a sample: 一个样本(a case?) > 一组抽样
two sample t test: 两样本t检验(two cases ...?) > 两组样本t检验
sample size: 样本大小(case scale?) > 样本组容量
central tendency: 集中趋势(nondispersion?) > 中心趋势
dispersion: 离散(discrete?)趋势 > 分散趋势
SD=standard deviation: 标准方差(..variance?) > 标准离差
df=degrees of freedom: 自由度(size of freedom?) > 自由维度(dimensions of freedom)
Why practitioners discretize their continuous data
Yihui asked this question yesterday. My supervisor Dr. Hau also criticized routine grouping discretization. I encountered two plausible reasons in 2007 classes, one negative, the other at least conditionally positive.
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 betterthe green continuous line, or the discretized blue dashes? R simulation code is given.
《结构方程模型及其应用》(侯, 温, & 成,2004)部分章节R代码
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 ChiSquare吻合。不过LISREL其它拟合指标用的是Normal Theory Weighted Least Squares ChiSquare?，所以看上去比Minimum Fit Function ChiSquare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)
[update] AMos, Mplus 与 SAS/STAT CALIS 缺省报告与使用的都是 Minimum Fit Function ChiSquare，可通过各种软件(Albright & Park, 2009)的结果对比查验。Normal Theory Weighted Least Squares ChiSquare并非总是比Minimum Fit Function ChiSquare报告更“好”的拟合结果（虽然常常如此）。Olsson, Foss, 和 Breivik (2004) 用模拟数据对比了二者，确证Minimum Fit Function ChiSquare计算得到的拟合指标在小样本之外的情形都比Normal Theory Weighted Least Squares ChiSquare的指标更适用。
R的sem包目前在迭代初值的计算上还做得不够好。我遇到的几个缺省初值下迭代不收敛案例，将参数设定合理范围的任意初值（比如TX，TY都设成01之间的正实数）之后都收敛了。

Albright, J. J., & Park., H. M. (2009). Confirmatory factor analysis using Amos, LISREL, Mplus, and SAS/STAT CALIS. The University Information Technology Services Center for Statistical and Mathematical Computing, Indiana University. Retrieved July 7, 2009, from http://www.indiana.edu/~statmath/stat/all/cfa/cfa.pdf.
Olsson, U. H., Foss, T., & Breivik, E. (2004). Two equivalent discrepancy functions for maximum likelihood estimation: Do their test statistics follow a noncentral chisquare distribution under model misspecification? Sociological Methods Research, 32(4), 453500.
[update]R中的sem包妙处之一是可在线实现结构方程应用界面。下面这个最粗陋的例子对应原书Chap3_1_4_CFA_MB.LS8的结果。
荣耀属于sem package的作者、Rweb的作者、以及服务器运算资源的提供者。
##Input Correlation Matrix R.DHP<matrix(0,ncol=17,nrow=17); R.DHP[col(R.DHP) >= row(R.DHP)] < c( 1, .34,1, .38,.35,1, .02,.03,.04,1, .15,.19,.14,.02,1, .17,.15,.20,.01,.42,1, .20,.13,.12,.00,.40,.21,1, .32,.32,.21,.03,.10,.10,.07,1, .10,.17,.12,.02,.15,.18,.23,.13,1, .14,.16,.15,.03,.14,.19,.18,.18,.37,1, .14,.15,.19,.01,.18,.30,.13,.08,.38,.38,1, .18,.16,.24,.02,.14,.21,.21,.22,.06,.23,.18,1, .19,.20,.15,.01,.14,.24,.09,.24,.15,.21,.21,.45,1, .18,.21,.18,.03,.25,.18,.18,.18,.22,.12,.24,.28,.35,1, .08,.18,.16,.01,.22,.20,.22,.12,.12,.16,.21,.25,.20,.26,1, .12,.16,.25,.02,.15,.12,.20,.14,.17,.20,.14,.20,.15,.20,.50,1, .20,.16,.18,.04,.25,.14,.21,.17,.21,.21,.23,.15,.21,.22,.29,.41,1 ); R.DHP<t(R.DHP); colnames(R.DHP)<rownames(R.DHP)<paste('X',1:17,sep=''); print('Inputted Correlation Matrix'); print(R.DHP); ## ## ##Input Model Specification of Chap3_1_4_CFA_MB.LS8 require(sem); model.B < matrix(ncol=3,byrow=TRUE,data=c( 'X1 <> X1' , 'TD1_1' , NA , 'X2 <> X2' , 'TD2_2' , NA , 'X3 <> X3' , 'TD3_3' , NA , 'X5 <> X5' , 'TD5_5' , NA , 'X6 <> X6' , 'TD6_6' , NA , 'X7 <> X7' , 'TD7_7' , NA , 'X8 <> X8' , 'TD8_8' , NA , 'X9 <> X9' , 'TD9_9' , NA , 'X10<> X10', 'TD10_10', NA , 'X11<> X11', 'TD11_11', NA , 'X12<> X12', 'TD12_12', NA , 'X13<> X13', 'TD13_13', NA , 'X14<> X14', 'TD14_14', NA , 'X15<> X15', 'TD15_15', NA , 'X16<> X16', 'TD16_16', NA , 'X17<> X17', 'TD17_17', NA , 'xi1<> xi1', NA , '1' , 'xi2<> xi2', NA , '1' , 'xi3<> xi3', NA , '1' , 'xi4<> xi4', NA , '1' , 'xi5<> xi5', NA , '1' , 'xi1<> xi2', 'PH12' , NA , 'xi1<> xi3', 'PH13' , NA , 'xi1<> xi4', 'PH14' , NA , 'xi1<> xi5', 'PH15' , NA , 'xi2<> xi3', 'PH23' , NA , 'xi2<> xi4', 'PH24' , NA , 'xi2<> xi5', 'PH25' , NA , 'xi3<> xi4', 'PH34' , NA , 'xi3<> xi5', 'PH35' , NA , 'xi4<> xi5', 'PH45' , NA , 'X1 < xi1' , 'LX1_1' , NA , 'X2 < xi1' , 'LX2_1' , NA , 'X3 < xi1' , 'LX3_1' , NA , 'X5 < xi2' , 'LX5_2' , NA , 'X6 < xi2' , 'LX6_2' , NA , 'X7 < xi2' , 'LX7_2' , NA , 'X8 < xi1' , 'LX8_1' , NA , 'X9 < xi3' , 'LX9_3' , NA , 'X10< xi3' , 'LX10_3' , NA , 'X11< xi3' , 'LX11_3' , NA , 'X12< xi4' , 'LX12_4' , NA , 'X13< xi4' , 'LX13_4' , NA , 'X14< xi4' , 'LX14_4' , NA , 'X15< xi5' , 'LX15_5' , NA , 'X16< xi5' , 'LX16_5' , NA , 'X17< xi5' , 'LX17_5' , NA ) ); class(model.B)<'mod'; ## ## N=350;##sample size; ##R.DHP[4,4] excludes X4 ## Result (summary(sem.B<sem(model.B, R.DHP[4,4], N))); ## Residuals (round(residuals(sem.B),3)); ##################### boxplot.matrix = function(M,ylim=c(1,1)) { M = as.matrix(M); boxplot(c(M[row(M)>col(M)]),at=1,xlab='',ylab='',ylim=ylim); points(rep(1,length(c(M[row(M)>col(M)]))),c(M[row(M)>col(M)]),pch='',col='red'); stem(c(M[row(M)>col(M)])); boxplot.stats(c(M[row(M)>col(M)])); } #################### boxplot(residuals(sem.B));
Paper for 1st Chinese useR! Conference: Web Powered by R, or R Powered by Web
欢迎在本部的同学明天上午到现场看李崇亮同学演示，地点见会议主页
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
[update2009.07.11]文中MediaWiki与R集成的实验平台已不再开放编辑权限，原例转到另一个平台上（界面是西班牙语，找不到英语界面的平台）。希望能有帮手提供linux服务器自建一个平台。
Misunderstanding of Eq. 4 in Singer’s (1998) SAS PROC MIXED paper
Singer (1998, p. 327, Eq. 4) gave a big covariance matrix as the following 
...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 kth 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 kth 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 kth 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 kth 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. 323355.
气泡图击败Data Snoop
“Confidence interval of Rsquare”, but, which one?
In linear regression, confidence interval (CI) of population DV is narrower than that of predicted DV. With the assumption of generalizability, CI of at is
,
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 ztransform of Pearson's r, CI of the resampled 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 ztransform 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 Rfunction ci.R2(...) within package MBESS (Kelley, 2008).
In literature, "CI(s) of Rsquare" 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:
WordPress (and WPMU) Plugin for R Web Interface
Download: RwebFriend.zip [Update] Including Chinese UTF8 Version 
Plugin Name: RwebFriend 
Plugin URL: http://xiaoxu.lxxm.com/RwebFriend 
Description: Set Rweb url options and transform [rcode]...[/rcode] or <rcode>...</rcode> tagpair into TEXTAREA which supports direct submit to web interface of R.
*Credit notes：codes of two relevant plugins are studied and imported. One of the plugins deals with auto html tags within TEXTAREA tagpair, the other stops WordPress to autotransform quotation marks. 
Version: 1.0 
Author: Xiaoxu LI 
Author URI: http://xiaoxu.lxxm.com/ 
Setup:Wordpress 3.5

WordPress 3.4 
Usage: 
[update] The free Chinese wordpress platform yo2.cn has installed this plugin. See my demo.
More online demos  http://wiki.qixianglu.cn/rwebfriendttest/
[update, June 2009] 72pines.com (here!) installed this plugin. Try
[update2009JUL18]Test installed packages of Rweb:
http://pbil.univlyon1.fr/cgibin/Rweb/Rweb.cgi
https://rweb.stat.umn.edu/cgibin/Rweb/Rweb.cgi
Type III ANOVA in R
Type III ANOVA SS for factor A within interaction of factor B is defined as , wherein A:B is the pure interaction effect orthogonal to main effects of A, B, and intercept. There are some details in R to get pure interaction dummy IV(s).
Data is from SAS example PROC GLM, Example 30.3: Unbalanced ANOVA for TwoWay 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))$'Sum of Sq'[2])))
##
##
Currently, Anova(...) of Prof John Fox's car package (V. 1.28 or 1.29) used "impure" interaction dummy IV(s), which made its type III result relying upon the order of factor levels. I think in its next version, the "pure" interaction dummy IV(s) will be adopted to give consistent type III SS.
[update:]
In Prof John FOX's car package, with parameter contrasts in inputted lm object, Example(Anova) gave type III SS consistent to other softwares. In this case, the code line should be 
Anova(lm(y~drug*disease, contrasts=list(drug=contr.sum, disease=contr.sum)),type='III');
Contrasts patterns are defined within lm(...) rather than Anova(...). An lm object with default contrasts parameter is inappropriate to calculate type III SS, or the result will rely on the level names in any nominal factor 
require(car);
M2<Moore;
M2$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 Rhelp 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 batchmode 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
DV predicted by two IVs, vs. triangular pyramid
 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 ).

Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences(3rd ed.) Mahwah, NJ: Lawrence Erlbaum Associates.