update johannes comments

R/XcmsExperiment.R

@@ -1380,7 +1380,7 @@ setMethod(
         rtMap <- param@rtMap
         if (length(rtMap) != length(object))
             stop("Mismatch between the number of files matched to lamas: ",
-                 length(rtMap), "and files in the object: ", length(object))
+                 length(rtMap), " and files in the object: ", length(object))
 
         # Make model and adjust retention for each file
         rt_adj <- bpmapply(rtMap, rt_raw, idx, FUN = function(x, y, i, param) {

R/do_adjustRtime-functions.R

@@ -593,23 +593,23 @@ adjustRtimeSubset <- function(rtraw, rtadj, subset,
 #'
 #' @description
 #' Alignment is achieved using the ['adjustRtime()'] method with a `param` of
-#' class `LamaParama`. This method #' corrects retention time by aligning
-#' chromatographic data with an external reference dataset. Peaks in the
-#' experimental chromatographic data are aligned to predefined landmark
-#' features called "lamas" based on their mass-to-charge ratio (m/z) and
-#' retention time.
-#'
-#' This alignment algorithm matches chromatographic peaks from the experimental
-#' data to the lamas, fitting a model based on this match to adjust their
-#' retention times and minimize discrepancies. This adjustment is performed
-#' file by file. Adjustable parameters such as `ppm`, `tolerance`, and
-#' `toleranceRt` define acceptable deviations during the matching process.
-#' It's crucial to note that only lamas and chromatographic peaks exhibiting a
-#' one-to-one mapping are considered when estimating retention time shifts. If
-#' a file has no peaks matching with lamas, no adjustment will be performed,
-#' and the file will be returned as-is. Users can evaluate this matching, for
-#' example, by checking the number of matches and ranges of the matching
-#' peaks, by first running `[matchLamasChromPeaks()]`.
+#' class `LamaParama`. This method corrects retention time by aligning
+#' chromatographic data with an external reference dataset.
+#'
+#' Chromatographic peaks in the experimental data are first matched to
+#' predefined (external) landmark features based on their mass-to-charge ratio
+#' and retention time and subsequently the data is aligned by minimizing the
+#' differences in retention times between the matched chromatographic peaks and
+#' lamas. This adjustment is performed file by file.
+#'
+#' Adjustable parameters such as `ppm`, `tolerance`, and `toleranceRt` define
+#' acceptable deviations during the matching process. It's crucial to note that
+#' only lamas and chromatographic peaks exhibiting a one-to-one mapping are
+#' considered when estimating retention time shifts. If a file has no peaks
+#' matching with lamas, no adjustment will be performed, and the the retention
+#' times will be returned as-is. Users can evaluate this matching, for example,
+#' by checking the number of matches and ranges of the matching peaks, by first
+#' running `[matchLamasChromPeaks()]`.
 #'
 #' Different warping methods are available; users can choose to fit a *loess*
 #' (`method = "loess"`, the default) or a *gam* (`method = "gam"`) between the
@@ -622,9 +622,9 @@ adjustRtimeSubset <- function(rtraw, rtadj, subset,
 #'
 #' Other functions related to this method:
 #'
-#'  - `LamaParama()`: return the respective the parameter object for alignment
-#'    using `adjustRtime()` function. Is also the input for functions listed
-#'    below.
+#'  - `LamaParama()`: return the respective parameter object for alignment
+#'    using `adjustRtime()` function. It is also the input for the functions
+#'    listed below.
 #'
 #'  - `matchLamasChromPeaks()`: quickly matches each file's ChromPeaks
 #'    to Lamas, allowing the user to evaluate the matches for each file.