Package 'plaqr'

a "plaqr" object obtained from a call to plaqr

additional parameters which will be ignored

a "plaqr" object.

a character vector with entries matching nonlinear terms in fit.

a character vector with length equal to the number of nonlinear terms in select (if select is NULL, the length must be equal to the number of nonlinear terms in fit). The first entry renames the first nonlinear term for plotting purposes, and so on. Note that select can reorder the nonlinear terms (see the examples).

a formula object, with the response on the left of a ~ operator, and the linear terms, separated by + operators, on the right. Any terms on the right of the ~ operator that also appear in nonlinVars will be included in the model as spline terms, not linear terms.

a one-sided formula object, with a ~ operator to the left of the nonlinear terms seperated by + operators. A term appearing in both formula and nonlinVars will be treated as a nonlinear term. If nonlinVars is not NULL, then an intercept will automatically be included in the model (despite a -1 or 0 term included in formula).

the quantile to be estimated, this is a number strictly between 0 and 1 (for now).

a data.frame in which to interpret the variables named in the formula, or in the subset and the weights argument. If this is missing, then the variables in the formula should be on the search list. This may also be a single number to handle some special cases – see below for details.

an optional vector specifying a subset of observations to be used in the fitting process.

vector of observation weights; if supplied, the algorithm fits to minimize the sum of the weights multiplied into the absolute residuals. The length of weights must be the same as the number of observations. The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous.

a function to filter missing data. This is applied to the model.frame after any subset argument has been used. The default (with na.fail) is to create an error if any missing values are found. A possible alternative is na.omit, which deletes observations that contain one or more missing values.

if TRUE then the model frame is returned. This is essential if one wants to call summary subsequently.

the algorithmic method used to compute the fit. There are several options: The default method is the modified version of the Barrodale and Roberts algorithm for $l_1$-regression, used by l1fit in S, and is described in detail in Koenker and d'Orey(1987, 1994), default = "br". This is quite efficient for problems up to several thousand observations, and may be used to compute the full quantile regression process. It also implements a scheme for computing confidence intervals for the estimated parameters, based on inversion of a rank test described in Koenker(1994). For larger problems it is advantagous to use the Frisch–Newton interior point method "fn". And very large problems one can use the Frisch–Newton approach after preprocessing "pfn". Both of the latter methods are described in detail in Portnoy and Koenker(1997). There is a fifth option "fnc" that enables the user to specify linear inequality constraints on the fitted coefficients; in this case one needs to specify the matrix R and the vector r representing the constraints in the form $Rb \geq r$. See the examples. Finally, there are two penalized methods: "lasso" and "scad" that implement the lasso penalty and Fan and Li's smoothly clipped absolute deviation penalty, respectively. These methods should probably be regarded as experimental.

a list giving contrasts for some or all of the factors default = NULL appearing in the model formula. The elements of the list should have the same name as the variable and should be either a contrast matrix (specifically, any full-rank matrix with as many rows as there are levels in the factor), or else a function to compute such a matrix given the number of levels.

a list of length equal to the number of nonlinear effects containing arguments to pass to the bs function for each term. Each element of the list is either NULL or a list with named elements correpsonding to the arguments in bs. If not NULL, the first element of splinesettings corresponds to the first nonlinear effect and so on.

additional arguments for the fitting routines (see the rq function in the ‘quantreg’ package ).

Coefficients from the fitted model

optionally the model matrix, if x=TRUE.

optionally the response, if y=TRUE.

the residuals from the fit.

the vector dual variables from the fit.

fitted values from the fit.

the formula that was used in the rq function.

the value of the objective function at the solution.

optionally the model frame, if model=TRUE

the linear terms used in the model fit.

the nonlinear terms used in the model fit.

the values of the nonlinear terms.

a plaqreffect object returned from nonlinEffect.

vector of indices of nonlinear terms in x to be plotted, by default all.

if TRUE, a rugplot for the x-coordinate is plotted.

if TRUE, the x-values of the rug plot are jittered.

title(s) as vector of character strings, by default titles are chosen for each plot as “Effect of CovariateName (tau=tau)”.

number of pages desired for the plots.

the type of plot that should be drawn.

additional arguments for the plotting algorithm.

a fitted model of class "plaqr" or "rq" to be used for prediction.

the prediction level required. The lower quantile is (1-level)/2 and the upper quantile is .5 + level/2.

an optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.

additional argument(s) for methods.

an object returned from nonlinEffect.

optional arguments.

an object returned from threshold.

optional arguments.

any model with a numerical response.

the desired threshold value. All values above t will be labeled “1”and all values below t will be labeled “0”.

an optional data frame in which to look for variables with which to predict. If omitted, no prediction is done.

additional argument(s) for methods in the predict function.

if newdata is not NULL, then pred.class is a vector of predicted classes for newdata. If newdata is NULL, then pred.class is NULL.

the threshold.

a vector of the predicted classes of the data used in fit.

a vector of the true classes of the data used in fit.

a scalar equal to the mean(train.class != true.class).

the number of observations in class“1” using the data used in fit.

the number of observations in class “1” using the data used in fit.

the number of observations truly in class “0”, but predicted to be in class “1” using the data used in fit.

the number of observations truly in class “1”, but predicted to be in class “1” using the data used in fit.

the call of fit.

the formula used in fit.

a vector of values to be transformed (the response variable)

a real-valued transformation parameter. 0 corresponds to the log transformation and 1 corresponds to the identity. See Geraci and Jones (2015) for more information on the one-parameter, symmetric transformation.

If TRUE, the inverse transformation is done to transform the variable back to the original scale. If FALSE, the standard transformation is computed.

a formula object, with the response on the left of a ~ operator, and the linear terms, separated by + operators, on the right. Any terms on the right of the ~ operator that also appear in nonlinVars will be included in the model as spline terms, not linear terms.

a one-sided formula object, with a ~ operator to the left of the nonlinear terms seperated by + operators. A term appearing in both formula and nonlinVars will be treated as a nonlinear term. If nonlinVars is not NULL, then an intercept will automatically be included in the model (despite a -1 or 0 term included in formula).

the quantile to be estimated, this is a number strictly between 0 and 1 (for now).

a data.frame in which to interpret the variables named in the formula, or in the subset and the weights argument. If this is missing, then the variables in the formula should be on the search list. This may also be a single number to handle some special cases – see below for details.

a real-valued sequence of possible transformation parameters. 0 corresponds to the log transformation and 1 corresponds to the identity. The transformation is symmetric so a negative transformation parameter is redundant and can be avoided. See Geraci and Jones (2015) for more information on the one-parameter, symmetric transformation.

a confint confidence interval for the transformation parameter will be created if confint is a number between 0 and 1 (otherwise automatically creates 95% CI). Otherwise, no confidence interval will be created. The bootstrap is used to create the confidence interval.

the number of bootstrap replications for the confidence interval. If no confidence interval is being created, this argument is ignored.

an optional vector specifying a subset of observations to be used in the fitting process.

vector of observation weights; if supplied, the algorithm fits to minimize the sum of the weights multiplied into the absolute residuals. The length of weights must be the same as the number of observations. The weights must be nonnegative and it is strongly recommended that they be strictly positive, since zero weights are ambiguous.

a function to filter missing data. This is applied to the model.frame after any subset argument has been used. The default (with na.fail) is to create an error if any missing values are found. A possible alternative is na.omit, which deletes observations that contain one or more missing values.

the algorithmic method used to compute the fit. There are several options: The default method is the modified version of the Barrodale and Roberts algorithm for $l_1$-regression, used by l1fit in S, and is described in detail in Koenker and d'Orey(1987, 1994), default = "br". This is quite efficient for problems up to several thousand observations, and may be used to compute the full quantile regression process. It also implements a scheme for computing confidence intervals for the estimated parameters, based on inversion of a rank test described in Koenker(1994). For larger problems it is advantagous to use the Frisch–Newton interior point method "fn". And very large problems one can use the Frisch–Newton approach after preprocessing "pfn". Both of the latter methods are described in detail in Portnoy and Koenker(1997). There is a fifth option "fnc" that enables the user to specify linear inequality constraints on the fitted coefficients; in this case one needs to specify the matrix R and the vector r representing the constraints in the form $Rb \geq r$. See the examples. Finally, there are two penalized methods: "lasso" and "scad" that implement the lasso penalty and Fan and Li's smoothly clipped absolute deviation penalty, respectively. These methods should probably be regarded as experimental.

a list giving contrasts for some or all of the factors default = NULL appearing in the model formula. The elements of the list should have the same name as the variable and should be either a contrast matrix (specifically, any full-rank matrix with as many rows as there are levels in the factor), or else a function to compute such a matrix given the number of levels.

a list of length equal to the number of nonlinear effects containing arguments to pass to the bs function for each term. Each element of the list is either NULL or a list with named elements correpsonding to the arguments in bs. If not NULL, the first element of splinesettings corresponds to the first nonlinear effect and so on.

The transformation parameter

The values of the transformed response

If a confidence interval is created, this is the confidence interval for the transformation parameter. Otherwise, NULL.

If a confidence interval is created, a B by n matrix containing the indices used in each bootstrap sample. Otherwise, NULL.

If a confidence interval is created, a B length vector containing the transformation parameter estimated in each bootstrap sample. Otherwise, NULL.