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/R/fit_Tweedieverse.R
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| a | b/R/fit_Tweedieverse.R | ||
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| 1 | fit.Tweedieverse <- function(features, |
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| 2 | metadata, |
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| 3 | base_model = 'CPLM', |
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| 4 | link = "log", |
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| 5 | formula = NULL, |
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| 6 | random_effects_formula = NULL, |
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| 7 | cutoff_ZSCP = 0.3, |
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| 8 | criteria_ZACP = 'BIC', |
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| 9 | adjust_offset = TRUE, |
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| 10 | correction = 'BH', |
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| 11 | cores = 4, |
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| 12 | optimizer = 'nlminb', |
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| 13 | na.action = na.exclude) { |
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| 14 | |||
| 15 | ################################################################ |
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| 16 | # Set the formula default to all fixed effects if not provided # |
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| 17 | ################################################################ |
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| 18 | |||
| 19 | if ("offset" %in% colnames(metadata)) { |
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| 20 | all_available_metadata <- setdiff(colnames(metadata), "offset") |
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| 21 | if (is.null(formula)) |
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| 22 | formula <- |
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| 23 | as.formula(paste("expr ~ ", paste(all_available_metadata, collapse = "+"))) |
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| 24 | if (adjust_offset) |
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| 25 | formula <- update(formula, . ~ . - offset(log(offset))) |
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| 26 | } else{ |
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| 27 | if (is.null(formula)) |
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| 28 | formula <- |
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| 29 | as.formula(paste("expr ~ ", paste(colnames(metadata), collapse = "+"))) |
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| 30 | } |
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| 31 | |||
| 32 | |||
| 33 | ############################################################## |
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| 34 | # Call per-feature base models and return results for output # |
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| 35 | ############################################################## |
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| 36 | |||
| 37 | if (base_model == 'CPLM') { |
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| 38 | fit <- fit.CPLM( |
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| 39 | features = features, |
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| 40 | metadata = metadata, |
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| 41 | link = link, |
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| 42 | formula = formula, |
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| 43 | random_effects_formula = random_effects_formula, |
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| 44 | correction = correction, |
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| 45 | cores = cores, |
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| 46 | optimizer = optimizer, |
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| 47 | na.action = na.action |
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| 48 | ) |
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| 49 | |||
| 50 | } |
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| 51 | |||
| 52 | if (base_model == 'ZICP') { |
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| 53 | fit <- fit.ZICP( |
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| 54 | features = features, |
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| 55 | metadata = metadata, |
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| 56 | link = link, |
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| 57 | formula = formula, |
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| 58 | random_effects_formula = random_effects_formula, |
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| 59 | correction = correction, |
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| 60 | cores = cores, |
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| 61 | optimizer = optimizer, |
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| 62 | na.action = na.action |
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| 63 | ) |
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| 64 | } |
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| 65 | |||
| 66 | |||
| 67 | if (base_model == 'ZSCP') { |
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| 68 | fit <- fit.ZSCP( |
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| 69 | features = features, |
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| 70 | metadata = metadata, |
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| 71 | link = link, |
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| 72 | formula = formula, |
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| 73 | random_effects_formula = random_effects_formula, |
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| 74 | cutoff_ZSCP = cutoff_ZSCP, |
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| 75 | correction = correction, |
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| 76 | cores = cores, |
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| 77 | optimizer = optimizer, |
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| 78 | na.action = na.action |
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| 79 | ) |
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| 80 | } |
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| 81 | |||
| 82 | if (base_model == 'ZACP') { |
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| 83 | fit <- fit.ZACP( |
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| 84 | features = features, |
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| 85 | metadata = metadata, |
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| 86 | link = link, |
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| 87 | formula = formula, |
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| 88 | random_effects_formula = random_effects_formula, |
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| 89 | criteria_ZACP = criteria_ZACP, |
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| 90 | correction = correction, |
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| 91 | cores = cores, |
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| 92 | optimizer = optimizer, |
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| 93 | na.action = na.action |
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| 94 | ) |
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| 95 | } |
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| 96 | return(fit) |
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| 97 | } |