ncol=10) +
roweff %*% t(coleff)
#Apply Plaid Biclustering
res < biclust(test, method=BCPlaid())
#Apply default diagnosticTest
out < diagnosticTest(BCresult=res, data=test, save_F=TRUE, number=1,
statistics=c("F","Tukey","ModTukey","Tusell","Mandel","LBI","JandG"),
samplingtypes=c("Permutation","SemiparPerm","SemiparBoot",
"PermutationCor","SamplingCor","NormSim"))
out[[1]]$table
## End(Not run)