window of 1500

inst/examples/beer.Rmd

@@ -28,7 +28,7 @@ timeseries <- matrix(X@.Data, ncol=M)
 w <- sapply(data.frame(timeseries), function(x) any(x < threshold))
 # extract that subset by id 
 W <- timeseries[,w]
-sample <- 500 # sample length
+sample <- 1500 # sample length
 dev <- sapply(as.data.frame(W), which.min)
 drop <- which(dev - sample < 1)
 if(length(drop) > 0){
@@ -66,7 +66,7 @@ dat <- melt(data.frame(Variance=var, Autocorrelation=acor))
 To compare against the expected distribution of these statistics, we create another set of simulations without conditioning on having experienced a chance transition, on which we perform the identical analysis.  
 
 ``` {r simdatf_null}
-null <- timeseries[1000:1501,]
+null <- timeseries[1000:2501,]
 null <- as.data.frame(cbind(time = 1:dim(null)[1], null))
 ndf <- melt(null, id="time")
 names(ndf) = c("time", "reps", "value")

inst/examples/beer.md

@@ -46,7 +46,7 @@ timeseries <- matrix(X@.Data, ncol=M)
 w <- sapply(data.frame(timeseries), function(x) any(x < threshold))
 # extract that subset by id 
 W <- timeseries[,w]
-sample <- 500 # sample length
+sample <- 1500 # sample length
 dev <- sapply(as.data.frame(W), which.min)
 drop <- which(dev - sample < 1)
 if(length(drop) > 0){
@@ -72,7 +72,7 @@ zoom <- df
 ggplot(subset(zoom, reps %in% levels(zoom$reps)[1:9])) + geom_line(aes(time, value)) + facet_wrap(~reps, scales="free")
 ```
 
-![plot of chunk example-trajectories](http://farm9.staticflickr.com/8516/8592953281_bb7ec80511_o.png) 
+![plot of chunk example-trajectories](http://farm9.staticflickr.com/8369/8593100341_4e2af5fec3_o.png) 
 
 
 Compute model-based warning signals on all each of these.  
@@ -92,7 +92,7 @@ To compare against the expected distribution of these statistics, we create anot
 
 
 ```r
-null <- timeseries[1000:1501,]
+null <- timeseries[1000:2501,]
 null <- as.data.frame(cbind(time = 1:dim(null)[1], null))
 ndf <- melt(null, id="time")
 names(ndf) = c("time", "reps", "value")
@@ -111,7 +111,7 @@ ggplot(dat) + geom_histogram(aes(value, y=..density..), binwidth=0.3, alpha=.5)
  geom_density(data=nulldat, aes(value), adjust=2) + xlab("Kendall's tau") + theme_bw()
 ```
 
-![plot of chunk fig](http://farm9.staticflickr.com/8518/8592953433_2e184d3044_o.png) 
+![plot of chunk fig](http://farm9.staticflickr.com/8108/8593100533_533e2d0c46_o.png) 
 
 
 

inst/examples/beer_dat.csv

@@ -1,25 +1,27 @@
 "","variable","value"
-"1","Variance",0.435792828685259
-"2","Variance",0.509800796812749
-"3","Variance",0.0161593625498008
-"4","Variance",0.141800796812749
-"5","Variance",-0.468239043824701
-"6","Variance",-0.0520478087649402
-"7","Variance",0.956207171314741
-"8","Variance",0.0816892430278885
-"9","Variance",0.173737051792829
-"10","Variance",0.941290836653387
-"11","Variance",-0.327426294820717
-"12","Variance",0.406342629482072
-"13","Autocorrelation",0.0609083665338645
-"14","Autocorrelation",0.389641434262948
-"15","Autocorrelation",-0.143203187250996
-"16","Autocorrelation",-0.0640318725099602
-"17","Autocorrelation",-0.403474103585657
-"18","Autocorrelation",-0.076207171314741
-"19","Autocorrelation",0.648637450199203
-"20","Autocorrelation",-0.230533864541833
-"21","Autocorrelation",-0.107059760956175
-"22","Autocorrelation",0.207394422310757
-"23","Autocorrelation",-0.145880478087649
-"24","Autocorrelation",0.538231075697211
+"1","Variance",-0.209310252996005
+"2","Variance",0.645493120284066
+"3","Variance",0.157986684420772
+"4","Variance",-0.737260541500222
+"5","Variance",-0.447833111407013
+"6","Variance",-0.817232134931203
+"7","Variance",0.238093209054594
+"8","Variance",-0.709791389258766
+"9","Variance",0.615680426098535
+"10","Variance",0.337430980914336
+"11","Variance",-0.153015534842432
+"12","Variance",0.83688948069241
+"13","Variance",-0.61149045716822
+"14","Autocorrelation",-0.140282290279627
+"15","Autocorrelation",0.571103417665335
+"16","Autocorrelation",0.2305867731913
+"17","Autocorrelation",-0.579675099866844
+"18","Autocorrelation",-0.428012427873946
+"19","Autocorrelation",-0.19937505548158
+"20","Autocorrelation",0.230494451841988
+"21","Autocorrelation",-0.492623169107856
+"22","Autocorrelation",0.27037017310253
+"23","Autocorrelation",0.172885929871283
+"24","Autocorrelation",-0.0338854860186418
+"25","Autocorrelation",0.582636484687084
+"26","Autocorrelation",-0.484349755881048