granova.1w: Graphic display for one-way ANOVA In granova: Graphical Analysis of Variance

Description

Graphic to display data for a one-way analysis of variance, and also to help understand how ANOVA works, how the F statistic is generated for the data in hand, etc. The graphic may be called 'elemental' or 'natural' because it is built upon the key question that drives one-way ANOVA.

Usage

 ```1 2 3 4 5``` ```granova.1w(data, group = NULL, dg = 2, h.rng = 1.25, v.rng = 0.2, box = FALSE, jj = 1, kx = 1, px = 1, size.line = -2.5, top.dot = 0.15, trmean = FALSE, resid = FALSE, dosqrs = TRUE, ident = FALSE, pt.lab = NULL, xlab = NULL, ylab = NULL, main = NULL, ...) ```

Arguments

 `data` Dataframe or vector. If a dataframe, the two or more columns are taken to be groups of equal size (whence `group` is NULL). If `data` is a vector, `group` must be a vector, perhaps a factor, that indicates groups (unequal group sizes allowed with this option). `group` Group indicator, generally a factor in case `data` is a vector. `dg` Numeric; sets number of decimal points in output display, default = 2. `h.rng` Numeric; controls the horizontal spread of groups, default = 1.25 `v.rng` Numeric; controls the vertical spread of points, default = 0.25. `box` Logical; provides a bounding box (actually a square) to the graph; default FALSE. `jj` Numeric; sets horizontal jittering level of points; when pairs of ordered means are close to one another, try jj < 1; default = 1. `kx` Numeric; controls relative sizes of `cex`, default = 1.0 `px` Numeric; controls relative sizes of `cex.axis`, default = 1.0 `size.line` Numeric; controls vertical location of group size and name labels, default = -2.5. `top.dot` Numeric; controls hight of end of vertical dotted lines through groups; default = .15. `trmean` Logical; marks 20% trimmed means for each group (as green cross) and prints out those values in output window, default = FALSE. `resid` Logical; displays marginal distribution of residuals (as a 'rug') on right side (wrt grand mean), default = FALSE. `dosqrs` Logical; ensures plot of squares (for variances); when FALSE or the number of groups is 2, squares will be suppressed, default = TRUE. `ident` Logical; allows user to identify specific points on the plot, default = FALSE. `pt.lab` Character vector; allows user to provide labels for points, else the rownames of xdata are used (if defined), or if not labels are 1:N (for N the total number of all data points), default = NULL. `xlab` Character; horizontal axis label, default = NULL. `ylab` Character; vertical axis label, default = NULL. `main` Character; main label, top of graphic; can be supplied by user, default = NULL, which leads to printing of generic title for graphic. `...` Optional arguments to be passed to `identify`, for example `offset`

Details

The central idea of the graphic is to use the fact that a one way analysis of variance F statistic is the ratio of two variances each of which can usefully be presented graphically. In particular, the sum of squares between (among) can be represented as the sum of products of so-called effects (each being a group mean minus the grand mean) and the group means; when these effects are themselves plotted against the group means a straight line necessarily ensues. The group means are plotted as (red triangles along this line. Data points (jittered) for groups are displayed (vertical axis) with respect to respective group means. One-way ANOVA residuals can be displayed (set resid=TRUE) as a rug plot (on right margin); the standard deviation of the residuals, when squared, is just the mean square within, which corresponds to area of blue square. The conventional F statistic is just a ratio of the between to the within mean squares, or variances, each of which corresponds to areas of squares in the graphic. The blue square, centered on the grand mean vertically and zero for the X-axis, corresponds to mean square within (with side based on [twice] the pooled standard deviation); the red square corresponds to the mean square between, also centered on the grand mean. Use of effects to locate the groups in the order of the observed means, from left to right (by increasing size) yields this 'elemental' graphic for this commonly used statistical method.

Groups need not be of the same sizes, nor do data need to reflect any particular distributional characteristics. Skewness, outliers, clustering of data points, and various other features of the data may be seen in this graphic, possibly identified using point labels. Trimmed means (20%) can also be displayed if desired. Finally, by redisplaying the response data in two or more versions of the graphic it can be useful to visualize various effects of non-linear data transformations. (`ident=TRUE`).

Value

Returns a list with two components:

 `grandsum` Contains the basic ANOVA statistics: the grandmean, the degrees of freedom and mean sums of squares between and within groups, the F statistic, F probability and the ratio between the sum of squares between groups and the total sum of squares. `stats` Contains a table of statistics by group: the size of each group, the contrast coefficients used in plotting the groups, the weighted means, means, and 20% trimmed means, and the group variances and standard deviations.

Author(s)

Robert M. Pruzek RMPruzek@yahoo.com,

James E. Helmreich James.Helmreich@Marist.edu

References

Fundamentals of Exploratory Analysis of Variance, Hoaglin D., Mosteller F. and Tukey J. eds., Wiley, 1991.

`granova.2w`, `granova.contr`, `granova.ds`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27``` ``` data(arousal) #Drug A granova.1w(arousal[,1:2], h.rng = 1.6, v.rng = 0.5, top.dot = .35) ######################### library(MASS) wt.gain <- anorexia[, 3] - anorexia[, 2] granova.1w(wt.gain, group = anorexia[, 1], size.line = -3) ########################## data(poison) ##Note violation of constant variance across groups in following graphic. granova.1w(poison\$SurvTime, group = poison\$Group, ylab = "Survival Time") ##RateSurvTime = SurvTime^-1 granova.1w(poison\$RateSurvTime, group = poison\$Group, ylab = "Survival Rate = Inverse of Survival Time") ##Nonparametric version: RateSurvTime ranked and rescaled ##to be comparable to RateSurvTime; ##note labels as well as residual (rug) plot below. granova.1w(poison\$RankRateSurvTime, group = poison\$Group, ylab = "Ranked and Centered Survival Rates", main = "One-way ANOVA display, poison data (ignoring 2-way set-up)", res = TRUE) ```

Example output

```Loading required package: car
\$grandsum
Grandmean        df.bet       df.with        MS.bet       MS.with
22.35          1.00         18.00         73.73          6.86
F.stat        F.prob SS.bet/SS.tot
10.75          0.00          0.37

\$stats
Size Contrast Coef Wt'd Mean  Mean Trim'd Mean Var. St. Dev.
Placebo   10         -1.92     20.43 20.43       20.30 5.83     2.41
Drug.A    10          1.92     24.27 24.27       24.45 7.89     2.81

\$grandsum
Grandmean        df.bet       df.with        MS.bet       MS.with
2.76          2.00         69.00        307.32         56.68
F.stat        F.prob SS.bet/SS.tot
5.42          0.01          0.14

\$stats
Size Contrast Coef Wt'd Mean  Mean Trim'd Mean  Var. St. Dev.
Cont   26         -3.21     -0.49 -0.45       -1.16 63.82     7.99
CBT    29          0.24      3.63  3.01        1.80 53.41     7.31
FT     17          4.50      5.15  7.26        7.91 51.23     7.16

\$grandsum
Grandmean        df.bet       df.with        MS.bet       MS.with
0.48         11.00         36.00          0.20          0.02
F.stat        F.prob SS.bet/SS.tot
9.01          0.00          0.73

\$stats
Size Contrast Coef Wt'd Mean Mean Trim'd Mean Var. St. Dev.
3     4         -0.27      0.21 0.21        0.21 0.00     0.02
9     4         -0.24      0.24 0.24        0.24 0.00     0.01
2     4         -0.16      0.32 0.32        0.32 0.01     0.08
12    4         -0.15      0.32 0.32        0.32 0.00     0.03
6     4         -0.14      0.34 0.34        0.34 0.00     0.05
8     4         -0.10      0.38 0.38        0.38 0.00     0.06
1     4         -0.07      0.41 0.41        0.41 0.00     0.07
7     4          0.09      0.57 0.57        0.57 0.02     0.16
10    4          0.13      0.61 0.61        0.61 0.01     0.11
11    4          0.19      0.67 0.67        0.67 0.07     0.27
5     4          0.34      0.82 0.82        0.82 0.11     0.34
4     4          0.40      0.88 0.88        0.88 0.03     0.16

\$grandsum
Grandmean        df.bet       df.with        MS.bet       MS.with
2.62         11.00         36.00          5.17          0.24
F.stat        F.prob SS.bet/SS.tot
21.53          0.00          0.87

\$stats
Size Contrast Coef Wt'd Mean Mean Trim'd Mean Var. St. Dev.
4     4         -1.46      1.16 1.16        1.16 0.04     0.20
5     4         -1.23      1.39 1.39        1.39 0.31     0.55
10    4         -0.93      1.69 1.69        1.69 0.13     0.36
11    4         -0.92      1.70 1.70        1.70 0.49     0.70
7     4         -0.76      1.86 1.86        1.86 0.24     0.49
1     4         -0.14      2.49 2.49        2.49 0.25     0.50
8     4          0.09      2.71 2.71        2.71 0.17     0.42
6     4          0.41      3.03 3.03        3.03 0.18     0.42
12    4          0.47      3.09 3.09        3.09 0.06     0.24
2     4          0.65      3.27 3.27        3.27 0.68     0.82
9     4          1.64      4.26 4.26        4.26 0.06     0.23
3     4          2.18      4.80 4.80        4.80 0.28     0.53

\$grandsum
Grandmean        df.bet       df.with        MS.bet       MS.with
2.49         11.00         36.00          3.70          0.19
F.stat        F.prob SS.bet/SS.tot
19.18          0.00          0.85

\$stats
Size Contrast Coef Wt'd Mean Mean Trim'd Mean Var. St. Dev.
4     4         -1.38      1.11 1.11        1.11 0.03     0.18
5     4         -1.13      1.36 1.36        1.36 0.28     0.53
10    4         -0.82      1.67 1.67        1.67 0.10     0.31
11    4         -0.80      1.69 1.69        1.69 0.50     0.71
7     4         -0.67      1.82 1.82        1.82 0.24     0.49
1     4         -0.10      2.39 2.39        2.39 0.30     0.55
8     4          0.23      2.72 2.72        2.72 0.19     0.44
6     4          0.55      3.04 3.04        3.04 0.18     0.42
12    4          0.61      3.09 3.09        3.09 0.05     0.22
2     4          0.66      3.15 3.15        3.15 0.39     0.62
9     4          1.29      3.78 3.78        3.78 0.03     0.16
3     4          1.55      4.04 4.04        4.04 0.03     0.16
```

granova documentation built on May 2, 2019, 9:36 a.m.