Function reference • dailymet

All functions

Rennes: Maximum Daily temperature (TX) in Rennes-Saint-Jacques (FR)

anova(<pgpTList>): Log-Likelihood Ratio for pgpTList objects

as.fevdTList(): Coerce into a fevdTList object.

as.rqTList(): Create a rqTList Object

autolayer(<rqTList>): Autolayer Method for the Class "rqTList".

autoplot(<dailyMet>): Autoplot a daily meteorological series.

autoplot(<phasesMatrix>): Autoplots a phasesMatrix Object

autoplot(<rqTList>): Autoplot Method for the Class "rqTList".

checkTrigNames(): Check a Vector of Names for Trigonometric Components

clusters3(): Compute Clusters

coSd(): Estimated Coefficients with Standard Errors

coSd(<fevd>): Coefficients and Standard Errors

coSd(<fevdTList>): Coefficients and Standard Errors

coSd(<mlePP>): Coefficients and Standard Errors

coSd(<rq>): Coefficients and Standard Errors

coSd(<rqTList>): Coefficients and Standard Errors

dailyMet(): Create an Object with S3 Class "dailyMet"

dailymet-package: Utility Functions to Model Daily Meteorological Variables

designVars(): Create variables by using "design functions" of the date such as trigonometric, polynomial, splines, ...

exceed(): Exceedances Over Threshold(s)

exceed(<pgpTList>): Exceedances Over Threshold(s)

fevdTList(): Create a fevdTList Object

findStationMF(): Find a "Meteo France" Station using a Description, Id or Name

format(<quantile.pgpTList>): Round and Format Quantiles

isNested(): Checks that two pgpTList Objects correspond to Nested Models.

lastFullYear(): Find the Last Full Year

logLik(<pgpTList>): logLik Method for `pgpTList` Objects.

makeNewData(): Prepare a Data Object for a Prediction

makeNewData(<pgpTList>): Prepare a Data Object for a Prediction

modelMatrices(): Extract or Compute the Model Matrices related to an Object

modelMatrices(<fevd>): Extract or Compute the Model Matrices related to a fevd Object.

parInfo(): Provide Information about the Parameters of a Fitted Model Object

parInfo(<pgpTList>): Provide Information about the Parameters of a pgpTList Object.

pgpTList(): Fit a non-stationary Poisson-GP Model using several Thresholds computed by Quantile Regression.

phases(): Phases of Sine Waves from the Trigonometric Coefficients

predict(<fevd>): Predicted Values based on a fevd Object

predict(<pgpTList>): Predict a `pgpTList` Object.

predict(<rqTList>): Predict from a rqTList Object.

print(<phasesMatrix>): Print a phasesMatrix Object

quantMax(<pgpTList>): Compute Quantiles for the Maximum the Marks of a Poisson-GP Model

quantile(<fevd>): Compute Quantile for fevd Objects

quantile(<pgpTList>): Compute Quantiles for the Maximum the Marks of a Poisson-GP Model

readECA(): Read an ECA File and Add Extra Varables

readMet(): Read a csv File Containing Daily Meteorological Timeseries and Add Extra Variables

residuals(<fevd>): Generalized Residuals for some fevd Objects

residuals(<pgpTList>): Generalized Residuals for a TVGEV Model

rqTList(): Create A rqTList Object by Repeated Calls to rq

seasCenter(): Extract a Time Window in Year from a Data Frame.

simulate(<pgpTList>): Simulate a `pgpTList` Object.

sinBasis(): Create a Basis of Sine Waves with Given Phases

stationsMF: Stations Météo-France

subset(<dailyMet>): Subset the data part of a dailyMet object

summary(<dailyMet>): Summary Method

summary(<pgpTList>): Summary Method for `pgpTList` Objects.

tau(): Probability for a Quantile Regression Object

theta(): Compute the GP or GEV Coefficients for an Object with Class "fevd"

tsDesign(): Designs For Time Series Regression

xi(): Compute an Estimate of the Tail Coefficient 'xi'

xi(<rqTList>): Compute an Estimate of the Tail Coefficient 'xi' using Quantile Regression Results