plotMS: Model series plot

Description

This function produces a graphical output that allows the examination of the effect of using different model specifications (design) on the predictive performance of these models (a model series). It generally is used to access the results of functions buildMS and statsMS, but can be easily adapted to work with any model structure and performance measure.

Usage

plotMS(obj, grid, line, ind, type = c("b", "g"), pch = c(20, 2),
  size = 0.5, arrange = "desc", color = NULL, xlim = NULL,
  ylab = NULL, xlab = NULL, at = NULL, ...)

Arguments

obj

Object of class data.frame, generally returned by statsMS, containing a 1) series of performance statistics of several models, and 2) the design information of each model. See

grid

Vector of integer values or character strings indicating the columns of the data.frame containing the design data which will be gridded using the function levelplot. See Detail

line

Character string or integer value indicating which of the performance statistics (usually calculated by statsMS) should be plotted using the function xyplot. See Details

ind

Integer value indicating for which group of models the mean rank is to be calculated. See Details for more information.

type

Vector of character strings indicating some of the effects to be used when plotting the performance statistics using xyplot. Defaults to type = c("b", "g"). See panel.xyplot

pch

Vector with two integer values specifying the symbols to be used
to plot points. The first sets the symbol used to plot the performance
statistic, while the second sets the symbol used to plot the mean rank of
the indicator set using argument ind

size

Numeric value specifying the size of the symbols used for
plotting the mean rank of the indicator set using argument ind.
Defaults to size = 0.5. See grid.points for more
i

arrange

Character string indicating how the model series should be
arranged, which can be in ascending (asc) or descending (desc)
order. Defaults to arrange = "desc". See arrange

color

Vector defining the colors to be used in the grid produced by
function levelplot. If NULL, defaults to
color = cm.colors(n), where n is the number of unique values
in the columns defined by argument

xlim

Numeric vector of length 2, giving the x coordinates range. If
NULL (which is the recommended value), defaults to
xlim = c(0.5, dim(obj)[1] + 0.5). This is, so far, the optimum range
for adequate plotting.

ylab

Character vector of length 2, giving the y-axis labels. When
obj is a data.frame returned by statsMS, and the
performance statistic passed to argument line is one of those
calculated by statsMS

xlab

Character vector of length 1, giving the x-axis labels. Defaults
to xlab = "Model ranking".

at

Numeric vector indicating the location of tick marks along the x
axis (in native coordinates).

...

Other arguments for plotting, although most of these have no been
tested. Argument asp, for example, is not effective since the function
automatically identifies the best aspect for plotting based on the dimensions
of the design data.

`Value`

An object of class "trellis" consisting of a model series
plot.

`Warning`

Use the original functions xyplot and
levelplot for higher customization.

`Details`

This section gives more details about arguments obj, grid,
line, arrange, and ind.
obj{
The argument obj usually constitutes a data.frame returned by
statsMS. However, the user can use any data.frame object as far
as it contains the two basic units of information needed:
design data passed with argumentgrid
performance statistic passed with argumentline
}
grid{
The argument grid indicates the design data which is used to
produce the grid output in the top of the model series plot. By design
we mean the data that specify the structure of each model and how they differ
from each other. Suppose that eight linear models were fit using three types
of predictor variables (a, b, and c). Each of these
predictor variables is available in two versions that differ by their
accuracy, where 0 means a less accurate predictor variable, while
1 means a more accurate predictor variable. This yields 2^3 = 8 total
possible combinations. The design data would be of the following form:
> design
  a b c
1 0 0 0
2 0 0 1
3 0 1 0
4 1 0 0
5 0 1 1
6 1 0 1
7 1 1 0
8 1 1 1
}
line{
The argument line corresponds to the performance statistic that is
used to arrange the models in ascending or descending order, and to produce
the line output in the bottom of the model series plot. For example, it can
be a series of values of adjusted coefficient of determination, one for each
model:
adj_r2 <- c(0.87, 0.74, 0.81, 0.85, 0.54, 0.86, 0.90, 0.89)
}
arrange{
The argument arrange automatically arranges the model series
according to the performance statistics selected with argument line.
If obj is a data.frame returned by statsMS(), then the
function uses standard arranging approaches. For most performance
statistics, the models are arranged in descending order. The exception is
when "r2", "adj_r2" or "ADJ_r2" are used, in which case
the models are arranged in ascending order. This means that the model with
lowest value appears in the leftmost side of the model series plot, while the
models with the highest value appears in the rightmost side of the plot.
> arrange(obj, adj_r2)
  id a b c adj_r2
1  5 1 0 1   0.54
2  2 0 0 1   0.74
3  3 1 0 0   0.81
4  4 0 1 0   0.85
5  6 0 1 1   0.86
6  1 0 0 0   0.87
7  8 1 1 1   0.89
8  7 1 1 0   0.90
This results suggest that the best performing model is that of id = 7,
while the model of id = 5 is the poorest one.
}
ind{
The model series plot allows to see how the design influences model
performance. This is achieved mainly through the use of different colors in
the grid output, where each unique value in the design data is
represented by a different color. For the example given above, one could
try to see if the models built with the more accurate versions of the
predictor variables have a better performance by identifying their relative
distribution in the model series plot. The models placed at the
rightmost side of the plot are those with the best performance.
The argument ind provides another tool to help identifying how the
design, more specifically how each variable in the design data,
influences model performance. This is done by simply calculating the mean
ranking of the models that were built using the updated version of each
predictor variable. This very same mean ranking is also used to rank the
predictor variables and thus identify which of them is the most important.
After arranging the design data described above using the adjusted
coefficient of determination, the following mean rank is obtained for each
predictor variable:
> rank_center
     a    b    c
1 5.75 6.25 5.25
This result suggests that the best model performance is obtained when using
the updated version of the predictor variable b. In the model series
plot, the predictor variable b appears in the top row, while the
predictor variable c appears in the bottom row.
}

`References`

Deepayan Sarkar (2008). Lattice: Multivariate Data Visualization with
R. Springer, New York. ISBN 978-0-387-75968-5.
Roger D. Peng (2008). A method for visualizing multivariate time series
data. Journal of Statistical Software. v. 25 (Code Snippet), p. 1-17.
Roger D. Peng (2012). mvtsplot: Multivariate Time Series Plot. R
package version 1.0-1. http://CRAN.R-project.org/package=mvtsplot.

`See Also`

levelplot, xyplot,
mvtsplot.

`Examples`

Run this code# This example follows the discussion in section "Details"
# Note that the data.frame is created manually
id <- c(1:8)
design <- data.frame(a = c(0, 0, 1, 0, 1, 0, 1, 1),
                     b = c(0, 0, 0, 1, 0, 1, 1, 1),
                     c = c(0, 1, 0, 0, 1, 1, 0, 1))
adj_r2 <- c(0.87, 0.74, 0.81, 0.85, 0.54, 0.86, 0.90, 0.89)
obj <- cbind(id, design, adj_r2)
p <- plotMS(obj, grid = c(2:4), line = "adj_r2", ind = 1,
            color = c("lightyellow", "palegreen"),
            main = "Model Series Plot")
print(p)
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