Documentation check for typos

R/checkPeakMatrix.R

@@ -1,5 +1,5 @@
 #' Check if peak matrix is in format features in rows, samples in columns 
-#' and that all cell contain numeric values. 
+#' and that all cells contain numeric values. 
 #' 
 #' All functions in pmp pacakge expect input peak matrix to have samples 
 #' as columns and measured features in rows. This function will check input 
@@ -8,7 +8,7 @@
 #' of samples in peak matrix.
 #'
 #' @param peak_data peak matrix
-#' @param classes Vector of class labels
+#' @param classes vector of class labels
 #' @return matrix where samples are represented in columns and features in rows
 #' @examples 
 #' 

R/data.R

@@ -4,5 +4,5 @@
 #'
 #' @format List of testing data sets
 # 
-#' @source N/A
+#' @source https://www.ebi.ac.uk/metabolights/MTBLS79
 "testData"

R/filters.R

@@ -10,27 +10,27 @@ NULL
 #' This tool facilitates the removal of such features from the data matrix, 
 #' as defined using an appropriate blank' sample.
 #'
-#' @param df Peak intensity matrix
-#' @param fold_change Minimum fold change between analytical and blank samples.
-#' @param classes Vector of class labels
-#' @param blank_label Class label used to identify blank samples
-#' @param qc_label Class label for QC sample. If not NULL will use QC samples 
+#' @param df peak intensity matrix
+#' @param fold_change minimum fold change between analytical and blank samples.
+#' @param classes vector of class labels
+#' @param blank_label class label used to identify blank samples
+#' @param qc_label class label for QC sample. If not NULL will use QC samples 
 #'to calculate the mean intensity
-#' @param remove Remove blank samples from peak matrix or not
-#' @param fraction_in_blank Number between 0 to 1 to specify fraction in how 
+#' @param remove remove blank samples from peak matrix or not
+#' @param fraction_in_blank number between 0 to 1 to specify fraction in how 
 #'many blanks peaks should be present
-#' @return List of filtered peak intensity matrix and matrix with flags
+#' @return list of filtered peak intensity matrix and matrix with flags
 #' 
 #' @examples
 #' attach (testData)
 #' out <- filter_peaks_by_blank(df=testData$data, fold_change=1.2, 
-#' classes=testData$class, blank_label='Blank', qc_label=NULL, 
-#' remove=FALSE, fraction_in_blank=0)
+#'    classes=testData$class, blank_label='Blank', qc_label=NULL, 
+#'    remove=FALSE, fraction_in_blank=0)
 #' 
 #' @export
 
 filter_peaks_by_blank <- function(df, fold_change, classes, blank_label, 
-qc_label=NULL, remove=TRUE, fraction_in_blank=0) {
+        qc_label=NULL, remove=TRUE, fraction_in_blank=0) {
 
     df <- check_peak_matrix(peak_data=df, classes=classes)
 
@@ -86,26 +86,26 @@ qc_label=NULL, remove=TRUE, fraction_in_blank=0) {
 #' relative proportion (minimum fraction) of samples containing non-missing 
 #' values.
 #'
-#' @param df Peak intensity matrix
-#' @param min_frac Threshold of fraction of detection
-#' @param classes Vector of class labels
-#' @param method Method to use. 'QC' - withing QC samples, within' - within 
+#' @param df peak intensity matrix
+#' @param min_frac threshold of fraction of detection
+#' @param classes vector of class labels
+#' @param method method to use. 'QC' - withing QC samples, within' - within 
 #'each sample class or across' - across all samples
-#' @param qc_label Class label for QC sample
+#' @param qc_label class label for QC sample
 #' 
-#' @return List of filtered peak intensity matrix and matrix with flags
+#' @return list of filtered peak intensity matrix and matrix with flags
 #' 
 #' @examples 
 #' attach (testData)
 #' 
 #' out <- filter_peaks_by_fraction(df=testData$data, min_frac=1, 
-#' classes=testData$class, method='QC', qc_label='QC')
+#'     classes=testData$class, method='QC', qc_label='QC')
 #'     
 #' out <- filter_peaks_by_fraction(df=testData$data, min_frac=1, 
-#' classes=testData$class, method='across', qc_label='QC')
+#'     classes=testData$class, method='across', qc_label='QC')
 #' 
 #' out <- filter_peaks_by_fraction(df=testData$data, min_frac=1, 
-#' classes=testData$class, method='within', qc_label='QC')
+#'     classes=testData$class, method='within', qc_label='QC')
 #' 
 #' @export
 
@@ -153,10 +153,10 @@ filter_peaks_by_fraction <- function(df, min_frac, classes=NULL,
 
 #' Remove features from peak intensity matrix
 #'
-#' Filter to remove features
+#' Filter to remove features.
 #'
-#' @param df Peak intensity matrix
-#' @param rem_index Logical vector containing TRUE vales for features to remove
+#' @param df peak intensity matrix
+#' @param rem_index logical vector containing TRUE vales for features to remove
 #' 
 #' @return data frame, filtered peak intensity matrix
 #' 
@@ -187,16 +187,16 @@ remove_peaks <- function(df, rem_index) {
 #' relative standard deviation of intensity values for a given feature within 
 #' specified QC samples.
 #'
-#' @param df Peak intensity matrix
-#' @param max_rsd Threshold of QC RSD\% value
-#' @param classes Vector of class labels
-#' @param qc_label Class label for QC sample
-#' @return List of filtered peak intensity matrix and matrix with flags
+#' @param df peak intensity matrix
+#' @param max_rsd threshold of QC RSD\% value
+#' @param classes vector of class labels
+#' @param qc_label class label for QC sample
+#' @return list of filtered peak intensity matrix and matrix with flags
 #' @examples 
 #' 
 #' attach (testData)
 #' out <- filter_peaks_by_rsd(df=testData$data, max_rsd=20,
-#'    classes=testData$class, qc_label='QC')
+#'     classes=testData$class, qc_label='QC')
 #' 
 #' @export
 
@@ -228,10 +228,10 @@ filter_peaks_by_rsd <- function(df, max_rsd, classes, qc_label) {
 #' must first be addressed. This tool facilitates the removal of samples 
 #' containing a user-defined maximum percentage of missing values.
 #'
-#' @param df Peak intensity matrix
-#' @param max_perc_mv Threshold of missing value percentage.
-#' @param classes Vector of class labels
-#' @return List of filtered peak intensity matrix and matrix with flags
+#' @param df peak intensity matrix
+#' @param max_perc_mv threshold of missing value percentage.
+#' @param classes vector of class labels
+#' @return list of filtered peak intensity matrix and matrix with flags
 #' 
 #' @examples 
 #' 

R/glog_transformation.R

@@ -3,7 +3,7 @@
 
 NULL
 
-#' variance stabilising (extended) generalised logarithm transformation
+#' Variance stabilising (extended) generalised logarithm transformation
 #'
 #' https://doi.org/10.1186/1471-2105-8-234
 #'
@@ -17,12 +17,12 @@ glog <- function(y, y0=0, lambda){
     return(z)
 }
 
-#' Internal function for max. likelihood optimisation of glog params
+#' Internal function for max. likelihood optimisation of glog params.
 #' Calculates the alternative Jacobian function described in
 #' https://doi.org/10.1186/1471-2105-8-234
 #'
-#' @param y values.
-#' @param y0 offset applied to y (default=0).
+#' @param y values
+#' @param y0 offset applied to y (default=0)
 #' @param lambda lambda
 #' @return numeric, optimised glog parameter
 
@@ -55,11 +55,11 @@ SSE <- function(lambda, y0=0, y) {
     return(s)
 }
 
-#' If glog optimisation fails, this function will will scale values in the 
-#' peak matrix to the 1 / mean (total signal) over all samples.
+#' If glog optimisation fails, this function will scale values in the 
+#' peak matrix to the 1/mean(total signal) over all samples.
 #' 
-#' @param df Peak intensity matrix
-#' @return Scaled peak matrix
+#' @param df peak intensity matrix
+#' @return scaled peak matrix
 
 glog_rescale_data <- function(df){
     # 
@@ -76,9 +76,9 @@ glog_rescale_data <- function(df){
 #' https://doi.org/10.1186/1471-2105-8-234
 #'
 #' @param df Peak intensity matrix
-#' @param classes Vector of class labels
-#' @param qc_label Class label for QC sample
-#' @param store_lambda If value of optimised lambda parameter needs to be
+#' @param classes vector of class labels
+#' @param qc_label class label for QC sample
+#' @param store_lambda if value of optimised lambda parameter needs to be
 #'returned
 #' @examples
 #' attach (testData)

R/mv_imputation.R

@@ -4,9 +4,9 @@
 NULL
 
 #' Replace missing values for specific peak matrix feature
-#' @param x Index of matrix feature
-#' @param df A peak matrix with features in the rows, samples in the columns
-#' @param vals Vector object with the same length as number of features in 
+#' @param x index of matrix feature
+#' @param df a peak matrix with features in the rows, samples in the columns
+#' @param vals vector object with the same length as number of features in 
 #'peak matrix to replca NA values with
 #' 
 #' @return feature at specified index 'x' with replaced missing values
@@ -19,8 +19,8 @@ replace_na <- function(x, df, vals){
 }
 
 #' Imput missing values using mode of each feature
-#' @param df A peak matrix with features in the rows, samples in the columns
-#' @param method Mode to use for missing value imputation. 'mn' for mean and 
+#' @param df a peak matrix with features in the rows, samples in the columns
+#' @param method mode to use for missing value imputation. 'mn' for mean and 
 #''md'for median.
 #' 
 #' @return data frame of missing value imputed peak intensity matrix
@@ -42,16 +42,16 @@ impute_mode <- function (df, method){
 
 #' Missing value imputation using different algorithms
 #'
-#' @param df A peak matrix with features in the rows, samples in the columns
-#' @param method Missing value imputation method. Supported methods are 'knn',
-#''rf', 'bpca', 'sv', 'mn' and 'md'.
-#' @param k Number of neighbors to be used in the imputation 
-#' @param rowmax Fraction of missing values per row.
-#' @param colmax Fraction of missing values per column.
-#' @param maxp Number of features to run on single core. If set to NULL will
-#'use total number of features.
-#' @param check_df If set to TRUE will check if input data needs to be
-#'transposed, so that features are in rows.
+#' @param df a peak matrix with features in the rows, samples in the columns
+#' @param method missing value imputation method. Supported methods are 'knn',
+#''rf', 'bpca', 'sv', 'mn' and 'md'
+#' @param k number of neighbors to be used in the imputation 
+#' @param rowmax fraction of missing values per row
+#' @param colmax fraction of missing values per column
+#' @param maxp number of features to run on single core. If set to NULL will
+#'use total number of features
+#' @param check_df if set to TRUE will check if input data needs to be
+#'transposed, so that features are in rows
 #' @return data frame of missing value imputed peak intensity matrix
 #' 
 #' @examples 

R/normalisation.R

@@ -1,8 +1,8 @@
 #' Normalise peak table to the total sum of peak intensities
-#' @param df Data frame
-#' @param check_df If set to TRUE will check if input data needs to be
-#'transposed, so that features are in rows.
-#' @return Normalised peak matrix.
+#' @param df data frame
+#' @param check_df ff set to TRUE will check if input data needs to be
+#'transposed, so that features are in rows
+#' @return normalised peak matrix
 #' @examples 
 #' attach (testData)
 #' out <- normalise_to_sum (testData$data)
@@ -19,11 +19,11 @@ normalise_to_sum <- function(df, check_df=TRUE) {
 
 #' Normalise peak table using PQN method
 #'
-#' @param df Data frame.
-#' @param classes Vector of class labels. 
-#' @param qc_label Label used for QC samples. If set to 'all', all samples will
+#' @param df data frame
+#' @param classes vector of class labels
+#' @param qc_label label used for QC samples. If set to 'all', all samples will
 #'be used to calculate correction factor
-#' @return List of normalised data set and correction coefficients
+#' @return list of normalised data set and correction coefficients
 #' @examples 
 #' attach (testData)
 #' pqn_normalisation(df=testData$data, classes=testData$class, qc_label='QC')