Removed package dependence

ontogenerator · Jul 17, 2018 · 7ad8349 · 7ad8349
1 parent 9de3ea0
commit 7ad8349
Show file tree

Hide file tree

Showing 32 changed files with 472 additions and 394 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,25 +1,26 @@
 Package: cpdetectorr
 Type: Package
 Title: Change point estimation from sequences of choices
-Version: 1.0.0.0000
-Date: 2015-09-25
-Authors@R: person("Vladislav", "Nachev", email = "[email protected]", role = c("aut", "cre"))
+Version: 1.1.0
+Date: 2018-07-17
+Authors@R: person("Vladislav", "Nachev", email = "[email protected]",
+  role = c("aut", "cre"))
 URL: http://github.com/ontogenerator/cpdetectorr
 BugReports: http://github.com/ontogenerator/cpdetectorr/issues
 Description: An R implementation of the learning curve analysis method published
-    in Gallistel et al. (2004). From a sequence of responses the changes in response
-    (e.g. from one response frequency to another or from one response level to
-    another) are estimated using different statistical tests and a threshold
-    criterion.
+  in Gallistel et al. (2004). From a sequence of responses the changes in response
+  (e.g. from one response frequency to another or from one response level to
+  another) are estimated using different statistical tests and a threshold
+  criterion.
 References: Gallistel CR, Fairhurst S, Balsam P (2004) The learning curve:
-    Implications of a quantitative analysis. PNAS 101:13124-13131. doi: 10.1073/
-    pnas.0404965101
+  Implications of a quantitative analysis. PNAS 101:13124-13131. doi: 10.1073/
+  pnas.0404965101
 Depends:
-    R (>= 3.2.2)
+  R (>= 3.2.2)
 Imports:
-    dplyr
+  stats
 Suggests:
-    ggplot2
-License: GPL (>=2)
+  dplyr, ggplot2
+License: GPL (>=3)
 LazyData: TRUE
-RoxygenNote: 5.0.1
+RoxygenNote: 6.0.1
diff --git a/NAMESPACE b/NAMESPACE
@@ -1,4 +1,3 @@
 # Generated by roxygen2: do not edit by hand
 
 export(cp_wrapper)
-import(dplyr)
diff --git a/R/cp_wrapper.R b/R/cp_wrapper.R
@@ -3,31 +3,32 @@
 #' @param isDiscrete A boolean parameter, true if the data to be analyzed are discrete,
 #' false if continuous, e.g. succesive intervals
 #' @param test Name of the test to be performed. The following tests are implemented:
-#' "binomial" (random rate), "chisquare", "ttest", "KS" (Komolgorov-Smirnov). The binomial test is
-#' used for finding changes in the rate parameter of a random rate process, either intermittently
-#' (isDiscrete = TRUE) or continuously (isDiscrete = FALSE) sampled. The chi square test is used with
-#' data where there has been a frequency change, as in, e.g. correct choices. The t test should be
-#' used for normally distributed data. The Komolgorov-Smirnof test is used for data with nonnormal or
-#' unknown distribution.
-#' @param Crit A real-valued decision criterion on logit. Values recommended by Gallistel et al. (2004)
-#' are between 1.3 and 6, which correspond to p values of 0.05 and 0.000001, respectively.
-#' The values are the logarithms of the odds against the null (no-change) hypothesis.
-#' logit = log[(1-p)/p], where p is the desired significance level
+#' "binomial" (random rate), "chisquare", "ttest", "KS" (Komolgorov-Smirnov). The binomial
+#' test is used for finding changes in the rate parameter of a random rate process, either
+#' intermittently (isDiscrete = TRUE) or continuously (isDiscrete = FALSE) sampled.
+#' The chi square test is used with data where there has been a frequency change, as in,
+#' e.g. correct choices. The t test should be used for normally distributed data.
+#' The Komolgorov-Smirnof test is used for data with nonnormal or unknown distribution.
+#' @param Crit A real-valued decision criterion on logit. Values recommended by
+#' Gallistel et al. (2004) are between 1.3 and 6, which correspond to p values of 0.05 and
+#' 0.000001, respectively. The values are the logarithms of the odds against the null
+#' (no-change) hypothesis. logit = log[(1-p)/p], where p is the desired significance level
 #' @return A data frame with the number of Trials (Trial) for each change point detected,
 #' the cumulative number of correct responses, or successes, (CumSs),
-#' and the ratio of correct/total choices (Slopes) for the trials since the last change point
-#' and the current change point
+#' and the ratio of correct/total choices (Slopes) for the trials since the last change
+#' point and the current change point
 #' @references Gallistel CR, Fairhurst S, Balsam P (2004) The learning curve:
-#' Implications of a quantitative analysis. PNAS 101:13124-13131. doi: 10.1073/pnas.0404965101
+#' Implications of a quantitative analysis. PNAS 101:13124-13131.
+#' doi: 10.1073/pnas.0404965101
 #' @examples
 #' # generate dummy data with a change point at trial 120
-#' input <- c(rbinom(120,1,0.5),rbinom(200,1,0.8))
+#' input <- c(rbinom(120, 1, 0.5), rbinom(200, 1, 0.8))
 #' cp_wrapper(input, TRUE, "chisquare", 3)
 #' cp_wrapper(input, TRUE, "binomial", 3)
 #'
 #' # use included eyeblink data set:
 #' eyeblink[,] # inspect data set
-#' cp_wrapper(eyeblink, TRUE, "chisquare", 4)
+#' cp_wrapper(eyeblink, TRUE, "chisquare", 3)
 #' # using small criterion, e.g. 2, fails due to computational problems
 #' # better use "binomial" instead
 #' cp_wrapper(eyeblink, TRUE, "binomial", 2)
@@ -47,32 +48,33 @@
 #' CumRespMeasure = cumsum(plusmaze)[,])
 #' # plot cumulative learning curve with change points
 #' ggplot(my.data) + geom_line(aes(Trial,CumRespMeasure)) +
-#'  geom_point(data=cp.1, aes(Trial, CumSs), shape=1, size = 6, color = "blue") +
-#'  geom_point(data=cp.2, aes(Trial, CumSs), shape=2, size = 3, color = "green") +
-#'  geom_point(data=cp.3, aes(Trial, CumSs), shape=3, size = 3, color = "red")
+#'  geom_point(data = cp.1, aes(Trial, CumSs), shape = 1, size = 6, color = "blue") +
+#'  geom_point(data = cp.2, aes(Trial, CumSs), shape = 2, size = 3, color = "green") +
+#'  geom_point(data = cp.3, aes(Trial, CumSs), shape = 3, size = 3, color = "red")
 #'
 #' # plot average response rate per trial
-#' ggplot() + geom_step(data=cp.1, aes(Trial,Slopes)) +
+#' ggplot() + geom_step(data = cp.1, aes(Trial, Slopes)) +
 #'  ylab("Average Response Rate per Trial")
 #'
 #' @export
 
 cp_wrapper <- function(input, isDiscrete, test, Crit)
 {
-  if(!is.logical(isDiscrete)) stop("Second input (isDiscrete) must be boolean")
+  if (!is.logical(isDiscrete)) stop("Second input (isDiscrete) must be boolean")
 
   input <- unlist(input, use.names = FALSE)
-  Cum <- unlist(cumsum(input),use.names = FALSE)
+  Cum <- unlist(cumsum(input), use.names = FALSE)
 
 
-  if ((test=="binomial")&(!identical(input %% 1,mat.or.vec(length(input),nc=1)))&(isDiscrete))
+  if ((test == "binomial") & (!identical(input %% 1, mat.or.vec(length(input), nc = 1))) &
+      (isDiscrete))
     stop("When the binomial test is used with discrete-trial data,
          the data must be integer valued")
 
-  if (isDiscrete==0 & test=="chisquare")
+  if (!isDiscrete & test == "chisquare")
     stop("Cannot use chi square test when data are successive intervals")
 
-  if ((test=="chisquare")&(sum(input + !input)!=length(input)))
+  if ((test == "chisquare") & (sum(input + !input) != length(input)))
     stop("When chi square test is used, data must be binary, i.e. 0 or 1")
 
   switch(test,
@@ -84,8 +86,8 @@ cp_wrapper <- function(input, isDiscrete, test, Crit)
            # the total number of observations is less than 8
          },
          KS = {
-           CritLength <- 7 # K-S test is not valid when there are fewer than 4 data in either
-           # of the two samples
+           CritLength <- 7 # K-S test is not valid when there are fewer than 4 data in
+           # either of the two samples
          },
          ttest = {
            CritLength <- 2 # when t test is used there must be at least 3 data
@@ -95,108 +97,110 @@ cp_wrapper <- function(input, isDiscrete, test, Crit)
 
   outputlist <- c() # resetting the output
 
-  if (test=="chisquare"|test=="binomial")
+  if (test == "chisquare" | test == "binomial")
   {
-    CP <- matrix(c(0,0),ncol=2)
+    CP <- matrix(c(0, 0), ncol = 2)
     outputlist <- list(Cumt = Cum) # Initializiing for while loop. The Cumt vector will
     # be truncated as change points are found
     outputlist$r <- 1 # Initializing for while loop
 
-    while (!is.null(outputlist$r)&(length(outputlist$Cumt)>CritLength))
+    while (!is.null(outputlist$r) & (length(outputlist$Cumt) > CritLength))
     {
-      if (!isDiscrete){ # data are continuous
+      if (!isDiscrete) { # data are continuous
         R <- cpc(outputlist$Cumt) # putative inflection points
         L <- rrc(outputlist$Cumt, R) # logit vector for continuous case
-      } else { # data are discrete
+      } else {# data are discrete
         R <- cpd(outputlist$Cumt) # putative inflection points
 
         switch(test,
-               binomial = { # if binomial test is to be used
-                 L <- rrd(outputlist$Cumt,R) # logit vector
+               binomial = {# if binomial test is to be used
+                 L <- rrd(outputlist$Cumt, R) # logit vector
                },
-               chisquare={ # if chisquare test is to be used
-                 L <- chi2logit(outputlist$Cumt,R) # logit vector
+               chisquare = {# if chisquare test is to be used
+                 L <- chi2logit(outputlist$Cumt, R) # logit vector
                },
                stop("For discrete data test can only be 'binomial' or 'chisquare' ")
         )
       }
-      outputlist <- trun(outputlist$Cumt,R,L,Crit) #Cumt is the truncated cumulative
+      outputlist <- trun(outputlist$Cumt, R, L, Crit) #Cumt is the truncated cumulative
       # record; Lt is the logit vector up to the point of
       # truncation (not used); r is the change point; r is empty if there is
       # no significant change point
 
-      if (!is.null(outputlist$r)){ # if there is a change point, update change-point array
-        if(!isDiscrete) # In the continuous case, the row count goes in the
+      if (!is.null(outputlist$r)) { # if change point, update change-point array
+        if (!isDiscrete) # In the continuous case, the row count goes in the
           # y-column of the output array (the event count); in all other cases, it goes
           # in the x column. In the continuous case, the x column
           # contains the successive event times
         {
-          cumrCP <- CP[length(CP[,1]),2] + outputlist$r # Add Cumt row for latest change point
-          # to last change point to get Cum row of latest change point
-          CP <- rbind(CP,c(Cum[cumrCP], cumrCP)) # Value of cumulative record at the change point
+          cumrCP <- CP[length(CP[, 1]), 2] + outputlist$r # Add Cumt row for latest change
+          # point to last change point to get Cum row of latest change point
+          CP <- rbind(CP,c(Cum[cumrCP], cumrCP)) # Value of cumulative record at the
+          # change point
         }else{# In the discrete case, the row data go in the first column of CP
-          cumrCP <- CP[length(CP[,1]),1] + outputlist$r # Add Cumt row for latest change point
-          # to last change point to get Cum row of latest change point
-          CP <- rbind(CP,c(cumrCP, Cum[cumrCP])) # Value of cumulative record at the change point
+          cumrCP <- CP[length(CP[, 1]), 1] + outputlist$r # Add Cumt row for latest
+          # change point to last change point to get Cum row of latest change point
+          CP <- rbind(CP, c(cumrCP, Cum[cumrCP])) # Value of cumulative record at the
+          # change point
         }
 
       } # end of updating change-point array
 
-    } # end of while loop for finding successive change points when the binomial test is used
-  } # end of section that computes change-point array when binomial or chi square test is used
+    } # end of while loop for finding successive change points with binomial test
+  } # end of section that computes change-point array with binomial or chi square test
 
-  if (test=="ttest"|test=="KS")
+  if (test == "ttest" | test == "KS")
   {
-    outputlist <- list(newinput = input) # Initializing for while loop. These vectors will be
-    # truncated as CPs are found out
-    CP <- matrix(c(0,0),ncol=2) # Initializing for while loop
+    outputlist <- list(newinput = input) # Initializing for while loop. These vectors will
+    # be truncated as CPs are found out
+    CP <- matrix(c(0, 0), ncol = 2) # Initializing for while loop
     outputlist$r <- 1 # Initializing for while loop
 
-    while (!is.null(outputlist$r)&(length(outputlist$newinput)>CritLength))
+    while (!is.null(outputlist$r) & (length(outputlist$newinput) > CritLength))
     {
       outputlist$newCum <- cumsum(outputlist$newinput)
-      if (!isDiscrete){ # data are continuous
+      if (!isDiscrete) {# data are continuous
         R <- cpc(outputlist$newCum) # putative inflection points
-      } else { # data are discrete
+      } else {# data are discrete
         R <- cpd(outputlist$newCum) # putative inflection points
       } #computing R vector
 
       switch(test,
-             KS = { # if K-S test is to be used
-               outputlist$r <- KS(outputlist$newinput,R, Crit) # logit vector
+             KS = {# if K-S test is to be used
+               outputlist$r <- KS(outputlist$newinput, R, Crit) # logit vector
              },
-             ttest={ # if t test is to be used
-               outputlist$r <- cpt(outputlist$newinput,R, Crit) # logit vector
+             ttest = {# if t test is to be used
+               outputlist$r <- cpt(outputlist$newinput, R, Crit) # logit vector
              }
       ) # end of computing new changepoint
-      if (!is.null(outputlist$r)){ # if there is a change point, update change-point array and
-        # truncate newinput
-        cumrCP <- CP[length(CP[,1]),1] + outputlist$r # Add Cumt row for latest change point
-        # to last change point to get Cum row of latest change point
-        CP <- rbind(CP,c(cumrCP, Cum[cumrCP])) # Value of cumulative record at the change point
+      if (!is.null(outputlist$r)) {# if there is a change point, update change-point array
+        # and truncate newinput
+        cumrCP <- CP[length(CP[, 1]), 1] + outputlist$r # Add Cumt row for latest change
+        # point to last change point to get Cum row of latest change point
+        CP <- rbind(CP, c(cumrCP, Cum[cumrCP])) # Value of cumulative record at the change
+        # point
         outputlist$newinput <-
-          outputlist$newinput[(outputlist$r+1):length(outputlist$newinput)] # Truncated data vector
+          outputlist$newinput[(outputlist$r + 1):length(outputlist$newinput)]
+        # Truncated data vector
 
       } # end of updating change-point array & truncating
     } # end of while loop for computing CP array when K-S or t test are used
   } # end of section that computes CP array when K-S or t test are used
 
   # Adding final point to output array
-  if(isDiscrete) #
+  if (isDiscrete) #
   {
-    CP <- as.data.frame(rbind(CP, c(length(Cum), Cum[length(Cum)]))) # last row of CP array
-    # gives coordinates of final point in cumulative record
+    CP <- as.data.frame(rbind(CP, c(length(Cum), Cum[length(Cum)])))
     names(CP) <- c("Trial", "CumSs")
-    # last row of CP array gives coordinates of final point in cumulative record
-    CP %>% mutate_(Slopes = ~(CumSs-lead(CumSs))/(Trial-lead(Trial)),
-                   Slopes = ~ifelse(is.na(Slopes), lag(Slopes), Slopes))
-    #depends on the dplyr package
-  } else { # in continuous case, row count goes in y column
-    CP <- as.data.frame(rbind(CP, c(Cum[length(Cum)], length(Cum)))) # last row of CP array
-    # gives coordinates of final point in cumulative record
+  } else {# in continuous case, row count goes in y column
+    CP <- as.data.frame(rbind(CP, c(Cum[length(Cum)], length(Cum))))
     names(CP) <- c("Time", "Events")
-    # last row of CP array gives coordinates of final point in cumulative record
-    CP %>% mutate_(Slopes = ~(Events-lead(Events))/(Time-lead(Time)),
-                   Slopes = ~ifelse(is.na(Slopes), lag(Slopes), Slopes))
+
   }
+  # last row of CP array gives coordinates of final point in cumulative record
+  CP$Slopes <- (lead_v(CP[, 2]) - CP[, 2]) / (lead_v(CP[, 1]) - CP[, 1])
+  CP$Slopes <- ifelse(is.na(CP$Slopes), lag_v(CP$Slopes), CP$Slopes)
+  CP
 }
+
+