Finalizing Multiple Sequence Alignment
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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",
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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))
|
||||||
|
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: GenBank sequences and a multiple fasta file
|
||||||
|
if (!require("ape")) install.packages("ape")
|
||||||
|
library(ape) # needed for read.GenBank
|
||||||
|
# fetch the mtDNA sequences
|
||||||
|
mtDNA.MultiSeqs.list<-read.GenBank(c("AF011222","AF254446","X90314","AF089820",
|
||||||
|
"AF176766","AF451972", "AY079510",
|
||||||
|
"AF050738","AF176722","AF315498",
|
||||||
|
"AF176731","AF451964"), as.character=TRUE)
|
||||||
|
# look at species names
|
||||||
|
mtDNA.Species<-attr(mtDNA.MultiSeqs.list,"species")
|
||||||
|
# use species as name instead of genbank id
|
||||||
|
names(mtDNA.MultiSeqs.list)<-mtDNA.Species
|
||||||
|
names(mtDNA.MultiSeqs.list)
|
||||||
|
# need to fix some names
|
||||||
|
names(mtDNA.MultiSeqs.list)[1] <- paste("German_Neanderthal",sep="")
|
||||||
|
names(mtDNA.MultiSeqs.list)[2] <- paste("Russian_Neanderthal",sep="")
|
||||||
|
names(mtDNA.MultiSeqs.list)[3] <- paste("Human")
|
||||||
|
names(mtDNA.MultiSeqs.list)[6] <- paste("Puti_Orangutan",sep="")
|
||||||
|
names(mtDNA.MultiSeqs.list)[12] <- paste("Jari_Orangutan",sep="")
|
||||||
|
names(mtDNA.MultiSeqs.list)
|
||||||
|
# look at one of the sequences using $
|
||||||
|
mtDNA.MultiSeqs.list$Human
|
||||||
|
length(mtDNA.MultiSeqs.list$Human)
|
||||||
|
## Convert to Biostrings object for the sequences
|
||||||
|
if (!require("BiocManager")) install.packages("BiocManager")
|
||||||
|
library(BiocManager)
|
||||||
|
if (!require("Biostrings")) BiocManager::install("Biostrings")
|
||||||
|
library(Biostrings)
|
||||||
|
# loop through the list to create vector of strings for Biostrings input
|
||||||
|
Names.vec <- c() # initialize speices names string vector
|
||||||
|
Seqs.vec <- c() # initialize sequence string vector
|
||||||
|
for (mtDNA.name in names(mtDNA.MultiSeqs.list))
|
||||||
|
{
|
||||||
|
Names.vec <- c(Names.vec,mtDNA.name) # concatenate vector
|
||||||
|
Seqs.vec <-c(Seqs.vec,paste(mtDNA.MultiSeqs.list[[mtDNA.name]],collapse=""))
|
||||||
|
}
|
||||||
|
mtDNA.multSeqs.bstr <- DNAStringSet(Seqs.vec) # convert to Biostring
|
||||||
|
# name the Biostring sequences and compute stats
|
||||||
|
names(mtDNA.multSeqs.bstr) <- Names.vec # count nucs and sequence lengths
|
||||||
|
num.nts <- alphabetFrequency(mtDNA.multSeqs.bstr)[,1:4]
|
||||||
|
mtDNA.lengths <- rowSums(num.nts)
|
||||||
|
proportion.nts <- num.nts/mtDNA.lengths
|
||||||
|
num.nts
|
||||||
|
names(mtDA.multSeqs.bstr)
|
||||||
|
names(mtDNA.multSeqs.bstr)
|
||||||
|
mtDNA.multSeqs.bstr
|
||||||
|
mtDNA.multSeqs.bstr
|
||||||
|
mtDNA.multSeqs.bstr
|
||||||
|
mtDNA.multSeqs.bstr --help
|
||||||
|
print(mtDNA.multSeqs.bstr)
|
||||||
|
print(mtDNA.multSeqs.bstr, -n40)
|
||||||
|
print(mtDNA.multSeqs.bstr, -n 40)
|
||||||
|
print(mtDNA.multSeqs.bstr, n=20)
|
||||||
|
class(mtDNA.multSeqs.bstr)
|
||||||
|
print(mtDNA.multSeqs.bstr)
|
||||||
|
?print()
|
||||||
|
?print
|
||||||
|
table(mtDNA.multSeqs.bstr)
|
||||||
|
mtDNA.multSeqs.bstr
|
||||||
|
mtDNA.multSeqs.bstr$width
|
||||||
|
mtDNA.multSeqs.bstr[,1]$width
|
||||||
|
mtDNA.multSeqs.bstr[1,]$width
|
||||||
|
mtDNA.multSeqs.bstr[1]$width
|
||||||
|
mtDNA.multSeqs.bstr[1]
|
||||||
|
mtDNA.multSeqs.bstr[1]$seq
|
||||||
|
mtDNA.multSeqs.bstr[1]$width
|
||||||
|
mtDNA.multSeqs.bstr[1]$names
|
||||||
|
mtDNA.multSeqs.bstr$names
|
||||||
|
# name the Biostring sequences and compute stats
|
||||||
|
names(mtDNA.multSeqs.bstr) <- Names.vec # count nucs and sequence lengths
|
||||||
|
mtDNA.multSeqs.bstr$names
|
||||||
|
mtDNA.multSeqs.bstr$Names
|
||||||
|
mtDNA.lengths
|
||||||
|
table(mtDNA.lengths, Names.vec)
|
||||||
|
cbind(mtDNA.lengths, Names.vec)
|
||||||
|
sort(cbind(mtDNA.lengths, Names.vec))
|
||||||
|
cbind(mtDNA.lengths, Names.vec)
|
||||||
|
cbind(mtDNA.lengths, Names.vec)
|
||||||
|
table(cbind(mtDNA.lengths, Names.vec))
|
||||||
|
rbind(cbind(mtDNA.lengths, Names.vec))
|
||||||
|
sort(rbind(cbind(mtDNA.lengths, Names.vec)))
|
||||||
|
rbind(mtDNA.lengths, Names.vec)
|
||||||
|
cbind(mtDNA.lengths, Names.vec)
|
||||||
|
max(cbind(mtDNA.lengths, Names.vec))
|
||||||
|
max(cbind(mtDNA.lengths, Names.vec))[,1]
|
||||||
|
max(cbind(mtDNA.lengths, Names.vec)[,1])
|
||||||
|
max(cbind(mtDNA.lengths, Names.vec)[1,])
|
||||||
|
max(cbind(mtDNA.lengths, Names.vec)[,1])
|
||||||
|
max(cbind(mtDNA.lengths, Names.vec))
|
||||||
|
cbind(mtDNA.lengths, Names.vec)
|
||||||
|
nlengthnames <- cbind(mtDNA.lengths, Names.vec)
|
||||||
|
max(nlengthnames[,1])
|
||||||
|
nlengthnames <- cbind(mtDNA.lengths, Names.vec)
|
||||||
|
nlengthnames[which.max(nlengthnames[,1])]
|
||||||
|
idx <- which.max(nlengthnames[,1])
|
||||||
|
idx
|
||||||
|
nlengthnames[idx, idx]
|
||||||
|
nlengthnames[idx]
|
||||||
|
nlengthnames
|
||||||
|
nlengthnames[idx,]
|
||||||
|
proportion.nts <- num.nts/mtDNA.lengths
|
||||||
|
proportion.nts
|
||||||
@ -48,7 +48,7 @@ mtDNA.multSeqs.bstr <- DNAStringSet(Seqs.vec) # convert to Biostring
|
|||||||
|
|
||||||
# name the Biostring sequences and compute stats
|
# name the Biostring sequences and compute stats
|
||||||
names(mtDNA.multSeqs.bstr) <- Names.vec # count nucs and sequence lengths
|
names(mtDNA.multSeqs.bstr) <- Names.vec # count nucs and sequence lengths
|
||||||
# num.nts <- alphabetFrequency(mtDNA.multSeqs.bstr)[,1:4]
|
num.nts <- alphabetFrequency(mtDNA.multSeqs.bstr)[,1:4]
|
||||||
mtDNA.lengths <- rowSums(num.nts)
|
mtDNA.lengths <- rowSums(num.nts)
|
||||||
proportion.nts <- num.nts/mtDNA.lengths
|
proportion.nts <- num.nts/mtDNA.lengths
|
||||||
|
|
||||||
@ -56,3 +56,40 @@ proportion.nts <- num.nts/mtDNA.lengths
|
|||||||
nlengthnames <- cbind(mtDNA.lengths, Names.vec)
|
nlengthnames <- cbind(mtDNA.lengths, Names.vec)
|
||||||
idx <- which.max(nlengthnames[,1])
|
idx <- which.max(nlengthnames[,1])
|
||||||
nlengthnames[idx,]
|
nlengthnames[idx,]
|
||||||
|
|
||||||
|
|
||||||
|
#### Part B: Multiple Sequence Alignment
|
||||||
|
if (!require("BiocManager")) install.packages("BiocManager")
|
||||||
|
library(BiocManager)
|
||||||
|
if (!require("msa")) BiocManager::install("msa")
|
||||||
|
library(msa)
|
||||||
|
mtDNA.msa <- msa(mtDNA.multSeqs.bstr,method="ClustalOmega")
|
||||||
|
msaPrettyPrint(mtDNA.msa, file="mtDNA.pdf", output="pdf", showNames="left",
|
||||||
|
showLogo="none", askForOverwrite=FALSE, verbose=TRUE )
|
||||||
|
## loop to make results data frame
|
||||||
|
num_seqs <- length(Names.vec)
|
||||||
|
# initialize data frame
|
||||||
|
align.stats.df <- data.frame(species=rep(NA,num_seqs), seqlen=rep(0,num_seqs),
|
||||||
|
numgaps=rep(0,num_seqs), nt_a=rep(NA,num_seqs),
|
||||||
|
nt_c=rep(NA,num_seqs), nt_g=rep(NA,num_seqs),
|
||||||
|
nt_t=rep(NA,num_seqs))
|
||||||
|
# DNAbin type required for dist.dna and helpful for other calculations
|
||||||
|
mtDNA.msa.DNAbin <- as.DNAbin(mtDNA.msa)
|
||||||
|
for (i in 1:num_seqs){
|
||||||
|
seq_name <- Names.vec[i]
|
||||||
|
seq.vec <- as.character(mtDNA.msa.DNAbin[i,])
|
||||||
|
num.gaps <- sum(seq.vec=="-")
|
||||||
|
prop.nt.i <- proportion.nts[i,]
|
||||||
|
align.stats.df[i,] <- c(seq_name, mtDNA.lengths[i], num.gaps,
|
||||||
|
round(prop.nt.i[1],digits=2), round(prop.nt.i[2],digits=2),
|
||||||
|
round(prop.nt.i[3],digits=2), round(prop.nt.i[4],digits=2))
|
||||||
|
}
|
||||||
|
|
||||||
|
# write to file
|
||||||
|
write.table(align.stats.df,file="alignstats.tab",sep = "\t", row.names=FALSE, quote=FALSE)
|
||||||
|
|
||||||
|
# you can use $ operator to grab a named column from a data.frame
|
||||||
|
# similar to grabbing a named variable from a list
|
||||||
|
align.stats.df$species
|
||||||
|
align.stats.df$nt_a # strings by default
|
||||||
|
as.numeric(align.stats.df$nt_a) # convert to numeric
|
||||||
|
|||||||
Binary file not shown.
13
alignstats.tab
Normal file
13
alignstats.tab
Normal file
@ -0,0 +1,13 @@
|
|||||||
|
species seqlen numgaps nt_a nt_c nt_g nt_t
|
||||||
|
German_Neanderthal 379 81 0.31 0.32 0.13 0.24
|
||||||
|
Russian_Neanderthal 345 76 0.33 0.34 0.11 0.22
|
||||||
|
Human 360 76 0.33 0.34 0.11 0.22
|
||||||
|
Gorilla_beringei_beringei 374 96 0.29 0.36 0.13 0.23
|
||||||
|
Pan_troglodytes_troglodytes 340 105 0.32 0.36 0.1 0.22
|
||||||
|
Puti_Orangutan 354 90 0.31 0.4 0.1 0.19
|
||||||
|
Gorilla_gorilla_gorilla 367 71 0.3 0.35 0.14 0.21
|
||||||
|
Gorilla_beringei_graueri 374 105 0.28 0.35 0.14 0.23
|
||||||
|
Pan_troglodytes_schweinfurthii 339 110 0.34 0.35 0.09 0.22
|
||||||
|
Pan_troglodytes_ellioti 411 111 0.33 0.36 0.1 0.21
|
||||||
|
Pan_troglodytes_verus 339 39 0.34 0.37 0.1 0.2
|
||||||
|
Jari_Orangutan 345 111 0.32 0.39 0.1 0.19
|
||||||
14
mtDNA.aux
Normal file
14
mtDNA.aux
Normal file
@ -0,0 +1,14 @@
|
|||||||
|
\relax
|
||||||
|
\savedseqlength{1}{1}{369}
|
||||||
|
\savedseqlength{1}{2}{374}
|
||||||
|
\savedseqlength{1}{3}{374}
|
||||||
|
\savedseqlength{1}{4}{354}
|
||||||
|
\savedseqlength{1}{5}{345}
|
||||||
|
\savedseqlength{1}{6}{360}
|
||||||
|
\savedseqlength{1}{7}{379}
|
||||||
|
\savedseqlength{1}{8}{345}
|
||||||
|
\savedseqlength{1}{9}{340}
|
||||||
|
\savedseqlength{1}{10}{339}
|
||||||
|
\savedseqlength{1}{11}{411}
|
||||||
|
\savedseqlength{1}{12}{339}
|
||||||
|
\gdef \@abspage@last{2}
|
||||||
308
mtDNA.log
Normal file
308
mtDNA.log
Normal file
@ -0,0 +1,308 @@
|
|||||||
|
This is pdfTeX, Version 3.141592653-2.6-1.40.24 (TeX Live 2022/Arch Linux) (preloaded format=pdflatex 2022.11.8) 21 NOV 2022 16:54
|
||||||
|
entering extended mode
|
||||||
|
restricted \write18 enabled.
|
||||||
|
%&-line parsing enabled.
|
||||||
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|
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(/tmp/Rtmp9tSm8e/seq57d10064df.fasta:
|
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|
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|
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|
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|
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\par
|
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|
l.25 ...hade}{/tmp/Rtmp9tSm8e/seq57d10064df.fasta}
|
||||||
|
|
||||||
|
I suspect you've forgotten a `}', causing me to apply this
|
||||||
|
control sequence to too much text. How can we recover?
|
||||||
|
My plan is to forget the whole thing and hope for the best.
|
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|
|
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|
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|
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|
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|
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|
l.25 ...hade}{/tmp/Rtmp9tSm8e/seq57d10064df.fasta}
|
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|
|
||||||
|
I can't figure out why you would want to use a tab mark
|
||||||
|
here. If you just want an ampersand, the remedy is
|
||||||
|
simple: Just type `I\&' now. But if some right brace
|
||||||
|
up above has ended a previous alignment prematurely,
|
||||||
|
you're probably due for more error messages, and you
|
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|
might try typing `S' now just to see what is salvageable.
|
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|
|
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|
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|
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|
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|
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|
& & @
|
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|
l.25 ...hade}{/tmp/Rtmp9tSm8e/seq57d10064df.fasta}
|
||||||
|
|
||||||
|
I can't figure out why you would want to use a tab mark
|
||||||
|
here. If you just want an ampersand, the remedy is
|
||||||
|
simple: Just type `I\&' now. But if some right brace
|
||||||
|
up above has ended a previous alignment prematurely,
|
||||||
|
you're probably due for more error messages, and you
|
||||||
|
might try typing `S' now just to see what is salvageable.
|
||||||
|
|
||||||
|
! Misplaced alignment tab character &.
|
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|
\msfline ->\par & & &
|
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|
& @
|
||||||
|
l.25 ...hade}{/tmp/Rtmp9tSm8e/seq57d10064df.fasta}
|
||||||
|
|
||||||
|
I can't figure out why you would want to use a tab mark
|
||||||
|
here. If you just want an ampersand, the remedy is
|
||||||
|
simple: Just type `I\&' now. But if some right brace
|
||||||
|
up above has ended a previous alignment prematurely,
|
||||||
|
you're probably due for more error messages, and you
|
||||||
|
might try typing `S' now just to see what is salvageable.
|
||||||
|
|
||||||
|
! Misplaced alignment tab character &.
|
||||||
|
\msfline ->\par & & & &
|
||||||
|
@
|
||||||
|
l.25 ...hade}{/tmp/Rtmp9tSm8e/seq57d10064df.fasta}
|
||||||
|
|
||||||
|
I can't figure out why you would want to use a tab mark
|
||||||
|
here. If you just want an ampersand, the remedy is
|
||||||
|
simple: Just type `I\&' now. But if some right brace
|
||||||
|
up above has ended a previous alignment prematurely,
|
||||||
|
you're probably due for more error messages, and you
|
||||||
|
might try typing `S' now just to see what is salvageable.
|
||||||
|
|
||||||
|
Runaway argument?
|
||||||
|
! Paragraph ended before \check@letter was complete.
|
||||||
|
<to be read again>
|
||||||
|
\par
|
||||||
|
l.25 ...hade}{/tmp/Rtmp9tSm8e/seq57d10064df.fasta}
|
||||||
|
|
||||||
|
I suspect you've forgotten a `}', causing me to apply this
|
||||||
|
control sequence to too much text. How can we recover?
|
||||||
|
My plan is to forget the whole thing and hope for the best.
|
||||||
|
|
||||||
|
Runaway argument?
|
||||||
|
! Paragraph ended before \firstchar@get was complete.
|
||||||
|
<to be read again>
|
||||||
|
\par
|
||||||
|
l.25 ...hade}{/tmp/Rtmp9tSm8e/seq57d10064df.fasta}
|
||||||
|
|
||||||
|
I suspect you've forgotten a `}', causing me to apply this
|
||||||
|
control sequence to too much text. How can we recover?
|
||||||
|
My plan is to forget the whole thing and hope for the best.
|
||||||
|
|
||||||
|
Runaway argument?
|
||||||
|
! Paragraph ended before \firstchar@get was complete.
|
||||||
|
<to be read again>
|
||||||
|
\par
|
||||||
|
l.47 \end{texshade}
|
||||||
|
|
||||||
|
I suspect you've forgotten a `}', causing me to apply this
|
||||||
|
control sequence to too much text. How can we recover?
|
||||||
|
My plan is to forget the whole thing and hope for the best.
|
||||||
|
|
||||||
|
Runaway argument?
|
||||||
|
! Paragraph ended before \res@get was complete.
|
||||||
|
<to be read again>
|
||||||
|
\par
|
||||||
|
l.47 \end{texshade}
|
||||||
|
|
||||||
|
I suspect you've forgotten a `}', causing me to apply this
|
||||||
|
control sequence to too much text. How can we recover?
|
||||||
|
My plan is to forget the whole thing and hope for the best.
|
||||||
|
|
||||||
|
! Misplaced alignment tab character &.
|
||||||
|
\seq@line ->\par &
|
||||||
|
@
|
||||||
|
l.47 \end{texshade}
|
||||||
|
|
||||||
|
I can't figure out why you would want to use a tab mark
|
||||||
|
here. If you just want an ampersand, the remedy is
|
||||||
|
simple: Just type `I\&' now. But if some right brace
|
||||||
|
up above has ended a previous alignment prematurely,
|
||||||
|
you're probably due for more error messages, and you
|
||||||
|
might try typing `S' now just to see what is salvageable.
|
||||||
|
|
||||||
|
. . . . . [1
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||||
|
Here is how much of TeX's memory you used:
|
||||||
|
8179 strings out of 478238
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97095 string characters out of 5850456
|
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|
1334999 words of memory out of 5000000
|
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|
26433 multiletter control sequences out of 15000+600000
|
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|
471599 words of font info for 39 fonts, out of 8000000 for 9000
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1141 hyphenation exceptions out of 8191
|
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|
541i,6n,65p,182b,4214s stack positions out of 5000i,500n,10000p,200000b,80000s
|
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|
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|
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|
0.pfb></usr/share/texmf-dist/fonts/type1/public/amsfonts/cm/cmsy10.pfb></usr/sh
|
||||||
|
are/texmf-dist/fonts/type1/public/amsfonts/cm/cmtt10.pfb>
|
||||||
|
Output written on mtDNA.pdf (2 pages, 86917 bytes).
|
||||||
|
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|
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|
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|
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|
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|
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|
|
||||||
48
mtDNA.tex
Normal file
48
mtDNA.tex
Normal file
@ -0,0 +1,48 @@
|
|||||||
|
\documentclass[10pt]{article}
|
||||||
|
|
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|
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|
|
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|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
|
||||||
|
\pagestyle{empty}
|
||||||
|
|
||||||
|
\begin{document}
|
||||||
|
\begin{texshade}{/tmp/Rtmp9tSm8e/seq57d10064df.fasta}
|
||||||
|
\seqtype{N}
|
||||||
|
\shadingmode{identical}
|
||||||
|
\threshold{50}
|
||||||
|
\showconsensus[ColdHot]{bottom}
|
||||||
|
\shadingcolors{blues}
|
||||||
|
\hidelogoscale
|
||||||
|
\shownames{left}
|
||||||
|
\nameseq{1}{Gorilla gorilla gorilla}
|
||||||
|
\nameseq{2}{Gorilla beringei beringei}
|
||||||
|
\nameseq{3}{Gorilla beringei graueri}
|
||||||
|
\nameseq{4}{Puti Orangutan}
|
||||||
|
\nameseq{5}{Jari Orangutan}
|
||||||
|
\nameseq{6}{Human}
|
||||||
|
\nameseq{7}{German Neanderthal}
|
||||||
|
\nameseq{8}{Russian Neanderthal}
|
||||||
|
\nameseq{9}{Pan troglodytes troglodytes}
|
||||||
|
\nameseq{10}{Pan troglodytes schweinfurthii}
|
||||||
|
\nameseq{11}{Pan troglodytes ellioti}
|
||||||
|
\nameseq{12}{Pan troglodytes verus}
|
||||||
|
\shownumbering{right}
|
||||||
|
\showlegend
|
||||||
|
\end{texshade}
|
||||||
|
\end{document}
|
||||||
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Reference in New Issue
Block a user