update response matrixSource:
update the globally
response matrix and the unglobally normalized
door_response_matrix_non_normalized by introducing new
consensus response data of given receptor.
update_door_database(receptor, permutation = TRUE, perm, response_matrix_nn = door_default_values("door_response_matrix_non_normalized"), response_matrix = door_default_values("door_response_matrix"), responseRange = door_default_values("door_response_range"), weightGlobNorm = door_default_values("door_global_normalization_weights"), select.MDValue = door_default_values("select.MDValue"), strict = TRUE, overlapValues = door_default_values("overlapValues"), door_excluded_data = door_default_values("door_excluded_data"), plot = FALSE)
character string, name of given odorant receptor.
logical, if TRUE, the sequence is chosen from permutation, if FALSE, sequence is chosen by the routine process.
a matrix with one sequence of study names per row, if empty, all possible permutations of study names will be provided.
data frame, response data that has not been globally normalized.
data frame, globally normalized response data.
data frame, response range of studies.
data frame, weight matrix for global normalization.
the minimum mean distance between studies to perfom a merge (used if permutation == FALSE or if permutation == TRUE AND strict == TRUE)
logical, if TRUE merging a permutation will be stopped once a single merge has a mean distance above select.MDValue (only valid if permutation == TRUE)
minimum overlap between studies to perfom a merge
the data frame that contains the list of excluded data sets.
The merging sequence could be arranged by the routine process (using
model_response or taking the optimized sequence that is chosen
from permutations. The mean correlation between merged responses and each
original recording will be computed for each permutation, the optimozed
sequence is with the highest correlation.