nickpettican

#!/usr/bin/perl

use diagnostics;
use strict;
use warnings;
use Getopt::Long;

# Defining bin size and parameters

print "Enter file name:\n";

nickpettican

# CHAPTER 6:

# TWO SAMPLES

# The classical tests:
# - Comparing two variances: Fisher's F test
var.test
# - Comparing two sample means with normal errors: Student's t test
t.test
# - Comparing two means with non-normal errors: Wilcoxon's test

nickpettican

# CHAPTER 7 
# LINEAR REGRESSION
reg.data <- read.csv("c:\\MSc\\Statistics\\Data\\tannin.csv")
attach(reg.data)
names(reg.data)
plot(tannin,growth,pch=21,bg="blue")
# LINEAR REGRESSION IN R
lm(growth∼tannin)
abline(lm(growth∼tannin),col="green")
fitted <- predict(lm(growth∼tannin))

nickpettican

# ANOVA: ANALYSIS OF VARIANCE

# Regression and ANOVA are identical approaches except for the nature of the explanatory variables

# STATISTICAL BACKGORUND

# The emphasis of ANOVA is on hypothesis testing
# To estimate means and standard errors of differences between means
# Comparing two means by t-test: (difference between two means)/standard error of the difference -> compare with critical value
# In ANOVA we compare 3 or more means

nickpettican

# write out table with data
write.table(merged_data, file='merged_data.tsv', quote=FALSE, sep='\t')

# open multiple datasets
temp = list.files(pattern="*.tsv")
gsub('.stv','',temp)
gsub('_output.tsv','_abundance',temp)
temp2 <- gsub('_output.tsv','_abundance',temp)
df <- read.table(temp[1])
df <- read.table(temp[1],col.names=c('ID','T','P',temp2[1]))

nickpettican

#!/usr/bin/env python

import os
import csv

DATADIR = "C:\Users\User\Downloads\HavasLynx"
DATAFILE = "tin00028.tsv"
EUCODES = "2letterEU.txt"
OUTFILE = "internetuse_EU.csv"

nickpettican

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