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+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1,2)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1,2,3)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+#g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.breaks <- g.hist$breaks # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.breaks[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+alpha.LM
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+#plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
+plot(yeast)
+hist(yeast)
+hist(g.vec)
+g.pois
+g.mean
+alpha.LM
+alpha.ML
+degree(g)
+sort(degree(g))
+sort(degree(g),decreasing=FALSE)
+sort(degree(g),decreasing=F)
+sort(degree(g),decreasing=false)
+sort(degree(g), decreasing = TRUE)
+head(sort(degree(g), decreasing = TRUE))
+stddev(degree(g))
+sd(degree(g))
+tail(sort(degree(g), decreasing = TRUE))
+plot(log(g.breaks.clean), log(g.probs.clean))
+# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
+# Degree Distribution
+# Professor: Dr. McKinney, Spring 2022
+# Noah Schrick - 1492657
+library(igraph)
+library(igraphdata)
+data(yeast)
+g <- yeast
+g.netname <- "Yeast"
+################# Set up Work #################
+g.vec <- degree(g)
+g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
+" Network"))
+legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
+"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
+"#006CD1", "#E66100", "#D35FB7"))
+g.mean <- mean(g.vec)
+g.seq <- 0:max(g.vec) # x-axis
+################# Guessing Alpha #################
+alpha.guess <- 1.5
+lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
+################# Poisson #################
+g.pois <- dpois(g.seq, g.mean, log=F)
+lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
+################# Linear model: Least-Squares Fit #################
+g.breaks <- g.hist$breaks[-c(1)] # remove 0
+g.probs <- g.hist$density[-1] # make lengths match
+# Need to clean up probabilities that are 0
+nz.probs.mask <- g.probs!=0
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
+summary(g.fit)
+alpha.LM <- coef(g.fit)[2]
+lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
+################# Max-Log-Likelihood #################
+n <- length(g.breaks.clean)
+kmin <- g.breaks.clean[1]
+alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
+alpha.ML
+lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
+plot(log(g.breaks.clean), log(g.probs.clean))
+g.breaks.clean <- g.breaks[nz.probs.mask]
+g.probs.clean <- g.probs[nz.probs.mask]
+plot(log(g.breaks.clean), log(g.probs.clean))
+BiocManager::install()
+simDats <- createSimulation2(data.type = "discrete", avg.maf = 0.2, sim.type = "mainEffect",
+pct.train = 0.5, pct.holdout = 0.5, pct.validation = 0,
+main.bias = 0.4, pct.signals = 0.2)
+# npdro::createSimulation2() example for gwas simulation
+library(npdro)
+# npdro::createSimulation2() example for gwas simulation
+if (!require("npdro")) install.packages("npdro")
+library(npdro)
+# npdro::createSimulation2() example for gwas simulation
+install_github("insilico/npdro")
+# npdro::createSimulation2() example for gwas simulation
+if (!require("devtools")) install.packages("devtools")
+library(devtools)
+install_github("insilico/npdro")
+# npdro::createSimulation2() example for gwas simulation
+if (!require("devtools")) install.packages("devtools")
+?createSimulation2
+# npdro::createSimulation2() example for gwas simulation
+if (!require("devtools")) install.packages("devtools")
+library(devtools)
+install_github("insilico/npdro")
+library(npdro)
+## Set Working Directory to file directory - RStudio approach
+setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
+gen.gwas = function(samples, vars, batch_size=1000){
+for (x in 1:ceiling(samples/batch_size)){
+curr_batch <- samples - (batch_size*(x-1))
+batch_gen <- ifelse(curr_batch > batch_size, batch_size, curr_batch)
+data <- createSimulation2(data.type = "discrete", avg.maf = 0.2,
+sim.type = "mainEffect",
+pct.train = 1.0,
+pct.imbalance=round(runif(n = 1, min = 0.1, max = 0.9),2),
+main.bias = 0.4, pct.signals = 0.2,
+num.samples = batch_gen,
+num.variables = vars)
+rownames(data$train) <- as.integer(rownames(data$train))+
+(batch_size*(x-1))
+if (x == 1){
+write.table(data$train, "artif_gwas.csv", row.names = TRUE)
+} else{
+write.table(data$train, "artif_gwas.csv", row.names = TRUE,
+append = TRUE, col.names = FALSE)
+}
+}
+return(data$train)
+}
+?createSimulation2
diff --git a/Presentations/.~lock.Schrick-Noah_CS-6643_Proposal.ppt# b/Presentations/.~lock.Schrick-Noah_CS-6643_Proposal.ppt#
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-,noah,NovaArchSys,06.12.2022 17:33,file:///home/noah/.config/libreoffice/4;
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diff --git a/Presentations/.~lock.Schrick-Noah_CS-6643_Update.ppt# b/Presentations/.~lock.Schrick-Noah_CS-6643_Update.ppt#
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-,noah,NovaArchSys,06.12.2022 17:29,file:///home/noah/.config/libreoffice/4;
\ No newline at end of file
diff --git a/Reports/Schrick-Noah_CS-6643_Final-Report.odt b/Reports/Schrick-Noah_CS-6643_Final-Report.odt
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