The function is modified from the same-name function in packge BsMD with the purpose of providing more usage comfort (correct effect sizes in case of factors, automatic annotation, automatic labelling of the most significant factors only).
DanielPlot(fit, ...)
# S3 method for design
DanielPlot(fit, ..., response = NULL)
# S3 method for default
DanielPlot(fit, code = FALSE, autolab = TRUE, alpha = 0.05, faclab = NULL,
block = FALSE, datax = TRUE, half = FALSE, pch = "*",
cex.fac = par("cex.lab"), cex.lab = par("cex.lab"),
cex.pch = par("cex"), cex.legend = par("cex.lab"),
main = NULL, subtitle=NULL, ...)an experimental design of class design
with the type element of the design.info attribute
containing “FrF2” or “pb”
OR
object of class lm. Fitted model from lm or aov.
further arguments to be passed to the default function,
or graphical parameters to be passed to plot;
note that one should not use pch for split-plot designs.
NULL or a character string that specifies response variable to be used,
must be an element of response.names(obj);
if NULL, the first response from response.names(obj) is used
logical. If TRUE labels “A”,“B”, etc. are used
instead of the names of the coefficients (factors). A legend linking
codes to names is provided.
If TRUE, only the significant factors according to the Lenth method
(significance level given by alpha) are labelled.
significanc level for the Lenth method
NULL or list.
If NULL, point labels are automatically determined according to the
setting of code (i.e. A,B,C etc. for code=TRUE, natural effect
names otherwise) and autolab (i.e. all effects are labelled if autolab=FALSE,
only significant effects are labelled if autolab=TRUE).
Otherwise, faclab can be used for manual labelling of certain effects
and should be a list with idx (integer vector referring to
position of effects to be labelled) and lab (character vector of labels)
components.
logical. If TRUE, the first factor is labelled as “BK” (block).
logical. If TRUE, the x-axis is used for the factor effects the
the y-axis for the normal scores. The opposite otherwise.
logical. If TRUE, half-normal plot of effects is display.
numeric or character. Points character.
numeric. Factor label character size.
numeric. Labels character size.
numeric. Points character size.
numeric. Legend size in case of codes.
NULL or character. Title of plot. If NULL, automatic title is generated.
NULL or character. Sub title of plot. Should not be used for split-plot designs, because automatic subtitle is generated for these.
The function invisibly returns a data frame with columns: x, y,
no, effect, coded (if coded plot was requested)
and pchs, for the coordinates, the position numbers,
the effect names, the coded effect names, and the plotting characters
for plotted points.
The plotting characters are particularly useful for split-plot designs and can be used for subsequent separate plotting of whole-plot and split-plot effects, if necessary.
The design underlying fit
has to be a (regular or non-regular) fractional factorial 2-level design.
Effects (except for the intercept) are displayed in a normal or half-normal
plot with the effects in the x-axis by default.
If fit is a design with at least one response variable
rather than a linear model fit,
the lm-method for class design is applied to it with
degree high enough that at least one effect is assigned to each column of the Yates matrix,
and the default method for DanielPlot is afterwards applied to the
resulting linear model.
For split-plot designs, whole plot effects are shown as different plotting characters, because they are potentially subject to larger variability, and one should not be too impressed, if they look impressively large, as this may well be indication of plot-to-plot variability rather than a true effect.
Box G. E. P, Hunter, W. C. and Hunter, J. S. (2005) Statistics for Experimenters, 2nd edition. New York: Wiley.
Daniel, C. (1959) Use of Half Normal Plots in Interpreting Two Level Experiments. Technometrics 1, 311--340.
Daniel, C. (1976) Application of Statistics to Industrial Experimentation. New York: Wiley.
Lenth, R.V. (1989) Quick and easy analysis of unreplicated factorials. Technometrics 31, 469--473.
Lenth, R.V. (2006) Lenth s Method for the Analysis of Unreplicated Experiments. To appear in Encyclopedia of Statistics in Quality and Reliability, Wiley, New York. Downloadable at http://www.wiley.com/legacy/wileychi/eqr/docs/sample_1.pdf.
qqnorm, halfnormal,
LenthPlot, BsMD-package