spelling fixes

R/elastic_lpcr_regression.R

@@ -1,14 +1,14 @@
-#' Elastic Logisitc Prinipcal Component Regression
+#' Elastic logistic Principal Component Regression
 #'
 #' This function identifies a logistic regression model with phase-variability
 #' using elastic pca
 #'
 #' @param f matrix (\eqn{N} x \eqn{M}) of \eqn{M} functions with \eqn{N} samples
 #' @param y vector of size \eqn{M} lables
 #' @param time vector of size \eqn{N} describing the sample points
-#' @param pca.method string specifing pca method (options = "combined",
+#' @param pca.method string specifying pca method (options = "combined",
 #' "vert", or "horiz", default = "combined")
-#' @param no scalar specifify number of principal components (default=5)
+#' @param no scalar specify number of principal components (default=5)
 #' @param smooth_data smooth data using box filter (default = F)
 #' @param sparam number of times to apply box filter (default = 25)
 #' @return Returns a lpcr object containing
@@ -18,7 +18,7 @@
 #' \item{warp_data}{fdawarp object of aligned data}
 #' \item{pca}{pca object of principal components}
 #' \item{Loss}{logistic loss}
-#' \item{pca.method}{string specifing pca method used}
+#' \item{pca.method}{string specifying pca method used}
 #' @keywords srvf alignment regression
 #' @references J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic
 #'  Functional Principal Component Regression,” Statistical Analysis and Data

R/elastic_mlpcr_regression.R

@@ -1,14 +1,14 @@
-#' Elastic Multinomial Logisitc Prinipcal Component Regression
+#' Elastic Multinomial logistic Principal Component Regression
 #'
 #' This function identifies a multinomial logistic regression model with phase-variability
 #' using elastic pca
 #'
 #' @param f matrix (\eqn{N} x \eqn{M}) of \eqn{M} functions with \eqn{N} samples
 #' @param y vector of size \eqn{M} labels
 #' @param time vector of size \eqn{N} describing the sample points
-#' @param pca.method string specifing pca method (options = "combined",
+#' @param pca.method string specifying pca method (options = "combined",
 #' "vert", or "horiz", default = "combined")
-#' @param no scalar specifify number of principal components (default=5)
+#' @param no scalar specify number of principal components (default=5)
 #' @param smooth_data smooth data using box filter (default = F)
 #' @param sparam number of times to apply box filter (default = 25)
 #' @return Returns a mlpcr object containing
@@ -19,7 +19,7 @@
 #' \item{warp_data}{fdawarp object of aligned data}
 #' \item{pca}{pca object of principal components}
 #' \item{Loss}{logistic loss}
-#' \item{pca.method}{string specifing pca method used}
+#' \item{pca.method}{string specifying pca method used}
 #' @keywords srvf alignment regression
 #' @references J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic
 #'  Functional Principal Component Regression,” Statistical Analysis and Data

R/elastic_pcr_regression.R

@@ -1,14 +1,14 @@
-#' Elastic Linear Prinipcal Component Regression
+#' Elastic Linear Principal Component Regression
 #'
 #' This function identifies a regression model with phase-variability
 #' using elastic pca
 #'
 #' @param f matrix (\eqn{N} x \eqn{M}) of \eqn{M} functions with \eqn{N} samples
 #' @param y vector of size \eqn{M} responses
 #' @param time vector of size \eqn{N} describing the sample points
-#' @param pca.method string specifing pca method (options = "combined",
+#' @param pca.method string specifying pca method (options = "combined",
 #' "vert", or "horiz", default = "combined")
-#' @param no scalar specifify number of principal components (default=5)
+#' @param no scalar specify number of principal components (default=5)
 #' @param smooth_data smooth data using box filter (default = F)
 #' @param sparam number of times to apply box filter (default = 25)
 #' @param parallel run in parallel (default = F)
@@ -20,7 +20,7 @@
 #' \item{warp_data}{fdawarp object of aligned data}
 #' \item{pca}{pca object of principal components}
 #' \item{SSE}{sum of squared errors}
-#' \item{pca.method}{string specifing pca method used}
+#' \item{pca.method}{string specifying pca method used}
 #' @keywords srvf alignment regression
 #' @references J. D. Tucker, J. R. Lewis, and A. Srivastava, “Elastic
 #'  Functional Principal Component Regression,” Statistical Analysis and Data

R/function_group_warp_bayes.R

@@ -5,7 +5,7 @@
 #' @param f matrix (\eqn{N} x \eqn{M}) of \eqn{M} functions with \eqn{N} samples
 #' @param time sample points of functions
 #' @param iter number of iterations (default = 150000)
-#' @param powera Dirchelet prior parameter (default 1)
+#' @param powera Dirichlet prior parameter (default 1)
 #' @param times factor of length of subsample points to look at (default = 5)
 #' @param tau standard deviation of Normal prior for increment (default ceil(times*.4))
 #' @param gp number of colors in plots (default seq(dim(f)[2]))

R/joint_gauss_model.R

@@ -3,7 +3,7 @@
 #' This function models the functional data using a Gaussian model extracted from
 #' the principal components of the srvfs using the joint model
 #'
-#' @param warp_data fdawarp objecet from \link{time_warping} of aligned data
+#' @param warp_data fdawarp object from \link{time_warping} of aligned data
 #' @param n number of random samples (n = 1)
 #' @param no number of principal components (n=4)
 #' @return Returns a fdawarp object containing \item{fs}{random aligned samples}

R/jointfPCA.R

@@ -3,12 +3,12 @@
 #' This function calculates amplitude and phase joint  functional principal component
 #' analysis on aligned data
 #'
-#' @param warp_data fdawarp objecet from \link{time_warping} of aligned data
-#' @param no number of prinicpal components to extract
+#' @param warp_data fdawarp object from \link{time_warping} of aligned data
+#' @param no number of principal components to extract
 #' @param id integration point for f0 (default = midpoint)
 #' @param C balance value (default = NULL)
 #' @param ci geodesic standard deviations (default = c(-1,0,1))
-#' @param showplot show plots of prinipal directions (default = T)
+#' @param showplot show plots of principal directions (default = T)
 #' @return Returns a list containing \item{q_pca}{srvf principal directions}
 #' \item{f_pca}{f principal directions}
 #' \item{latent}{latent values}

R/pair_align_functions.R

@@ -9,7 +9,7 @@
 #' @param lambda controls amount of warping (default = 0)
 #' @param method controls which optimization method (default="DP") options are
 #' Dynamic Programming ("DP"), Coordinate Descent ("DP2"), Riemannian BFGS
-#' ("RBFGS"), Simultaneous Alignment ("SIMUL"), Dirchelet Bayesian ("dBayes"),
+#' ("RBFGS"), Simultaneous Alignment ("SIMUL"), Dirichlet Bayesian ("dBayes"),
 #' and Expo-Map Bayesian ("expBayes")
 #' @param w controls LRBFGS (default = 0.01)
 #' @param iter number of mcmc iterations for mcmc method (default 2000)

R/pair_align_functions_bayes.R

@@ -9,7 +9,7 @@
 #' @param iter number of iterations (default = 15000)
 #' @param times factor of length of subsample points to look at (default = 5)
 #' @param tau standard deviation of Normal prior for increment (default ceil(times*.4))
-#' @param powera Dirchelet prior parameter (default 1)
+#' @param powera Dirichlet prior parameter (default 1)
 #' @param showplot shows plots of functions (default = T)
 #' @param extrainfo T/F whether additional information is returned
 #' @return Returns a list containing \item{f1}{function 1}

R/predict.lpcr.R

@@ -1,4 +1,4 @@
-#' Elastic Prediction for functional logisitc PCR Model
+#' Elastic Prediction for functional logistic PCR Model
 #'
 #' This function performs prediction from an elastic logistic fPCR regression model
 #'  with phase-variability
@@ -8,7 +8,7 @@
 #' @param y An optional vector of labels to calculate PC. If omitted, PC is NULL
 #' @param ... additional arguments affecting the predictions produced
 #' @return Returns a list containing
-#' \item{y_pred}{predicted probabilites of the class of newdata}
+#' \item{y_pred}{predicted probabilities of the class of newdata}
 #' \item{y_labels}{class labels of newdata}
 #' \item{PC}{probability of classification}
 #' @keywords srvf alignment regression

R/predict.mlpcr.R

@@ -1,4 +1,4 @@
-#' Elastic Prediction for functional multinomial logisitc PCR Model
+#' Elastic Prediction for functional multinomial logistic PCR Model
 #'
 #' This function performs prediction from an elastic multinomial logistic fPCR regression model
 #'  with phase-variability
@@ -8,7 +8,7 @@
 #' @param y An optional vector of labels to calculate PC. If omitted, PC is NULL
 #' @param ... additional arguments affecting the predictions produced
 #' @return Returns a list containing
-#' \item{y_pred}{predicted probabilites of the class of newdata}
+#' \item{y_pred}{predicted probabilities of the class of newdata}
 #' \item{y_labels}{class labels of newdata}
 #' \item{PC}{probability of classification per class}
 #' \item{PC.comb}{total probability of classification}

R/tolerance.R

@@ -89,7 +89,7 @@ bootTB <- function(f, time, a=.05, p=.99, B=500, no = 5, parallel=T){
 #' Tolerance Bound Calculation using Elastic Functional PCA
 #'
 #' This function computes tolerance bounds for functional data containing
-#' phase and amplitude variation using principal component anlaysis
+#' phase and amplitude variation using principal component analysis
 #'
 #' @param f matrix of functions
 #' @param time vector describing time sampling