sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
70e048cb1b3773befca52d0d1e224eb7e1b4e73b148f9f160a61cb743cca97b6 | R | 1,831 | 47 | # this file takes a single input, the sample_name (such as 'STEPN47_Region_2'). One can simply create a variable
# by running something like: "sample_name <- 'STEPN47_Region_2'", and run this script line by line, skipping the
# first part where the sample_name is read from supplied arguments, or can execute this scri... |
87ecb817e832e53f6c9089418b0adcda93f7edb0b76dd7663e6cd5e54d0969a6 | R | 1,833 | 66 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
### Data preparation ----------------------------------------------------------
d <- read_rds("09.pc_comparison/input_data.rds")
y_test <- d$Test$y
x_test <- read_rds("09.pc_comparison/input_hpcs.rds")
x_test <- x_test$Test
rm(... |
b9e8d453463c971486fb38b0772757bd6302c28651c78900430547bdaeecf057 | R | 1,834 | 52 | # LDA.R
rm(list=ls())
#1.data preparation----
map=read.csv('./01_taxa/feat_genus.csv',header=T)
otu=map[,c(6:238)]/100
rowSums(otu)
rownames(otu)=map$Row.names
library("OTUtable")
#abundance: The minimum threshold for percentage of reads attributed to a taxon in at least one sample
#persistence: The minimum threshol... |
8153619eaad4bbc157729c1155fb4e39ab417073854ab7f0497d45e9f49f03fb | R | 1,835 | 50 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
setwd("/homes/amkusmec/kernels_in_GP/")
library(tidyverse)
library(argparse)
source("src/BGGE2.R")
parser <- ArgumentParser()
parser$add_argument("--seed", "-s", type = "integer")
args <- parser$parse_args()
# The phenotypes have already been scaled and center... |
96eb06c48509c85623524fb0005244ec90dac2b4062f04e963148ebef9887e94 | R | 1,836 | 66 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
### Data preparation ----------------------------------------------------------
d <- read_rds("09.pc_comparison/input_data.rds")
y_test <- d$Test$y
x_test <- read_rds("09.pc_comparison/input_hpcs.rds")
x_test <- x_test$Test
rm(... |
e9ed6599383cf8c5cd5400345dca482f5bb907f14200e909f226edeb0030cc4c | R | 1,845 | 53 |
# printEvalRes ------------------------------------------------------------
#' @name printEvalDE
#' @aliases printEvalDE
#' @title Summary table of power assessment
#' @description This function takes as input a result object from \code{\link{evaluateDE}}
#' and prints out a table to summarize important error-rates-r... |
a525744fded997ea90d4164ef9f40facb70424db0726dda769597d31eff4e42a | R | 1,846 | 51 | compile_ieeg_csv_files <- function(roi, sub, sample_freq, timelock_folder, keyword_include = "*", keyword_exclude = "9999"){
hc_elecs <- list.files(path(here(), "data_mount", "remote", "pacman", "preprocessing", sub, "ieeg", timelock_folder))
hc_elecs <- hc_elecs[grepl(keyword_include, hc_elecs) & !grepl(keywor... |
c3e6c95989232ab651d8320d4ff67451e209fb3035016cbbbfa99c27f58acf35 | R | 1,848 | 66 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
### Data preparation ----------------------------------------------------------
d <- read_rds("09.pc_comparison/input_data.rds")
y_test <- d$Test$y
x_test <- read_rds("09.pc_comparison/input_hpcs.rds")
x_test <- x_test$Test
rm(... |
a74b8556ec36b7a57769a725ac5dfe8f363f8e93aaaf279f00198c79ebcc6e6a | R | 1,851 | 45 |
test_that("Epoch resampling works as expected", {
set.seed(42)
row_num <- 10
col_num <- 100
dummy_data <- matrix(
rnorm(row_num * col_num),
nrow = row_num,
dimnames = list(paste0("Elec", seq_len(row_num)), NULL)
)
expected_times <- seq(0, by = 0.01, length.out = col_num) # 100 Hz
dummy_epoch... |
ba330c7ed531f5475e7a4d0396c0b57c0e3493af18f586a06e307e241036e856 | R | 1,851 | 69 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
library(argparse)
source("src/gPCs_tuner_classes.R")
parser <- ArgumentParser()
parser$add_argument("--model", type = "integer")
parser$add_argument("--fold", type = "integer")
args <- parser$parse_args()
#... |
4121921870db96e0d3fff9593587995b15911eb3cdf75c41be8c5467b4385ecf | R | 1,857 | 47 | # cl <<- initialize_multiprocessing()
#
# ### adapted from https://github.com/statOmics/MSqRobSum/blob/master/vignettes/msqrobsum.Rmd
#
# library(limma)
# library(MSnbase)
#
# # read example dataset @ MSqRob vignette
# data_path = "C:/Users/Frank/Downloads/peptides.txt.gz"
# data_path = gzfile(data_path)
# exprs_col = ... |
af627b12dc4e8757104aeaa24117fa138178776a61d3351b323174314a303357 | R | 1,863 | 53 | merge_theta_and_behavioral_data <- function(roi, sub, distance_df, timepoints, keyword_include = "*", keyword_exclude = "choice"){
shift <- 0
hc_elecs <- list.files(path(here(), "data", "theta_csvs", roi, sub))
hc_elecs <- hc_elecs[grepl(keyword_include, hc_elecs) & !grepl(keyword_exclude, hc_elecs)]
elec... |
9a100d271a3974f7f36bc51b4a1bc53c39d80e3a8b0e0e0e2b21641b4f6b9a4d | R | 1,865 | 53 | # arguments
args <- commandArgs(trailingOnly=T)
tf <- as.character(args[1])
outFolder <- as.character(args[2])
k <- as.numeric(args[3])
featureList <- args[4]
#listFile <- '../encoded/mad-max-myc-2all/list.txt'
#outFolder <- '../input/mad-max-myc-2all'
#k <- 10
# re-assemble outFolder (to avoid problem with tailing ... |
338b1fe13c63322a00a8554cb428a375459650fa5ff224528dbf95a2d2bd1684 | R | 1,875 | 59 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/12a.ReLU_hPC_tuner.R")[10:86]))
# Creates a directory for intermediate caching of history objects
cv_hp_d... |
0c3757492454c58cc79a4f76615054b54df01fa322454e17a355b6aa4cff5967 | R | 1,879 | 42 | install.packages("VennDiagram")
library(VennDiagram) #ÒýÓðü
diffFile="diff.txt" #²îÒì·ÖÎöµÄ½á¹ûÎļþ
moduleFile="module_red.txt" #Ä£¿é»ùÒòµÄÎļþ
setwd("C:\\Users\\wx197\\Desktop\\×ÔѧÍø113»úÆ÷ѧϰ\\205.geoML×ÊÁÏ\\205.geoML×ÊÁÏ\\11.venn") #ÉèÖù¤×÷Ŀ¼
geneList=list()
#¶ÁÈ¡²... |
2765122d726d685dd2489da4cb9a60173af88e80f82afc67643987919d8c7a39 | R | 1,888 | 63 | ##' ONB = orth(mat) is an orthonormal basis for the range of matrix
##' mat. That is, ONB' * ONB = I, the columns of ONB span the same
##' space as the columns of mat, and the number of columns of ONB is
##' the rank of mat.
##' @title Calculate an orthonormal basis
##' @param mat matrix to calculate orthonormal base
... |
92362b0699e3ae1776d52bfc08e22dc44b9bfc783e96c25ae84102b1102f1ae3 | R | 1,889 | 59 | require(keras)
build_model <- function(hp) {
num_blocks <- as.integer(hp["conv1d_num_blocks"])
num_layers <- as.integer(hp["conv1d_num_layers"])
model <- keras_model_sequential()
for (i in 1:num_blocks) {
for (j in 1:num_layers) {
if (i == 1 & j == 1) {
model |> layer_conv_1d(filters = ... |
72ca35df1cd6ae9bd1e9b60af3b1a0f0e03f7f5849a76ae12ea55e205fa04ff3 | R | 1,890 | 58 | require(keras)
build_model <- function(hp) {
num_blocks <- as.integer(hp["conv1d_num_blocks"])
num_layers <- as.integer(hp["conv1d_num_layers"])
model <- keras_model_sequential()
for (i in 1:num_blocks) {
for (j in 1:num_layers) {
if (i == 1 & j == 1) {
model |> layer_conv_1d(filters = ... |
7f84691c860a17367770160ba535239c13e1692e333d7fefc82ecad33ecdcff9 | R | 1,893 | 58 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source("src/int_tuner_classes.R")
### Load the tuned hyperparameters --------------------------------------------
# `epochs` and `batch_size` have to be explicitly converted to R objects for
# the keras fit ... |
0347c7d261a84cb0f83563609cc76de6d110eddd1b3914219a65e05219bc67a8 | R | 1,905 | 74 | library(readr)
library(dplyr)
library(lme4)
library(lmerTest)
library(emmeans)
library(broom)
df_all <- read_csv("../results/csv/mismatch_peaks.csv")
# select just mismatch 1
dm1 <- df_all %>% filter(mismatch == "mismatch_1" )
dm1 <- df_all %>% filter(mismatch == "mismatch_1" & jitter_no != 0)
dvs <- c(
"mmn_am... |
d3137673a771d49c59b094569c55d299211445972171eb7c1b807012a42c8e0a | R | 1,905 | 60 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(kerastuneR)
source(textConnection(readLines("10.interactions/02a.tanh_PC_int_tuner.R")[11:105]))
### Load the tuned hyperparameters
# `epochs` and `batch_size` have t... |
12cc4566e17f81c2573a751d7e2b9d9085f72b79595cad5028be2c2244d0b15d | R | 1,919 | 60 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(kerastuneR)
source(textConnection(readLines("10.interactions/03a.sigmoid_PC_int_tuner.R")[11:105]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have... |
a78f6e6f8a910fe140231e05663fccfb38e5a5c5ed367cabf5ea75fd06876eb1 | R | 1,921 | 61 | # To combine the two controls (control-peri and experimental-pre), set
# KEEP.DATA in VsFreq.r to true, run each condition separately and save
# the saved data:
# vi VsFreq.r
# set KEEP.DATA to TRUE
# vi DoAll.r
# set CC_INACT to 2
# set INACT_TIME to 0
# source("DoAll.r")
# KeepX ->... |
bdb6d90d5ce31c012612370a1ffb7b8616ce73be6104da5a6a0c54faa8abcbac | R | 1,923 | 48 | #' Training KDM Biological Age algorithm using the NHANES III and projecting into NHANES IV dataset.
#'
#' @title kdm_nhanes
#' @description Train KDM algorithm in NHANES III and project into NHANES IV.
#' @param biomarkers A character vector indicating the names of the biomarkers included in the KDM Biological Age alg... |
d814bdef6cbdbba84efa23427e64082b6a1781ee1a5fc774cb15d40702e2f01a | R | 1,923 | 65 | get_r_version <- function() {
return(paste0(version$major, ".", sub("\\..*", "", version$minor)))
}
is_installed <- function(pkg) {
suppressMessages({
require(pkg, quietly = TRUE, warn.conflicts = FALSE, character.only = TRUE)
})
}
read_packages <- function(packages_file = "packages.txt") {
pa... |
9217e79d58e075179d6e9994cb8869578ae7e7ae8a58130be1f7730511e1bda0 | R | 1,925 | 54 | #' Benchmarking: Plotting the output of the function `bench_calcAUC()`
#'
#' This function plots the output of the function `bench_calcAUC()`
#'
#' @param out The output of the function `bench_calcAUC()`
#' @param tile_cols Color vector for the tiles plot layer
#' @param tile_leg_title Title of tile legend
#' @param ct... |
6c817805136a79f8d59b12262de962d242b0f911c8bb7c6b6bd4b8ee2491ea6e | R | 1,932 | 61 | #!/usr/bin/env Rscript
library(Seurat)
library(glue)
library(reticulate)
library(sceasy)
file_path <- toString(commandArgs(TRUE)[1])
ass <- toString(commandArgs(TRUE)[2])
conda_env <- toString(commandArgs(TRUE)[3])
drop_predictions <- toString(commandArgs(TRUE)[4])
use_condaenv(conda_env)
sc <- import("scanpy", conv... |
b16c7ccd8d85f45de0f9666ed558d0edd9a490abb2d2d7674876f4020f943d8b | R | 1,951 | 48 | #' Training Phenotypic Age algorithm using the NHANES III and projecting into NHANES IV dataset.
#'
#' @title phenoage_nhanes
#' @description Train Phenotypic Age algorithm in NHANES III and project into NHANES IV.
#' @param biomarkers A character vector indicating the names of the biomarkers included in the Phenotypic... |
edea67f01d194a79008049704a0084881eb1a6be43ed96522083850ffa4ccdd9 | R | 1,960 | 51 |
rm(list = ls(all.names = TRUE)) # clear everything from memory
cat("\014") # clear terminal (send the control+L character)
library(msdap) # load MS-DAP R package
dataset = import_dataset_metamorpheus(path = "C:/VU/code/R/msdap/docker/temp/exampledata/dataset_Klaassen2018_pmid26931375", protein_qval_threshold = 0.05, ... |
f7c308a9e03eb2425eb149b90d6cd40b01ebb80f58ea5d45ea28424b8df10af6 | R | 1,973 | 47 | install.packages("reshape2")
#install.packages("ggplot2")
#ÒýÓðü
library(reshape2)
library(ggplot2)
setwd("C:\\Users\\wx197\\Desktop\\×ÔѧÍø113»úÆ÷ѧϰ\\205.geoML×ÊÁÏ\\205.geoML×ÊÁÏ\\07.boxplot") #ÉèÖù¤×÷Ŀ¼
#¶¨ÒåÏäÏßͼµÄº¯Êý
bioBoxplot=function(inputFile=null, outFile=null, titleName=null){
#¶Á... |
f6c19e2dae3e991df3aa2770411309ba4ab94b1e6977cd3f54bf34f97fae43e7 | R | 1,975 | 50 | # Usage: Rscript transfer_table.R <trait1_name> <trait2_name> <input_directory>
args <- commandArgs(trailingOnly = TRUE)
if (length(args) != 3) {
stop("Usage: Rscript transfer_table.R <trait1_name> <trait2_name> <input_directory>", call. = FALSE)
}
trait1 <- args[1]
trait2 <- args[2]
input_dir <- args[3]
# --- 1. ... |
76d601da7102ed9cf196f592de57cad9af31cac360af96ddb89594129fc3fdf0 | R | 1,984 | 58 | library(here)
library(ggplot2)
library(rlang)
library(rstatix)
library(nlme)
library(cowplot)
library(ggbeeswarm)
library(ggpubr)
library(ggsignif)
setwd("Desktop/JacobsLab/")
data<-read.csv("SLC Spontaneous Weights - Sheet1.csv", header=TRUE)
names(data)
generate_boxplots <- function(input_data, X, Y, min,max){
d... |
aa4b15b50020ad743057eaa5fe6ef9fc536c28c91d77c77fab78e8dc8b52c48e | R | 1,984 | 53 | #' Benchmarking: Plotting the average AUC of a list of outputs from `bench_calcAUC()`
#'
#' This function plots a list of outputs from the function `bench_calcAUC()`
#'
#' @param out_list The list of outputs from the function `bench_calcAUC()`
#' @param bar_cols Color vector for the bar plot layer
#' @param ct_mapping ... |
6be2e09f8227d12e448f9a13aa93a628307cd76e2cc7e5e68505077a8a3e2c9f | R | 2,003 | 62 | # PrefDirection.r
# Return list of sites with pref direction of units (0 if none)
###########################################################################
# Set basic parameters
# AREA = "PRR"
# MEMORY = F
# STIMULATE = F
# SACONLY = F
# CC_INACT = 0
#########################################################... |
3871edcf0505bf9db916ac2de1c8103eadb3634d8f625960da2def1fe7e88de8 | R | 2,007 | 56 | rsqrd <- function(observe, model){
ss_res <- sum((observe-model)^2)
y_bar <- mean(observe)
ss_tot <- sum((observe-y_bar)^2)
return (1-ss_res/ss_tot)
}
################################
args <- commandArgs(trailingOnly = TRUE)
#prefix <- '../tianyin-method/data/GATA/GATA3_TCATGC20NCG_GATA_10'
#outFile <- '../o... |
a569945751af77f9f8e4c982f9aeeca8e9fba64c13508dbe7f2dca7f0f368b08 | R | 2,015 | 67 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
# dir.create("results/alt_DNNs")
# dir.create("results/alt_DNNs/intermediates")
extract_layer <- function(model, df_in) {
last_layer <- length(model$layers) - 1
tf_in... |
ddd2cff450028a2c1b6d7da64bc7e7d8c37c4ca6083c2843d9a235db01a1d46d | R | 2,018 | 65 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("~/BGGE/R/BGGE2.R")
ref <- read_csv("ref_fixed.csv", show_col_types = FALSE) %>%
mutate(yNA = if_else(Set == "Train", GrainYield, as.numeric(NA)),
yS = scale(yNA, center = TRUE, scale = TRUE) %>% drop())
yc <- attr(ref$yS, "... |
71247874ab03c7c0fded5337bd80b6bea9ea73b4eb59b129e120df0ec476e420 | R | 2,029 | 73 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
# Function for rmse
rmse <- function(y, yhat) sqrt(mean((yhat - y)^2))
# Phenotypic data
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y_train <- ref %>%
filter(Set == "Train") %>%
pull(GrainYi... |
e3ff78841bb0eb44645a8be645e672baea8778db855d1f82b54f54440869122d | R | 2,033 | 69 | #####################################
# Example of meta d calculation for individual subject and
# exemple of trace plots and posterior distribution plots
# using the Function_metad_indiv.R
# AM 2018
#####################################
## Packages ----------------------------------------------------------------
li... |
1c545b5998af201617fe84ccc107960245fcdcf2df2adb10d622e96850aed6a3 | R | 2,035 | 67 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
library(argparse)
source("src/soil_tuner_classes.R")
parser <- ArgumentParser()
parser$add_argument("--model", type = "integer")
parser$add_argument("--fold", type = "integer")
args <- parser$parse_args()
#... |
2c01b07bd79b8830da39079031f52c1b0b97bf82286d785bd15a8ebb47f9e4e2 | R | 2,035 | 65 | #' Generic function for resampling objects
#'
#' This function allows you to resample an object to a different sampling frequency.
#'
#' @rdname resample-Epoch-method
#' @export
setGeneric("resample", function(x, ...) standardGeneric("resample"))
#' Resample an Epoch object to a different frequency
#'
#' This func... |
21e9785b2f1c84e089b4291426d8ce601879afd6fff2f1a2853ef834b1fe6b11 | R | 2,038 | 70 | #' Benchmarking: Calculate structural-aware metrics
#'
#' This function calculates silhouette/cLISI score for each cell type
#'
#' @param Obj A Seurat Object
#' @param cluster The analyzing clusters' name
#' @param Metric The calculated metric, "Silhouette" or "cLISI".
#' @param reduction The dimensionality reduction w... |
17c350ddfffc548774e23ef44f20d489cea73300bcf6c2785f5c5bac6e116810 | R | 2,054 | 62 | #' Performance of the model
#'
#' This function calculates the performance of the model by generating a confusion table between the predicted and real data
#'
#' @param out The predicted cell types
#' @param cluster A cell type annotation
#'
#' @return A list containing a performance dataframe and the confusion table
#... |
04681335b24af71b724a5f688f1102383d7ae05b503c141b67c66b5cb32de5fa | R | 2,064 | 14 | "d1" <-
c(1.92602, 2.42255, 1.08184, 2.46053, 2.1405, 2.42255, 2.80936, 1.52716, 1.14312, 2.01421, 2.04624, 1.45695, 1.95735, 2.46053, 1.29609, 1.73277, 2.04624, 2.1405, 1.26978, 2.49991, 3.13258, 2.17413, 1.95735, 2.20869, 2.01421, 1.81219, 2.01421, 2.34815, 1.29609, 1.36203)
"c1" <-
c(-0.296291, -0.30792, -0.290137, ... |
72cbfd97a81e0d171577c63879dd3c733860c9e6c05db1f5564fb9e52c886c04 | R | 2,077 | 73 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("~/BGGE/R/BGGE2.R")
# The phenotypes have already been scaled and centered
ref <- read_csv("processed/reference_genetic.csv", show_col_types = FALSE) |>
mutate(yS2 = (GrainYield - mean(yNA, na.rm = TRUE))/sd(yNA, na.rm = TRUE))
ug <- u... |
f3dd319c7ea2034977ff90481140c4277d3f15803f1982a0227d2cdcf50c0883 | R | 2,078 | 72 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("~/BGGE/R/BGGE2.R")
# The phenotypes have already been scaled and centered
ref <- read_csv("processed/reference_genetic.csv", show_col_types = FALSE) |>
mutate(yS2 = (GrainYield - mean(yNA, na.rm = TRUE))/sd(yNA, na.rm = TRUE))
ug <- u... |
4117d98c8610d67d729c61822f09d97cd7570d50c8aa348655728136183e2d57 | R | 2,082 | 69 | # ------------- Figure S6 --------------
#----Figure S6A----
library(survival)
library(survminer)
library(patchwork)
load("./figure5/lassco/data.RData")
coefficient <- read.csv("./figure5/lassco/coeffident.csv")
coef_vec <- setNames(coefficient$X1, coefficient$regulor)
calc_df <- function(tpm, meta){
tpm... |
c19dade32a3f87a7510a5c5d5a2112497bb0b9053c08ad7e5757d1992b1827e0 | R | 2,091 | 72 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("~/BGGE/R/BGGE2.R")
# The phenotypes have already been scaled and centered
ref <- read_csv("processed/reference_genetic.csv", show_col_types = FALSE) |>
mutate(yS2 = (GrainYield - mean(yNA, na.rm = TRUE))/sd(yNA, na.rm = TRUE))
ug <- u... |
d24f22715428361cf230850d3797f0348e4b6bddc6a176543db86b6c1ddbec68 | R | 2,100 | 59 | library(data.table)
library(ReX)
library(ggplot2)
path = "...\\results_HCP\\ReX_files"
#PCC
path_pcc <- file.path(path, "rex_AICHA_PCC_80_samples.csv")
data_pcc <- fread(path_pcc)
x <- data_pcc[, 4:ncol(data_pcc)]
sub <- data_pcc[,2]
sess <- data_pcc[,3]
x <- as.matrix(x)
sub <- as.matrix(sub)
sess <- as.matrix(sess)... |
994a93cf7bc664f62e922d1e3a4fcd4960f7ee344f3717586167d21a6eba7b06 | R | 2,109 | 77 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
# Function for rmse
rmse <- function(y, yhat) sqrt(mean((yhat - y)^2))
# Phenotypic data
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y_train <- ... |
5f63cf5f2ee14c3aa7b4eea5462042706f0d6920f7eb885ad772c814fcfd7a1c | R | 2,113 | 74 | # Select2.r
# Pick the right files to run (using cell database)
MEMORY = F
verbose = F
###########################################################################
options(stringsAsFactors = FALSE)
a = b = NULL
if (MONK=="zen" || MONK=="both") {
a = read.table(
paste0("/data/coord/zen/zenunits", ifelse(MEMORY,... |
311e35c804abaf978b9eb78bb87c44d557788321ca280d16afb17ad11448db2a | R | 2,114 | 56 | library(here)
library(ggplot2)
library(cowplot)
library(readr)
here::i_am("src/Figure_5_ASO_Microbiome.R")
### Read in files ---
beta_diversity <- read_rds(here("results/ASO/figures/lumcol_beta_diversity.RDS"))
# beta_diversity <- ggdraw(add_sub(beta_diversity,
# bquote(paste(~italic(R)^2,"=0... |
b170e940a8693cee14baf3fd2ccea08a67fc08fa7f3ab036c0bad4ac96c6d2e9 | R | 2,114 | 89 | #' Helper function to simulate IRT data
#'
#' Function to simulate data that can be used to fit IRT models.
#'
#' @param n.obs Number of observations that should be included in the data set
#' @param n.items Number of items that should be simulated
#' @param discrimination Standard deviation on the log scale
#' @par... |
074259517ed75b32dfe6176da33d867e47878a7e09f1803948ed54566640f914 | R | 2,116 | 64 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/08a.tanh_PC_tuner.R")[7:84]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to be explicitly converted to R objects for
# the keras fit ... |
614f927e9100994e1aaf78bc58cc6b72eb1a895bbd3372acd864d80422c5f1a1 | R | 2,118 | 65 | ##############################
## Utils
##############################
#' Get configuration data from remote URL
#'
#' @return A list of project configurations
get_config_data <- function() {
url <- "https://raw.githubusercontent.com/Jiefei-Wang/EpochData/refs/heads/main/config.json"
repos <- osf_retriev... |
ce9627ae23a08102969c7a6246cbb4dd02ca9daeaca82635afe3c702e5e23db8 | R | 2,118 | 57 | source('/cluster/home/chencheng/Mac_gaba/prepare.R')
library(ArchR)
library(parallel)
library(tidyverse)
library(GenomicRanges)
addArchRThreads(threads = 30)
library(argparse)
parser <- ArgumentParser(description='extarct bedfiles from ArchR project')
parser$add_argument('-G', '--group', help='specify a column in colD... |
d7717938732c199a5d13817c555c0224d984391aaa8ed6e7c14c27d7fc66d13f | R | 2,120 | 62 | # ------------- Figure 7 --------------
#----Figure 7A----
library(oncoPredict)
library(ggplot2)
library(tidyverse)
library(ggpubr)
library(DescTools)
library(testthat)
read_data <- function(path) {
data <- readRDS(path)
cat("Dimensions:", dim(data), "\n")
print(data[1:5, 1:5])
return(data)
}
# re... |
6ef360e3360a7c0510690ec82e073d6ab09178cf9992a7c63cbc5ccfc2ccb9d6 | R | 2,125 | 60 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
### Load the hybrid marker matrix ---------------------------------------------
# From Lopez-Cruz et al. (2023)
X <- read_csv("../../genomes2fields_curated/data/data_g2f/GENO.csv", show_col_types = FALSE)
x_taxa <- X[[1L]]
x_taxa <- str_replace(... |
364bd5da9ef2d6ae71cc7a70e1b3401dcc0156171327308f261bf4c4ca5a05ae | R | 2,127 | 77 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
# Function for rmse
rmse <- function(y, yhat) sqrt(mean((yhat - y)^2))
# Phenotypic data
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y_train <- ... |
f9b5a84cd1ca95a274e78dcd2c01c5244b0cd52d14807745a9d394239b969c63 | R | 2,128 | 64 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
source(textConnection(readLines("09.pc_comparison/06a.sigmoid_PC_tuner.R")[7:84]))
###Load the tuned hyperparameters
# `epochs` and `batch_size` have to be explicitly converted to R objects for
# the keras f... |
1e337cb6c445c46924ef706e0ff48489c32b2d28ab7ff14c19ec72168d6ec23c | R | 2,134 | 67 | library(Seurat)
library(reticulate)
library(anndata)
library(dplyr)
library(tidyverse)
set_meta_pb <- function(data, metadata, ind_name, cov_names){
metadata$index <- metadata[,ind_name]
metadata <- metadata[!duplicated(metadata$index),][,c('index',cov_names)]
rownames(metadata) <- metadata$index... |
e91235d6218bc289cfe1d6984576a5e37e927c379e505275d8d9199dbcdb911c | R | 2,135 | 55 | #if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("limma")
#ÒýÓðü
library(limma)
inputFile="geneMatrix.txt" #±í´ïÊý¾ÝÎļþ
conFile="s1.txt" #¶ÔÕÕ×éµÄÑùÆ·ÐÅÏ¢Îļþ
treatFile="s2.txt" #ʵÑé×éµÄÑùÆ·ÐÅÏ¢Îļþ
geoID="GS... |
9768ff20b39ec2a0451c15f0cdb8bb8102b30fabad62b602691c0ee4673d728c | R | 2,140 | 58 | library(rsfcNet)
library(RcppCNPy)
library(data.table)
path = "...\\results_ABIDEI\\feature_importance"
path_save = "...\\results_ABIDEI\\feature_importance\\"
path_varconet_aal_abide <- file.path(path, "AAL_feature_importance.csv")
data_varconet_aal_abide <- as.matrix(fread(path_varconet_aal_abide))
path_varconet_... |
768824874d78c1bab8166c1ada337e620da1b688a3ac356cad701e71bcebf389 | R | 2,141 | 69 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
library(argparse)
source("src/gPCs_tuner_classes.R")
### Passes the random seed for weight initialization and reproducibility
parser <- ArgumentParser()
parser$add_argument("--seed", type = "integer")
args <-... |
b42015c2456092598dea9a8112cbc203fdad52811e3eb56f3d6c3524edf59bfc | R | 2,147 | 83 | ##' Conjugate gradient optimization
##' @param nlnet The nlnet
##' @param trainIn Training data
##' @param trainOut fitted data
##' @param verbose logical, print messages
##' @return ...
##' @author Henning Redestig, Matthias Scholz
optiAlgCgd <- function(nlnet,
trainIn,
tr... |
28c650f9e9de828bacf58f458f8bcf8aa8edd3193c65440ddb7270bda5ee54d6 | R | 2,150 | 45 |
#' Plot differential detection results as a histogram
#'
#' @param dataset dataset object
#' @param zscore_threshold cutoff used in the plot (for absolute values)
#' @returns list of ggplot objects with 1 plot per contrast. If no differential detection data is available, returns an empty list
#' @export
plot_different... |
2d1c2252139c85c1eb387925a0f739f3ad906b3e67bc4aa73c81ff9722552c4c | R | 2,151 | 76 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
source("06b.tanh_SNPs_build_model.R")
### Load the tuned hyperparameters
hp <- read_rds("tanh_opt_hp.rds")
y <- read_rds("../pc_comparison/input_data.rds")
y_train <- y$Train$y
y_test <- y$Test$y
rm(y); gc()
### Mod... |
605f42f1e1489324db104a7bf7f989075a1d81ab5779f2300160aaa88a29d75d | R | 2,156 | 70 | #' Person parameter distribution
#'
#' Visualizes the distribution of estimated person ability (theta) parameters
#' from a fitted mirt model. Works with both unidimensional and multidimensional
#' models. For multidimensional models, dimensions are shown with separate fill
#' colors.
#'
#' @param model an object of cl... |
e355cf3c44313a9fc8651f306cdfcc677969c5661d6a6d13652e28beb1e29124 | R | 2,157 | 76 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
source("04b.relu_SNPs_build_model.R")
### Load the tuned hyperparameters
hp <- read_rds("dense_opt_hp.rds")
y <- read_rds("../pc_comparison/input_data.rds")
y_train <- y$Train$y
y_test <- y$Test$y
rm(y); gc()
### Mo... |
ef5c891dca079d6d34dfa3e7c56ac4228b1ae0b95e259b07d080599db690caa8 | R | 2,158 | 57 | #install.packages("ggplot2")
#install.packages("ggrepel")
#???ð?
library(dplyr)
library(ggplot2)
library(ggrepel)
logFCfilter=0.585 #logFC????????
adj.P.Val.Filter=0.05 #????????pÖµ????????
diffFile="all.txt" #???л????????????Ľ????ļ?
geneFile="model.genes.txt" #Ä... |
cb0bc7c89ae0da1e4fd242e41d809f684e15f6c3d60f78225f8f93eafb19e87a | R | 2,159 | 21 | # Step 3: spaceranger
## What is spaceranger? {-}
`spaceranger` is a set of analysis pipelines that process Visium Spatial Gene Expression data with brightfield and fluorescence microscope images.
The following links and the flowchart provide useful information about `spaceranger` and how to run it on histology ima... |
d71dedf470ea1f1bb426a6457e09f96d9066c95f78a0f2948509b55df78594e1 | R | 2,159 | 59 | # Preparation
setwd("C:/Users/Masterthesis_Mayla/Raw/activityAndSleep")
# install.packages("jsonlite")
library(jsonlite)
# Change input and output path accordingly
inputPath <- "C:/Users/Masterthesis_Mayla/Raw/activityAndSleep/"
outputPath <- "C:/Users/Masterthesis_Mayla/Step2_cleaning_ActivityandSleep/Result/"
if ... |
d9b0d62823b4652e1815d9dccfa3371ab0b731f738ef03d449fe316c61641035 | R | 2,161 | 61 |
####################
# HYPNOGRAM PLOTTING
####################
# load data
hyp <- read.csv(file="//lexport/iss01.charpier/analyses/stephen.whitmarsh/data/hspike/hypnogram_table.txt", sep=',', header=TRUE, dec='.', na.strings = " ")
hyp$label[hyp$label == "PRESLEEP"] = "AWAKE"
hyp$label[hyp$label == "POSTSLEEP"] = "AW... |
9bff40c4d3f65b1f8b38b05f84e6d6b10972e85411645c40b0674fbc7339bdfd | R | 2,162 | 76 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
source("09b.tanh_hPC_build_model.R")
### Load the tuned hyperparameters
hp <- read_rds("tanh_hPC_opt_hp.rds")
y <- read_rds("../pc_comparison/input_data.rds")
y_train <- y$Train$y
y_test <- y$Test$y
rm(y); gc()
### ... |
c5c07aaa757acabb07a1fa9b1bee4cf4b97146caced9e255bb0277c57426ef50 | R | 2,165 | 60 | # doAll.r
# Batch: Rscript doAll.r out.doAlls/unique.date
# Use _mvXXXms to get the right data, then copy it into the right subdirectory
OVERRIDE <<- T
options(warn=-1)
rm(AREA.OVERRIDE,MONK.OVERRIDE,MERGE.OVERRIDE,ALIGN.OVERRIDE,NEAR.OVERRIDE,
CC.INACT.OVERRIDE,
SORT_BY_SACCADE.OVERRIDE,SAC_TYPE.OVERRIDE) # I... |
4966cc19f9944c79b9e7dfd535f65f980cb93f7fc5d0476e75af974fd739ecc0 | R | 2,168 | 76 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
source("07b.relu_hPC_build_model.R")
### Load the tuned hyperparameters
hp <- read_rds("dense_hPC_opt_hp.rds")
y <- read_rds("../pc_comparison/input_data.rds")
y_train <- y$Train$y
y_test <- y$Test$y
rm(y); gc()
###... |
a6b248594500bef0ec4514db364c56fe96ab6f955a1452fb1c15f77d62c13479 | R | 2,174 | 76 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
source("05b.sigmoid_SNPs_build_model.R")
### Load the tuned hyperparameters
hp <- read_rds("sigmoid_opt_hp.rds")
y <- read_rds("../pc_comparison/input_data.rds")
y_train <- y$Train$y
y_test <- y$Test$y
rm(y); gc()
#... |
cbe0a429917c7ec3d16e5de7921827adad39317da58f967e28482f20e8e68204 | R | 2,184 | 69 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
# Get the hyperparameters for the 4 best models
best_hp <- read_rds("10.interactions/tanh_best_hp.rds")
# Load the cross-validation results
cv_res <- list.files("10.interactions/opt_tanh_cvFit", "*\\.rds",
full.names = TRUE... |
e050ffa1a2912b77a4028791ee4dcdfdb6164da5eb1010247d9dd8c9a7aa95de | R | 2,186 | 76 | ### FOR RUNNING ON BEOCAT
setwd("/homes/amkusmec/interactions")
library(tidyverse)
library(keras)
source("08b.sigmoid_hPC_build_model.R")
### Load the tuned hyperparameters
hp <- read_rds("sigmoid_hPC_opt_hp.rds")
y <- read_rds("../pc_comparison/input_data.rds")
y_train <- y$Train$y
y_test <- y$Test$y
rm(y); gc()
... |
12bc82f7dc5fb379f168734eea65b3860095f574a1d6c56ea77988e8497c30c2 | R | 2,193 | 69 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
# Get the hyperparameters for the 4 best models
best_hp <- read_rds("10.interactions/sigmoid_best_hp.rds")
# Load the cross-validation results
cv_res <- list.files("10.interactions/opt_sigmoid_cvFit", "*\\.rds",
full.names ... |
abfe137337b4157bb21a91dece39362c0f721aa3ccb8b414397d85e497245920 | R | 2,193 | 80 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
### Data preparation ----------------------------------------------------------
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y_train <- ref %>%
filter(Set == "Train") %>%
pull(GrainYield) %>%
... |
6e7bd441db10d2acf7bc27b7d740e091e743e8b70556a5ce5b6450eda9cb4cb2 | R | 2,196 | 69 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
# Get the hyperparameters for the 4 best models
best_hp <- read_rds("10.interactions/tanh_hPC_best_hp.rds")
# Load the cross-validation results
cv_res <- list.files("10.interactions/opt_tanh_hPC_cvFit", "*\\.rds",
full.name... |
88fc04637f1ba9498907f6d6f4351240090e370afb0fb49b22bbc3ea45271759 | R | 2,199 | 69 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
# Get the hyperparameters for the 4 best models
best_hp <- read_rds("10.interactions/dense_hPC_best_hp.rds")
# Load the cross-validation results
cv_res <- list.files("10.interactions/opt_dense_hPC_cvFit", "*\\.rds",
full.na... |
dbfa067a675f37da94925f995bb4a4ab6e072d7fbb154beb8d21afcc32933c04 | R | 2,200 | 62 | #' Plotting itemfit estimates
#'
#' This function takes a fitted mirt-model and visualizes item infit and outfit estimates. The function builds on `mirt::itemfit()`. Currently only supported `fact_stats = "infit"`.
#'
#'
#' @param model an object of class `SingleGroupClass` returned by the function `mirt()`.
#' @p... |
1abdfcbda0865c4efdac1cc9a530576505bf3f830f76cb9a27c9c9b08d425cd3 | R | 2,205 | 69 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
# Get the hyperparameters for the 4 best models
best_hp <- read_rds("10.interactions/sigmoid_hPC_best_hp.rds")
# Load the cross-validation results
cv_res <- list.files("10.interactions/opt_sigmoid_hPC_cvFit", "*\\.rds",
ful... |
43f7df7a2df13aece8764798b3ab0f9ea032e3cdad64634584f481a18f33bfe3 | R | 2,205 | 82 | #' Plotting item information curves
#'
#' This function takes a fitted mirt-model and visualizes items information curves.
#'
#'
#' @param model an object of class `SingleGroupClass` returned by the function `mirt()`.
#' @param items numerical vector indicating which items to plot.
#' @param facet Should all items b... |
fcaab0d20742855be134568aef03c7fb141f3955abfea1b4e1785c106da16fbb | R | 2,206 | 69 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
# Get the hyperparameters for the 4 best models
best_hp <- read_rds("10.interactions/dense_best_hp.rds")
# Load the cross-validation results
cv_res <- list.files("10.interactions/opt_dense_cvFit", "*\\.rds",
full.names = TR... |
b6ea72b4c51ef415c19225e93c24c084676079339ce7de00b737a4c88e8b83bc | R | 2,208 | 80 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
### Data preparation ----------------------------------------------------------
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y_train <- ref %>%
filter(Set == "Train") %>%
pull(GrainYield) %>%
... |
7581cd5244aaf847ac95a728fac1cabd9791e38bc9477477091d388756889be2 | R | 2,213 | 52 | #' Generate bulk tissue profiles from purified cell types
#'
#' Given some reference DNA methylation profiles, this function combines them
#' in a weighted sum using the user-provided proportions to construct bulk
#' tissue profiles in known ratios. There is also the option to specifiy the
#' addition of a proportion o... |
047b818dad21725a4a39d0275a46e42f99a724284235d9278a9354bf03fb78aa | R | 2,224 | 96 | #####################################
# Estimate metacognitive sensibility (meta d') for individual subject
#
# Adaptation in R of matlab function 'fit_meta_d_mcmc.m'
# by Steve Fleming
# for more details see Fleming (2017). HMeta-d: hierarchical Bayesian
# estimation of metacognitive efficiency from confidence rating... |
bb871556259c67f654f6db04c1479aa4d1c559021df7f90af1af1fa517a76896 | R | 2,240 | 74 | #' Benchmarking of the marker detection ability
#'
#' This function calculates the marker detection rate per case
#'
#' @param input_list List combining several outputs of ds_feature_importance
#' @param ref_list List of reference markers
#' @param xlab The text for the x axis name
#' @param ylab The text for the y axi... |
4e727cbcd5e1adfb998544a7e332b01bac48b29f9431816e64e3e5aa2dde74f6 | R | 2,245 | 59 | #' Visualize item person map and scale properties based on Rasch model
#'
#' This function takes a fitted mirt-model and visualizes and plots item-person-map (also known as Kernel-Density Plots or Wright maps) on the left, and add a scale characteristic curve, scale information curve, and a marginal reliability curve o... |
f53bc9ab9a3ecf383d93a53ffa8e0f972e79cea2e2ad4b74a5c3837d3d2c8cac | R | 2,267 | 67 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(kerastuneR)
library(abind)
source(textConnection(readLines("10.interactions/02a.tanh_PC_int_tuner.R")[11:105]))
# Creates a directory for intermediate caching of histo... |
cfcd57908d8a2285d4da2bdc1c0b208110be114394509b9fd773675bf3b81949 | R | 2,283 | 88 | ---
title: "Plot Consistency Matrices"
output: html_notebook
---
Version 1.0, July 2025, SA
This script creates plots of the matrices in Fig. S1F
Input: adjMat.csv and data_cluster_labels.csv
# Packages and functions
```{r}
library(corrplot)
```
# Plot Consistency Matrices
## Read in the data
```{r}
#### re... |
c334ba6b8064ab3e04081ce711359e937670a587c67d3802061a78ecd96c686b | R | 2,287 | 75 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
library(argparse)
source("src/int_tuner_classes.R")
parser <- ArgumentParser()
parser$add_argument("--model", type = "integer")
parser$add_argument("--fold", type = "integer")
args <- parser$parse_args()
##... |
b668616ffadb1d1345fce2f213e61333373b395b59f7657c34eb55a61791b7b3 | R | 2,288 | 69 | ## Differential Expression Analysis with DESeq2
## RNA-seq analysis pipeline for anoxia vs normoxia comparison
rm(list = ls())
# Load required libraries
library(DESeq2)
library(EnhancedVolcano)
# Read raw count data
raw_df <- read.table('heart.rawReadCounts.txt', header = TRUE, row.names = 1)
# Read sample informat... |
21ed8464852ef5404c9bf5598107e488418b5955f50793b4ae50c97dcfbdff71 | R | 2,290 | 87 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("~/BGGE/R/BGGE2.R")
ref <- read_csv("ref_fixed.csv", show_col_types = FALSE) %>%
mutate(yNA = if_else(Set == "Train", GrainYield, as.numeric(NA)),
yS = scale(yNA, center = TRUE, scale = TRUE) %>% drop())
yc <- attr(ref$yS, "... |
7b3f2453b802d6785514c93447bd7ee67c44e1f2476161e8deaae80b0762e5b8 | R | 2,290 | 73 | ---
title: "Plot individual cluster profiles comprising the superclusters"
output: html_notebook
---
Version 1.0, July 2025, SA
This script plots the connectivity profiles for individual clusters that comprise the superclusters. See "Neocortical Cluster Label" column of Table S1 for information on cluster numbers.
... |
000863c59a9f8e6ceca4aedb0e74568ba7fc1929f4f21f4709c7698337b793fd | R | 2,295 | 98 | cli::cli_h2("┗ [Vasc-AoP] Loading utils")
#--------------#
####🔺Pipes ####
#--------------#
"%ni%" <- Negate("%in%")
"%s+%" <- function(lhs, rhs) paste0(lhs, rhs)
"%ne%" <- function(lhs, rhs) {
if (is.null(lhs) || rlang::is_empty(lhs) || (length(lhs) == 1 && lhs == "")) {
return(rhs)
} else {
... |
6dd4edfad43b6ecee0da2e8cf3400081fab00a85861bfa2e50a9ee16902dcb84 | R | 2,304 | 75 | ##' Later
##' @param nlnet The nlnet
##' @param trainIn training data
##' @param trainOut fitted data
##' @return error
##' @author Henning Redestig, Matthias Scholz
errorHierarchic <- function(nlnet, trainIn, trainOut) {
weights <- nlnet@weights$current()
if(nlnet@inverse) {
numElements <- nlnet@net[1] *... |
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