diff --git a/.Rhistory b/.Rhistory
deleted file mode 100644
index c83e194..0000000
--- a/.Rhistory
+++ /dev/null
@@ -1,512 +0,0 @@
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2)
-################# Linear model: Least-Squares Fit #################
-g.breaks <- g.hist$breaks[-c(1,2,3)] # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.breaks[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2)
-################# Linear model: Least-Squares Fit #################
-g.breaks <- g.hist$breaks[-c(1)] # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.breaks[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2)
-################# Linear model: Least-Squares Fit #################
-#g.breaks <- g.hist$breaks[-c(1)] # remove 0
-g.breaks <- g.hist$breaks # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.breaks[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2)
-################# Linear model: Least-Squares Fit #################
-g.breaks <- g.hist$breaks[-c(1)] # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.probs[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean)/kmin)
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
-alpha.LM
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2)
-################# Linear model: Least-Squares Fit #################
-g.breaks <- g.hist$breaks[-c(1)] # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.probs[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=5)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2)
-################# Linear model: Least-Squares Fit #################
-g.breaks <- g.hist$breaks[-c(1)] # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.probs[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2)
-################# Linear model: Least-Squares Fit #################
-g.breaks <- g.hist$breaks[-c(1)] # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.probs[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4)
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
-################# Linear model: Least-Squares Fit #################
-g.breaks <- g.hist$breaks[-c(1)] # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.probs[nz.probs.mask]
-#plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
-plot(yeast)
-hist(yeast)
-hist(g.vec)
-g.pois
-g.mean
-alpha.LM
-alpha.ML
-degree(g)
-sort(degree(g))
-sort(degree(g),decreasing=FALSE)
-sort(degree(g),decreasing=F)
-sort(degree(g),decreasing=false)
-sort(degree(g), decreasing = TRUE)
-head(sort(degree(g), decreasing = TRUE))
-stddev(degree(g))
-sd(degree(g))
-tail(sort(degree(g), decreasing = TRUE))
-plot(log(g.breaks.clean), log(g.probs.clean))
-# Homework 4 for the University of Tulsa' s CS-7863 Network Theory Course
-# Degree Distribution
-# Professor: Dr. McKinney, Spring 2022
-# Noah Schrick - 1492657
-library(igraph)
-library(igraphdata)
-data(yeast)
-g <- yeast
-g.netname <- "Yeast"
-################# Set up Work #################
-g.vec <- degree(g)
-g.hist <- hist(g.vec, freq=FALSE, main=paste("Histogram of the", g.netname,
-" Network"))
-legend("topright", c("Guess", "Poisson", "Least-Squares Fit",
-"Max Log-Likelihood"), lty=c(1,2,3,4), col=c("#40B0A6",
-"#006CD1", "#E66100", "#D35FB7"))
-g.mean <- mean(g.vec)
-g.seq <- 0:max(g.vec) # x-axis
-################# Guessing Alpha #################
-alpha.guess <- 1.5
-lines(g.seq, g.seq^(-alpha.guess), col="#40B0A6", lty=1, lwd=3)
-################# Poisson #################
-g.pois <- dpois(g.seq, g.mean, log=F)
-lines(g.seq, g.pois, col="#006CD1", lty=2, lwd=3)
-################# Linear model: Least-Squares Fit #################
-g.breaks <- g.hist$breaks[-c(1)] # remove 0
-g.probs <- g.hist$density[-1] # make lengths match
-# Need to clean up probabilities that are 0
-nz.probs.mask <- g.probs!=0
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.probs[nz.probs.mask]
-plot(log(g.breaks.clean), log(g.probs.clean))
-g.fit <- lm(log(g.probs.clean)~log(g.breaks.clean))
-summary(g.fit)
-alpha.LM <- coef(g.fit)[2]
-lines(g.seq, g.seq^(-alpha.LM), col="#E66100", lty=3, lwd=3)
-################# Max-Log-Likelihood #################
-n <- length(g.breaks.clean)
-kmin <- g.breaks.clean[1]
-alpha.ML <- 1 + n/sum(log(g.breaks.clean/kmin))
-alpha.ML
-lines(g.seq, g.seq^(-alpha.ML), col="#D35FB7", lty=4, lwd=3)
-plot(log(g.breaks.clean), log(g.probs.clean))
-g.breaks.clean <- g.breaks[nz.probs.mask]
-g.probs.clean <- g.probs[nz.probs.mask]
-plot(log(g.breaks.clean), log(g.probs.clean))
-#### Part B: Loop Recursion Warmup
-m <- 3 # row edges
-n <- 6 # col edges
-path_matrix <- matrix(1, nrow=m+1, ncol=n+1)
-for (i in seq(2,m+1)){
-for (j in seq(2, n+1)){
-path_matrix[i,j] <- path_matrix[i-1,j] + path_matrix[i,j-1]
-}
-}
-path_matrix
-path_matrix[m][n]
-path_matrix[m]
-path_matrix[n]
-path_matrix[m,n]
-path_matrix[m+1,n+1]
-calc.num.paths <- function(n,m){
-path_matrix <- matrix(1, nrow=m+1, ncol=n+1)
-for (i in seq(2,m+1)){
-for (j in seq(2, n+1)){
-path_matrix[i,j] <- path_matrix[i-1,j] + path_matrix[i,j-1]
-}
-}
-path_matrix[m+1,n+1]
-}
-#### Part B: Loop Recursion Warmup
-calc.num.paths <- function(n,m){
-path_matrix <- matrix(1, nrow=m+1, ncol=n+1)
-for (i in seq(2,m+1)){
-for (j in seq(2, n+1)){
-path_matrix[i,j] <- path_matrix[i-1,j] + path_matrix[i,j-1]
-}
-}
-path_matrix[m+1,n+1]
-}
-m <- 5 # row edges
-n <- 5 # col edges
-calc.num.paths(n,m)
-m <- 5 # row edges
-n <- 6 # col edges
-calc.num.paths(n,m)
-m <- 10 # row edges
-n <- 10 # col edges
-calc.num.paths(n,m)
-factorial(n+m)/(factorial(n)*factorial(m))
-m <- 5 # row edges
-n <- 5 # col edges
-calc.num.paths(n,m)
-factorial(n+m)/(factorial(n)*factorial(m))
-m <- 5 # row edges
-n <- 6 # col edges
-calc.num.paths(n,m)
-factorial(n+m)/(factorial(n)*factorial(m))
-m <- 10 # row edges
-n <- 10 # col edges
-calc.num.paths(n,m)
-factorial(n+m)/(factorial(n)*factorial(m))
-h1n1.Cali
-h1n1.Cali.dna.vec <- fasta2vec("FJ969540.1.fasta")
-#### Part A: EMBOSS pairwise alignment server and influenza
-## Load associated supportive libraries
-if (!require("seqinr")) install.packages("seqinr")
-library(seqinr)
-## Load in the fasta file
-fasta2vec <- function(fasta.file){
-if (!require("seqinr")) install.packages("seqinr")
-library(seqinr)
-fasta <- read.fasta(file=fasta.file, as.string= TRUE)
-fasta.string <- fasta[[1]][1]
-fasta.list <- strsplit(fasta.string,"")
-fasta.vec <- unlist(fasta.list)
-}
-h1n1.Cali.dna.vec <- fasta2vec("FJ969540.1.fasta")
-## Set Working Directory to file directory - RStudio approach
-setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
-h1n1.Cali.dna.vec <- fasta2vec("FJ969540.1.fasta")
-h1n1.Cali.dna.vec[1:5]
-?count
-count(h1n1.Cali.dna.vec[1:5],2)
-count(h1n1.Cali.dna.vec[1:5],2)["aa"]
-count(h1n1.Cali.dna.vec[1:5],11)
-count(h1n1.Cali.dna.vec[1:5],1)
-#### Part C: Dinucleotide Signals
-calc.sliding.cpg <- function(fastaVec, slideWin){
-# allocate memory for odds ratio output
-cg_dinuc_oddsRatio <- double()
-n<-length(fastaVec)
-for (i in 1:(n-slideWin)){
-# array slice the vectpr on sliding window size and obtain dinuc appearances
-dinucs<-count(fastaVec[i:(i+slideWin)],2)
-# retrieve number of times "CG" appeared
-cg_dinuc_count <- dinucs["cg"]
-# get counts of all nucleotides
-nucs<-count(fastaVec[i:(i+slideWin)],1)
-# obtain times dinuc pairing appeared per times the indiv nucs appeared
-cg_dinuc_oddsRatio[i] <-
-cg_dinuc_count/(nucs["c"]*nucs["g"])
-}
-return(cg_dinuc_oddsRatio) # returns vector
-}
-apoe.fasta.vec <- fasta2vec("apoe.fasta")
-cpg_vec <- calc.sliding.cpg(apoe.fasta.vec,150)
-plot(cpg_vec,type="l",main="Observed vs Expected CG",xlab="Base Index", ylab="Obs/Exp")
-cpg_vec