ClaraHapp / MFPCA
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man/PACE.Rd has changed.
NEWS.md has changed.
man/dot-PACE.Rd has changed.
man/fpcaBasis.Rd has changed.

@@ -5,7 +5,7 @@
 5 5 #' Calculate univariate functional PCA 6 6 #'  7 7 #' This function is a slightly adapted version of the  8 - #' \code{\link[refund]{fpca.sc}} function in the \pkg{refund} package for  8 + #' \code{fpca.sc} function in the \strong{refund} package for  9 9 #' calculating univariate functional principal components based on a smoothed  10 10 #' covariance function. The smoothing basis functions are penalized splines. 11 11 #'
@@ -25,7 +25,7 @@
 25 25 #' covariance estimate? Defaults to \code{FALSE}. 26 26 #' @param cov.weight.type The type of weighting used for the smooth covariance 27 27 #' estimate. Defaults to \code{"none"}, i.e. no weighting. Alternatively,  28 - #' \code{"counts"} (corresponds to \code{\link[refund]{fpca.sc}} ) weights the pointwise estimates of the covariance function 28 + #' \code{"counts"} (corresponds to \code{fpca.sc} in \strong{refund}) weights the pointwise estimates of the covariance function 29 29 #' by the number of observation points. 30 30 #'  31 31 #' @return \item{fit}{The approximation of \code{Y.pred} (if \code{NULL}, the
@@ -37,7 +37,7 @@
 37 37 #' were calculated.} \item{sigma2}{The estimated variance of the measurement  38 38 #' error.} \item{estVar}{The estimated smooth variance function of the data.} 39 39 #'  40 - #' @seealso \code{\link[refund]{fpca.sc}}, \code{\link{PACE}} 40 + #' @seealso \code{\link{PACE}} 41 41 #'  42 42 #' @references Di, C., Crainiceanu, C., Caffo, B., and Punjabi, N. (2009).  43 43 #' Multilevel functional principal component analysis. Annals of Applied
@@ -139,7 +139,7 @@
 139 139 #'  140 140 #' This function calculates a univariate functional principal components  141 141 #' analysis by smoothed covariance based on code from  142 - #' \code{\link[refund]{fpca.sc}} (package \pkg{refund}). 142 + #' \code{fpca.sc} in package \strong{refund}. 143 143 #'  144 144 #' @section Warning: This function works only for univariate functional data  145 145 #' observed on one-dimensional domains.
@@ -157,19 +157,19 @@
 157 157 #' @param nbasis An integer, representing the number of B-spline basis  158 158 #' functions used for estimation of the mean function and bivariate smoothing  159 159 #' of the covariance surface. Defaults to \code{10} (cf.  160 - #' \code{\link[refund]{fpca.sc}}). 160 + #' \code{fpca.sc} in \strong{refund}). 161 161 #' @param pve A numeric value between 0 and 1, the proportion of variance  162 162 #' explained: used to choose the number of principal components. Defaults to  163 - #' \code{0.99} (cf. \code{\link[refund]{fpca.sc}}). 163 + #' \code{0.99} (cf. \code{fpca.sc} in \strong{refund}). 164 164 #' @param npc An integer, giving a prespecified value for the number of  165 165 #' principal components. Defaults to \code{NULL}. If given, this overrides  166 - #' \code{pve} (cf. \code{\link[refund]{fpca.sc}}). 166 + #' \code{pve} (cf. \code{fpca.sc} in \strong{refund}). 167 167 #' @param makePD Logical: should positive definiteness be enforced for the  168 168 #' covariance surface estimate? Defaults to \code{FALSE} (cf.  169 - #' \code{\link[refund]{fpca.sc}}). 169 + #' \code{fpca.sc} in \strong{refund}). 170 170 #' @param cov.weight.type The type of weighting used for the smooth covariance  171 171 #' estimate. Defaults to \code{"none"}, i.e. no weighting. Alternatively,  172 - #' \code{"counts"} (corresponds to \code{\link[refund]{fpca.sc}} ) weights the 172 + #' \code{"counts"} (corresponds to \code{fpca.sc} in \strong{refund}) weights the 173 173 #' pointwise estimates of the covariance function by the number of observation 174 174 #' points. 175 175 #'
@@ -186,12 +186,12 @@
 186 186 #' (if \code{predData} is \code{NULL}).} \item{npc}{The number of functional  187 187 #' principal components: either the supplied \code{npc}, or the minimum number 188 188 #' of basis functions needed to explain proportion \code{pve} of the variance  189 - #' in the observed curves (cf. \code{\link[refund]{fpca.sc}}).}  189 + #' in the observed curves (cf. \code{fpca.sc} in \strong{refund}).}  190 190 #' \item{sigma2}{The estimated measurement error variance (cf.  191 - #' \code{\link[refund]{fpca.sc}}).} \item{estVar}{The estimated smooth 191 + #' \code{fpca.sc} in \strong{refund}).} \item{estVar}{The estimated smooth 192 192 #' variance function of the data.} 193 193 #'  194 - #' @seealso \code{\link[funData]{funData}}, \code{\link[refund]{fpca.sc}},  194 + #' @seealso \code{\link[funData]{funData}},  195 195 #' \code{\link{fpcaBasis}}, \code{\link{univDecomp}} 196 196 #'  197 197 #' @export PACE

@@ -174,7 +174,7 @@
 174 174 #' This function calculates a functional principal component basis 175 175 #' representation for functional data on one-dimensional domains. The FPCA is 176 176 #' calculated via the \code{\link{PACE}} function, which is built on 177 - #' \link[refund]{fpca.sc} in the \pkg{refund} package. 177 + #' \code{fpca.sc} in the \strong{refund} package. 178 178 #'  179 179 #' @param funDataObject An object of class \code{\link[funData]{funData}}  180 180 #' containing the observed functional data samples and for which the FPCA is
@@ -182,19 +182,19 @@
 182 182 #' @param nbasis An integer, representing the number of B-spline basis  183 183 #' functions used for estimation of the mean function and bivariate smoothing  184 184 #' of the covariance surface. Defaults to \code{10} (cf.  185 - #' \code{\link[refund]{fpca.sc}}). 185 + #' \code{fpca.sc} in \strong{refund}). 186 186 #' @param pve A numeric value between 0 and 1, the proportion of variance  187 187 #' explained: used to choose the number of principal components. Defaults to  188 - #' \code{0.99} (cf. \code{\link[refund]{fpca.sc}}). 188 + #' \code{0.99} (cf. \code{fpca.sc} in \strong{refund}). 189 189 #' @param npc An integer, giving a prespecified value for the number of  190 190 #' principal components. Defaults to \code{NULL}. If given, this overrides  191 - #' \code{pve} (cf. \code{\link[refund]{fpca.sc}}). 191 + #' \code{pve} (cf. \code{fpca.sc} in \strong{refund}). 192 192 #' @param makePD Logical: should positive definiteness be enforced for the  193 193 #' covariance surface estimate? Defaults to \code{FALSE} (cf.  194 - #' \code{\link[refund]{fpca.sc}}). 194 + #' \code{fpca.sc} in \strong{refund}). 195 195 #' @param cov.weight.type The type of weighting used for the smooth covariance 196 196 #' estimate in \code{\link{PACE}}. Defaults to \code{"none"}, i.e. no weighting. Alternatively,  197 - #' \code{"counts"} (corresponds to \code{\link[refund]{fpca.sc}} ) weights the pointwise estimates of the covariance function 197 + #' \code{"counts"} (corresponds to \code{fpca.sc} in \strong{refund}) weights the pointwise estimates of the covariance function 198 198 #' by the number of observation points. 199 199 #'  200 200 #' @return \item{scores}{A matrix of scores (coefficients) with dimension
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