Hang's Blog

A meaningful kindergarten opening ceremony

2017-08-25T20:41:00.000+01:00

I attended the opening ceremony of my daughter’s kindergarten today. It was a simple ceremony, lasting about an hour. Nevertheless, the activities for the ceremony were impressive. I found they are very meaningful. They had been definitely carefully designed.

The first procedure was the new students entered the site. The older student raised sunflowers to make an arch path to welcome the new students. The new students happily passed through the beautiful path and then sat on the benches in front of the stage. The older students pave the road for the younger students. What a good meaning!

After a short welcome speech by the headmaster, the chorus began. Three songs were sung: “I like the flowers” (in English), “Mir sind e Schuel” (in Swiss German) and “S gaat besser mitenand” (in Swiss German). The first song is for expressing the children’s love to nature. The title of the second song means “I am a student”. Singing this song, the new students declared they became a student from the day. The last song was sung only by the older students. Its main message of the song is that we all belong to each other and we will care for each other. All the three songs deliver meaningful and kind messages.

At the end, all student went to the playground and released balloons into the sky. This was the climax of the ceremony. The balloon represents hope and dream. All the students release their dreams together, and since then they will realise their dreams bit by bit together. This activity marked a successful ending for the ceremony.

The students participating the event may not fully understand all these meanings, but the experience will enter their memories and they will receive all the good messages eventually.

The linear programming problem of life

2017-06-24T23:49:00.000+01:00

I watched the movie “Mao's Last Dancer” tonight. It reminds me that I still cannot change many things in my lift the meaningful way they should be because I am not doing well enough. In the linear programming problem of life, the way for you to guarantee the solution to reach your expectation is to relax the constraints in your control. It often means increasing your production.

Young people’s attitude towards having children

2017-04-02T22:03:00.000+01:00

Young people have an undermining attitude towards having children. On one hand, they place a greater emphasis than their parents' generation on personal freedom, rational choice, and hedonistic values. On the other hand, the costs, financial and opportunity cost, of raising a child increase in many societies.

Asking people at their 20s or 30s whether they want to have children, they often give answers based on whether they like children, with a differentiation between liking playing with others’ children and taking care of their own children. The value of children to their life and happiness is barely taken into their consideration, not to mention motivations such as the responsibility of continuing their bloodline for the family or contributing to sustaining the development of the society. The decision of having children is more of a thing about personal feeling of dealing with children.

Young people who decide to have children attach more importance to the quality than their parents’ generation. They thus spend more money and time on their children’s daily lives, education, interests, etc. As such spendings are a long term “investment” that the outcomes are not seen immediately, parents often decide what and how much to invest by comparing with others, which is likely to lead all parents to spend more. This is for sure the case in societies like China, and probably also in more developed societies. Many developed countries heavily subsidise childcare. This could reduce the financial costs of having and raising children. A study shows finances is still the main reason for not having children in the USA (see http://www.gallup.com/poll/164618/desire-children-norm.aspx). In these countries, however, the opportunity cost for young people to have children is also high, because they have better opportunities to live a happy life without having a child.

Land Transfer in Rural China

2016-03-12T22:02:00.000+00:00

Two forms of land transfer

As farmers in rural China immigrate to cities, their farm land, which is only allowed to be used for farming, in villages needs to be transferred to others. In most cases, one's land is transferred to his family members in his village. The closer family members have higher priority to receive the land. This is the norm that all farmers follow. The farmers still have the sense that land belongs to their family. So when an family member does not use the land he is distributed, it should naturally be transferred to other family members. Only when no member in this family can receive the land, it can go to someone outside the family. The next adequate receiver is usually the neighbours, who are just less intimate than family members. The land is seldom transferred to someone outside the village (natural village, or group under administrative village) as all the land is owned by the collective that is made up of all members in the village.

The above describes the spontaneous transfer among farmers in the same village or occasionally involving farmers in nearby villages. The amount of land involved in a single transfer is generally very small, but most of the transfers in rural China are conducted in this way. These transfers are unorganised and informal. Contracts are not used in these transfers. They often bring about disputes and conflicts. The government wants to reduce this form of transfer and encourage the transfer in a more standard way that use contracts. Nowadays, more and more transfers use contracts. The form of transfer usually involves a large amount of land and many households. The transferee are usually legal entities such as agricultural companies, farmers' cooperative and family farm. Compared with farmers' spontaneous transfer, the rents paid to the transferrers are higher.

Therefore, two forms of land transfer take place in rural China, the farmers' spontaneous transfer and the organised transfer. Still, the former is the majority in rural China.

Three types of land rights

In rural China, the farm land in a village is own by the collective that is made up of all famers in the village. This is, the collective has the ownership of the land. The collective contracts out the land to each household. The most recent contracts between farmers and the collective are signed around 1998 and the tenancy is 30 years. Farmers have the right to possess, use, profit from and transfer the land out during the tenancy. These rights together are termed as farmers' contractual right. When a farmer transfer his land to another farmers, the transferee will obtain the right to operate the land.

Ownership, contractual right and right to operate are the types of property rights that matter for the farm land in rural China. The contracting between the collective and farmers separates the ownership and contractual right, and the land transfer separates the contractual right and right to operate.

Writing PhD Thesis Conclusion

2015-08-04T15:48:00.001+01:00

Writing a conclusion is an important part of thesis dissertation writing. Ideally, a good conclusion should be able to provide a good picture of what the thesis is about. The conclusion should also give a clear impression that the purpose of the thesis has been achieved. The conclusion of a research reaffirms the thesis statement, discusses the issues, and reaches a final judgment. However, the conclusion is not just a summary – it is statement based on your reasoning on the evidence you have accumulated. This is where you share with readers the conclusions you have reached from your research.Writing PhD ConclusionIn a nutshell, the conclusion should stress the importance of the thesis statement, give the thesis a sense of completeness, and leave a final impression on the reader. The conclusion is generally the place to explore the implications of your topic or argument. The conclusion chapter seeks to:

Tie together, integrate, and synthesize the various issues raised in the discussion sections, while reflecting the introductory thesis statement(s) or objectives
Provide answers to the thesis research question(s)
Identify the theoretical and policy implications of the study with respect to the overall study area
Attempts to carry the examiner or reader to a new level of perception about the thesis
Highlight the study limitations
Recommend direction and areas for future research

There are many variations and styles on how to write a thesis conclusion. However, there are some common elements of a conclusion:

A summary of the main text or main points of the study
A deduction made on the basis of the main body (i.e. Concluding statements)
The writer’s personal opinion on what has been discussed
A statement about the limitations of the work
The implication of the work for future research (i.e. Recommendations)
Other important facts and figures not mentioned in the main body

Below are some tips that will help in writing a PhD thesis conclusion:

In your conclusion, you must extract the key aspects of relevant literature and explain how these are justified or contradicted your research.
When discussing the limitations of your research, do not be too negative or over-modest about your achievements. What is most important is to emphasise the contribution that your research project will make.
The conclusion should synthesise what has been previously discussed. It must pull together all of the parts of your argument and refer the reader back to the focus that you have outlined in your introduction and to the central topic.
Be very systematic, brief, and don’t introduce any new information. The conclusion should add to the overall quality and impact of the research. It should be able to stand on its own and provide a justification and defence of the thesis.

source: http://writepass.com/journal/2013/06/writing-your-phd-thesis-conclusion/

Optimization in R

2015-05-15T11:13:00.000+01:00

Many statistical techniques involve optimization. The path from a set of data to a statistical estimate often lies through a patch of code whose purpose is to find the minimum (or maximum) of a function. Likelihood-based methods (such as structural equation modeling, or logistic regression) and least squares estimates all depend on optimizers for their estimates and for certain goodness-of-fit tests. Base-R offers the optim function for general-purpose optimization. Through a conversation with John Nash, author and maintainer of optim and the newer optimx, learn about the pitfalls of optimization and some of the tools that R offers.

Statistics is nothing if not an exercise in optimization, beginning with the sample average and moving on from there. The average of a group of numbers minimizes the sum of squared deviations. In other words, the average minimizes the sum of terms like (x_i-avg)^2. The median minimizes the sum of absolute deviations—terms like |x_i-med|.

The underlying strategy for most statistical reasoning is:

1. Write down a probability model that should account for the data.
2. This model will contain some unknown constants, or parameters.
3. Collect the data.
4. Find values of the parameters that best account for the data.

That last point contains the optimization. Maximum likelihood is an optimization procedure that selects the most plausible parameter values for the data you got. Parameters can be estimated in a number of ways, but all of them involve an optimization.

Interestingly, you can optimize most of the models taught in a beginning statistics course with a closed-form expression. Mathematically, finding the mean and variance of normal data, estimating a proportion, fitting a regression line, and modeling treatment and block effects in experimental design are all optimization problems. Their solutions emerge as the solution of a system of linear equations. The statistician hands over the problem to a software package, trusting that the linear algebra algorithm will produce stable estimates of the parameters.

For just about any model outside of the linear models I mentioned, closed-form solutions do not exist. You need not go far to find practical examples. Consider a bank that wants to predict whether prospective customers will default on their mortgages. The bank's historical data show the default status (a binary outcome coded Yes-No) of numerous customers, together with personal and financial information, such as employment status, years with current employer, salary, marital status. Estimating the contributions of each of these pieces of information boils down to estimating the parameters of a logistic equation. No closed-form expression exists for the best parameters of this model, so a numerical procedure is required to estimate the parameters and optimize the likelihood.

The same is true of other generalized linear models, structural equation models, and many other models that are used in modern statistics. Most of the heavy lifting in these problems, and the software that delivers the results, relies on a numerical optimization algorithm.

The usual graduate program in statistics, even at a good school, teaches you a lot about the theoretical properties of these estimates. And the theory, to be sure, paints an optimistic picture. Theory shows that under certain conditions, most people forget with time, and given a large enough sample size, the solution to the optimization problem (the maximum likelihood estimate) is the solution to the estimation problem. Solving the maximum likelihood problem gives you the estimates necessary to complete specification of the model. Even better, the estimates behave nicely (are normally distributed). You can even estimate how accurate they are from the curvature of the maximum likelihood function near its optimum value. At least, if the sample size is large enough, these nice properties will hold.

You then hand the problem over to an optimization algorithm, confident that your work is done. But is that confidence well placed?

Institutions, Procedures, Norms

2015-04-09T13:22:00.000+01:00

One of the noteworthy aspects of the framing offered by Victor Nee and Mary Brinton of the assumptions of the new institutionalism is the very close connection they postulate between institutions and norms. (See the prior posting on this subject). So what is the connection between institutions and norms?

The idea that an institution is nothing more than a collections of norms, formal and informal, seems incomplete on its face. Institutions also depend on rules, procedures, protocols, sanctions, and habits and practices. These other social behavioral factors perhaps intersect in various ways with the workings of social norms, but they are not reducible to a set of norms. And this is to say that institutions are not reducible to a collection of norms.

Consider for example the institutions that embody the patient safety regime in a hospital. What are the constituents of the institutions through which hospitals provide for patient safety? Certainly there are norms, both formal and informal, that are deliberately inculcated and reinforced and that influence the behavior of nurses, pharmacists, technicians, and doctors. But there are also procedures -- checklists in operating rooms; training programs -- rehearsals of complex crisis activities; routinized behaviors -- "always confirm the patient's birthday before initiating a procedure"; and rules -- "physicians must disclose financial relationships with suppliers". So the institutions defining the management of patient safety are a heterogeneous mix of social factors and processes.

A key feature of an institution, then, is the set of procedures and protocols that it embodies. In fact, we might consider a short-hand way of specifying an institution in terms of the set of procedures it specifies for behavior in stereotyped circumstances of crisis, conflict, cooperation, and mundane interactions with stakeholders. Organizations have usually created specific ways of handling typical situations: handling an intoxicated customer in a restaurant, making sure that no "wrong site" surgeries occur in an operating room, handling the flow of emergency supplies into a region when a large disaster occurs. The idea here is that the performance of the organization, and the individuals within it, will be more effective at achieving the desired goals of the organization if plans and procedures have been developed to coordinate actions in the most effective way possible. This is the purpose of an airline pilot's checklist before takeoff; it forces the pilot to go through a complete procedure that has been developed for the purpose of avoiding mistakes. Spontaneous, improvised action is sometimes unavoidable; but organizations have learned that they are more effective when they thoughtfully develop procedures for handling their high-risk activities.

This is the point at which the categories of management oversight and staff training come into play. It is one thing to have designed an effective set of procedures for handling a given complex task; but this achievement is only genuinely effective if agents within the organization in fact follow the procedures and protocols. Training is the umbrella activity that describes the processes through which the organization attempts to achieve a high level of shared knowledge about the organization's procedures. And management oversight is the umbrella activity that describes the processes of supervision and motivation through which the organization attempts to ensure that its agents follow the procedures and protocols.

In fact, one of the central findings in the area of safety research is that the specific content of the procedures of an organization that engages in high-risk activities is crucially important to the overall safety performance of the organization. Apparently small differences in procedure can have an important effect on safety. To take a fairly trivial example, the construction of a stylized vocabulary and syntax for air traffic controllers and pilots increases safety by reducing the possibility of ambiguous communications; so two air traffic systems that were identical except with respect to the issue of standardized communications protocols will be expected to have different safety records. Another key finding falls more on the "norms and culture" side of the equation; it is frequently observed that high-risk organizations need to embody a culture of safety that permeates the whole organization.

We might postulate that norms come into the story when we get to the point of asking what motivates a person to conform to the prescribed procedure or rule -- though there are several other social-behavioral mechanisms that work at this level as well (trained habits, well enforced sanctions, for example). But more fundamentally, the explanatory value of the micro-institutional analysis may come in at the level of the details of the procedures and rules in contrast to other possible embodiments -- rather than at the level of the question, what makes these procedures effective in most participants' conduct?

We might say, then, that an institution can be fully specified when we provide information about:

the procedures, policies, and protocols it imposes on its participants
the training and educational processes the institution relies on for instilling appropriate knowledge about its procedures and rules in its participants
the management, supervision, enforcement, and incentive mechanisms it embodies to assure a sufficient level of compliance among its participants
the norms of behavior that typical participants have internalized with respect to action within the institution

And the distinctive performance characteristics of the institution may derive from the specific nature of the arrangements that are described at each of these levels.

System safety is a good example to consider from the point of view of the new institutionalism. Two airlines may have significantly different safety records. And the explanation may be at any of these levels: they may have differences in formalized procedures, they may have differences in training regimes, they may have differences in management oversight effectiveness, or they may have different normative cultures at the rank-and-file level. It is a central insight of the new institutionalism that the first level may be the most important for explaining the overall safety records of the two companies, even though mechanisms may fail at any of the other levels as well. Procedural differences generally lead to significant and measurable differences in the quality of organizational results.

Source: http://understandingsociety.blogspot.ie/2009/02/institutions-procedures-norms.html

Count Occurrences of Factor in R

2015-02-25T17:46:00.000+00:00

events <- data.frame(type = factor(c('A', 'A', 'B'), c('A','B','C')), quantity = c(1, 2, 1))

# Method 1
table(events$type)

# Method 2
xtabs(quantity~type, events)

# Method 3
aggregate(quantity~type, events, FUN=sum)

# Method 4
library(plyr)
ddply(events, .(type), summarise, quantity = sum(quantity), .drop=FALSE)

Adjacency Matrix is an amazing Tool for Analysing Networks

2015-02-19T01:51:00.001+00:00

I work a lot with adjacency matrix, a way of representing a relationship network. Adjacency matrix transfers a network graph into a well developed mathematical form. This opens the door to analysing networks using rigorous mathematical tools. The more I use it, the more useful I find this invention is.

1. In addition to indicating which nodes link to which nodes, with or without direction -- the most fundamental function, the matrix can conveniently reflect how important the links are (adding weight values to the corresponding elements).

2. Doing various elementary transformations of the adjacency matrix can achieve a variety of changes of the network, such as changing the order of the nodes, merging the relationships of two nodes.

3. One can assign attributes to nodes by multiplying the matrix (by row or column) with a vector storing the attributes.

Dropping Factor Levels in R

2015-02-03T19:00:00.000+00:00

drop.levels {gdata} # Drop unused factor levels
droplevels {base} # The function droplevels is used to drop unused levels from a factor or, more commonly, from factors in a data frame.
When creating data frame or importing data into data frame (loading data with read.table, read.csv or the like), character vectors are converted to factors by default. To avoid this, set option (stringsAsFactors = FALSE)

Asking for Comments via Emails

2015-01-14T10:24:00.000+00:00

Here are a few phrases you can use to ask colleagues for suggestions, or feedback, on a document you’ve prepared.

If you want to make sure that other members of your team are informed about the work you’ve been doing, you can use sentences like these to express interest in their opinions:

 I’d be grateful for any comments or suggestions you might have.
 I’d like to get some feedback on the report before we send it out.
 Please let me know if you’d like to propose any changes.

If you want to make a stronger request for feedback, making it clear that you expect a response from your readers, you can use sentences like these:

 Please take a look and let me know what you think of this draft.
 I’d like to receive detailed feedback on this version of the document.
 I’d appreciate it if each of you can take a few minutes to review and comment on the report.

Write an email to a group of colleagues, announcing that you have completed a business document for your company and that you would like them to read and review it before you submit it to your supervisor. Be sure to include the following points:

 The type of document you are sending
 A way to find the document (for example, as a file attached to your email)
 The type of comments you are asking for
 A deadline for submitting comments

NB: You can invent details such as the name of the company or product if you want, but it is not necessary to complete the task

Subject: Document for review

Dear Emily,

I should be glad if you could review the attached document. Can you please let me

know if you have any comments?

I would like to receive your feedback by 5:00pm next Wednesday, so that I can

make any changes you feel are necessary.

Thank you very much for your attention. I look forward to hearing from you.

Regards,

Sandra

Subject: Draft of quarterly sales report

Dear Colleagues,

I’ve just completed a draft version of our department’s sales report for the most recent quarter, which you will find attached as a Word document.

I’ve been asked to submit the final version of this report to our head office no later than 9:00am on Friday, and I’d very much like to hear your feedback on the draft before I send it out. If you have any comments or suggestions, I’ll need to receive them by 3:00pm Thursday so I can refer to them when I prepare the final version on Thursday afternoon.

I look forward to hearing from all of you.

Thanks and best regards,

Simon

Convert Matrix into Symmetric in R

2014-11-12T18:54:00.000+00:00

require(Matrix):

forceSymmetric(dat, uplo="U"))

Assign the lower triangular part to the upper triangular part:

dat[upper.tri(dat)] <- t(dat)[lower.tri(dat)]

micEcon package:

symMatrix(dat[upper.tri(dat, TRUE)], nrow=nrow(dat), byrow=TRUE)

Transforming NetLogo Simulated Networks into UCINET for Analysis

2014-06-23T20:27:00.000+01:00

There's a program called NetLogo. It's an open-source agent-based modeling program. And it's awesome, although you have to spend some time with it. There are some example models that are automatically downloaded into a library when you download the program, and several of them are network simulators (they'll simulate a virus in a network, diffusion of a directed network, the formation of giant components, preferential attachment, etc).

There's a program called UCINET too. It's a social network analysis program which works with a freeware program called NETWORK which allows you to visualize networks. UCINET can only be run on Windows, and you can get a free trial you 60 days. With it, you can take a network generated in NetLogo and run all sorts of network diagnostics and alter your network image in whatever ways you like (I have yet to figure out if there is a way to keep track of the time variable somehow- perhaps as a link or turtle attribute, which could then be input as a node attribute in NETWORK).

They should probably be able to talk to each other, right? And then you could create rules for behavior in your NetLogo, simulate a network on those rules, analyze that network or a set of random simulations, and see if your rules are creating the types of networks you see in reality. That would be pretty cool, not? In the absence of knowing much about programming, here's a semi-labor intensive way to make this happen (doubtless R or some other program can do a lot of this already):

(1) In NetLogo:
File > Models Library > Networks > Preferential Attachment > Open
Select the Procedures tab
At the very top of Procedures, above the setup procedures, add "turtles-own [ my-list-partners; ]"
In the Main Procedures section, in the "to go" command, add "ask turtles [ set my-list-partners ([who] of link-neighbors) ]" just above the "tick" command
Select the Interface tab (and pray you don't see an error message)
Setup
Go until you have a network you like the size of
File > Export > World... > Save it somewhere
(there may be a more efficient way to create an output with just the variables of interest, but the below commands will work as well)

(2) In Excel:
Open the network's csv file you just created
Delete columns B:M
Delete rows 1:13
Delete rows below your initial ego/alter list (everything including and below "PATCHES")
Edit > Replace... > type in "[" into the "Find what:" space, leave "Replace with:" blank > Replace All > OK
type in "]" into the "Find what:" space, leave "Replace with:" blank > Replace All > OK > Close
Select column B (the commands below might depend on your version of Word)
Data > Text to Columns... > Delimited > Next > select "Space" as the delimiter and deselect "Tab" > Next > Finish
Hand select the frame with the data
Copy

(3) In UCINET 6
Data > Date editors > DL Editor
In the DL Editor, select the data tab
Select the first cell
Paste
Under "Data format:", select "Nodelist (1-mode)"
File > Save UCINET dataset > name it and save it somewhere
Close the DL editor
Visualize > NetDraw

(4) In NetDraw (which is automatically downloaded as part of UCINET)
File > Open > Ucinet dataset > Network
Select your file, which will end with ##h
Click OK

You can play with it from there. There's also an ability to upload attributes into NetDraw (like turtle color or breed), or resize nodes according to their degree, or any of a number of other options. If you're network in NetLogo has several components, UCINET can handle that too. I imagine there's some way to do a lot of the data reformatting/exporting/
importing automatically. You could also probably apply a similar alteration in code to other network models within NetLogo and use the same procedures to get it into UCINET.

Best of luck! Let me know if anything interesting comes of this/you find a more efficient way to accomplish the same thing.

Source: http://sexandstats.blogspot.ie/2011/09/network-analysis-transforming-netlogo.html

Common Words in Spoken Dialogues

2014-06-12T14:57:00.000+01:00

There are a number of words that are common in spoken dialogues that do not occur in written forms. This section discusses how such words should be transcribed. Where possible, we use the spelling from Quirk et al. (1985).

Filled Pauses Filled pauses are very common in natural dialogue. There seem to be two types, ones that sound like "uh" and ones that sound like "um". The endings of these words are often prolonged, thus tempting transcribers to label it is "ummm". Rather, these words should be classified as either "um" or "uh", and transcribed as such. We also include "er", which is more common in British accents. Note that the filled-pauses should never be transcribed as partial words.

um	Filled pause.
uh	Filled pause.
er	Filled pause. More common in British English.

Acknowledgments

The following is a list of commonly occurring acknowledgments, and how they should be spelt.

okay	Agreement. Speakers will often produce variants of this, such as "kay", "mkay", "umkay". All of these variants should be spelt as "okay".
uh-huh	Agreement.
uh-hm	Agreement.
mm-hm	Agreement.
uh-uh	Disagreement.
mm	Agreement, stalling for time
huh	Request for clarification. Puzzlement.
hm	Stalling for time.
nah	Informal version of "no".
nope	Informal version of "no".
a-ha	Interjection denoting surprise, as in "aha< I found it", rather than "uh-huh" as an acknowledgement ha Interjection, similar to "aha".
oh	Surprise. ooh As in "ooh, that's gross."
yeah	Informal version of "yes"
yep	Informal version of "yes"

Contractions

Contractions, that are common, should be written as one word. The following is a list of common contraction endings. Note that there can often be an ambiguity as to whether the speaker was saying the words as one or as two individual words, especially since words are often blurred together. If in doubt, annotate the word pair as two separate words, spelling out the second in full.

'll	for "will"
've	for "have"
n't	for "not"
're	for "are"
's	for "is"

All other contractions are left to the transcriber's discretion as to whether they should be transcribed as one word or two.
Word Pairs There are some word pairs that are so altered (in pronounciation) that they seem to be one lexical item. Such pairs can be transcribed as single words. Below, we give some common word pairs.

lemme	for "let me"
wanna	for "want to"
gonna	for "going to"
gotta	for "going to"

Extracted from PostScript by R. Paul McCarty 2001/07/30

On Paper Reading

2014-06-06T11:15:00.002+01:00

Paper is not something you should read a large number of, but something you should read a number of times.

Rereading papers can be more useful than starting new papers, conditional on careful selection of papers to read.

A paper is good because it clearly tell you where it comes from and where it goes to, and can inspire readers with various interests.

Start to Change

2014-05-19T07:45:00.001+01:00

I feel the situation starts to change from this week on. I will become more efficient at work gradually. Here I list the tasks to do for this week (20140519-20140525):

Submit the abstract by Monday;
Interview (QQ msgs) Li and Dad for setting up the ABM;
Finalize the ABM of peer effects among smallholders;
Complete the statistical analysis on the simulation data;
Complete a first draft paper for peer effects (including all sections);
Start to do the daily recaps (new words, paper(s) read, being honesty, etc.);
Figure out how to import the map to NetLogo;
Email to a prof.

Living beside the Sea

2014-05-17T15:04:00.001+01:00

My current living place is 2 minutes walk to the sea. Every morning I cycle along the seaside to my work, and every evening I cycle back along the seaside. Every weekend morning I practise Tai Chi facing the sea.

I once had a strong expectation to the sea. I grew up in a very inland place. To see the sea was one of my dreams when I was a child. The dream had not been realised until I was 25 years old. Now I am so close to the sea. I can see it when I lay on the bed. I can hear and smell it on my way to work and on my way home.

Sea is often compared to the board minds or deep thoughts, which I am short of. A Chinese saying goes: "the wise delight in water, the benevolent delight in mountains". Water is flexible and profound.

Sea is also linked to boldness and tenacity. People obtain such qualities in the fight with sea. This seems true. People living in the coastal places appear more adventurous than those dwelling in inland places. For example, the southerners in China are more likely to do business than the northerners.

Codes for Replacing Special Values in R

2014-03-20T20:40:00.000+00:00

grep {base} Pattern Matching and Replacement.

E.g., sub(“”, 0 ,dat)

dat[dat==“”] = 0 # replace empty cell in dat as 0
replace(x, list, values)

E.g., dat <- replace(dat, is.na(dat), 0)

na.strings=c("","NA") # replace empty cell as NA when importing data

Five Common Relationships among Three Variables in a Statistical Model

2014-03-12T19:54:00.000+00:00

In a statistical model–any statistical model–there is generally one way that a predictor X and a response Y can relate:

This relationship can take on different forms, of course, like a line or a curve, but there’s really only one relationship here to measure.

Usually the point is to model the predictive ability, the effect, of X on Y.

In other words, there is a clear response variable*, although not necessarily a causal relationship. We could have switched the direction of the arrow to indicate that Y predicts X or used a two-headed arrow to show a correlation, with no direction, but that’s a whole other story.

For our purposes, Y is the response variable and X the predictor.

But a third variable–another predictor–can relate to X and Y in a number of different ways. How this predictor relates to X and Y changes how we interpret the relationship between X and Y.These relationships have common names, but the names sometimes differ across fields, so you may be familiar with a different name than the ones I give below. In any case, the names are less important than:

(1) making sure the relationship you are testing is the one that answers your research question and

(2) the relationship reflects the data.

So let’s look at some possible relationships, once we add a second predictor, Z.

1. Covariate correlated with X

In this model, the Covariate, Z, is correlated with X, and both predict Y.

Because X and Z are not independent, there will usually be some joint effect on Y–some part of the relationship between X and Y that can’t be distinguished from the relationship between Z and Y.

The relationship between X and Y is no longer the full effect of X on Y.

It’s the marginal, unique effect of X on Y, after controlling for the effect of Z.

While the model fit as a whole will include both the joint and the unique effects of both X and Z on Y, the regression coefficient for X will only include its unique effect.

When both X and Z are observed variables, this is nearly always the situation.

As long as the correlation is moderate, it’s still possible to measure the unique effect of X. If it gets too high, however, you will start to hit a point of multicollinearity in which the model has problems calculating estimates.

A good example of this kind of relationship would be in a study that measures the nutritional composition of soil cores at different altitudes and moisture levels.

Because water flows downhill, lower altitudes (X) tend to be more moist (Z). So while we can’t completely separate altitude from moisture levels, as long as they’re only moderately correlated, we should be able to find the unique effect of altitude on potassium levels.

Test this effect by including X and Z in a model together. To understand how much Z and X overlap in their explanation of Y, rerun the model without Z and look at how much X’s coefficient changes.

2. Covariate Independent of X

When a covariate Z is NOT related to X, it has a slightly different effect. It needs to be able to predict Y as well to be useful in the model, but the effects of X and Z don’t overlap.

Including Z in the model often leads to the relationship between X and Y becomingmore significant because Z has explained some of the otherwise unexplained variance in Y.

An example of this kind of covariate is when an experimental manipulation (X) on response time (Y) only becomes significant when we control for finger dexterity levels (Z).

If finger dexterity has a large effect on response time and we don’t account for it, all of that variation due to dexterity will go into unexplained error–the denominator of our test statistic.

Controlling for that variance means it is no longer unexplained, and is removed from the denominator.

Test this type of effect by running hierarchical regression (add each predictor in on a separate step).

3. Spurious Relationship
A confounding variable Z creates a spurious relationship between X and Y because Z is related to both X and Y.

This is the relationship seen in most “correlation is not causation” examples: The amount of ice cream consumption (X) in a month predicts number of shark attacks (Y). Do sharks like eating ice-cream laden people? No.

This spurious relationship is created by a confounder that leads to increases in both ice-cream consumption and shark attacks: temperature (Z). People both eat more ice cream and swim in the ocean in hotter months.

It can be difficult to distinguish between this relationship and #1 above through testing alone. This is a situation where you have to entertain spuriousness as a possibility and question your results.

4. Mediation
Mediation indicates a specific causal pathway. It occurs when at least part of the reason X affects Y is through Z. X affects Z and Z affects Y.

There are then two effects of X on Y: the indirect effect of X on Y through Z and the direct effect of X on Y.

An example here would be if the relationship between stress (X) and depression (Y) was mediated through increased levels of stress hormones (Z).

There are a number of ways to test for mediation, including running a series of regression models and through path analysis. More modern approaches recommend testing the strength of the indirect effect.

5. Moderation
Despite the similarity in the names, a moderation is an entirely different beast than mediation.

Moderation indicates that the effect of X on Y is different for different values of Z. In other words, Z moderates (affects) the effect of X on Y.

Maybe when Z is large, there is a strong positive relationship between X and Y, but when Z is small, there is no relationship between X and Y.

So essentially, we’re saying that X predicts Y in different ways, depending on the value of Z.

X and Z can be correlated or not.

One example of moderation can be seen in the relationship between depression (X), physical health scores (Y), and poverty status (Z). Poverty status (Yes/No) would be said to moderate the relationship between depression and physical health if there was a weak negative relationship among people not in poverty, but a strong negative relationship among people in poverty.

Test moderation by including an interaction term between X and Z.

All of these relationships can occur with more than two predictors in the model, and a model can contain more than one of them. Figuring out the potential relationships among variables is often the fun part of data analysis.

Source: http://www.theanalysisfactor.com/five-common-relationships-among-three-variables-in-a-statistical-model/

当前学习中的几对关系

2013-12-27T19:36:00.000+00:00

刚才和来做访问的汪老师谈到当前我们的学习中存在的一些问题，表现为以下几对关系。

一对是学与思的关系。我们的问题是有时候注重学习而不够注重思考，有时候注重思考而不够在注重学习。前者的表现在于：一是没能较好地将所学的概念、理论同实际问题、特别是自己所研究的话题结合起来。有效的学习者总是注意在学习新知的过程中始终想到这个知识点对我的研究的问题有哪些帮助、我可以怎样它运用到自己的研究中去。把别人的研究作为一个自己研究的一个隐喻，去挖掘其中可资借鉴的部分。这一方面使得学习更有目标性，可以带着目标去学，这样可以强化记忆和认知，帮助我们学习；另一方面促进读对于研究主题的思考，可以从不同角度去认识自己的研究主体。一个可能的弊端是，这可能导致对所学知识的认识比较狭窄和片面，不能很好进行一般化。避免的途径首先是从整体上把握这个知识，其次是在不同领域和主题中进行运用。二是没有注意将所学的知识放在自身的中知识体系之中。这首先要求要构建自身的知识体系，这样的各个知识都有它的位置和坐标。“学而不思则罔，死而不学则殆”。

后者说的是没有把所思、所悟放在一定的理论框架之中、没有认真得调查前人做了哪些研究，因为思考所得的学术价值不高。这首先要求掌握一定的核心理论，形成自己观察世界的视角。

二是思与写的关系。我们往往是想的多、写下来的少。这既不能很好地对所思进行梳理和总结，也不利于认识的积累和提高。此外，写的多的一种重要好处可以练笔。

你所在研究团队强吗

2013-12-19T08:46:00.000+00:00

一个研究团队、实验室或科研小组如果处在这样的情形中，那它就还比较弱：

搞技术的人（如玩统计、计量的人，编程的人）比搞理论的人更吃香；
申请经费要按照经费提供者、政策制定者的意图来写研究计划，而不是根据本团队的兴趣来写；
各个成员研究的主题很分散（研究的方法和所采用的理论不同没有关系）。

如果出在这样的情形中，那它就没多大前途：

毕业的学生（如硕士生、博士生）大部分不再从事科研，甚至不从事与研究相关的工作（甚至他们进来的就是不是冲着科研来的）；
比较强的人（包括在某一方面有专长的人，比如搞技术的人），无论是在读的学生、还是其他研究人员，会轻易地离开这个团队；
不重视基础理论研究，一心只做能拿到经费的、发文章快的研究；
团队中比较强的人（比如PI）跟比较弱的人（比如硕、博士生）在学术上的交流很少或没有。

如果问，按照这些标准，本人所在的研究团队怎么样，结论是它不强，但还比较有前途。

Some Famous Metaphors

2013-12-08T12:20:00.002+00:00

The Big Bang.
Fred Hoyle

All the world’s a stage, and all the men and women merely players. They have their exits and their entrances.
William Shakespeare

Art washes away from the soul the dust of everyday life.
Pablo Picasso

I am the good shepherd, … and I lay down my life for the sheep.
The Bible, John 10:14-15

All religions, arts and sciences are branches of the same tree.
Albert Einstein

Chaos is a friend of mine.
Bob Dylan

All our words are but crumbs that fall down from the feast of the mind.
Khalil Gibran

If you want a love message to be heard, it has got to be sent out. To keep a lamp burning, we have to keep putting oil in it.
Mother Teresa

America has tossed its cap over the wall of space.
John F. Kennedy

A hospital bed is a parked taxi with the meter running.
Groucho Marx

A good conscience is a continual Christmas.
Benjamin Franklin

Let us be grateful to people who make us happy, they are the charming gardeners who make our souls blossom.
Marcel Proust

And your very flesh shall be a great poem.
Walt Whitman

Advertising is the rattling of a stick inside a swill bucket.
George Orwell

Dying is a wild night and a new road.
Emily Dickinson

Fill your paper with the breathings of your heart.
William Wordsworth

Conscience is a man’s compass.
Vincent Van Gogh

Bedtime Vocabulary

2013-12-02T16:28:00.000+00:00

Below list the English vocabulary I discussed with my dorm mates.

Vocabulary	Contributor	Date
sanitation	Wei	25/11/2013
fingers crossed	Hang	26/11/2013
for god's sake	Hang	27/11/2013
stochastic	Puqing	28/11/2013
swindle	Wei	29/11/2013
patriot, patriotic	Wei	30/11/2013
swindle	Wei	01/12/2013
oasis	Puqing	02/12/2013
millennium	Wei	03/12/2013
naught	Hang	04/12/2013
orchard	Puqing	06/12/2013
horticulture	Wei	07/12/2013
laundry detergent	Hang	08/12/2013
obesity	Hang	19/12/2013
obese, over-weighted, big-boned	Puqing & Hang	21/12/2013
--	--	//2013

Positive Personality Adjectives List

2013-11-25T10:21:00.000+00:00

Able 有能力的，能干的
Active 活跃的，积极主动的
Adaptable 能适应的，可修改的
Adventuresome 敢冒险的，爱冒险的，冒险性的
Adventurous 冒险的，大胆的，充满危险的
Ambitious 雄心勃勃的，有野心的
Appreciative 怀有感激的，赏识的，承认有价值的
Authentic 地道的，正宗的，真实可信的
Aware 知晓的，意识到的，察觉的，有...方面知识的，懂世故的

Balanced 平衡的，和谐安定的
Bold 英勇大胆的，厚颜无耻的
Brave 勇敢的

[Note: Differences between Brave and Bold:

Brave is typically used to describe an action that is positive in nature and often selfless, for the benefit of others possibly at the peril of or lack of concern for oneself.
Bold is forceful and in your face. A bold action is not necessarily a positive action. Bold can apply to things other than actions as well, such as referring to a very strong cologne as a Bold scent (because it is powerful and in your face).
You would not refer to inanimate objects as Brave.]

Calm 平静的，心如止水的
Capable 能胜任的，有能力的，有才华的
Carefree 无忧无虑的，不负责任的 free from anexity and responsibility
Caring 表示或感到关心的，有同情心的 displaying kindness or concern for others
Cheerful 高兴的，愉快的
Clever 聪明的，机灵的，熟练的
Compassionate 富于同情心的，慈悲的 felling or showing sympothy or concern for others
Concerned 有关的，与...相关的，关心的
Confident 有信心的，确信的
Conscientious 尽责的，认真的，本着良心的，小心谨慎的 wishing to do one’s work or duty well and thoroughly; relating to a person’s conscience
Considerate 体贴的；体谅的；考虑周到的
Courageous 有胆量的，勇敢的 not deterred by danger or pain; brave
Creative 创造性的 relating to or involving the use of the imagination or original ideas to create something
Curious 有好奇心的，有求知欲的

Dainty 讲究的，秀美的，美味的 delicately small and pretty; fastidious, especially concerning food
Daring 大胆的，勇敢的 (of a person or action) adventurous or audaciously bold
Dedicated 专注的，专用的，献身的
Dependable 可靠的，可信赖的 trustworthy and reliable
Determined 坚决的，有决心的，决定了的
Devoted 献身的，投入的，忠诚的 very loving and loyal
Diligent 勤勉的，用功的，费尽心血的 having or showing care or conscietiouness in one's work or duites
Direct 直接的，直系的，亲身的
Directed 定向的；经指导的；被控制的
Disciplined 守纪律的，受过训练的
Discriminating 有识别力的，差别的 having or show refined taste or good judgement
Distinct 明显的；独特的；清楚的；有区别的
Dynamic 动态的，有活力的

Eager 渴望的，意愿强烈的
Easy-going 随和的，好相处的
Empathetic 富有同情心的，心意共鸣的
Enduring 持久的
Energetic 精力充沛的
Enterprising 具有企业家特质的，富有创新精神的
Enthusiastic 热情的，
Ethical 伦理的
Exceptional 例外的
Excited 兴奋的
Exuberant

Fair 公平的
Fascinating 迷人的，妙极的
Feisty
Fervent
Flexible 灵活的
Forgiving 谅解的，宽恕的
Friendly 友好的
Fun 有趣的

Generous
Gentle
Genuine
Giving
Gutsy

Happy
Hard Working
Healthy
Helpful
Honest
Honorable
Humble
Humorous

Idealistic
Imaginative
Independent
Ingenious
Inquisitive
Insightful
Integrity
Intelligent
Interesting
Intuitive
Inventive

Jolly

Jovial

Joyful

Keen

Kind

Knowledgeable

Laid-back

Leader

Light-hearted

Likable

Lively

Loveable

Loving

Loyal

Magical

Mannerly

Mature

Mellow

Memorable

Mighty

Motivated

Natural

Neat

Noble

Nurturing

Observant

Open-minded

Optimistic

Organized

Original

Out-going

Patient

Patriotic

Peaceful

Perceptive

Perky

Persevering

Persistent

Pleasant

Popular

Positive

Practical

Principled

Private

Problem-solver

Proud

Quick-witted

Quiet

Rational

Real

Reasonable

Reflective

Reliable

Resilient

Resourceful

Respectful

Responsible

Self-confident

Self-directed

Self-sacrificing

Self-starter

Self-sufficient

Sensitive

Sharp

Sincere

Skillful

Smart

Sociable

Spirited

Spiritual

Spontaneous

Stable

Steady

Strong

Studious

Successful

Supportive

Surprising

Sympathetic

Talented

Thorough

Thoughtful

Tireless

Tolerant

Trusting

Trustworthy

Truthful

Understanding

Unique

Unselfish

Upbeat

Vigilant

Warm

Wise

Witty

Worthy

China’s New Reform Blueprint Has Arrived

2013-11-17T09:00:00.001+00:00

A few days ago, a cautiously and a little vaguely worded communique caused some questions on Xi government's determination to deepen reforms. However, the specific plan for recasting economy released on Friday has largely reassured people.

The Decision on Major Issues Concerning Comprehensively Deepening Reforms promises a handful of potentially significant changes.

几天前，一份措辞含糊的会议公报引发了外界对习近平改革决心的质疑，但中国最高领导层周五公布了一项重塑经济的详细计划，在很大程度上打消了人们的一些疑虑。

周五公布的这份长达20页的文件相当于一项改革蓝图。中国领导人在文件中承诺将放开金融行业、放宽其他一些行业的外资准入限制、让市场决定资源价格并赋予农民更多财产权利。

China’s new leadership has released its first major reform blueprint.

The document said China would significantly ease its one-child policy, allowing couples to have two children if one of the parents is an only child. Currently, Chinese couples are restricted to one child except under some circumstances, such as rural dwellers, pilot programs in a number of areas and among ethnic minorities.

China also plans to abolish a controversial labor camp system in what Xinhua described as “part of efforts to improve human rights and judicial practices.” Under the system, which has been in place since 1957, police are allowed to imprison people in labor camps for up to four years without formal arrest or trial.

Other proposed reforms include a system for insuring bank deposits, an increase in the amount of profits state-owned companies pay the government and an increased emphasis on environmental protection in the evaluation of officials.

As with all Chinese government policies, the devil will be in the implementation.

http://www.china.org.cn/china/third_plenary_session/2013-11/16/content_30620736.htm

http://news.xinhuanet.com/politics/2013-11/15/c_118164235.htm

http://blogs.wsj.com/chinarealtime/2013/11/15/china-to-ease-one-child-policy-abolish-labor-camps/

http://blogs.wsj.com/chinarealtime/2013/10/30/editorial-hints-at-xis-challenge/

http://cn.wsj.com/gb/20131116/bch132712.asp