1. 授权72pines.com的管理员在判断有必要的情况下将本文转为private.

2. 我还没有试用文中推荐的技术，转载不表示对该技术的适用性背书[update]试用体验甚好，速度优于我目前所用的中文大学vpn，自动切换效果尤佳。

3.[update]题图T恤亦为转载广告，是否公益待考。图链接为男款，亦有女款。不过，今天(2009JUL01)发现已断货。

本文将提供一种一劳永逸的翻墙方式（ssh -D），实施之后，那道墙——对你来说——将从此透明。

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The first is a variant of the old Golden Hammer law -- if the only tool is ANOVA, every continuous predictor need discretization. The second reason is empirical -- ANOVA with discretization steals df(s). Let's demo it with a diagram.
The red are the population points, and the black are samples. Which predicts the population better--the green continuous line, or the discretized blue dashes? R simulation code is given.

用John FOX教授的sem包试写了这本教材的几个例子，结果都与LISREL8报告的Minimum Fit Function Chi-Square吻合。不过LISREL其它拟合指标用的是Normal Theory Weighted Least Squares Chi-Square，所以看上去比Minimum Fit Function Chi-Square报告的结果要好那么一些些。LISREL历史上推的GFI/AGFI曾因为经常将差报好被批评，圈内朋友私下嘲笑这样LISREL就更好卖了，买软件的用户也高兴将差报好，只有读Paper的人上当。

目前只写了chap3_1..到Chap3_2_...，一共5(或6)个例子。尺有所短，寸有所长--sem包不会自动报告所有修正指数，不能做样本量不一样多的多组模型，对复杂的模型要写的代码太多。不过，在已经尝试的几个例子里，有一个是LISREL跑不出来但sem包能跑出结果的。目前sem包还没有达到结构方程众多商业软件的成熟水准，但R庞大的义工武器库已经使sem包至少已经在Missing Data Multiple Imputation、Bootstrapping等等应用上胜出一筹。所以例子中还附带了缺失数据一讲Multiple Impuation的R示范代码。

欢迎各位有兴趣作类似尝试的同学将结果email我， 可以陆续更新到下面的GPL版权代码集合中。

代码下载：lixiaoxu.googlepages.com (中大镜像)

论文下载(Googlepages, 中文大学镜像)
RWebFriend for Wordpress 在线示例(yo2.cn上的示例,  奇想录上的临时示例)

Google Presentation 在线演示

...if we combine the variance components for the two random effects together into a single matrix, we would find a highly structured block diagonal matrix. For example, if there were three students in each class, we would have:

If the number of students per class varied, the size of each of these submatrices would also vary, although they would still have this common structure. The variance in MATHACH for any given student is assumed to be ...

Quiz:

1. Sampling K repetitions with replacement, denotes MATHACH of the k-th random student within a given class, say, the first class. What is the population variance of the Y series? A: , B: .

2. Sampling repetitions with replacement, denotes MATHACH(s) of the k-th pair of random students within a given class, say, the first class. Sometime the 2nd sampled student is just the 1st one by chance. What is the population covariance of the and series? A: 0, B: .

3. Sampling K repetitions with replacement, denotes MATHACH of the k-th random student within one randomly sampled class for each repetition. What is the population variance of the Y series? A: , B: .

4. Sampling repetitions with replacement, denotes MATHACH(s) of the k-th pair of random students within one random class sampled for each pair of students. Sometime the 2nd sampled student is just the 1st one by chance. What is the population covariance of the and series? A: 0, B: .

Correct answers: A A B B.

Your plausible incorrect answers for Quiz 1 and 2 just tell how the matrix caused my misunderstanding when I read the paper the first time. It is difficult to give names for the randomly sampled classes, and the matrix columns and rows.

--
Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics. 24. 323-355.

Data Snoop, 民科的神奇直线(google 始作俑者):

气泡图，数据击败Data Snoop:

R tip：

n=20;plot(rnorm(n),rnorm(n),cex=sqrt(abs(rnorm(n)))*10,pch=1,col=1:n);

,

while CI of is

.

The pivot methods of both are quite similar as following.

,

so .

,

so

of linear regression is the point estimate of

for fixed IV(s) model. Or, it is the point estimate of wherein denotes the correlation of Y and , the linear composition of random IV(s) . The CI of is wider than that of with the same and confidence level.

[update] It is obvious that CI of  relies on the distribution presumption of IV(s) and DV, as fixed IV(s) are just special cases of generally random IV(s). Usually, the presumption is that all IV(s) and DV are from multivariate normal distribution.

In the bivariate normal case with a single random IV, through Fisher's z-transform of Pearson's r, CI of the re-sampled can also be constructed. Intuitively, it should be wider than CI of .

Thus,

CI of can be constructed as . With the reverse transform , the CI bounds of are

and

.

In multiple p IV(s) case, Fisher's z-transform is

.

Although it could also be used to construct CI of , it is inferior to noncentral F approximation of R (Lee, 1971). The latter is the algorithm adopted by MSDOS software R2 (Steiger & Fouladi, 1992) and R-function ci.R2(...) within package MBESS (Kelley, 2008).

In literature, "CI(s) of R-square" are hardly the literal CI(s) of in replication once more. Most of them actually refer to CI of . Authors in social science unfamiliar to hate to type when they feel convenient to type r or R. Users of experimentally designed fixed IV(s) should have reported CI of . However, if they were too familiar to Steiger's software R2 to ignore his series papers on CI of effect size, it would be significant chance for them to report a loose CI of , even in a looser name "CI of ".

----

Lee, Y. S. (1971). Some results on the sampling distribution of the multiple correlation coefficient. Journal of the Royal Statistical Society, B, 33, 117–130.

Kelley, K. (2008). MBESS: Methods for the Behavioral, Educational, and Social Sciences. R package version 1.0.1. [Computer software]. Available from http://www.indiana.edu/~kenkel

Steiger, J. H., & Fouladi, R. T. (1992). R2: A computer program for interval estimation, power calculation, and hypothesis testing for the squared multiple correlation. Behavior research methods, instruments and computers, 4, 581–582.

R Code of Part I:

R Code of Part II:

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[update] Recommend an updated and speedy Rweb server– http://pbil.univ-lyon1.fr/cgi-bin/Rweb/Rweb.cgi

[update, June 2009] 72pines.com (here!) installed this plugin. Try

Data is from SAS example PROC GLM, Example 30.3: Unbalanced ANOVA for Two-Way Design with Interaction

##
##Data from http://www.otago.ac.nz/sas/stat/chap30/sect52.htm
##
drug <- as.factor(c(t(t(rep(1,3)))%*%t(1:4))); ##Factor A
disease <- as.factor(c(t(t(1:3)) %*% t(rep(1,4))));##Factor B
y <- t(matrix(c(
42 ,44 ,36 ,13 ,19 ,22
,33 ,NA ,26 ,NA ,33 ,21
,31 ,-3 ,NA ,25 ,25 ,24
,28 ,NA ,23 ,34 ,42 ,13
,NA ,34 ,33 ,31 ,NA ,36
,3 ,26 ,28 ,32 ,4 ,16
,NA ,NA ,1 ,29 ,NA ,19
,NA ,11 ,9 ,7 ,1 ,-6
,21 ,1 ,NA ,9 ,3 ,NA
,24 ,NA ,9 ,22 ,-2 ,15
,27 ,12 ,12 ,-5 ,16 ,15
,22 ,7 ,25 ,5 ,12 ,NA
),nrow=6));
## verify data with http://www.otago.ac.nz/sas/stat/chap30/sect52.htm
(cbind(drug,disease,y));
##
## make a big table
y <- c(y);
drug <- rep(drug,6);
disease <- rep(disease,6);
##
## Design the PURE interaction dummy variables
m <- model.matrix(lm(rep(0,length(disease)) ~ disease + drug +disease:drug));
##! If lm(y~ ...) is used, the is.na(y) rows will be dropped. The residuals will be orthogonal to observed A, & B rather than designed cell A & B. It will be Type II SS rather than Type III SS.
c <- attr(m,"assign")==3;
(IV_Interaction <-residuals( lm(m[,c] ~ m[,!c])));
##
## verify data through type I & II ANOVA to http://www.otago.ac.nz/sas/stat/chap30/sect52.htm
## Type I ANOVA of A, defined by SS_A --
anova(lm(y~drug*disease));
##
## Type II ANOVA of A, defined by SS_{A+B}-SS_B --
require(car);
Anova(lm(y~drug*disease),type='II');
anova(lm(y~disease),lm(y~drug + disease))
##
##
## Type III ANOVA of A defined by SS_{A:B+A+B}-SS_{A:B+B}
t(t(c( anova(lm(y~IV_Interaction+disease),lm(y~disease * drug))$'Sum of Sq'[2]
,anova(lm(y~IV_Interaction+drug),lm(y~disease*drug))$'Sum of Sq'[2]
,anova(lm(y~disease+drug),lm(y~disease*drug))$'Sum of Sq'[2])))
##
##

Currently, Anova(...) of Prof John Fox's car package (V. 1.2-8 or 1.2-9) used "impure" interaction dummy IV(s), which made its type III result relying upon the order of factor levels. I think in its next version, the "pure" interaction dummy IV(s) will be adopted to give consistent type III SS.

[update:]

In Prof John FOX's car package, with parameter contrasts in inputted lm object, Example(Anova) gave type III SS consistent to  other softwares. In this case, the code line should be --

Anova(lm(y~drug*disease, contrasts=list(drug=contr.sum, disease=contr.sum)),type='III');

Contrasts patterns are defined within lm(...) rather than Anova(...). An lm object with default contrasts parameter is inappropriate to calculate type III SS, or the result will rely on the level names in any nominal factor --

require(car);
M2<-Moore;
M2$f1<-M2$fcategory;
M2$f2<-as.factor(- as.integer(M2$fcategory));
mod1<-lm(formula = conformity ~ f1 * partner.status,data=M2);
mod2<-lm(formula = conformity ~ f2 * partner.status,data=M2);
c(Anova(mod1,type='III')$'Sum Sq'[3],Anova(mod2,type='III')$'Sum Sq'[3])

There was hot discussion of type III ANOVA on R-help newsgroup. Thomas Lumley thought Types of SS nowadays don't have to make any real sense --

http://tolstoy.newcastle.edu.au/R/help/05/04/3009.html

This is one of many examples of an attempt to provide a mathematical answer to something that isn't a mathematical question.

As people have already pointed out, in any practical testing situation you have two models you want to compare. If you are working in an interactive statistical environment, or even in a modern batch-mode system, you can fit the two models and compare them. If you want to compare two other models, you can fit them and compare them.

However, in the Bad Old Days this was inconvenient (or so I'm told). If you had half a dozen tests, and one of the models was the same in each test, it was a substantial saving of time and effort to fit this model just once.

This led to a system where you specify a model and a set of tests: eg I'm going to fit y~a+b+c+d and I want to test (some of) y~a vs y~a+b, y~a+b vs y~a+b+c and so on. Or, I want to test (some of) y~a+b+c vs y~a+b+c+d, y~a+b+d vs y~a+b+c+d and so on. This gives the "Types" of sums of squares, which are ways of specifying sets of tests. You could pick the "Type" so that the total number of linear models you had to fit was minimized. As these are merely a computational optimization, they don't have to make any real sense. Unfortunately, as with many optimizations, they have gained a life of their own.

The "Type III" sums of squares are the same regardless of order, but this is a bad property, not a good one. The question you are asking when you test "for" a term X really does depend on what other terms are in the model, so order really does matter. However, since you can do anything just by specifying two models and comparing them, you don't actually need to worry about any of this.

-thomas

-- Diagram from Wiki

It is easier to imagine relation in three spatial vectors by their angles, than by their correlations. For standardized and s , , cosines of three angles of the triangular pyramid determinate the correlation matrix, thus, all statistics of the regressions and . Unexpected but imaginative results on the impact of introducing are --

1. Both s are nearly independent of . Togethor they predict almost perfectly ( and ).

2. Both s are almost perfectly correlated with . Togethor, one of the regressive coefficient is significantly negative (, and respectively).

3. Redundancy (Cohen, Cohen, West, & Aiken, 2003) increases to full and then decreases to zero and even negative (, and closes from to then to ).