```
time <- seq(1:24) #create a 24 hour time
pure <- matrix(time,24,18)
colnames(pure) <- paste0("H",1:18)
pure <- data.frame(time,cos((pure - col(pure))*pi/12)*3 + 3)
#18 different phases but scaled to 0-6 match mood data
matplot(pure[-1],type="l",main="Pure circadian arousal rhythms",
xlab="time of day",ylab="Arousal")
op <- par(mfrow=c(2,2))
cosinor.plot(1,3,pure)
cosinor.plot(1,5,pure)
cosinor.plot(1,8,pure)
cosinor.plot(1,12,pure)
p <- cosinor(pure) #find the acrophases (should match the input)
#now, test finding the acrophases for different subjects on 3 variables
#They should be the first 3, second 3, etc. acrophases of pure
pp <- matrix(NA,nrow=6*24,ncol=4)
pure <- as.matrix(pure)
pp[,1] <- rep(pure[,1],6)
pp[1:24,2:4] <- pure[1:24,2:4]
pp[25:48,2:4] <- pure[1:24,5:7] *2 #to test different variances
pp[49:72,2:4] <- pure[1:24,8:10] *3
pp[73:96,2:4] <- pure[1:24,11:13]
pp[97:120,2:4] <- pure[1:24,14:16]
pp[121:144,2:4] <- pure[1:24,17:19]
pure.df <- data.frame(ID = rep(1:6,each=24),pp)
colnames(pure.df) <- c("ID","Time",paste0("V",1:3))
cosinor("Time",3:5,"ID",pure.df)
op <- par(mfrow=c(2,2))
cosinor.plot(2,3,pure.df,IDloc=1,ID="1")
cosinor.plot(2,3,pure.df,IDloc=1,ID="2")
cosinor.plot(2,3,pure.df,IDloc=1,ID="3")
cosinor.plot(2,3,pure.df,IDloc=1,ID="4")
#now, show those in one panel as spagetti plots
op <- par(mfrow=c(1,1))
cosinor.plot(2,3,pure.df,IDloc=1,ID="1",multi=TRUE,ylim=c(0,20),ylab="Modeled")
cosinor.plot(2,3,pure.df,IDloc=1,ID="2",multi=TRUE,add=TRUE,lty="dotdash")
cosinor.plot(2,3,pure.df,IDloc=1,ID="3",multi=TRUE,add=TRUE,lty="dashed")
cosinor.plot(2,3,pure.df,IDloc=1,ID="4",multi=TRUE,add=TRUE,lty="dotted")
set.seed(42) #what else?
noisy <- pure
noisy[,2:19]<- noisy[,2:19] + rnorm(24*18,0,.2)
n <- cosinor(time,noisy) #add a bit of noise
small.pure <- pure[c(8,11,14,17,20,23),]
small.noisy <- noisy[c(8,11,14,17,20,23),]
small.time <- c(8,11,14,17,20,23)
cosinor.plot(1,3,small.pure,multi=TRUE)
cosinor.plot(1,3,small.noisy,multi=TRUE,add=TRUE,lty="dashed")
# sp <- cosinor(small.pure)
# spo <- cosinor(small.pure,opti=TRUE) #iterative fit
# sn <- cosinor(small.noisy) #linear
# sno <- cosinor(small.noisy,opti=TRUE) #iterative
# sum.df <- data.frame(pure=p,noisy = n, small=sp,small.noise = sn,
# small.opt=spo,small.noise.opt=sno)
# round(sum.df,2)
# round(circadian.cor(sum.df[,c(1,3,5,7,9,11)]),2) #compare alternatives
#
# #now, lets form three "subjects" and show how the grouping variable works
# mixed.df <- rbind(small.pure,small.noisy,noisy)
# mixed.df <- data.frame(ID=c(rep(1,6),rep(2,6),rep(3,24)),
# time=c(rep(c(8,11,14,17,20,23),2),1:24),mixed.df)
# group.df <- cosinor(angle="time",x=2:20,code="ID",data=mixed.df)
# round(group.df,2) #compare these values to the sp,sn,and n values done separately
```

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