Fit a bimodal gaussian distribution to a set of observations.
Fit a bimodal gaussian distribution to a set of observations.
fitBimodal(x, prob = 0.95, coverage = 0.8, size = 10, assign = FALSE, boolean = FALSE, verbose = TRUE, maxit = 5000, maxrestarts = 100, bySampling = FALSE, nsamp = 2000, force.tries = FALSE, ...) fitModal(x, m, prob = 0.95, coverage = 0.8, size = 10, assign = FALSE, boolean = FALSE, verbose = TRUE, maxit = 5000, maxrestarts = 100, bySampling = FALSE, nsamp = 200, ...)
| x | a named numeric vector of cells/observations or a matrix of genes X cells (variables X observations). If the latter, the column means are first computed. |
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| prob | a numeric value >= 0 and <= 1; the minimum posterior probability required for an observation to be assigned to a mode. Default: 0.95 |
| coverage | the fraction of observations that must have a posterior probability higher than <prob> to one of two modes in order for the distribution to qualify as bimodal. Default: 0.8 |
| size | the minimum number of observations that must be assigned to a mode in order for the distribution to qualify as bimodal. Default: 10 |
| assign | if set to TRUE, returns a list of length two containing the vector names that were assigned to each mode. Default: FALSE |
| boolean | if set to TRUE, returns a boolean value indicating whether the distribution is bimodal. Default: FALSE |
| verbose | print progress messages. Default: TRUE |
| maxit | the maximum number of iterations. Default: 5000 |
| maxrestarts | the maximum number of restarts allowed. See |
| m | number of components (modes). Default: 2 |
| x | a named numeric vector of cells/observations or a matrix of genes X cells (variables X observations). If the latter, the column means are first computed. |
| m | numeric value indicating the modality to test; 1 for unimodal; 2 for bimodal; etc. |
| prob | a numeric value >= 0 and <= 1; the minimum posterior probability required for an observation to be assigned to a mode. Default: 0.95 |
| coverage | the fraction of observations that must have a posterior probability higher than <prob> to one of two modes in order for the distribution to qualify as bimodal. Default: 0.8 |
| size | the minimum number of observations that must be assigned to a mode in order for the distribution to qualify as bimodal. Default: 10 |
| assign | if set to TRUE, returns a list of length two containing the vector names that were assigned to each mode. Default: FALSE |
| boolean | if set to TRUE, returns a boolean value indicating whether the distribution is bimodal. Default: FALSE |
| verbose | print progress messages. Default: TRUE |
| maxrestarts | the maximum number of restarts allowed. See |
| maxit | the maximum number of iterations. Default: 5000 |
| m | number of components (modes). Default: 2 |
The posterior probabilities of each observation to one of two modes. If boolean = TRUE, return a boolean value indicating whether bimodality was found. If assign = TRUE, return a list of length two with the observations (IDs) in each mode.
The posterior probabilities of each observation to one of two modes. If boolean = TRUE, return a boolean value indicating whether bimodality was found. If assign = TRUE, return a list of length two with the observations (IDs) in each mode.
#> Error in infercna(m = useData(), dipcells = dipcells): unused argument (dipcells = dipcells)# Malignant cells only (remove columns corresponding to dipcells) cna = cna[, !colnames(cna) %in% unlist(dipcells)]#> Error in eval(expr, envir, enclos): object 'cna' not found#> Error in splitGenes(cna, by = "chr"): object 'cna' not found#> Error in lapply(X = X, FUN = FUN, ...): object 'cnaByChr' not found#> Error in lapply(X = X, FUN = FUN, ...): object 'cnaByChr' not found#> Error in lapply(X = X, FUN = FUN, ...): object 'cnaByChr' not found#> Error in infercna(m = useData(), dipcells = dipcells): unused argument (dipcells = dipcells)# Malignant cells only (remove columns corresponding to dipcells) cna = cna[, !colnames(cna) %in% unlist(dipcells)]#> Error in eval(expr, envir, enclos): object 'cna' not found#> Error in splitGenes(cna, by = "chr"): object 'cna' not found#> Error in lapply(X = X, FUN = FUN, ...): object 'cnaByChr' not found#> Error in lapply(X = X, FUN = FUN, ...): object 'cnaByChr' not found#> Error in lapply(X = X, FUN = FUN, ...): object 'cnaByChr' not found