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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,5 +1,5 @@ | ||
| Package: RMINC | ||
| Version: 1.5.2.1 | ||
| Version: 1.5.2.2 | ||
| Date: 2017-05-31 | ||
| Title: Statistical Tools for Medical Imaging NetCDF (MINC) Files | ||
| Authors@R: c(person("Jason", "Lerch", role = "aut", email = "[email protected]"), | ||
|
|
@@ -20,6 +20,7 @@ Imports: | |
| batchtools, | ||
| dplyr (>= 0.7.6), | ||
| tidyr (>= 0.8.1), | ||
| readr , | ||
| lme4 (>= 1.1-13), | ||
| purrr (>= 0.2.5), | ||
| shiny (>= 1.1.0), | ||
|
|
@@ -61,4 +62,4 @@ OS_type: unix | |
| BugReports: https://github.com/Mouse-Imaging-Centre/RMINC/issues | ||
| URL: https://github.com/Mouse-Imaging-Centre/RMINC, | ||
| https://wiki.mouseimaging.ca/display/MICePub/RMINC | ||
| RoxygenNote: 6.0.1 | ||
| RoxygenNote: 6.1.0 | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,194 @@ | ||
| #' Effect Sizes | ||
| #' | ||
| #' Takes the output of a minc modelling function and computes the unbiased | ||
| #' hedges g* and variance of hedges g* | ||
| #' @param buffer The results of a vertex/anat/mincLm run | ||
| #' @param predictors A vector of factor predictor names. By default the effect size | ||
| #' be computed for all treatment-coded factor columns. | ||
| #' @details This code implements the methods from Nakagawa, S., Cuthill, I.C., 2007. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. Camb. Philos. Soc. 82, 591-05. https://doi.org/10.1111/j.1469-185X.2007.00027.x | ||
| #' for computing effect size of group comparisons from a GLM. | ||
| #' | ||
| #' For now, interactions are explicitly excluded from being predictors. To get | ||
| #' effect size for interactions, use the interaction() function to create a new | ||
| #' treatment coded factor to use as a predictor. | ||
| #' @return A matrix with columns of hedgesg-<factorlevel> and hedgesg_var-<factorlevel> for each factor predictor in the GLM | ||
| #' or for each column supplied. | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' getRMINCTestData() | ||
| #' # read the text file describing the dataset | ||
| #' gf <- read.csv("/tmp/rminctestdata/test_data_set.csv") | ||
| #' # run a linear model relating the data in all voxels to Genotype | ||
| #' vs <- mincLm(jacobians_fixed_2 ~ Sex, gf) | ||
| #' effectsize <- mincEffectSize(vs) | ||
| #' } | ||
| #' @export | ||
| vertexEffectSize <- function(buffer, predictors = NULL) | ||
| { | ||
| #Nakagawa, S., Cuthill, I.C., 2007. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. Camb. Philos. Soc. 82, 591-605. https://doi.org/10.1111/j.1469-185X.2007.00027.x | ||
| #Original unbiased corrector from paper replaced with | ||
| #unbiased corrector function J from https://en.wikipedia.org/wiki/Effect_size#Hedges'_g | ||
| #Watch out for exploding gamma function | ||
| J <- function(a) { | ||
| out <- tryCatch({ | ||
| return (gamma(a / 2) / (sqrt(a / 2) * gamma((a - 1) / 2))) | ||
| }, | ||
| error = function(cond) { | ||
| return(1) | ||
| }, | ||
| warning = function(cond) { | ||
| return(1) | ||
| }) | ||
| return(out) | ||
| } | ||
| # g_biased = t ( n1 + n2 ) / sqrt(n1*n2)*sqrt(df) | ||
| # g_unbiased = g_biased * J(n1 + n2 - 2) | ||
| # g_variance_unbiased = (n1 + n2) / (n1*n2) + g_unbiased**2 / (2 * (n1 + n2 - 2)) | ||
| # n1 study group | ||
| # n2 reference group | ||
| # N = n1+n2 | ||
| # NN = n1*n2 | ||
|
|
||
| #Scrub columns not needed | ||
| originalMincAttrs <- mincAttributes(buffer) | ||
| stattype <- originalMincAttrs$`stat-type` | ||
| # Remove coefficients from buffer | ||
| if (!is.null(stattype)) { | ||
| for (nStat in 1:length(stattype)) { | ||
| if (stattype[nStat] == 'beta' || | ||
| stattype[nStat] == 'R-squared' || | ||
| stattype[nStat] == "logLik" || stattype[nStat] == "F") { | ||
| if (!exists('indicesToRemove')) { | ||
| indicesToRemove = nStat | ||
| } | ||
| else { | ||
| indicesToRemove = c(indicesToRemove, nStat) | ||
| } | ||
| } | ||
| } | ||
| if (exists('indicesToRemove')) { | ||
| updatedAttrs <- originalMincAttrs | ||
| updatedAttrs$`stat-type` <- | ||
| updatedAttrs$`stat-type`[-indicesToRemove] | ||
| updatedAttrs$dimnames[[2]] <- | ||
| updatedAttrs$dimnames[[2]][-indicesToRemove] | ||
|
|
||
| buffer <- buffer[, -indicesToRemove] | ||
| buffer <- setMincAttributes(buffer, updatedAttrs) | ||
| } | ||
| } | ||
|
|
||
| #Extract model, contrasts from model call | ||
| model_call <- attr(buffer, "call") | ||
| model_call[[1]] <- quote(model.matrix) | ||
| names(model_call)[names(model_call) == "formula"] <- "" | ||
| mod_mat <- eval(model_call) | ||
| conts <- attr(mod_mat, "contrasts") | ||
| cat_vars <- | ||
| names(conts)[vapply(conts, function(x) | ||
| all(x == "contr.treatment"), logical(1))] | ||
|
|
||
| #Checking for assumptions regarding computing | ||
| if (is.null(conts)) | ||
| stop("No factors in model, cannot compute g* statistics") | ||
|
|
||
| if (is.null(cat_vars)) | ||
| stop("No treatment-coded factors in model, cannot compute g* statistics") | ||
|
|
||
| if (!is.null(predictors) && | ||
| !all(predictors %in% colnames(updatedAttrs$model))) | ||
| stop("Supplied predictors not found in model") | ||
|
|
||
| if (!is.null(predictors) && | ||
| !all(grepl(paste(cat_vars, collapse = "|"), predictors))) | ||
| stop("Supplied predictors are not treatment contrasts") | ||
|
|
||
| if (!is.null(predictors) && any(grepl(":", predictors))) | ||
| stop( | ||
| "Interactions in predictors are not currently supported, | ||
| generate treatment contrasts using interaction()" | ||
| ) | ||
|
|
||
| #If predictors aren't provided, ennumerate factors from the model | ||
| if (is.null(predictors)) { | ||
| predictors <- | ||
| grep( | ||
| ":", | ||
| grep( | ||
| paste(cat_vars, collapse = "|"), | ||
| updatedAttrs$dimnames[[2]], | ||
| value = TRUE | ||
| ), | ||
| invert = TRUE, | ||
| value = TRUE | ||
| ) | ||
| predictors <- gsub("tvalue-", "", predictors, fixed = TRUE) | ||
| } | ||
|
|
||
|
|
||
| #Compute reference group size, find number of total subjects, subtract all | ||
| #instaces of the treatment-coded factor, remaining number is reference group | ||
| referencegroupsize <- | ||
| matrix(1, nrow = 1, ncol = length(predictors)) | ||
| colnames(referencegroupsize) <- predictors | ||
|
|
||
| for (var in cat_vars) { | ||
| referencegroupsize[, grep(var, predictors, value = TRUE)] <- | ||
| NROW(updatedAttrs$model[, grep(":", | ||
| grep(var, colnames(updatedAttrs$model), value = TRUE), | ||
| invert = TRUE, | ||
| value = TRUE)]) - | ||
| sum(updatedAttrs$model[, grep(":", | ||
| grep(var, colnames(updatedAttrs$model), value = TRUE), | ||
| invert = TRUE, | ||
| value = TRUE)]) | ||
| } | ||
|
|
||
| n.cols <- length(predictors) | ||
| n.row <- 0 | ||
| if (is.matrix(buffer)) { | ||
| n.row <- nrow(buffer) | ||
| } | ||
| else { | ||
| n.row <- length(buffer) | ||
| } | ||
| output <- matrix(1, nrow = n.row, ncol = 2 * n.cols) | ||
| colnames(output) <- | ||
| c(paste0("hedgesg-", predictors), | ||
| paste0("hedgesg_var-", predictors)) | ||
| for (column in predictors) { | ||
| n1 <- sum(updatedAttrs$model[, column]) | ||
| n2 <- referencegroupsize[, column] | ||
| N <- n1 + n2 | ||
| NN <- n1 * n2 | ||
| df <- updatedAttrs$df[[1]][2] | ||
| output[, paste0("hedgesg-", column)] <- | ||
| buffer[, paste0("tvalue-", column)] * N / (sqrt(NN) * sqrt(df)) * J(N - | ||
| 2) | ||
| output[, paste0("hedgesg_var-", column)] <- | ||
| (N) / (NN) + output[, paste0("hedgesg-", column)] ** 2 / (2 * (N - 2)) | ||
| } | ||
| rownames(output) <- rownames(buffer) | ||
| attr(output, "likeVolume") <- attr(buffer, "likeVolume") | ||
|
|
||
| # run the garbage collector... | ||
| gcout <- gc() | ||
|
|
||
| return(output) | ||
| } | ||
|
|
||
| #' @describeIn vertexEffectSize mincEffectSize | ||
| #' @export | ||
| mincEffectSize <- function(buffer, predictors = NULL) { | ||
| eff_call <- match.call() | ||
| eff_call[[1]] <- quote(vertexEffectSize) | ||
| eval(eff_call) | ||
| } | ||
|
|
||
| #' @describeIn vertexEffectSize anatEffectSize | ||
| #' @export | ||
| anatEffectSize <- function(buffer, predictors = NULL) { | ||
| eff_call <- match.call() | ||
| eff_call[[1]] <- quote(vertexEffectSize) | ||
| eval(eff_call) | ||
| } |
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