##### Code for Marginal Sum of Squares test (also called lack of fit test)




#### Note this code assumes you have already imported data and created
# a regression object and an ANOVA object

###Importing data
#datum=read.csv(file.choose())
#head(datum)
### Run a regression - treat Protein as continuous
#results=lm(Mass~Protein) #Protein is continuous 
#summary(results)
### Run an ANOVA - treat Protein as categorical
#results2=lm(Mass~as.factor(Protein),data=datum) 
### use the as.factor function to get R to treat 'Protein' as continuous
#summary(results2)


### Conduct the f-drop test


anova(results,result2) 

### the two models you want to compare are included as arguments 
### order doesn't usually matter, but it's best to put the more complicated
### model first for those few cases where it does
###

### Note that the two models can't have the same number of parameters
### A significant p-value means the more complex model is a 
### significant improvement in fit
### A non-significant p-value means the simpler model is adequate
### Note that RSS (which is the same thing as SSE) is always lower 
### in the more complex model