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George Gamline By David Michael Stoll On Amazon Music, Gamline Trademark – viettingame


These smoothers are used to draw nonparametric-regression lines on scatterplots produced bythe scatterplot, scatterplotMatrix and several other car functions. The functions are not meant tobe called directly by the user, although the user can supply options via the smoother.args argument,the contents of which vary by the smoother (see Details below). The gamLine smoother uses thegam function in the mgcv package, the loessLine smoother uses theloess function in the stats package, and the quantregLine smoother uses therqss function in the quantreg package.

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gamLine(x, y, col, log.x, log.y, spread=FALSE, smoother.args, draw=TRUE, offset=0)

loessLine(x, y, col, log.x, log.y, spread=FALSE, smoother.args, draw=TRUE, offset=0)

quantregLine(x, y, col, log.x, log.y, spread=FALSE, smoother.args, draw=TRUE, offset=0)



$x$ coordinates of points.


$y$ coordinates of points.


line color.


TRUE if the $x$-axis is logged.


TRUE if the $y$-axis is logged.


the default is to plot only an estimated mean or median function. If this argument is TRUE, then a measure of spread is also plotted.


additional options accepted by the smoother, in the form of a list of named values (see Details below).


if TRUE, the default, draw the smoother on the currently active graph. If FALSE, return a list with coordinates x and y for the points that make up the smooth and if requested x.pos, y.pos, x.neg, y.neg for the spread smooths.

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For use when spread=TRUE, the vertical axis is sqrt(offset^2 + variance smooth).


The function loessLine is a re-implementation of the loess smootherthat was used in car prior to September 2012. The main enhancement is the ability toset more arguments through the smoother.args argument.

The function gamLine is more general than the loess fittingbecause it allows fitting a generalized additive model using splines. You can specify an errordistribution and link function.

The function quantregLine fits an additive model using splines with estimationbased on L1 regression for the median and quantile regression if you ask for the spread. It islikely to be more robust than the other smoothers.

The argument smoother.args is a list of named elements used to pass additional arguments to the smoother. As of November, 2016, the smoother is evaluated at an equally spaced grid of 50 points in the range of the horizontal variable. With any of the smoothers you can change to say 100 evaluation points by using the argument smoother.args=list(evaluation=100).

For loessLine the default value issmoother.args=list(lty=1, lwd=2, lty.spread=2, lwd.spread=1, span=2/3 (prior to 11/2016, span was 1/2),degree=2, family=”symmetric”, iterations=4). The arguments lty and lwd are the type and width respectively of the mean or median smooth, smooth.ltyand smooth.lwd are the type and color of the spread smooths if requested.The arguments span, degree and family arepassed to the loess function, iterations=4 robustness iterations.

For gamLine the default issmoother.args=list(lty=1, lwd=2, lty.spread=2, lwd.spread=1,k=-1, bs=”tp”, family=”gaussian”, link=NULL, weights=NULL)The first four arguments are as for loessLine. The next two arguments are passed to the gam function to control the smoothing:k=-1 allows gam to choose the number of splines in the basisfunction; bs=”tp” provides the type of spline basis to be used with “tp” for the default thin-plate splines. The last three arguments allow providinga family, link and weights as in generalized linear models. See examplesbelow. The spread argument is ignored unless family=”gaussian” and link=NULL.

For quantregLine the default issmoother.args=list(lty=1, lwd=2, lty.spread=2, lwd.spread=1,lambda=IQR(x)). The first fourarguments are as for loessLine. The last argument is passed to theqss function in quantreg. It is a smoothingparameter, here a robust estimate of the scale of the horizontal axis variable.This is an arbitrary choice, and may not work well in all circumstances.

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See Also

scatterplot, scatterplotMatrix, gam, loess, and rqss.


# NOT RUN {scatterplot(prestige ~ income, data=Prestige)scatterplot(prestige ~ income, data=Prestige, smoother=gamLine)scatterplot(prestige ~ income, data=Prestige, smoother=quantregLine)scatterplot(prestige ~ income | type, data=Prestige)scatterplot(prestige ~ income | type, data=Prestige, smoother=gamLine)scatterplot(prestige ~ income | type, data=Prestige, smoother=quantregLine)scatterplot(prestige ~ income | type, data=Prestige, smoother=NULL)scatterplot(prestige ~ income | type, data=Prestige, spread=TRUE)scatterplot(prestige ~ income | type, data=Prestige, smoother=gamLine, spread=TRUE)scatterplot(prestige ~ income | type, data=Prestige, smoother=quantregLine, spread=TRUE)scatterplot(weight ~ repwt | sex, spread=TRUE, data=Davis, smoother=loessLine)scatterplot(weight ~ repwt | sex, spread=TRUE, data=Davis, smoother=gamLine) # messes upscatterplot(weight ~ repwt | sex, spread=TRUE, data=Davis, smoother=quantregLine) # robustset.seed(12345)w

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