Part B and Part C
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1,2)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1,2,3)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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#g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.breaks <- g.hist$breaks # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.breaks[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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alpha.LM
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
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"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
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"#006CD1", "#E66100", "#D35FB7"))
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g.mean <- mean(g.vec)
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g.seq <- 0:max(g.vec) # x-axis
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################# Guessing Alpha #################
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alpha.guess <- 1.5
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lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
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################# Poisson #################
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g.pois <- dpois(g.seq, g.mean, log=F)
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lines(g.seq, g.pois, col="#006CD1", lty=2)
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################# Linear model: Least-Squares Fit #################
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g.breaks <- g.hist$breaks[-c(1)] # remove 0
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g.probs <- g.hist$density[-1] # make lengths match
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# Need to clean up probabilities that are 0
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nz.probs.mask <- g.probs!=0
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g.breaks.clean <- g.breaks[nz.probs.mask]
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g.probs.clean <- g.probs[nz.probs.mask]
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#plot(log(g.breaks.clean), log(g.probs.clean))
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g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
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summary(g.fit)
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alpha.LM <- coef(g.fit)[2]
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lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
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################# Max-Log-Likelihood #################
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n <- length(g.breaks.clean)
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kmin <- g.breaks.clean[1]
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alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
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alpha.ML
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lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
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# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
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# Degree Distribution
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# Professor: Dr. McKinney, Spring 2022
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# Noah Schrick - 1492657
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library(igraph)
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library(igraphdata)
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data(yeast)
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g <- yeast
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g.netname <- "Yeast"
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################# Set up Work #################
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g.vec <- degree(g)
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g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
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" Network"))
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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$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.probs[nz.probs.mask]
|
||||
plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
|
||||
plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.probs[nz.probs.mask]
|
||||
plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
## Set Working Directory to file directory - RStudio approach
|
||||
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
|
||||
#### Part A: Preparing Data
|
||||
load("sense.filtered.cpm.Rdata")
|
||||
# load phenotype (mdd/hc) data
|
||||
subject.attrs <- read.csv("Demographic_symptom.csv",
|
||||
stringsAsFactors = FALSE)
|
||||
if (!require("dplyr")) install.packages("dplyr")
|
||||
library(dplyr)
|
||||
# grab intersecting X (subject ids) and Diag (Diagnosis) from columns
|
||||
phenos.df <- subject.attrs %>%
|
||||
filter(X %in% colnames(sense.filtered.cpm)) %>%
|
||||
dplyr::select(X, Diag)
|
||||
mddPheno <- as.factor(phenos.df$Diag)
|
||||
# Normalized and transform
|
||||
library(preprocessCore)
|
||||
mddExprData_quantile <- normalize.quantiles(sense.filtered.cpm)
|
||||
mddExprData_quantileLog2 <- log2(mddExprData_quantile)
|
||||
# attach phenotype names and gene names to data
|
||||
colnames(mddExprData_quantileLog2) <- mddPheno
|
||||
rownames(mddExprData_quantileLog2) <- rownames(sense.filtered.cpm)
|
||||
rownames(sense.filtered.cpm)
|
||||
len(rownames(sense.filtered.cpm))
|
||||
length(rownames(sense.filtered.cpm))
|
||||
length(rownames(sense.filtered.cpm))
|
||||
@ -1 +1 @@
|
||||
,noah,NovaArchSys,29.09.2022 14:42,file:///home/noah/.config/libreoffice/4;
|
||||
,noah,NovaArchSys,30.09.2022 11:57,file:///home/noah/.config/libreoffice/4;
|
||||
@ -21,6 +21,7 @@ phenos.df <- subject.attrs %>%
|
||||
mddPheno <- as.factor(phenos.df$Diag)
|
||||
|
||||
# Normalized and transform
|
||||
if (!require("preprocessCore")) install.packages("preprocessCore")
|
||||
library(preprocessCore)
|
||||
mddExprData_quantile <- normalize.quantiles(sense.filtered.cpm)
|
||||
mddExprData_quantileLog2 <- log2(mddExprData_quantile)
|
||||
@ -29,4 +30,54 @@ colnames(mddExprData_quantileLog2) <- mddPheno
|
||||
rownames(mddExprData_quantileLog2) <- rownames(sense.filtered.cpm)
|
||||
|
||||
length(rownames(sense.filtered.cpm))
|
||||
#### Part B: Filter noise genes
|
||||
|
||||
|
||||
#### Part B: Filter noise genes
|
||||
# coefficient of variation filter sd(x)/abs(mean(x))
|
||||
CoV_values <- apply(mddExprData_quantileLog2,1,
|
||||
function(x) {sd(x)/abs(mean(x))})
|
||||
# smaller threshold, the higher the experimental effect relative to the
|
||||
# measurement precision
|
||||
sum(CoV_values<.045)
|
||||
# there is one gene that has 0 variation -- remove
|
||||
sd_values <- apply(mddExprData_quantileLog2,1, function(x) {sd(x)})
|
||||
rownames(mddExprData_quantileLog2)[sd_values==0]
|
||||
# filter the data matrix
|
||||
GxS.covfilter <- mddExprData_quantileLog2[CoV_values<.045 & sd_values>0,]
|
||||
dim(GxS.covfilter)
|
||||
|
||||
|
||||
#### Part C: Differential Expression with t-tests
|
||||
# convert phenotype
|
||||
pheno.factor <- as.factor(colnames(GxS.covfilter))
|
||||
pheno.factor
|
||||
str(pheno.factor)
|
||||
levels(pheno.factor)
|
||||
|
||||
## Run t-tests
|
||||
myrow <- 2 # first pick a gene row index to test
|
||||
mygene<-rownames(GxS.covfilter)[myrow]
|
||||
mygene
|
||||
|
||||
# a. traditional R interface
|
||||
mdd <- GxS.covfilter[myrow,pheno.factor=="MDD"]
|
||||
hc <- GxS.covfilter[myrow,pheno.factor=="HC"]
|
||||
t.result <- t.test(mdd,hc)
|
||||
t.result
|
||||
|
||||
# b. formula interface ~ saves a step
|
||||
t.result <- t.test(GxS.covfilter[myrow,] ~ pheno.factor)
|
||||
t.result
|
||||
p <- t.result$p.value
|
||||
t.result$statistic
|
||||
|
||||
## Plot the Data
|
||||
if (!require("ggplot2")) install.packages("ggplot2")
|
||||
library(ggplot2)
|
||||
# create data frame for gene
|
||||
mygene.data.df <- data.frame(gene=GxS.covfilter[myrow,],phenotype=pheno.factor)
|
||||
# boxplot
|
||||
p <- ggplot(mygene.data.df, aes(x=phenotype, y=gene, fill=phenotype)) +
|
||||
stat_boxplot(geom ='errorbar') + geom_boxplot()
|
||||
p <- p + xlab("MDD versus HC") + ylab(mygene)
|
||||
p
|
||||
|
||||
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Reference in New Issue
Block a user