Local Regression Dialog Functions

DESCRIPTION:

These functions are used by the Local Regression dialog.

USAGE:

menuLoess(formula, data, subset=T, na.omit.p=T, 
          weights=NULL, span=0.75, enp.target, degree.p=T, 
          cond.parametric=NULL, drop.quad=NULL, 
          normalize=TRUE, family=c("gaussian", "symmetric"), 
          surface=c("interpolate", "direct"), cell=0.2, 
          iterations=4, print.object.p=T, save.results=NULL, 
          save.fitted.p=F, save.resid.p=F, plotResidVsFit.p=F, 
          plotSqrtAbsResid.p=F, plotResponseVsFit.p=F,  
          plotQQ.p=F, plotRFSpread.p=F, plotCoplot.p=F, 
          smooths=F, rugplot=F, id.n=3, newdata=NULL, 
          predobj.name=NULL, predict.p=F, se.p=F) 
tabSummary.loess(object, print.object.p=T, 
          save.results=NULL, save.resid.p=F, save.fitted.p=F) 
tabPlot.loess(loessobj, plotResidVsFit.p=F, 
          plotSqrtAbsResid.p=F, plotResponseVsFit.p=F, 
          plotQQ.p=F, plotRFSpread.p=F, plotCoplot.p=F, 
          smooths=F, rugplot=F, id.n=3, ...) 
tabPredict.loess(object, newdata=NULL, save.name, 
       predict.p=F, se.p=F) 

REQUIRED ARGUMENTS:

formula
a formula object, with the response on the left of a ~ operator, and the terms, separated by "*" operators, on the right.
object
an object that inherits from class loess.
loessobj
an object that inherits from class loess.

OPTIONAL ARGUMENTS:

data
a data frame in which to interpret the variables named in 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.
weights
optional expression for weights to be given to indi- vidual observations in the sum of squared residuals that forms the local fitting criterion. By default, an un- weighted fit is carried out. If supplied, weights is treated as an expression to be evaluated in the same data frame as the model formula. It should evaluate to a non- negative numeric vector. If the different observations have nonequal variances, weights should be inversely pro- portional to the variances.
subset
expression saying which subset of the rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.
family
the assumed distribution of the errors. The values are "gaussian" or "symmetric". The first value is the default. If the second value is specified, a robust fitting procedure is used.
normalize
logical that determines if numeric predictors should be normalized. If TRUE, the standard normalization is used. If FALSE, no normalization is carried out.
span
smoothing parameter between 0 to 1, the higher the smoother the fitted cured.
enp.target
another way to specify the amount of smoothing. An approximation is used to compute a value of span that will yield approximately enp.target equivalent number of parameters.
degree.p
if FALSE, use locally-linear fitting, and if TRUE, use locally-quadratic fitting.
drop.quad
for cases with degree.p=TRUE and with two or more numeric predictors, this argument specifies those numeric predictors whose squares should be dropped from the set of fitting variables. The argument is a character string quoted with predictor names. Predictors are pasted together with comma as separator, sep=",".
cond.parametric
for two or more numeric predictors, this argument specifies those variables that should be conditionally-parametric. The argument is a character string quoted with predictor names. Predictors are pasted together with comma as separator, sep=",".
na.omit.p
if TRUE, then any observation with missing values are removed from the analysis. If FALSE and there are missing values then the function will exit with a message that missing values are not allowed. If na.omit.p is TRUE then na.action is set to na.omit in the call to lm. If na.omit.p is FALSE then na.action is set to na.fail in the call to lm.
surface
determines whether the fitted surface is computed directly at all points ("direct") or whether an interpolation method is used ("interpolate"). The latter, the default, is what most users should use unless special circumstances warrant.
cell
if interpolation is used to compute the surface, this argument specifies the maximum cell size of the k-d tree. Suppose k <- floor(n*cell*span) where n is the number of observations. Then a cell is further divided if the number of observations within it is greater than or equal to k.
iterations
if family is "symmetric", the number of iterations of the robust fitting method.
print.object.p
if TRUE, a short analysis of variance table is printed. This output is from the function print.aov.
save.results
a character string for the name of the data frame to save the fit and residuals in. If data frame with this name already exists in database 1 and it has the appropriate number of rows then the saved values will be appended to the data frame. If the object already exist in database 1 and it is not a data frame or it does not have the appropriate number of rows then a new name is created by appending a number to save.results and the results are saved in the data frame with the new name.
save.fitted.p
if TRUE, the fitted values from the regression are saved in the data frame save.results.
save.resid.p
if TRUE, the residuals from the regression are saved in the data frame save.results.
plotResidVsFit.p
if TRUE, a plot of the residuals versus the fitted values is created.
plotSqrtAbsResid.p
if TRUE, a plot of the absolute value of the square root of the residuals versus the fitted values is created. This plot is useful for checking for the constant variance assumption of the model.
plotResponseVsFit.p
if TRUE, a plot of the response versus the fitted values is created.
plotQQ.p
if TRUE, a Normal quantile-quantile plot of the residuals is created.
plotRFSpread.p
if TRUE, a residual-fit spread plot is created. This is a visual analog to the multiple R-squared statistic. It compares the spread of the fitted values to the spread of the residuals.
plotCoplot.p
if TRUE, the function graphs the fitted surface of a local regression model for one, two or three predictors. For one predictor, a curve is graphed against the predictor. For two or three predictors, a conditioning plot (coplot) is made against each predictor, conditional on the others. Each dependence panel of a conditioning plot shows a curve that is a slice through the surface and is based on an evaluation for evaluation equally-spaced values of the predictor ranging between values specified by ranges; in addition, confidence intervals at confidence equally-spaced values over the same range are shown. These arguments are specified during the creation of the preplot object by preplot.loess().
smooths
if TRUE a smooth curve, computed with loess.smooth is displayed on the Residuals vs Fit, Sqrt Abs Residuals vs Fit and Response vs Fit plots.
rugplot.p
if TRUE, a rugplot is displayed on the Residuals vs Fit, Sqrt Abs Residuals vs Fit and Response vs Fit plots. A rugplot is a sequence of vertical bars along the x-axis that mark the "observed" x values.
id.n
the number of extreme points that will be identified on the Residuals vs Fit, Sqrt Abs Residuals vs Fit, Residual's Normal QQ and Cook s Distance plots. The row names from the models data frame will be used to identify the points.
newdata
a data frame to use for computing predictions. It must contain the same names as the terms in the right side of the formula for the model. If missing, the predictions for the original data are computed.
predobj
a character string for the name of the data frame to save the predictions, standard errors and confidence intervals in. If data frame with this name already exists in database 1 and it has the appropriate number of rows then the values will be appended to the data frame. If the object already exist in database 1 and it is not a data frame or it does not have the appropriate number of rows then a new name is created by appending a number to predobj and the values are saved in data frame with the new name.
predict.p
if TRUE, the predicted values are saved in predobj.
se.p
if TRUE, the pointwise standard errors for the predictions will be stored in predobj.

VALUE:

an object of class "loess". See the help file for loess.object for details.

SIDE EFFECTS:

Plots will be drawn if requested. The objects save.results will be created or appended to if fitted values or residuals are saved.

SEE ALSO:

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