curvetest: Test Equality of Curves with Homoscedastic or Heteroscedastic...
In curvetest: The package will formally test two curves represented by discrete data sets to be statistically equal or not when the errors of the two curves were assumed either equal or not using the tube formula to calculate the tail probabilities.

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/curvetest.R

Main test routine for formally testing the equality of two curves represented by discrete data points over a domain with homoscedacity or heteroscedacity errors. curvetest is the wrapper of curvetest.raw.

curvetest(formula, data1 = NULL, data2 = NULL, equal.var = TRUE, alpha = 0.5, bw = NULL, plotit = TRUE, conf.level = 0.05, kernel = c("Triangle", "Gaussian", "Trio", "Uniform", "Triweight", "Epanechnikov", "Quartic"), nn = 100, myx = NULL, bcorrect = "simple",...)
curvetest.raw(fits1, fits2, equal.var, conf.level, plotit)
## S3 method for class 'curvetest'
print(x,...)

`formula`	A formula to the data set such as y~x.
`data1`	A data frame of 2 columns representing the underlying curve 1. The column names must agree with the names in formula.
`data2`	A data frame for curve 2. If it is NULL, the test is whether curve 1 is statistically equal to 0 over the defining domain.
`equal.var`	Whether the variances are equal. Default to TRUE.
`alpha`	Smoothing parameter.
`bw`	Window bandwidth for both curves.
`plotit`	WHether plot the fitted curves or not. Default: FALSE.
`conf.level`	The confidence level to claim the curves are different. Default: 0.05.
`kernel`	One of the kernel functions to use to fit the curves. Must be one of "Triangle", "Gaussian", "Trio","Uniform", "Triweight", "Epanechnikov", "Quartic". partial match is allowed.
`nn`	Number of data points in the test domain to calculate the curve values.
`myx`	x-values in the test domain to calculate the curve values. If it is specified, nn will be suppressed.
`bcorrect`	Boundary correction method. Right now, except for 'none', meaning no corrections, the only other opton is 'simple'.
`...`	When plotit is true, plot parameters can be specified such as pch, lty, col etc.
`fits1,fits2`	The fitted results from fitting the first or second curve by curvefit procedure.
`x`	Test results from curvetest.

The algorithm works by first fitting the curves using local regression method specified by formula on data1 and/or data 2 with smoothing parameters specified in the function calls. Then it will test on the fitted curve 1 and curve 2 to see if they are statistically equal or not.

An R object of class curvetest will be generatd, containing curve fitting and testing results.

`Statistic,p`	Test statistic and p value of testing whether $f_1(x)==f_2(x)$ or $f_1(x)==0$.
`eDF`	Estimated degree of freedom of the fitting.
`equal.var`	The model specification of whether the two variances are equal or not
`esigma1,esigma2`	Estimated varaince of the fitted curves.
`k0`	The calculated value from the tube. See detail in paper.
`fits1,fits2`	Objects of class 'curvefit' from curvefit ruotine.

Zhongfa Zhang, Jiayang Sun

Zhongfa Zhang, et al: Test Equality of Curves with Homoscedastic or Heteroscedastic Errors. To appear

curvefit, print.curvetest, plot.curvetest

    n1=150; n2=155 ##numbers of data points for the two curves. 
    f1<-f2<-function(x){x*(1-x)+sin(2*pi*x)}; ##True functions.
    x1=seq(0,1, length=n1);
    x2=seq(0, 1, length=n2);
    y1=f1(x1)+rnorm(n1, 0, 0.2)
    y2=f2(x2)+rnorm(n2, 0, 0.2) ###Measured data for the two curves with noises. 
    curvetest(y~x,data.frame(x=x1,y=y1), data.frame(x=x2,y=y2), alpha =  0.7, equal.var=TRUE,plotit=TRUE)