Description Usage Arguments Details Note Author(s) See Also Examples
Methods to develop model validation and visualization of Integrated Regression Goodness of Fit technique.
1 2 3 | plotAsIntRegGOF(obj, covar = 1, ADD = FALSE, ...)
pointsAsIntRegGOF(obj,covar=1,...)
linesAsIntRegGOF(obj,covar=1,...)
|
obj |
An object of class |
covar |
Variable name, number or vector for which Int. Reg. is computed. If it is a number, it reference a covariate in the model frame, while if it is a name refer to data in data frame using in the fitting process. |
ADD |
If |
... |
Further parameters to for plotobj command. |
Currently, the implementation computes the accumulated residual
process against a single covariate (covar
). When the value
of covar
is set to 0, the response is used as the variable
whose residual are accumulated against.
Notice that if covar
is a vector its lenght should be equal
to the number of residuals.
lm
objects that does not have a data parameter set
when the call is executed does not work presently when the covar
parameter is different than 0.
Jorge Luis Ojeda Cabrera (jojeda@unizar.es).
lm
, glm
, nls
its
associated plot
method and intRegGOF
.
1 2 3 4 5 6 7 8 9 10 11 | n <- 50
d <- data.frame( X1=runif(n),X2=runif(n))
d$Y <- 1 + 2*d$X1 + rnorm(n,sd=.125)
par(ask=TRUE)
plot( d )
plotAsIntRegGOF(lm(Y~X1+X2,d),covar="X1")
plotAsIntRegGOF(a <- lm(Y~X1-1,d))
plotAsIntRegGOF(a,c("X1"))
plotAsIntRegGOF(a,0)
plotAsIntRegGOF(a,fitted(a))
par(ask=FALSE)
|
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