Last updated on 2024-10-31 10:50:28 CET.
| Package | NOTE | OK |
|---|---|---|
| fda.usc | 10 | 3 |
Current CRAN status: NOTE: 10, OK: 3
Version: 2.1.0
Check: Rd files
Result: NOTE
checkRd: (-1) FDR.Rd:23-24: Lost braces
23 | \item \code{out.FDR}{ \code{=TRUE}. If there are significative
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checkRd: (-1) FDR.Rd:25: Lost braces
25 | \item \code{pv.FDR}{ p-value for False Discovery Rate test.}
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71 | \item \code{cor}{ the value of distance correlation for each covariate.}
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72 | \item \code{maxLocal}{ index or locations of local maxima distance correlations.}
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checkRd: (-1) Outliers.fdata.Rd:53: Lost braces
53 | \item \code{outliers}{ Indexes of functional outlier.}
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54 | \item \code{dep.out}{ Depth value of functional outlier.}
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55 | \item \code{dep.out}{ Iteration in which the functional outlier is detected.}
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56 | \item \code{quantile}{ Threshold for outlier detection.}
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57 | \item \code{dep}{ Depth value of functional data.}
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checkRd: (-1) Outliers.fdata.Rd:83: Lost braces
83 | Cuevas A, Febrero M, Fraiman R. 2006. \emph{On the use of bootstrap for estimating functions with functional data.} Computational Statistics and Data Analysis 51: 1063{-}1074.
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checkRd: (-1) Outliers.fdata.Rd:85: Lost braces
85 | Febrero-Bande, M., Galeano, P., and Gonzalez-Manteiga, W. (2008). \emph{Outlier detection in functional data by depth measures with application to identify abnormal NOx levels}. Environmetrics 19, 4, 331{-}345.
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checkRd: (-1) Outliers.fdata.Rd:87: Lost braces
87 | Febrero-Bande, M., Galeano, P. and Gonzalez-Manteiga, W. (2007). \emph{A functional analysis of NOx levels: location and scale estimation and outlier detection}. Computational Statistics 22, 3, 411{-}427.
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checkRd: (-1) accuracy.Rd:46: Lost braces
46 | \item \code{accuracy}{ the accuracy classification score}
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47 | \item \code{recall}, \code{sensitivity,TPrate}{ \eqn{R=TP/(TP+FN)}}
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48 | \item \code{precision}{ \eqn{P=TP/(TP+FP)}}
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49 | \item \code{specificity},\code{TNrate}{ \eqn{TN/(TN+FP)}}
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50 | \item \code{FPrate}{ \eqn{FP/(TN+FP)}}
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51 | \item \code{FNrate}{ \eqn{FN/(TP+FN)}}
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52 | \item \code{Fmeasure}{ \eqn{2/(1/R+1/P)}}
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53 | \item \code{Gmean}{ \eqn{sqrt(R*TN/(TN+FP))}}
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54 | \item \code{kappa}{ the kappa index}
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55 | \item \code{cost}{ \eqn{sum(diag(tab)/rowSums(tab)*cost)/sum(cost)}}
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56 | \item \code{IOU}{ \eqn{TP/(TP+FN+FP)}} mean of Intersection over Union
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57 | \item \code{IOU4class}{ \eqn{TP/(TP+FN+FP)}} Intersection over Union by level
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61 | \item \code{MSE}{ Mean squared error, \eqn{\frac{\sum{(ypred- yobs)^2}}{n} }{\sum (ypred- yobs)^2 /n }}
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62 | \item \code{RMSE}{ Root mean squared error \eqn{\sqrt{\frac{\sum{(ypred- yobs)^2}}{n} }}{\sqrt(\sum (ypred- yobs)^2 /n )}}
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63 | \item \code{MAE}{ Mean Absolute Error, \eqn{\frac{\sum |yobs - ypred|}{n}}{\sum |yobs - ypred| /n}}
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checkRd: (-1) classif.DD.Rd:50-51: Lost braces
50 | \item \code{group.est} {Estimated vector groups by classified method
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52 | \item \code{misclassification} { Probability of misclassification.}
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53 | \item \code{prob.classification} { Probability of correct classification by group level.}
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54 | \item \code{dep} { Data frame with the depth of the curves for functional data (or points for multivariate data) in
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56 | \item \code{depth} { Character vector specifying the type of depth functions used.}
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57 | \item \code{par.depth} { List of parameters for \code{depth} function.}
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58 | \item \code{classif} { Type of classifier used.}
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59 | \item \code{par.classif}{ List of parameters for \code{classif} procedure.}
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60 | \item \code{w}{ Optional case weights.}
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61 | \item \code{fit}{ Fitted object by \code{classif} method using the depth as covariate.}
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103 | \item \code{formula}{ formula.}
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104 | \item \code{data}{ List that containing the variables in the model.}
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105 | \item \code{group}{ Factor of length \emph{n}}
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106 | \item \code{group.est}{ Estimated vector groups}
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107 | \item \code{prob.classification}{ Probability of correct classification by group.}
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108 | \item \code{prob.group}{ Matrix of predicted class probabilities. For each
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110 | \item \code{max.prob}{ Highest probability of correct classification.}
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65 | \item \code{formula}{ formula.}
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66 | \item \code{data}{ List that containing the variables in the model.}
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67 | \item \code{group}{ Factor of length \emph{n}}
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68 | \item \code{group.est}{ Estimated vector groups}
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69 | \item \code{prob.classification}{ Probability of correct classification by group.}
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70 | \item \code{prob.group}{ Matrix of predicted class probabilities. For each
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72 | \item \code{max.prob}{ Highest probability of correct classification.}
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57 | \item \code{formula}{ formula.}
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58 | \item \code{data}{ List that containing the variables in the model.}
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59 | \item \code{group}{ Factor of length \emph{n}}
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60 | \item \code{group.est}{ Estimated vector groups}
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61 | \item \code{prob.classification}{ Probability of correct classification by group.}
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62 | \item \code{prob.group}{ Matrix of predicted class probabilities. For each
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64 | \item \code{max.prob}{ Highest probability of correct classification.}
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54 | \item \code{formula}{ formula.}
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55 | \item \code{data}{ List that containing the variables in the model.}
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56 | \item \code{group}{ Factor of length \emph{n}}
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57 | \item \code{group.est}{ Estimated vector groups}
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58 | \item \code{prob.classification}{ Probability of correct classification by group.}
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59 | \item \code{prob.group}{ Matrix of predicted class probabilities. For each
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61 | \item \code{max.prob}{ Highest probability of correct classification.}
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21 | \item \code{mode.cond}{ Conditional mode.}
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22 | \item \code{x}{ Grid of length \code{n} where the the conditional density function is evaluated.}
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23 | \item \code{f}{ The conditional density function evaluated in \code{x}.}
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checkRd: (-1) create.fdata.basis.Rd:72: Lost braces
72 | \item \code{basis}{ basis}
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checkRd: (-1) create.fdata.basis.Rd:73: Lost braces
73 | \item \code{x}{ if \code{TRUE} the value of the rotated data (the centred data multiplied by the basis matrix) is returned}
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74 | \item \code{mean}{ functional mean of \code{fdataobj}}
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75 | \item \code{df}{ degree of freedom}
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76 | \item \code{type}{ type of basis}
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checkRd: (-1) dcor.xy.Rd:44: Lost braces
44 | \item \code{method}{ description of test}
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45 | \item \code{statistic}{ observed value of the test statistic}
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46 | \item \code{parameter}{ degrees of freedom}
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47 | \item \code{estimate}{ bias corrected distance correlation \code{bcdcor(x,y)}}
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48 | \item \code{p.value}{ p-value of the t-test}
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49 | \item \code{data.name}{ description of data}
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54 | \item \code{D1}{ the distance matrix of \code{x}}
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55 | \item \code{D2}{ the distance matrix of \code{y}}
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190 | 10: 419{-}440. %Febrero-Bande, M., Oviedo de la Fuente, M. (2012).
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75 | \item {\code{XYRP.test}}:{ \code{FDR.pv}: p-value using FDR, \code{proj.pv}: Matrix of p-values obtained for projections.}
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76 | \item {\code{MMD.test},\code{MMDA.test}}: {\code{stat}: Statistic, \code{p.value}: p-value, \code{thresh}: Threshold at level \code{alpha}.}
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77 | \item {\code{fEqDistrib.test}}: { \code{result}: \code{data.frame} with columns \code{Stat} and \code{p.value},
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49 | \item{pr}{code{logical}. If \code{TRUE} prints intermediate results.}
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34 | \item \code{stat}{ statistic value of test.}
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35 | \item \code{wm}{ statistic values of bootstrap resamples.}
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106 | linear differential operator; {\code{lambda.s},\code{Lfdobj.s}} for
| ^
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108 | \cr {\code{lambda.t},\code{Lfdobj.t}} for penalization of
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52 | \item \code{call}{ The matched call.}
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53 | \item \code{a.est}{ Intercept parameter estimated.}
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54 | \item \code{coefficientes}{ the matrix of the coefficients.}
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55 | \item \code{beta.est}{ A bivariate functional data object of class \code{bifd} with the estimated parameters of \eqn{\beta(s,t)}{\beta(s,t)}.}
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56 | \item \code{fitted.values}{ Estimated response.}
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57 | \item \code{residuals}{ \code{y} minus \code{fitted values}.}
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58 | \item \code{y}{ Functional response.}
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59 | \item \code{x}{ Functional explanatory data.}
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60 | \item \code{lambda.s}{ A roughness penalty with respect to \code{s} .}
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61 | \item \code{lambda.t}{ A roughness penalty with respect to \code{t}.}
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62 | \item \code{Lfdobj.s}{ A linear differential operator with respect to \code{s}.}
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63 | \item \code{Lfdobj.t}{ A linear differential operator with respect to \code{t}.}
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64 | \item \code{weights}{ Weights.}
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59 | \item \code{model}{ \code{fregre.pc}, \code{fregre.pls} or \code{fregre.basis} object.}
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60 | \item \code{beta.boot}{ functional beta estimated by the \code{nb} bootstrap regressions.}
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61 | \item \code{norm.boot}{ norm of diferences beetween the nboot betas estimated by bootstrap and beta estimated by regression model.}
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62 | \item \code{coefs.boot}{ matrix with the bootstrap estimated basis coefficients.}
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63 | \item \code{kn.boot}{ vector or list of length \code{nb} with index of the basis, PC or PLS factors selected in each bootstrap
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65 | \item \code{y.pred}{ predicted response values using \code{newX} covariates.}
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66 | \item \code{y.boot}{ matrix of bootstrap predicted response values using \code{newX} covariates.}
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67 | \item \code{newX}{ a \code{fdata} class containing the values of the model covariates at which predictions are required (only
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52 | \item \code{result:}{ List of non-parametric estimation by covariate.}
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53 | \item \code{fitted.values:}{ Estimated scalar response.}
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54 | \item \code{residuals:}{ \code{y} minus \code{fitted values}.}
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55 | \item \code{effects:}{ The residual degrees of freedom.}
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56 | \item \code{alpha:}{ Hat matrix.}
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57 | \item \code{family:}{ Coefficient of determination.}
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58 | \item \code{linear.predictors:}{ Residual variance.}
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59 | \item \code{deviance:}{ Scalar response.}
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60 | \item \code{aic:}{ Functional explanatory data.}
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61 | \item \code{null.deviance:}{ Non functional explanatory data.}
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62 | \item \code{iter}:{ Distance matrix between curves.}
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63 | \item \code{w:}{ beta coefficient estimated}
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64 | \item \code{eqrank:}{ List that containing the variables in the model.}
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65 | \item \code{prior.weights:}{ Asymmetric kernel used.}
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66 | \item \code{y:}{ Scalar response.}
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67 | \item \code{H:}{ Hat matrix, see Opsomer and Ruppert(1997) for more details.}
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68 | \item \code{converged:}{ conv.}
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44 | \item \code{basis.x}{ Basis used for \code{fdata} or \code{fd} covariates.}
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45 | \item \code{basis.b}{ Basis used for beta parameter estimation.}
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46 | \item \code{beta.l}{ List of estimated beta parameter of functional covariates.}
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47 | \item \code{data}{ List that containing the variables in the model.}
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48 | \item \code{formula}{ formula.}
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88 | \item \code{sr2:}{ Residual variance.}
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89 | \item \code{Vp:}{ Estimated covariance matrix for the parameters.}
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90 | \item \code{lambda:}{ A roughness penalty.}
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91 | \item \code{basis.x:}{ Basis used for \code{fdata} or \code{fd} covariates.}
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92 | \item \code{basis.b:}{ Basis used for beta parameter estimation.}
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93 | \item \code{beta.l:}{ List of estimated beta parameter of functional covariates.}
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94 | \item \code{data:}{ List that containing the variables in the model.}
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95 | \item \code{formula:}{ formula used in ajusted model.}
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96 | \item \code{formula.ini:}{ formula in call.}
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97 | \item \code{W:}{ inverse of covariance matrix}
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98 | \item \code{correlation:}{ See glsObject for the components of the fit. }
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43 | \item \code{sr2}{ Residual variance.}
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44 | \item \code{Vp}{ Estimated covariance matrix for the parameters.}
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45 | \item \code{lambda}{ A roughness penalty.}
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46 | \item \code{basis.x}{ Basis used for \code{fdata} or \code{fd} covariates.}
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47 | \item \code{basis.b}{ Basis used for beta parameter estimation.}
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48 | \item \code{beta.l}{ List of estimated beta parameter of functional covariates.}
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49 | \item \code{data}{ List that containing the variables in the model.}
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50 | \item \code{formula}{ formula.}
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52 | \item \code{call}{ The matched call.}
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53 | \item \code{residuals}{ \code{y} minus \code{fitted values}.}
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54 | \item \code{fitted.values}{ Estimated scalar response.}
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55 | \item \code{df.residual}{ The residual degrees of freedom.}
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56 | \item \code{r2}{ Coefficient of determination.}
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57 | \item \code{sr2}{ Residual variance.}
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58 | \item \code{H}{ Hat matrix.}
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59 | \item \code{y}{ Response.}
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60 | \item \code{fdataobj}{ Functional explanatory data.}
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61 | \item \code{mdist}{ Distance matrix between \code{x} and \code{newx}.}
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62 | \item \code{Ker}{ Asymmetric kernel used.}
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63 | \item \code{gcv}{ CV or GCV values.}
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64 | \item \code{h.opt}{ smoothing parameter or bandwidth that minimizes CV or GCV method.}
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65 | \item \code{h}{ Vector of smoothing parameter or bandwidth.}
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66 | \item \code{cv}{ List with the fitted values and residuals estimated by CV, without the same curve.}
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41 | \item \code{call}{ The matched call of \code{\link{fregre.pc}} function.}
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42 | \item \code{coefficients}{ A named vector of coefficients.}
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43 | \item \code{residuals}{ \code{y}-\code{fitted values}.}
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44 | \item \code{fitted.values}{ Estimated scalar response.}
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45 | \item \code{beta.est}{ beta coefficient estimated of class \code{fdata}}
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46 | \item \code{df.residual}{ The residual degrees of freedom. In ridge regression, \code{df(rn)} is the effective degrees of freedom.}
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47 | \item \code{r2}{ Coefficient of determination.}
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48 | \item \code{sr2}{ Residual variance.}
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49 | \item \code{Vp}{ Estimated covariance matrix for the parameters.}
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50 | \item \code{H}{ Hat matrix.}
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51 | \item \code{l}{ Index of principal components selected.}
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52 | \item \code{lambda}{ Amount of shrinkage.}
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53 | \item \code{P}{ Penalty matrix.}
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54 | \item \code{fdata.comp}{ Fitted object in \code{\link{fdata2pc}} function.}
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55 | \item \code{lm}{ \code{lm} object.}
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56 | \item \code{fdataobj}{ Functional explanatory data.}
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57 | \item \code{y}{ Scalar response.}
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47 | \item \code{fregre.pc}{ Fitted regression object by the best (\code{pc.opt}) components.}
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48 | \item \code{pc.opt}{ Index of PC components selected.}
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49 | \item \code{MSC.min}{ Minimum Model Selection Criteria (MSC) value for the (\code{pc.opt} components.}
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50 | \item \code{MSC}{ Minimum Model Selection Criteria (MSC) value for \code{kmax} components.}
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51 | \item \code{call}{ The matched call.}
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52 | \item \code{fitted.values}{ Estimated scalar response.}
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53 | \item \code{residuals}{ \code{y} minus \code{fitted values}.}
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54 | \item \code{df.residual}{ The residual degrees of freedom.}
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55 | \item \code{H}{ Hat matrix.}
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56 | \item \code{r2}{ Coefficient of determination.}
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57 | \item \code{sr2}{ Residual variance.}
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58 | \item \code{y}{ Scalar response.}
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59 | \item \code{fdataobj}{ Functional explanatory data.}
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60 | \item \code{XX}{ Non functional explanatory data.}
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61 | \item \code{mdist}{ Distance matrix between curves.}
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62 | \item \code{betah}{ beta coefficient estimated}
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63 | \item \code{data}{ List that containing the variables in the model.}
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64 | \item \code{Ker}{ Asymmetric kernel used.}
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65 | \item \code{h.opt}{ Value that minimizes CV or GCV method.}
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66 | \item \code{h}{ Smoothing parameter or bandwidth.}
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67 | \item \code{data}{ List that containing the
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69 | \item \code{gcv}{ GCV values.}
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70 | \item \code{formula}{ formula.}
| ^
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29 | \item \code{call}{ The matched call of \code{\link{fregre.pls}} function.}
| ^
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30 | \item \code{beta.est}{ Beta coefficient estimated of class \code{fdata}.}
| ^
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31 | \item \code{coefficients}{ A named vector of coefficients.}
| ^
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32 | \item \code{fitted.values}{ Estimated scalar response.}
| ^
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33 | \item \code{residuals}{\code{y}-\code{fitted values}.}
| ^
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34 | \item \code{H}{ Hat matrix.}
| ^
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35 | \item \code{df.residual}{ The residual degrees of freedom.}
| ^
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36 | \item \code{r2}{ Coefficient of determination.}
| ^
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37 | \item \code{GCV}{ GCV criterion.}
| ^
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38 | \item \code{sr2}{ Residual variance.}
| ^
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39 | \item \code{l}{ Index of components to include in the model.}
| ^
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40 | \item \code{lambda}{ Amount of shrinkage.}
| ^
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41 | \item \code{fdata.comp}{ Fitted object in \code{\link{fdata2pls}} function.}
| ^
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42 | \item \code{lm}{ Fitted object in \code{\link{lm}} function}
| ^
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43 | \item \code{fdataobj}{ Functional explanatory data.}
| ^
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44 | \item \code{y}{ Scalar response.}
| ^
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42 | \item \code{fregre.pls}{ Fitted regression object by the best (\code{pls.opt}) components.}
| ^
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43 | \item \code{pls.opt}{ Index of PLS components' selected.}
| ^
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44 | \item \code{MSC.min}{ Minimum Model Selection Criteria (MSC) value for
| ^
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46 | \item \code{MSC}{ Minimum Model Selection Criteria (MSC) value for \code{kmax} components.}
| ^
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17 | \item \code{DCP}{ Cook's Distance for Prediction.}
| ^
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18 | \item \code{DCE}{ Cook's Distance for Estimation.}
| ^
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19 | \item \code{DP}{ \eqn{\mbox{Pe}\tilde{\mbox{n}}\mbox{a's} }{} Distance.}
| ^
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33 | \item \code{quan.cook.for}{ Distance Cook Prediction Quantile.}
| ^
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34 | \item \code{quan.cook.est}{ Distance Cook Estimation Quantile.}
| ^
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35 | \item \code{quan.cook.Pena}{ Pena Distance Quantile.}
| ^
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36 | \item \code{mues.est}{ Sample Cook generated.}
| ^
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37 | \item \code{mues.pena}{ Sample Pena generated.}
| ^
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38 | \item \code{beta.boot}{ Functional beta estimated by bootstrap method.}
| ^
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69 | \item \code{cluster}{ Indexes of groups assigned.}
| ^
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70 | \item \code{centers}{ Curves centers.}
| ^
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46 | \item \code{gcv}{ Returns GCV values calculated for input parameters.}
| ^
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47 | \item \code{fdataobj}{ Matrix of set cases with dimension (\code{n} x \code{m}),
| ^
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50 | \item \code{fdata.est}{ Estimated \code{fdata} class object.}
| ^
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51 | \item \code{numbasis.opt}{ \code{numbasis} value that minimizes CV or GCV method.}
| ^
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52 | \item \code{lambda.opt}{ \code{lambda} value that minimizes CV or GCV method.}
| ^
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53 | \item \code{basis.opt}{ \code{basis} for the minimum CV or GCV method.}
| ^
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54 | \item \code{S.opt}{ Smoothing matrix for the minimum CV or GCV method.}
| ^
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55 | \item \code{gcv.opt}{ Minimum of CV or GCV method.}
| ^
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56 | \item \code{lambda}{ A roughness penalty. By default, no penalty \code{lambda=0}.}
| ^
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57 | \item \code{numbasis}{ Number of basis to use.}
| ^
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58 | \item \code{verbose}{ If \code{TRUE} information about GCV values
| ^
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59 | \item \code{gcv}{ GCV or CV for a vector of values of the smoothing parameter
| ^
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61 | \item \code{fdataobj}{ \code{\link{fdata}} class object.}
| ^
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62 | \item \code{fdata.est}{ Estimated \code{fdata} class object.}
| ^
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63 | \item \code{h.opt}{ \code{h} value that minimizes CV or GCV method.}
| ^
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64 | \item \code{S.opt}{ Smoothing matrix for the minimum CV or GCV method.}
| ^
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65 | \item \code{gcv.opt}{ Minimum of CV or GCV method.}
| ^
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66 | \item \code{h}{ Smoothing parameter or bandwidth.}
| ^
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51 | \item \code{fit}{ A vector of predictions or a matrix of predictions and bounds as above}
| ^
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52 | \item \code{se.fit}{ Associated standard error estimates of predictions}
| ^
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53 | \item \code{residual.scale}{ Residual standard deviations}
| ^
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54 | \item \code{df}{ Degrees of freedom for residual}
| ^
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checkRd: (-1) rp.flm.statistic.Rd:30-31: Lost braces in \itemize; \value handles \item{}{} directly
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checkRd: (-1) rp.flm.test.Rd:50-53: Lost braces in \itemize; meant \describe ?
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checkRd: (-1) rp.flm.test.Rd:101-102: Lost braces in \itemize; \value handles \item{}{} directly
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checkRd: (-1) rp.flm.test.Rd:105: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) summary.fregre.fd.Rd:28: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) summary.fregre.fd.Rd:29: Lost braces in \itemize; \value handles \item{}{} directly
checkRd: (-1) summary.fregre.fd.Rd:30: Lost braces in \itemize; \value handles \item{}{} directly
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-macos-arm64, r-release-macos-x86_64, r-release-windows-x86_64
Version: 2.1.0
Check: Rd cross-references
Result: NOTE
Found the following Rd file(s) with Rd \link{} targets missing package
anchors:
LMDC.select.Rd: gam
PCvM.statistic.Rd: fd
classif.DD.Rd: lda, qda
classif.ML.Rd: pca.fd, create.basis, rpart
classif.glm.Rd: pca.fd, create.basis
classif.gsam.Rd: pca.fd, create.basis
fda.usc-package.Rd: gam
fdata.deriv.Rd: Data2fd, deriv.fd
fdata2fd.Rd: Data2fd
flm.test.Rd: create.basis
fregre.basis.Rd: create.bspline.basis
fregre.basis.fr.Rd: linmod, create.bspline.basis
fregre.glm.Rd: pca.fd, create.basis
fregre.gls.Rd: corStruct, corClasses, varFunc, varFixed, varClasses,
glsControl, glsObject
fregre.gsam.Rd: s, te, gam, pca.fd, create.basis, summary.gam
fregre.gsam.vs.Rd: s, te, gam, summary.gam
fregre.lm.Rd: pca.fd, create.basis
norm.fdata.Rd: fd, inprod
plot.fdata.Rd: bifd
predict.fregre.lm.Rd: gam
semimetric.basis.Rd: create.bspline.basis, Data2fd, deriv.fd
Please provide package anchors for all Rd \link{} targets not in the
package itself and the base packages.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-windows-x86_64
Version: 2.1.0
Check: HTML version of manual
Result: NOTE
Found the following HTML validation problems:
GCCV.S.html:87:32 (GCCV.S.Rd:47): Warning: trimming empty <code>
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-x86_64