Added Vignette

Merge branch 'master' of github.com:ontogenerator/cpdetectorr

# Conflicts:
#	DESCRIPTION

vignettes/Detecting_change_points.R

@@ -0,0 +1,72 @@
+## ----setup, include = FALSE----------------------------------------------
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+
+## ------------------------------------------------------------------------
+library(cpdetectorr) # load package first
+set.seed(25)
+cp_wrapper(c(rbinom(50, 1, 0.3), rbinom(50, 1, 0.8)), TRUE, "binomial", 2)
+
+## ------------------------------------------------------------------------
+d_responses <- data.frame(Responses = c(rbinom(50, 1, 0.3), rbinom(50, 1, 0.8)))
+cp_wrapper(d_responses, TRUE, "chisquare", 2)
+
+## ------------------------------------------------------------------------
+eyeblink[,] # inspect data set
+
+## ---- eval = FALSE-------------------------------------------------------
+#  cp_wrapper(eyeblink, TRUE, "chisquare", 2)
+
+## ------------------------------------------------------------------------
+cp_wrapper(eyeblink, TRUE, "chisquare", 3)
+
+cp_wrapper(eyeblink, TRUE, "binomial", 2)
+
+## ------------------------------------------------------------------------
+library(ggplot2) # load the ggplot package
+eyeblinkdata <- data.frame(Trial = 1:length(eyeblink[,]),
+                           CumRespMeasure = cumsum(eyeblink)[,])
+changepoints <- cp_wrapper(eyeblink, TRUE, "binomial", 4) # save the output of the change point analysis
+#generate a cumulative response vs trial plot:
+ggplot(eyeblinkdata) + geom_line(aes(Trial, CumRespMeasure)) +
+  geom_point(data = changepoints, aes(Trial, CumSs), size = 3)
+
+## ------------------------------------------------------------------------
+plusmaze[,] # inspect data set
+(cp.1 <- cp_wrapper(plusmaze, TRUE, "binomial", 1.3)) #find the change points
+# plot average response rate per trial
+ggplot() + geom_step(data = cp.1, aes(Trial,Slopes)) +
+  ylab("Average Response Rate per Trial")
+# for comparison, the cumulative response vs trial plot, as in the example above:
+plusmazedata <- data.frame(Trial = 1:length(plusmaze[,]),
+                           CumRespMeasure = cumsum(plusmaze)[,])
+ggplot(plusmazedata) + geom_line(aes(Trial, CumRespMeasure)) +
+  geom_point(data = cp.1, aes(Trial, CumSs), size = 3)
+
+## ------------------------------------------------------------------------
+(cp.2 <- cp_wrapper(hopperentry, TRUE, "ttest", 4)) #find the change points
+# cumulative response vs trial plot
+hedata <- data.frame(Trial = 1:length(hopperentry[,]),
+                           CumRespMeasure = cumsum(hopperentry)[,])
+pl1 <- ggplotGrob(ggplot(hedata) + geom_line(aes(Trial, CumRespMeasure)) +
+  geom_point(data = cp.2, aes(Trial, CumSs), size = 3))
+# plot average response rate per trial
+pl2 <- ggplotGrob(ggplot(cp.2) + geom_step(aes(Trial, Slopes)) +
+  ylab("Average Response Rate per Trial"))
+# stack the two plots vertically using the grid package
+grid::grid.draw(rbind(pl1, pl2, size = "first"))
+
+## ------------------------------------------------------------------------
+(cp.3 <- cp_wrapper(matching, FALSE, "binomial", 2)) #find the change points
+# cumulative response vs trial plot
+matchingdata <- data.frame(Events = 1:length(matching[,]),
+                           Time = cumsum(matching)[,])
+pl3 <- ggplotGrob(ggplot(matchingdata) + geom_line(aes(Time, Events)) +
+  geom_point(data = cp.3, aes(Time, Events), size = 3))
+# plot average response rate per trial
+pl4 <- ggplotGrob(ggplot(cp.3) + geom_step(aes(Time, Slopes)) +
+  ylab("Events per Unit Time"))
+grid::grid.draw(rbind(pl3, pl4, size = "first"))
+