chrissyhroberts

#For the findings of the proposed study of diagnostic accuracy to be robust, we require a minimum number of infected and uninfected cases. The total number of samples required to accurately evaluate the diagnostic devices can be calculated4 using the formula 


#p  the expected sensitivity of the novel diagnostic
p<-0.99

# po is the minimum acceptable level of sensitivity
#pick one of these or use your own value(s)
po<-0.98

chrissyhroberts

require(season)

# use glm model in the first part, date can be full date or month (1-12),
res = cosinor(response~exposures+exposures2+exposures3, date=month, data=df,family=binomial(link='cloglog'),type = "monthly",cycles = 1)

summary(res)

plot(res)

chrissyhroberts

#draws a 96 cell grid in R and adds some data to the cells : useful for drawing plate maps for 96 well laboratory plates

title="PLATE X"
values<-paste(c("A","B","C"),1:3,sep="\n")

verticals<-c(rep(7.5,12),rep(6.5,12),rep(5.5,12),rep(4.5,12),rep(3.5,12),rep(2.5,12),rep(1.5,12),rep(0.5,12))
h2<-rep(seq(0.5,11.5,1),8)
#draw the grid
plot(c(0,12),c(0,8),pch=0,col="white",axes=FALSE,xlab="",ylab="")
abline(v=c(0:12))

chrissyhroberts

#Run a set of polynomial models on some x vs y data. 

#create a data set x and y
x<-1:1100
y<-sqrt(x^6+x/x^4)+x/x^x

#Define function to run models
polyfit<-function(x,order,raw=TRUE)
{
  fit<-lm(y~poly(x,order,raw=raw))