513 lines
19 KiB
R
513 lines
19 KiB
R
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
|
|
# 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",
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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=3)
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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)
|
|
# 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,
|
|
" 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))
|