Creating function for Traceback matrix walkback, and adding test input 3
This commit is contained in:
parent
5124a68d98
commit
e23e98ed15
922
.Rhistory
922
.Rhistory
@ -1,444 +1,3 @@
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.breaks[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1,2)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.breaks[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1,2,3)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.breaks[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.breaks[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
#g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
||||
g.breaks <- g.hist$breaks # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.breaks[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.probs[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
|
||||
alpha.LM
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.probs[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.probs[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.probs[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
# Need to clean up probabilities that are 0
|
||||
nz.probs.mask <- g.probs!=0
|
||||
g.breaks.clean <- g.breaks[nz.probs.mask]
|
||||
g.probs.clean <- g.probs[nz.probs.mask]
|
||||
#plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
|
||||
summary(g.fit)
|
||||
alpha.LM <- coef(g.fit)[2]
|
||||
lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
|
||||
################# Max-Log-Likelihood #################
|
||||
n <- length(g.breaks.clean)
|
||||
kmin <- g.breaks.clean[1]
|
||||
alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
|
||||
alpha.ML
|
||||
lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
|
||||
plot(yeast)
|
||||
hist(yeast)
|
||||
hist(g.vec)
|
||||
g.pois
|
||||
g.mean
|
||||
alpha.LM
|
||||
alpha.ML
|
||||
degree(g)
|
||||
sort(degree(g))
|
||||
sort(degree(g),decreasing=FALSE)
|
||||
sort(degree(g),decreasing=F)
|
||||
sort(degree(g),decreasing=false)
|
||||
sort(degree(g), decreasing = TRUE)
|
||||
head(sort(degree(g), decreasing = TRUE))
|
||||
stddev(degree(g))
|
||||
sd(degree(g))
|
||||
tail(sort(degree(g), decreasing = TRUE))
|
||||
plot(log(g.breaks.clean), log(g.probs.clean))
|
||||
# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
|
||||
# Degree Distribution
|
||||
# Professor: Dr. McKinney, Spring 2022
|
||||
# Noah Schrick - 1492657
|
||||
library(igraph)
|
||||
library(igraphdata)
|
||||
data(yeast)
|
||||
g <- yeast
|
||||
g.netname <- "Yeast"
|
||||
################# Set up Work #################
|
||||
g.vec <- degree(g)
|
||||
g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
|
||||
" Network"))
|
||||
legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
|
||||
"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
|
||||
"#006CD1", "#E66100", "#D35FB7"))
|
||||
g.mean <- mean(g.vec)
|
||||
g.seq <- 0:max(g.vec) # x-axis
|
||||
################# Guessing Alpha #################
|
||||
alpha.guess <- 1.5
|
||||
lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
|
||||
################# Poisson #################
|
||||
g.pois <- dpois(g.seq, g.mean, log=F)
|
||||
lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
|
||||
################# Linear model: Least-Squares Fit #################
|
||||
g.breaks <- g.hist$breaks[-c(1)] # remove 0
|
||||
g.probs <- g.hist$density[-1] # make lengths match
|
||||
@ -489,24 +48,465 @@ S[i,j]<- mismatch_score
|
||||
}
|
||||
}
|
||||
}
|
||||
len(S)
|
||||
size(S)
|
||||
nrows(S)
|
||||
nrow(S)
|
||||
col(S)
|
||||
S
|
||||
S[A][T]
|
||||
S[A,T]
|
||||
S
|
||||
S[A]
|
||||
S.A
|
||||
S.at(A)
|
||||
S[1.1]
|
||||
#### Part B: Alignment Score Matrix (F) and Traceback Matrix (T)
|
||||
x <- unlist(strsplit(x_str, ""))
|
||||
y <- unlist(strsplit(y_str, ""))
|
||||
x.len <- length(x)
|
||||
y.len <- length(y)
|
||||
Fmat<-matrix(0,nrow=x.len+1,ncol=y.len+1)
|
||||
Tmat<-Fmat # 0's to start
|
||||
rownames(Fmat)<-c("-",x); colnames(Fmat)<-c("-",y)
|
||||
rownames(Tmat)<-c("-",x); colnames(Tmat)<-c("-",y)
|
||||
# create first row and column
|
||||
Fmat[,1]<- seq(from=0,len=x.len+1,by=-abs(gap_penalty))
|
||||
Fmat[1,]<- seq(from=0,len=y.len+1,by=-abs(gap_penalty))
|
||||
Tmat[,1]<- rep(2,x.len+1) # 2 means align with a gap in the upper seq
|
||||
Tmat[1,]<- rep(3,y.len+1) # 3 means align with a gap in the side seq
|
||||
x
|
||||
T
|
||||
Tmat
|
||||
Fmat
|
||||
#### Part C: Building Fmat and Tmat
|
||||
my.numbers <- c(7,9,-4)
|
||||
max(my.numbers)
|
||||
which.max(my.numbers)
|
||||
#### Part C: Building Fmat and Tmat
|
||||
my.numbers <- c(9,9,-4)
|
||||
which.max(my.numbers)
|
||||
S[1,1]
|
||||
S["A", "T"]
|
||||
dna.letters("A")
|
||||
dna.letters
|
||||
?index()
|
||||
match("A", dna.letters)
|
||||
match("T", dna.letters)
|
||||
S[1,4]
|
||||
S
|
||||
S[1,2]
|
||||
rowname(Fmat[i])
|
||||
(Fmat[1])
|
||||
(Fmat[2])
|
||||
(Fmat[2,])
|
||||
rownames(Fmat)
|
||||
rownames(Fmat[1])
|
||||
rownames(Fmat[2])
|
||||
rownames(Fmat[2,1])
|
||||
rownames(Fmat)[1]
|
||||
rownames(Fmat)[2]
|
||||
S[rownames(Fmat)[2], colnames(Fmat)[2])
|
||||
S[rownames(Fmat)[2], colnames(Fmat)[2]]
|
||||
#### Part C: Building Fmat and Tmat
|
||||
for (i in 2:nrow(Fmat)){
|
||||
for (j in 2:ncol(Fmat)){ # use F recursive rules
|
||||
test_three_cases <- c(Fmat[i-1, j-1] + S[rownames(Fmat)[i], colnames(Fmat)[j]], # 1 mis/match
|
||||
# Fmat[i-1, j] + gap_penalty, # 2 up-gap
|
||||
Fmat[i, j-1] + gap_penalty) # 3 left-gap
|
||||
Fmat[i,j]=max(test_three_cases)
|
||||
Tmat[i,j]=which.max(test_three_cases)
|
||||
}
|
||||
}
|
||||
final_score <- Fmat[nrow(Fmat),ncol(Fmat)]
|
||||
Fmat
|
||||
final_score
|
||||
#### Part C: Building Fmat and Tmat
|
||||
for (i in 2:nrow(Fmat)){
|
||||
for (j in 2:ncol(Fmat)){ # use F recursive rules
|
||||
test_three_cases <- c(Fmat[i-1, j-1] + S[rownames(Fmat)[i], colnames(Fmat)[j]], # 1 mis/match
|
||||
Fmat[i-1, j] + gap_penalty, # 2 up-gap
|
||||
Fmat[i, j-1] + gap_penalty) # 3 left-gap
|
||||
Fmat[i,j]=max(test_three_cases)
|
||||
Tmat[i,j]=which.max(test_three_cases)
|
||||
}
|
||||
}
|
||||
final_score <- Fmat[nrow(Fmat),ncol(Fmat)]
|
||||
Fmat
|
||||
gap_penalty
|
||||
Fmat[1,5]
|
||||
Fmat[1,6]
|
||||
Tmat
|
||||
final_score
|
||||
## Aligning from Tmat
|
||||
m <- nrow(Fmat)
|
||||
n <- ncol(Fmat)
|
||||
top_seq <- list()
|
||||
side_seq <- list()
|
||||
while (m>0 && n>0){
|
||||
if (Tmat[m,n] == 3){
|
||||
top_seq.append(rownames(Tmat)[m])
|
||||
side_seq.append("-")
|
||||
m--
|
||||
} else if (Tmat[m,n] == 2){
|
||||
while (m>0 && n>0){
|
||||
if (Tmat[m,n] == 3){
|
||||
top_seq.append(rownames(Tmat)[m])
|
||||
side_seq.append("-")
|
||||
m--
|
||||
}
|
||||
else if (Tmat[m,n] == 2){
|
||||
while (m>0 && n>0){
|
||||
if (Tmat[m,n] == 3){
|
||||
top_seq.append(rownames(Tmat)[m])
|
||||
side_seq.append("-")
|
||||
m--
|
||||
}
|
||||
while (m>0 && n>0){
|
||||
if (Tmat[m,n] == 3){
|
||||
top_seq.append(rownames(Tmat)[m])
|
||||
side_seq.append("-")
|
||||
m--
|
||||
} else if (Tmat[m,n] == 2){
|
||||
n <- n-1
|
||||
while (m>0 && n>0){
|
||||
if (Tmat[m,n] == 3){
|
||||
top_seq.append(rownames(Tmat)[m])
|
||||
side_seq.append("-")
|
||||
m <- m-1
|
||||
} else if (Tmat[m,n] == 2){
|
||||
top_seq.append("-")
|
||||
side_seq.append(colnames(Tmat)[n])
|
||||
n <- n-1
|
||||
} else{
|
||||
top_seq.append(rownames(Tmat)[m])
|
||||
side_seq.append(colnames(Tmat)[n])
|
||||
m <- m-1
|
||||
n <- n-1
|
||||
}
|
||||
}
|
||||
while (m>0 && n>0){
|
||||
if (Tmat[m,n] == 3){
|
||||
top_seq <- append(top_seq, rownames(Tmat)[m])
|
||||
side_seq <- append(side_seq, "-")
|
||||
m <- m-1
|
||||
} else if (Tmat[m,n] == 2){
|
||||
top_seq <- append(top_seq, "-")
|
||||
side_seq <- append(side_seq, colnames(Tmat)[n])
|
||||
n <- n-1
|
||||
} else{
|
||||
top_seq <- append(top_seq, rownames(Tmat)[m])
|
||||
side_seq <- append(side_seq, colnames(Tmat)[n])
|
||||
m <- m-1
|
||||
n <- n-1
|
||||
}
|
||||
}
|
||||
top_seq
|
||||
side_seq
|
||||
top_seq[1]
|
||||
paste(top_seq, collapse=',')
|
||||
paste(top_seq, collapse=',')
|
||||
paste(side_seq, collapse=',')
|
||||
paste(rev(top_seq), collapse=',')
|
||||
paste(rev(side_seq), collapse=',')
|
||||
## Aligning from Tmat
|
||||
m <- nrow(Fmat)
|
||||
n <- ncol(Fmat)
|
||||
m
|
||||
Tmat[5,5]
|
||||
rownames(Tmat)[5]
|
||||
colnames(Tmat)[5]
|
||||
n
|
||||
rownames(Tmat)[5,7]
|
||||
rownames(Tmat)[7]
|
||||
colnames(Tmat)[7]
|
||||
paste(rev(top_seq), collapse=',')
|
||||
paste(rev(side_seq), collapse=',')
|
||||
side_seq <- list()
|
||||
top_seq <- list()
|
||||
top_seq <- append(top_seq, rownames(Tmat)[m])
|
||||
side_seq <- append(side_seq, colnames(Tmat)[n])
|
||||
paste(rev(top_seq), collapse=',')
|
||||
paste(rev(side_seq), collapse=',')
|
||||
m <- m-1
|
||||
n <- n-1
|
||||
top_seq <- append(top_seq, rownames(Tmat)[m])
|
||||
side_seq <- append(side_seq, colnames(Tmat)[n])
|
||||
m <- m-1
|
||||
n <- n-1
|
||||
paste(rev(top_seq), collapse=',')
|
||||
paste(rev(side_seq), collapse=',')
|
||||
top_seq <- append(top_seq, rownames(Tmat)[m])
|
||||
side_seq <- append(side_seq, colnames(Tmat)[n])
|
||||
m <- m-1
|
||||
n <- n-1
|
||||
paste(rev(top_seq), collapse=',')
|
||||
paste(rev(side_seq), collapse=',')
|
||||
?rbind
|
||||
curr_align_col <- rbind(x[n-1],y[m-1])
|
||||
curr_align_col
|
||||
## Aligning from Tmat
|
||||
m <- nrow(Tmat)
|
||||
n <- ncol(Tmat)
|
||||
seq_align <- character()
|
||||
while ((n+m) != 2){
|
||||
if (Tmat[m,n] == 3){
|
||||
curr_align_col <- rbind("-",y[m-1])
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
m <- m-1
|
||||
} else if (Tmat[m,n] == 2){
|
||||
curr_align_col <- rbind(x[n-1],"-")
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
n <- n-1
|
||||
} else{
|
||||
curr_align_col <- rbind(x[n-1], y[m-1])
|
||||
alignment <- cbind(curr_align_col, alignment)
|
||||
m <- m-1
|
||||
n <- n-1
|
||||
}
|
||||
}
|
||||
seq_align <- character()
|
||||
while ((n+m) != 2){
|
||||
if (Tmat[m,n] == 3){
|
||||
curr_align_col <- rbind("-",y[m-1])
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
m <- m-1
|
||||
} else if (Tmat[m,n] == 2){
|
||||
curr_align_col <- rbind(x[n-1],"-")
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
n <- n-1
|
||||
} else{
|
||||
curr_align_col <- rbind(x[n-1], y[m-1])
|
||||
seq_align <- cbind(curr_align_col, seq_align)
|
||||
m <- m-1
|
||||
n <- n-1
|
||||
}
|
||||
}
|
||||
?cbind
|
||||
## Aligning from Tmat
|
||||
n <- nrow(Tmat)
|
||||
m <- ncol(Tmat)
|
||||
seq_align <- character()
|
||||
while ((n+m) != 2){
|
||||
if (Tmat[m,n] == 3){
|
||||
curr_align_col <- rbind("-",y[m-1])
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
m <- m-1
|
||||
} else if (Tmat[m,n] == 2){
|
||||
curr_align_col <- rbind(x[n-1],"-")
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
n <- n-1
|
||||
} else{
|
||||
curr_align_col <- rbind(x[n-1], y[m-1])
|
||||
seq_align <- cbind(curr_align_col, seq_align)
|
||||
m <- m-1
|
||||
n <- n-1
|
||||
}
|
||||
}
|
||||
## Aligning from Tmat
|
||||
m <- nrow(Tmat)
|
||||
n <- ncol(Tmat)
|
||||
seq_align <- character()
|
||||
while ((n+m) != 2){
|
||||
if (Tmat[m,n] == 3){
|
||||
curr_align_col <- rbind("-",y[m-1])
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
m <- m-1
|
||||
} else if (Tmat[m,n] == 2){
|
||||
curr_align_col <- rbind(x[n-1],"-")
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
n <- n-1
|
||||
} else{
|
||||
curr_align_col <- rbind(x[n-1], y[m-1])
|
||||
seq_align <- cbind(curr_align_col, seq_align)
|
||||
m <- m-1
|
||||
n <- n-1
|
||||
}
|
||||
}
|
||||
seq_align
|
||||
curr_align_col
|
||||
x
|
||||
y
|
||||
n
|
||||
m
|
||||
## Aligning from Tmat
|
||||
n<-nrow(Tmat) # start at bottom right of Tmat
|
||||
m<-ncol(Tmat)
|
||||
alignment<-character()
|
||||
while( (n+m)!=2 ){
|
||||
if (Tmat[n,m]==1){
|
||||
# subtract 1 from x and y indices because they are
|
||||
# one row/col smaller than Tmat
|
||||
curr_align_col <- rbind(x[n-1],y[m-1])
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
n=n-1; m=m-1; # move back diagonally
|
||||
}else if(Tmat[n,m]==2){
|
||||
curr_align_col <- rbind(x[n-1],"-") # put gap in top seq
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
n=n-1 # move up
|
||||
}else{
|
||||
curr_align_col <- rbind("-",y[m-1]) # put gap in side seq
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
m=m-1 # move left
|
||||
}
|
||||
} # end while
|
||||
alignment
|
||||
## Aligning from Tmat
|
||||
n <- nrow(Tmat)
|
||||
m <- ncol(Tmat)
|
||||
seq_align <- character()
|
||||
while( (n+m)!=2 ){
|
||||
if (Tmat[n,m]==1){
|
||||
curr_align_col <- rbind(x[n-1],y[m-1])
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
n <- n-1
|
||||
m <- m-1
|
||||
}else if(Tmat[n,m]==2){
|
||||
curr_align_col <- rbind(x[n-1],"-") # put gap in top seq
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
n=n-1 # move up
|
||||
}else{
|
||||
curr_align_col <- rbind("-",y[m-1]) # put gap in side seq
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
m=m-1 # move left
|
||||
}
|
||||
} # end while
|
||||
alignment
|
||||
## Aligning from Tmat
|
||||
n <- nrow(Tmat)
|
||||
m <- ncol(Tmat)
|
||||
seq_align <- character()
|
||||
while( (n+m)!=2 ){
|
||||
if (Tmat[n,m]==1){
|
||||
curr_align_col <- rbind(x[n-1],y[m-1])
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
n <- n-1
|
||||
m <- m-1
|
||||
}else if(Tmat[n,m]==2){
|
||||
curr_align_col <- rbind(x[n-1],"-")
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
n <- n-1
|
||||
}else{
|
||||
curr_align_col <- rbind("-",y[m-1])
|
||||
seq_align <- cbind(curr_align_col,seq_align)
|
||||
m <- m-1
|
||||
}
|
||||
} # end while
|
||||
alignment
|
||||
seq_align
|
||||
#### Part D: Convert to functions
|
||||
make.alignment.matrices <- function(x_str, y_str, match_score, mismatch_score,
|
||||
gap_penalty){
|
||||
## Substitution Matrix
|
||||
dna.letters<-c("A","C","G","T")
|
||||
num.letters <- length(dna.letters)
|
||||
S<-data.frame(matrix(0,nrow=num.letters,ncol=num.letters)) # data frame
|
||||
rownames(S)<-dna.letters; colnames(S)<-dna.letters
|
||||
for (i in 1:4){
|
||||
for (j in 1:4){
|
||||
if(dna.letters[i]==dna.letters[j]){
|
||||
S[i,j]<- match_score
|
||||
}
|
||||
else{
|
||||
S[i,j]<- mismatch_score
|
||||
}
|
||||
}
|
||||
}
|
||||
## F Matrix and T Matrix
|
||||
x <- unlist(strsplit(x_str, ""))
|
||||
y <- unlist(strsplit(y_str, ""))
|
||||
x.len <- length(x)
|
||||
y.len <- length(y)
|
||||
Fmat<-matrix(0,nrow=x.len+1,ncol=y.len+1)
|
||||
Tmat<-Fmat # 0's to start
|
||||
rownames(Fmat)<-c("-",x); colnames(Fmat)<-c("-",y)
|
||||
rownames(Tmat)<-c("-",x); colnames(Tmat)<-c("-",y)
|
||||
# create first row and column
|
||||
Fmat[,1]<- seq(from=0,len=x.len+1,by=-abs(gap_penalty))
|
||||
Fmat[1,]<- seq(from=0,len=y.len+1,by=-abs(gap_penalty))
|
||||
Tmat[,1]<- rep(2,x.len+1) # 2 means align with a gap in the upper seq
|
||||
Tmat[1,]<- rep(3,y.len+1) # 3 means align with a gap in the side seq
|
||||
## Building Fmat and Tmat
|
||||
for (i in 2:nrow(Fmat)){
|
||||
for (j in 2:ncol(Fmat)){ # use F recursive rules
|
||||
test_three_cases <- c(Fmat[i-1, j-1] + S[rownames(Fmat)[i], colnames(Fmat)[j]], # 1 mis/match
|
||||
Fmat[i-1, j] + gap_penalty, # 2 up-gap
|
||||
Fmat[i, j-1] + gap_penalty) # 3 left-gap
|
||||
Fmat[i,j]=max(test_three_cases)
|
||||
Tmat[i,j]=which.max(test_three_cases)
|
||||
}
|
||||
}
|
||||
final_score <- Fmat[nrow(Fmat),ncol(Fmat)]
|
||||
return(list(Fmat=Fmat, Tmat=Tmat, score_out=final_score))
|
||||
}
|
||||
# load new input
|
||||
x_str2 <- "GATTA" # side sequence
|
||||
y_str2 <- "GAATTC" # top sequence
|
||||
match_score <- 2
|
||||
mismatch_score <- -1
|
||||
gap_penalty <- -2
|
||||
align.list2 <- make.alignment.matrices(x_str2, y_str2, match_score,
|
||||
mismatch_score, gap_penalty)
|
||||
align.list2$Fmat
|
||||
align.list2$Tmat
|
||||
align.list2$score_out
|
||||
if (!require("gplots")) install.packages("gplots")
|
||||
library(gplots)
|
||||
Fmat2 <- align.list2$Fmat
|
||||
col = c("black","blue","red","yellow","green")
|
||||
breaks = seq(min(Fmat2),max(Fmat2),len=length(col)+1)
|
||||
heatmap.2(Fmat2[-1,-1], dendrogram='none', density.info="none",
|
||||
Rowv=FALSE, Colv=FALSE, trace='none',
|
||||
breaks = breaks, col = col,
|
||||
sepwidth=c(0.01,0.01),
|
||||
sepcolor="black",
|
||||
colsep=1:ncol(Fmat2),
|
||||
rowsep=1:nrow(Fmat2))
|
||||
#### Part E: Traceback Matrix
|
||||
show.alignment <- function(x_str,y_str,Tmat){
|
||||
################ create the alignment
|
||||
# input Tmat and the two sequences: x side seq and y is top seq
|
||||
# make character vectors out of the strings
|
||||
x<-unlist(strsplit(x_str,""))
|
||||
y<-unlist(strsplit(y_str,""))
|
||||
n<-nrow(Tmat) # start at bottom right of Tmat
|
||||
m<-ncol(Tmat)
|
||||
alignment<-character()
|
||||
while( (n+m)!=2 ){
|
||||
if (Tmat[n,m]==1){
|
||||
# subtract 1 from x and y indices because they are
|
||||
# one row/col smaller than Tmat
|
||||
curr_align_col <- rbind(x[n-1],y[m-1])
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
n=n-1; m=m-1; # move back diagonally
|
||||
}else if(Tmat[n,m]==2){
|
||||
curr_align_col <- rbind(x[n-1],"-") # put gap in top seq
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
n=n-1 # move up
|
||||
}else{
|
||||
curr_align_col <- rbind("-",y[m-1]) # put gap in side seq
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
m=m-1 # move left
|
||||
}
|
||||
} # end while
|
||||
return(alignment)
|
||||
} # end function
|
||||
alignment2 <- show.alignment(x_str2,y_str2,align.list2$Tmat)
|
||||
alignment2
|
||||
write.table(alignment2,row.names=F,col.names=F,quote=F)
|
||||
## Input 3
|
||||
x_str3 <- "ATCGT" # side sequence
|
||||
y_str3 <- "TGGTG" # top sequence
|
||||
match_score <- 1
|
||||
mismatch_score <- -2
|
||||
gap_penalty <- -1
|
||||
align.list3 <- make.alignment.matrices(x_str3, y_str3, match_score,
|
||||
mismatch_score, gap_penalty)
|
||||
align.list3$Fmat
|
||||
align.list3$Tmat
|
||||
align.list3$score_out
|
||||
col = c("black","blue","red","yellow","green")
|
||||
breaks = seq(min(Fmat3),max(Fmat3),len=length(col)+1)
|
||||
heatmap.2(Fmat3[-1,-1], dendrogram='none', density.info="none",
|
||||
Rowv=FALSE, Colv=FALSE, trace='none',
|
||||
breaks = breaks, col = col,
|
||||
sepwidth=c(0.01,0.01),
|
||||
sepcolor="black",
|
||||
colsep=1:ncol(Fmat3),
|
||||
rowsep=1:nrow(Fmat3))
|
||||
Fmat3 <- align.list3$Fmat
|
||||
align.list3$Fmat
|
||||
Fmat3 <- align.list3$Fmat
|
||||
align.list3$Tmat
|
||||
align.list3$score_out
|
||||
o
|
||||
heatmap.2(Fmat3[-1,-1], dendrogram='none', density.info="none",
|
||||
Rowv=FALSE, Colv=FALSE, trace='none',
|
||||
breaks = breaks, col = col,
|
||||
sepwidth=c(0.01,0.01),
|
||||
sepcolor="black",
|
||||
colsep=1:ncol(Fmat3),
|
||||
rowsep=1:nrow(Fmat3))
|
||||
alignment3 <- show.alignment(x_str3,y_str3,align.list3$Tmat)
|
||||
alignment3
|
||||
write.table(alignment3,row.names=F,col.names=F,quote=F)
|
||||
|
||||
@ -163,3 +163,67 @@ heatmap.2(Fmat2[-1,-1], dendrogram='none', density.info="none",
|
||||
sepcolor="black",
|
||||
colsep=1:ncol(Fmat2),
|
||||
rowsep=1:nrow(Fmat2))
|
||||
|
||||
#### Part E: Traceback Matrix
|
||||
show.alignment <- function(x_str,y_str,Tmat){
|
||||
################ create the alignment
|
||||
# input Tmat and the two sequences: x side seq and y is top seq
|
||||
# make character vectors out of the strings
|
||||
x<-unlist(strsplit(x_str,""))
|
||||
y<-unlist(strsplit(y_str,""))
|
||||
|
||||
n<-nrow(Tmat) # start at bottom right of Tmat
|
||||
m<-ncol(Tmat)
|
||||
alignment<-character()
|
||||
while( (n+m)!=2 ){
|
||||
if (Tmat[n,m]==1){
|
||||
# subtract 1 from x and y indices because they are
|
||||
# one row/col smaller than Tmat
|
||||
curr_align_col <- rbind(x[n-1],y[m-1])
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
n=n-1; m=m-1; # move back diagonally
|
||||
}else if(Tmat[n,m]==2){
|
||||
curr_align_col <- rbind(x[n-1],"-") # put gap in top seq
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
n=n-1 # move up
|
||||
}else{
|
||||
curr_align_col <- rbind("-",y[m-1]) # put gap in side seq
|
||||
alignment <- cbind(curr_align_col,alignment)
|
||||
m=m-1 # move left
|
||||
}
|
||||
} # end while
|
||||
return(alignment)
|
||||
} # end function
|
||||
|
||||
alignment2 <- show.alignment(x_str2,y_str2,align.list2$Tmat)
|
||||
alignment2
|
||||
write.table(alignment2,row.names=F,col.names=F,quote=F)
|
||||
|
||||
## Input 3
|
||||
x_str3 <- "ATCGT" # side sequence
|
||||
y_str3 <- "TGGTG" # top sequence
|
||||
match_score <- 1
|
||||
mismatch_score <- -2
|
||||
gap_penalty <- -1
|
||||
|
||||
align.list3 <- make.alignment.matrices(x_str3, y_str3, match_score,
|
||||
mismatch_score, gap_penalty)
|
||||
align.list3$Fmat
|
||||
Fmat3 <- align.list3$Fmat
|
||||
align.list3$Tmat
|
||||
align.list3$score_out
|
||||
|
||||
col = c("black","blue","red","yellow","green")
|
||||
breaks = seq(min(Fmat3),max(Fmat3),len=length(col)+1)
|
||||
|
||||
heatmap.2(Fmat3[-1,-1], dendrogram='none', density.info="none",
|
||||
Rowv=FALSE, Colv=FALSE, trace='none',
|
||||
breaks = breaks, col = col,
|
||||
sepwidth=c(0.01,0.01),
|
||||
sepcolor="black",
|
||||
colsep=1:ncol(Fmat3),
|
||||
rowsep=1:nrow(Fmat3))
|
||||
|
||||
alignment3 <- show.alignment(x_str3,y_str3,align.list3$Tmat)
|
||||
alignment3
|
||||
write.table(alignment3,row.names=F,col.names=F,quote=F)
|
||||
|
||||
Binary file not shown.
BIN
Schrick-Noah_CS-6643_Lab-9.pdf
Normal file
BIN
Schrick-Noah_CS-6643_Lab-9.pdf
Normal file
Binary file not shown.
Loading…
x
Reference in New Issue
Block a user