Changed order 'site' and 'time' arguments is all TRIM calls

pkg/R/read_tdf.R

@@ -90,37 +90,37 @@ snifreport <- function(file, colclasses){
 
 #' Compute a summary of counts
 #'
-#' 
+#'
 #' Summarize counts over a trim input dataset. Sites without counts are removed
 #' before any counting takes place (since these will not be used when calling
 #' \code{\link{trim}}). For the remaining records, the total number of
 #' zero-counts, positive counts, total number of observed counts and the total
 #' number of missings are reported.
-#' 
+#'
 #' @param x A \code{data.frame} with annual counts per site.
 #' @param eps \code{[numeric]} Numbers smaller then \code{eps} are treated a zero.
-#' @param count.id \code{[character|numeric]}  index of the column containing the counts
-#' @param time.id \code{[character|numeric]}  index of the column containing the time codes
 #' @param site.id \code{[character|numeric]}  index of the column containing the site id's
-#' 
+#' @param time.id \code{[character|numeric]}  index of the column containing the time codes
+#' @param count.id \code{[character|numeric]}  index of the column containing the counts
+#'
 #' @return A \code{list} of class \code{count.summary} containing individual names.
 #' @export
 #' @examples
 #' data(skylark)
 #' count_summary(skylark)
-#' 
+#'
 #' s <- count_summary(skylark)
 #' s$zero_counts # obtain number of zero counts
-count_summary <- function(x, count.id="count",time.id="time",site.id="site", eps=1e-8){
-  
+count_summary <- function(x, count.id="count", site.id="site", time.id="time", eps=1e-8){
+
   site_count <- tapply(X = x[,count.id], INDEX = x[site.id], FUN=sum, na.rm=TRUE)
   ii <- abs(site_count) < eps
   sites_wout_counts <- character(0)
   if (any(ii)){
     sites_wout_counts <- names(site_count[ii])
     x <- x[!x[,site.id] %in% sites_wout_counts,,drop=FALSE]
   }
-  
+
   cnt <- x[,count.id]
   L <- list(
      sites = length(unique(x[,site.id]))
@@ -135,10 +135,10 @@ count_summary <- function(x, count.id="count",time.id="time",site.id="site", eps
 }
 
 #' print a count summary
-#' 
+#'
 #' @param x An R object
 #' @param ... unused
-#' 
+#'
 #' @export
 #' @keywords internal
 print.count.summary <- function(x,...){
@@ -152,7 +152,7 @@ print.count.summary <- function(x,...){
   printf("Total number of observed counts    %8d\n",x$total_observed)
   printf("Number of missing counts           %8d\n",x$missing_counts)
   printf("Total number of counts             %8d\n",x$total_counts)
-  
+
 }
 
 

pkg/R/trim.R

@@ -2,7 +2,7 @@
 #'
 #'
 #' Compute TRIM model parameters.
-#' 
+#'
 #'
 #' @section Models:
 #'
@@ -14,8 +14,8 @@
 #' In \bold{Model 1}, the imputed values are modeled as
 #'
 #' \eqn{\ln\mu_{ij} = \alpha_i,}
-#' 
-#' where \eqn{\alpha_i} is the site effect. This model implies that the counts 
+#'
+#' where \eqn{\alpha_i} is the site effect. This model implies that the counts
 #' vary accross sites, not over time. The model-based \link[=totals]{time totals} are equal to
 #' each time point and the model-based \link[=index]{indices} are all equal to one.
 #'
@@ -138,7 +138,7 @@
 #' @param x a \code{\link{trimcommand}}, a \code{data.frame}, or a \code{formula}
 #'  If \code{x} is a \code{formula}, the dependent variable (left-hand-side)
 #'  is treated as the 'counts' variable. The first and second independent variable
-#'  are treated as the 'time' and 'site' variable, \bold{in that specific order}. All
+#'  are treated as the 'site' and 'time' variable, \bold{in that specific order}. All
 #'  other variables are treated as covariates.
 #' @param ... Currently unused
 #'
@@ -151,7 +151,7 @@
 #'
 #' @examples
 #' data(skylark)
-#' m <- trim(count ~ time + site, data=skylark, model=2)
+#' m <- trim(count ~ site + time, data=skylark, model=2)
 #' summary(m)
 #' coefficients(m)
 #'
@@ -161,13 +161,13 @@
 #' # match weights to sites
 #' weights <- w[skylark$site]
 #' # run model
-#' m <- trim(count ~ time + site, data=skylark, model=3, weights=weights)
+#' m <- trim(count ~ site + time, data=skylark, model=3, weights=weights)
 #'
 #'
 #' # An example using change points, a covariate, and overdispersion
 #' # 1 is added as cp automatically
 #' cp <- c(2,6)
-#' m <- trim(count ~ time + site + Habitat, data=skylark, model=2, changepoints=cp, overdisp=TRUE)
+#' m <- trim(count ~ site + time + Habitat, data=skylark, model=2, changepoints=cp, overdisp=TRUE)
 #' coefficients(m)
 #' # check significance of changes in slope
 #' wald(m)
@@ -191,8 +191,8 @@ trim.trimcommand <- function(x,...){
   else                 covin <- list()
 
   trim_estimate(count=dat$count
-      , time.id = dat$time
       , site.id = dat$site
+      , time.id = dat$time
       , covars = dat[covars]
       , model = x$model
       , serialcor = x$serialcor
@@ -207,8 +207,8 @@ trim.trimcommand <- function(x,...){
 
 
 #' @param count.id \code{[character]} name of the column holding species counts
-#' @param time.id \code{[character]} name of the column holding the time of counting
 #' @param site.id \code{[character]} name of the column holding the site id
+#' @param time.id \code{[character]} name of the column holding the time of counting
 #' @param covars \code{[character]} name(s) of column(s) holding covariates
 #' @param model \code{[numeric]} TRIM model type 1, 2, or 3.
 #' @param weights \code{[numeric]} Optional vector of site weights. The length of
@@ -238,8 +238,8 @@ trim.data.frame <- function(x, count.id = "count", site.id="site", time.id="time
   # estimate the model and return
   trim_estimate(
     count = x[,count.id]
-    , time.id = x[,time.id]
     , site.id = x[,site.id]
+    , time.id = x[,time.id]
     , covars = x[covars]
     , model = model
     , serialcor=serialcor
@@ -253,15 +253,15 @@ trim.data.frame <- function(x, count.id = "count", site.id="site", time.id="time
 }
 
 #' @rdname trim
-#' @param data \code{[data.frame]} Data containing at least counts, times, and sites.
+#' @param data \code{[data.frame]} Data containing at least counts, sites, and times
 #' @export
 trim.formula <- function(x, data, model=2, weights=numeric(0)
           , serialcor=FALSE, overdisp=FALSE, changepoints=integer(0), stepwise=FALSE
           , autodelete=FALSE, ...){
   stopifnot(inherits(data,"data.frame"))
   L <- parse_formula(x, names(data))
   trim.data.frame(x=data
-      , count.id=L$count, time.id=L$time, site.id=L$site, covars = L$cov
+      , count.id=L$count, site.id=L$site, time.id=L$time, covars = L$cov
       , model=model, weights=weights
       , serialcor=serialcor, overdisp=overdisp, changepoints=changepoints
       , stepwise=stepwise, autodelete=autodelete, ...)
@@ -280,7 +280,7 @@ parse_formula <- function(x, vars){
   if (!all(valid_vars)){
     stop(sprintf("Variables %s not found in data", pr(all_vars[!valid_vars])))
   }
-  list(count = lhs, time = rhs[1], site=rhs[2], cov=rhs[-(1:2)])
+  list(count = lhs, site=rhs[1], time = rhs[2], cov=rhs[-(1:2)])
 }
 
 

pkg/R/trim_gof.R

@@ -21,18 +21,18 @@
 #' and \code{AIC}, for Chi-squared, Likelihoof Ratio and Akaike informatiuon content,
 #' respectively.
 #' @export
-#' 
+#'
 #' @family analyses
-#' @examples 
+#' @examples
 #' data(skylark)
-#' z <- trim(count ~ time + site, data=skylark, model=2)
+#' z <- trim(count ~ site + time, data=skylark, model=2)
 #' # prettyprint GOF information
 #' gof(z)
-#' 
+#'
 #' # get individual elements, e.g. p-value
 #' L <- gof(z)
 #' LR_p <- L$LR$p # get p-value for likelihood ratio
-#' 
+#'
 gof <- function(x) UseMethod("gof")
 
 # Here is a simple wrapper function for TRIM output lists.

pkg/R/trim_index.R

@@ -81,14 +81,14 @@
 #' @examples
 #'
 #' data(skylark)
-#' z <- trim(count ~ time + site, data=skylark, model=2)
+#' z <- trim(count ~ site + time, data=skylark, model=2)
 #' index(z)
 #' # mimic classic TRIM:
 #' index(z, "both")
 #' # Extract standard errors for the imputed data
 #' SE <- index(z,"imputed")$se_mod
 #' # Include covariates
-#' z <- trim(count ~ time + site + Habitat, data=skylark, model=2)
+#' z <- trim(count ~ site + time + Habitat, data=skylark, model=2)
 #' ind <- index(z, covars=TRUE)
 #' plot(ind)
 index <- function(x, which=c("imputed","fitted","both"), covars=FALSE, base=1) {
@@ -178,7 +178,7 @@ index <- function(x, which=c("imputed","fitted","both"), covars=FALSE, base=1) {
 #' @examples
 #'
 #' data(skylark)
-#' z <- trim(count ~ time + site + Habitat, data=skylark, model=2)
+#' z <- trim(count ~ site + time + Habitat, data=skylark, model=2)
 #' idx <- index(z, covars=TRUE)
 #' plot(idx, covar="Habitat", main="Skylark")
 #'

pkg/R/trim_overall.R

@@ -29,7 +29,7 @@
 #'
 #' # obtain the overall slope accross all change points.
 #' data(skylark)
-#' z <- trim(count ~ time + site, data=skylark, model=2)
+#' z <- trim(count ~ site + time, data=skylark, model=2)
 #' overall(z)
 #' plot(overall(z))
 #'
@@ -39,7 +39,7 @@
 #' L$coef
 #'
 #' # Obtain the slope from changepoint to changepoint
-#' z <- trim(count ~ time + site, data=skylark, model=2,changepoints=c(1,4,6))
+#' z <- trim(count ~ site + time, data=skylark, model=2,changepoints=c(1,4,6))
 #' # slope from time point 1 to 5
 #' overall(z,changepoints=c(1,5,7))
 overall <- function(x, which=c("imputed","fitted"), changepoints=numeric(0)) {
@@ -255,7 +255,7 @@ print.trim.overall <- function(x,...) {
 #'
 #' @examples
 #' data(skylark)
-#' m <- trim(count ~ time + site, data=skylark, model=2)
+#' m <- trim(count ~ site + time, data=skylark, model=2)
 #' plot(overall(m))
 #'
 #' @export

pkg/R/trim_post.R

@@ -42,7 +42,7 @@ print.trim <- function(x,...){
 #' @examples
 #'
 #' data(skylark)
-#' z <- trim(count ~ time + site,data=skylark,model=2,overdisp=TRUE)
+#' z <- trim(count ~ site + time, data=skylark, model=2, overdisp=TRUE)
 #'
 #' summary(z)
 summary.trim <- function(object,...) {
@@ -190,7 +190,7 @@ overdispersion <- function(x){
 #' @family analyses
 #' @examples
 #' data(skylark)
-#' z <- trim(count ~ time + site, data=skylark, model=2,overdisp=TRUE)
+#' z <- trim(count ~ site + time, data=skylark, model=2, overdisp=TRUE)
 #' coefficients(z)
 coef.trim <- function(object,
     representation=c("standard","trend","deviations"),...) {
@@ -244,7 +244,7 @@ coef.trim <- function(object,
 #' @family analyses
 #' @examples
 #' data(skylark)
-#' z <- trim(count ~ time + site, data=skylark, model=2,changepoints=c(3,5))
+#' z <- trim(count ~ site + time, data=skylark, model=2, changepoints=c(3,5))
 #' totals(z)
 #'
 #' totals(z, "both") # mimics classic TRIM
@@ -297,7 +297,7 @@ export.trim.totals <- function(x, species, stratum) {
 #' @family analyses
 #' @examples
 #' data(skylark)
-#' z <- trim(count ~ time + site, data=skylark, model=2);
+#' z <- trim(count ~ site + time, data=skylark, model=2);
 #' totals(z)
 #' vcv1 <- vcov(z)       # Use imputed data
 #' vcv2 <- vcov(z,"model") # Use modelled data
@@ -334,7 +334,7 @@ vcov.trim <- function(object, which = c("imputed","model"), ... ) {
 #' @family analyses
 #' @examples
 #' data(skylark)
-#' z <- trim(count ~ time + site, data=skylark, model=2);
+#' z <- trim(count ~ site + time, data=skylark, model=2);
 #' out <- results(z)
 results <- function(z) {
   stopifnot(inherits(z,"trim"))
@@ -380,7 +380,7 @@ plot.trim.results <- function(z, ...) {
 #' @examples
 #'
 #' data(skylark)
-#' z <- trim(count ~ time + site,data=skylark,model=2,overdisp=TRUE)
+#' z <- trim(count ~ site + time, data=skylark, model=2, overdisp=TRUE)
 #' now_what(z)
 now_what <- function(z) {
   stopifnot(inherits(z,"trim"))

pkg/R/trim_refine.R

@@ -5,8 +5,8 @@
 #' TRIM stepwise refinement
 #'
 #' @param count a numerical vector of count data.
-#' @param time.id a numerical vector time points for each count data point.
 #' @param site.id a numerical vector time points for each count data point.
+#' @param time.id a numerical vector time points for each count data point.
 #' @param covars an optional list of covariates
 #' @param model a model type selector
 #' @param serialcor a flag indication of autocorrelation has to be taken into account.
@@ -17,7 +17,7 @@
 #'   Usually this information is retrieved by a set of postprocessing functions
 #'
 #' @keywords internal
-trim_refine <- function(count, time.id, site.id, covars=list(),
+trim_refine <- function(count, site.id, time.id, covars=list(),
                         model=2, serialcor=FALSE, overdisp=FALSE,
                         changepoints=integer(0),weights=numeric(0))
 {
@@ -27,13 +27,13 @@ trim_refine <- function(count, time.id, site.id, covars=list(),
 
   # Always start with an estimation using all proposed changepoints
   cur_cp <- org_cp
-  z <- trim_workhorse(count, time.id, site.id, covars, model, serialcor, overdisp, cur_cp, weights)
+  z <- trim_workhorse(count, site.id, time.id, covars, model, serialcor, overdisp, cur_cp, weights)
 
   # # Hack: remove all except the first changepoints
   # n <- length(org_cp)
   # active[2:n] <- FALSE
   # cur_cp <- org_cp[active]
-  # z <- trim_workhorse(count, time.id, site.id, covars, model, serialcor, overdisp, cur_cp)
+  # z <- trim_workhorse(count, site.id, time.id, covars, model, serialcor, overdisp, cur_cp)
 
   for (iter in 1:100) {
     # Phase 1: can one of the changepoints be removed?
@@ -56,7 +56,7 @@ trim_refine <- function(count, time.id, site.id, covars=list(),
     # If a changepoint has been removed, we'll need to re-estimate the model
     if (removed) {
       cur_cp = org_cp[active]
-      z <- trim_workhorse(count, time.id, site.id, covars, model, serialcor, overdisp, cur_cp, weights)
+      z <- trim_workhorse(count, site.id, time.id, covars, model, serialcor, overdisp, cur_cp, weights)
     }
 
     # Phase 2: try to re-insert previously removed changepoints
@@ -103,7 +103,7 @@ trim_refine <- function(count, time.id, site.id, covars=list(),
     # If a changepoint has been re-inserted, we'll need to re-estimate the model
     if (added) {
       cur_cp = org_cp[active]
-      z <- trim_workhorse(count, time.id, site.id, covars, model, serialcor, overdisp, cur_cp, weights)
+      z <- trim_workhorse(count, site.id, time.id, covars, model, serialcor, overdisp, cur_cp, weights)
     }
 
     # Finished refinement?

pkg/R/trim_wald.R

@@ -53,10 +53,10 @@
 #'
 #' @examples
 #' data(skylark)
-#' z2 <- trim(count ~ time + site, data=skylark, model=2)
+#' z2 <- trim(count ~ site + time, data=skylark, model=2)
 #' # print info on significance of slope parameters
 #' print(z2)
-#' z3 <- trim(count ~ time + site, data=skylark, model=3)
+#' z3 <- trim(count ~ site + time, data=skylark, model=3)
 #' # print info on significance of deviations from linear trend
 #' wald(z3)
 wald <- function(x) UseMethod("wald")