##### Code for Basic linear Regression in R


### Importing data
# Don't call your data 'data' - that word already has a meaning in R


help(read.csv)
datum=read.csv(file.choose()) ### creates a data frame named datum from imported csv file


### check data was imported properly


names(datum) ### column headers of data file
head(datum) ### column headers of data file plus first 6 rows of data 
head(datum,10) ### number of rows of data can be changed
summary(datum) ### summary statistics for datum


### Create a scatterplot of data


#help(plot) ### help file for plot function
#plot(Y~X,data=datum) ### creates a scatter plot between 'Y' and 'X' variables in 'datum'
#Replace 'Y' and 'X' with whatever those columns are actually called
plot(Biomass~Rainfall,data=datum) ### scatterplot of data used in example


### Run regression using linear model


help(lm) ### help file for 'linear model' - most important function in class
#results=lm(Y~X,data=datum) ### runs a linear regression between 'Y' and 'X' columns in 'datum', call results 'results'
#Replace 'Y' and 'X' with whatever those columns are actually called
results=lm(Biomass~Rainfall,data=datum) ### linear regression of data used in example
summary(results) ### prints a summary of 'results'
# note slope and constant as well of t-tests and p-values of estimates
anova(results) ### generate an anova table of regression - not very useful as doesn't give anything other than p
# note F-statistic of full model - same as t-test in single variable regression
#coef(results) ### shows you the coefficient estimates from 'results'
#summary(results)$coef ### another way of extraction coefficients that also includes SE
#summary(results)$coef[1,] ### extracts all information for the intercept
#summary(results$coef[,2] ### extract standard errors for both variables


### Generate confidence intervals for parameters


confint(results) #calculates confidence limits on slope and intercept


### Plot regression line over data


#help(abline) ### function for plotting a straight line on plotted data
# requires either a lm object or values for a (intercept) and b (slope)
abline(results) ### plots results on the already created plot
# Note must have run regression first
# Note won't do anything if you haven't plotted data.