oce

Excel Marco for assigning stars for p values, delete diagonal pattern of cells

2015-05-13T11:57:00.001-07:00

***set e to bonferroni, if no bonferroni, set e to 1

Sub bstars()

Dim c As Range, e As Long
e = 10
For Each c In Selection
    If IsNumeric(c) And c < 0.001 / e Then c = "$^{***}$"
    If IsNumeric(c) And c < 0.01 / e Then c = "$^{**}$"
    If IsNumeric(c) And c < 0.05 / e Then c = "$^{*}$"
    If IsNumeric(c) And c < 0.1 / e Then c = "$^{#}$"
    If IsNumeric(c) And c < 0.01 Then c = "$^{#3}$"
    If IsNumeric(c) And c < 0.05 Then c = "$^{#2}$"
    If IsNumeric(c) And c < 0.1 Then c = "$^{#1}$"
    If IsNumeric(c) And c >= 0.1 Then c.ClearContents
Next

End Sub

Remove all cells in a matrix except the diagonal. (Copy from http://stackoverflow.com/questions/16057311/excel-macro-deleting-pattern-of-cells)

Sub diagonal()

Dim tmpRNG As Range
Set tmpRNG = Selection
Dim tmpOff As Long

tmpOff = tmpRNG.Row - tmpRNG.Column

Dim cell As Range
For Each cell In tmpRNG '.Cells
If cell.Row - tmpOff <> cell.Column Then cell.ClearContents
Next cell
End Sub

#The following two are modified just to remove all upper or lower diagonals...

Sub rmlower()

Dim tmpRNG As Range
Set tmpRNG = Selection
Dim tmpOff As Long

tmpOff = tmpRNG.Row - tmpRNG.Column

Dim cell As Range
For Each cell In tmpRNG '.Cells
If cell.Row - tmpOff >= cell.Column Then cell.ClearContents
Next cell
End Sub

Sub rmupper()

Dim tmpRNG As Range
Set tmpRNG = Selection
Dim tmpOff As Long

tmpOff = tmpRNG.Row - tmpRNG.Column

Dim cell As Range
For Each cell In tmpRNG '.Cells
If cell.Row - tmpOff <= cell.Column Then cell.ClearContents
Next cell
End Sub

Sub RoundNumbers()
Dim c As Range, e As Long
e = 2
For Each c In Selection
    If IsNumeric(c) And c <> "" Then c = Application.WorksheetFunction.Round(c, e)
Next
End Sub

stats::wilcox.test vs exactRankTests::wilcox.eact vs coin:: wilcoxsign_test

2015-03-21T03:53:00.002-07:00

1. wilcox.test() in the stats package
This function automatically switches to using a Normal
approximation when there are ties in the data:

  wilcox.test(x, y, paired=TRUE)$p.value
#[1] 0.05802402
(You can suppress the warning (due to ties) by specifying
the argument 'exact=FALSE'.)

This function also uses a continuity correction unless
told not to:

  wilcox.test(x, y, paired=TRUE, correct=FALSE)$p.value
#[1] 0.05061243

2. wilcox.exact() in pkg exactRankTests
This function can handle ties (using the "Wilcoxon" method)
with an 'exact' calculation:

  wilcox.exact(x, y, paired=TRUE)$p.value
#[1] 0.0546875

If you want the Normal approximation:

    wilcox.exact(x, y, paired=TRUE, exact=FALSE)$p.value#[1] 0.05061243  

3. wilcoxsign_test() in pkg coin
This is the most comprehensive of these functions.
It is also the only one that offers the "Pratt" method
of handling ties. It will default to this method and
a Normal approximation:

  pvalue(wilcoxsign_test(x ~ y))
#[1] 0.08143996

  pvalue(wilcoxsign_test(x ~ y, zero.method="Pratt",
         distribution="asympt"))
#[1] 0.08143996

You can get the results from wilcox.exact() with

  pvalue(wilcoxsign_test(x ~ y, zero.method="Wilcoxon",
         distribution="asympt"))
#[1] 0.05061243

and

  pvalue(wilcoxsign_test(x ~ y, zero.method="Wilcoxon",
         dist="exact"))
#[1] 0.0546875


Extract from: https://stat.ethz.ch/pipermail/r-help/2011-April/274931.html

Latex, Zotero Bibtex, APA6 class

2015-01-21T04:29:00.001-08:00

(This is my own note on resolving some issues on Latex.... )

Keep on having error msg with the bibtex export of Zotero.

Install plugin from here:
https://github.com/ZotPlus/zotero-better-bibtex

Then in Zotero export -> "Better bibLaTeX"

texmaker -
bibtex path to
"/usr/texbin/biber" %.bcf

\documentclass[floatsintext,man,12pt,a4paper]{apa6}

\usepackage[american]{babel}

\usepackage[utf8]{inputenc}

\usepackage{csquotes}

\usepackage[style=apa,sortcites=true,sorting=nyt,backend=biber]{biblatex} %uniquename=false if don't want abb.

\DeclareFieldFormat[article,misc,inbook,incollection,inproceedings,patent,thesis,unpublished]{apacase}{#1}

%\DeclareFieldFormat{apacase}{\MakeSentenceCase{#1}}

\AtEveryBibitem{

  \clearfield{labelmonth}

  \clearfield{labelday}

Optional parameters to finetune the placement of tables and figures:

h, here
t, top
b, bottom
p, page of float

and LaTeX will try to honore the placement with respect to actual place, the top or bottom of the page, or a separate page of floats. For example, when using ht LaTeX will try to put the figure at the insertion point, then on the top of the next page if it happens to violate its typesetting rules.

Force LaTeX to "insist" on these specifications by adding an exclamation mark (!) before the placement parameters, e.g. \begin{figure}[!ht].

Model fit indices reference

2015-01-16T04:40:00.002-08:00

(If you find this post from google, please note the following are just my only study notes and you may find them difficult to comprehend, I would suggest you to go to the original site (links provided) for detail information.)

Chi-square always sig. for large sample sizes (500+), hence type II error. Can look at RMSEA.

"RMSEA values of < 0.2 with sample sizes of 500+, and certainly 1000+, may indicate that the data do not underfit the model, and that the chi-square was inflated by sample size." - http://www.rasch.org/rmt/rmt254d.htm

MacCallum, Browne and Sugawara (1996) have used 0.01, 0.05, and 0.08 to indicate excellent, good, and mediocre fit, respectively. However, others have suggested 0.10 as the cutoff for poor fitting models. These are definitions for the population. That is, a given model may have a population value of 0.05 (which would not be known), but in the sample it might be greater than 0.10. - http://davidakenny.net/cm/fit.htm

Fit index ranges for structural equation modeling - http://www.psych-it.com.au/Psychlopedia/article.asp?id=277

Assess whole SEM - chi sq and fit index - http://zencaroline.blogspot.hk/2007/04/global-model-fit.html

- chi-sq p>.05, acceptable model fit
- penalty of model complexity (?)

Chi sq test
- H0: the proposed model holds in the population (sample covariance matrix = population covariance matrix) --> do NOT want to reject the H0!
-- the bigger the p the better

Reports of chi squre should be accompanied by degrees of freedom, sample size, and p-value. Example, χ2 (48, N=500)= 303.80, p < .001, TLI=.86, CFI=.90; or χ2 (15, N=2232)=10.91,p=.77
The χ2 associated with the model # is significant, χ2 (df, N=2232)=#, p=0.000, which suggests that the model is not consistent with the observed data.
Nonsignificant— χ2 (15, N=2232)=10.91,p=.77, suggesting that the proposed model is consistent with the observed data

Chi square is highly sensitive to departures from multivariate normality.

χ2 is sensitive to sample size. With large sample size, the chi-square values will be inflated (statistically significant), thus might erroneously implying a poor data-to-model fit (Schumacker & Lomax, 2004).
Small sample sizes - not enough power to detect the differences between several competing models using the chi squre statistic for model selection or evaluation.
Larger sample sizes - power may be so high --> poor model fit --> rejected models with only trivial misspecifications.
Large, complex problems (many variables and df), the observed chisquare will nearly always be statistically significant, even when there is a reasonably good fit to the data.

Most applications of confirmatory factor analysis require a subjective evaluation of whether or not a statistically significant chi-square is small enough to constitute an adequate fit.

Relative chi-square (or normal chi-square) = chi-square fit index / df

Make chi-sq test less dependent on sample size.
Wheaton (1987) advocated CMIN/DF not be used.
In the range of 2 to 1 or 3 to 1 indicate acceptable fit between the hypothetical model and the sample data (Carmnines&McIver,1981).
Different researchers have recommended using ratio as low as 2 or as high as 5 to indicate a reasonable fit (Marsh&Hocevar,1985).
A chi-squre/df ratio larger than 2 indicates an inadequate fit (Byrne,1989). chi-square/df ratio values lower than 2 are widely considered to represent a minimally plausible model (Byrne,1991, The Maslach Burnout Inventory: validating factorial structure and invariance across intermediates, secondary, and university educators. Multivariate Behavioral Research, 26 (4), 583-605)
The smaller the Chi-square, the better the fit of the model. It has been suggested that a Chi-square two or three times as large as the degrees of freedom is acceptable (Carmines
& McIver, 1981), but the fit is considered better the closer the Chi-square value
is to the degrees of freedom for a model (Thacker, Fields & Tetrick, 1989). In
the present sample, it was suggested that a ratio of 5 to 1 was “a useful rule
of thumb” (Jackson et al., 1993, p. 755). -- cf Timothy R. Hinkin (1995)
A Review of Scale Development Practices in the Study of Organizations.
Journal of Management, Vol. 21, No. 5.967-988

However, Chi-square test may be misleading. 1) The more complex the model, the more likely a good fit (i.e., the closer the researcher's model is to being just-identified, the more likely good fit will be found). 2) The larger the sample size, the more likely the rejection of the model and the more likely a Type II error (rejecting something true). In very large samples, even tiny differences between the observed model and the perfect-fit model may be found significant. 3) The chi-square fit index is also very sensitive to violations of the assumption of multivariate normality. When this assumption is known to be violated, the researcher may prefer Satorra-Bentler scaled chi-square, which adjusts model chi-square for non-normality.

Absolute fit indexes--directly assess how well a priori model reproduces the sample data

To address the limitations of chi-squre test, goodness-of-fit indexes as adjuncts to the chi-squre statistic are used to assess model fit
Model with many variables and small samples may be more inclined to experience degradation in absolute fit indexes than models with many variables and large sample size.
RMR(root mean square residual), the smaller the RMR, the better the model. An RMR of zero indicates a perfect fit. The closer the RMR to 0 for a model being tested, the better the model fit. RMR smaller than 0.05 indicates good fit.
SRMR (standardized RMR, root mean square residual)-- SRMR < = .05 means good fit, The smaller the SRMR, the better the model fit. SRMR = 0 indicates perfect fit. A value less than .08 is considered good fit. SRMR tends to be lower simply due to larger sample size or more parameters in the model. To get SRMR in AMOS, select Analyze, Calculate Estimates as usual. Then Select Plugins, Standardized RMR: this brings up a blank Standardized RMR dialog. Then re-select Analyze, Calculate Estimates, and the Standardized RMR dialog will display SRMR.
GFI should by equal to or greater than .90 to indicate good fit. GFI is less than or equal to 1. A value of 1 indicates a perfect fit. GFI tends to be larger as sample size increases. GFI> 0.95 indicates good fit. GFI index is roughly analogous to the multiple R square in multiple regression in that it represents the overall amount of the covariation among the observed variables that can be accounted for by the hypothesized model.
AGFI (adjusted GFI), AGFI adjusts the GFI for degree of freedom, resulting in lower values for models with more parameters. AGFI should also be at least .90, close to 1 indicates good fit. AGFI may underestimate fit for small sample sizes. AGFI's use has been declining and it is no longer considered a preferred measure of goodness of fit. AGFI > 0.9 indicates good fit.
CI (centrality index)--CI should be .90 or higher to accept the model.
CAK
CK (single sample cross-validation index)
MCI (centrality index
CN

Incremental fix index(comparative fi index)-- measure the proportionate improvement in fit by comparing a target model with a more restricted, nested baseline model. A null model in which all the observed variabels are uncorrelated is the most typically used baseline model
Baseline Comparisons-- comparing the given model with an alternative model

CFI (comparative fix index), close to 1 indicates a very good fit, > 0.9 or close to 0.95 indicates good fit, by convention, CFI should be equal to or greater than .90 to accept the model. CFI is independent of sample size. CFI is more appropriate than NFI in finite samples. NFI behaves erratically across ML and GLS, wheresas CFI behaved consistenly across the two estimation methods. CFI is recommended for routine use. Gerbing and Anderson (1993) recommended RNI and CFI, DELTA2 (IFI). When the sample size is small, both the CFI and TLI decrease as we increase the number of vairables in the models.
RNI, RNI is recommended for routine use. RNI is generally preferred over TLI. RNI> 0.95 indicates good fit.
BBI (Bentler-Bonett index), should be greater than .9 to consider fit good.
IFI (incremental fit index,also known as DELTA2), IFI should be equal to or greater than .90 to accept the model. IFI value close to 1 indicates good fit. IFI can be greater than 1.0 under certain circumstances. IFI is not recommended for routine use.
NFI (normed fit index, also known as the Bentler-Bonett normed fit index,DELTA1), 1 = perfect fit. NFI values above .95 are good, between .90 and .95 acceptable, and below .90 indicates a need to respecify the model. NFI greater than or equal to 0.9 indicates acceptable model fit. NFI less than 0.9 can usually be improved substantially. Some authors have used the more liberal cutoff of .80. NFI may underestimate fit for small samples. NFI does not reflect parsimony: the more parameters in the model, the larger the NFI coefficient, which is why NNFI (TLI) below is now preferred (NNFI incorporates a correction for model complexity, whereas the NFI does not). NFI depends on sample size, values of the NFI will be higher for larger sample sizes. NFI behaves erratically across estimation methods under conditions of small sample size. NFI is not a good indicator for evaluating model fit when the sample size is small.
NFI suggested relatively poorer model fit as missing data increased, with the bias generally more pronounced when data were MAR than when they were MCAR. Whereas NFI is still widely used, it is typically not among the recommended indices in recent reviews. Marsh et al., (1988) recommended against using NFI and in favor of TLI, because NFI, not TLI, is sensitive to sample size. When the sample size is small, both the CFI and TLI decrease as we increase the number of variables in the model.
NNFI(non-normed fit index,also called the Bentler-Bonett non-normed fit index, the Tucker-Lewis index, TLI,RHO2), NNFI is similar to NFI, but penalizes for model complexity. NNFI is not guaranteed to vary from 0 to 1. It is one of the fit indexes less affected by sample size. NNFI close to 1 indicates a good fit. TLI greater than or equal to 0.9 indicates acceptable model fit. By convention, NNFI values below .90 indicate a need to respecify the model. TLI less than 0.9 can usually be improved substantially. Some authors have used the more liberal cutoff of .80 since TLI tends to run lower than GFI. However, more recently, Hu and Bentler (1999) have suggested NNFI >= .95 as the cutoff for a good model fit. TLI is not associated with sample size. NNFI is recommended for routine use. NNFI is a more useful index than NFI. Hu and Bentler (1998,1999) support the continued use of TLI because TLI is relatively insensitive to sample size; TLI is sensitive to model missipecifications; is relatively insensitive to violations of assumptions of multivariate normality; is relatively insensitive to estimation method (maximum likelihood vs alternaitve methods). RNI is generally preferred over TLI.
NTLI, NTLI is recommended for routine use.
RFI (relative fit index, RHO1) is not guaranteed to vary from 0 to 1. RFI close to 1 indicates a good fit. Neither the NFI nor the RFI are recommended for routine use.

Parsimony-Adjusted Measures-- measures penalize for lack of parsimony.

PRATIO (parsimony ratio)
RMSEA (root mean square error of approximation),there is good model fit if RMSEA less than or equal to .05. There is adequate fit if RMSEA is less than or equal to .08. More recently, Hu and Bentler (1999) have suggested RMSEA <= .06 as the cutoff for a good model fit. RMSEA is a popular measure of fit. Less than .05 indicates good fit, =0.0 indicates exact fit, from .08 to .10 indicates mediocre fit, greater than .10 indicates poor fit. RMSEA is judged by a value of .05 or less as an indication of a good fit. A value of .08 or less is indicative of a “reasonable” error of approximation such that a model should not be used if it has an RMSEA greater than .1. Hu and Bentler (1995) suggested values below .06 indicate good fit. The RMSEA values are classified into four categories: close fit (.00–.05), fair fit (.05–.08), mediocre fit (.08–.10), and poor fit (over .10). RMSEA smaller than 0.05 indicates good fit. RMSEA tends to improve as we add variables to the model, expecially with larger sample size. One limitation of RMSEA is that it ignores the complexity of the model. The lack of fit of the hypothesized model to the population is known as the error of approximation. The RMSEA is a standardized measure of error of approximation. RMSEA value of .05 or less indicates a close approximation, values of up to .08 suggests a reasonable fit of the model in the population.
PCLOSE tests the null hypothesis that RMSEA is no greater than .05. If PCLOSE is less than .05, we reject the null hypothesis and conclude that the computed RMSEA is greater than .05, indicating lack of a close fit.
PGFI (parsimony goodness of fit index)
PNFI (parsimony normed fit index),There is no commonly agreed-upon cutoff value for an acceptable model.
PCFI (parsimony comparative fit index),There is no commonly agreed-upon cutoff value for an acceptable model.

Absolute fit indexes--directly assess how well a priori model reproduces the sample data
Information criteriosn index, goodness of fit measures based on information theory (do not have cutoffs like .90 or .95. Rather they are used in comparing models, with the lower value representing the better fit.)

CAK
CK
MCI (McDonald's centrality index)
CN(Hoelter's ctritical N)
AIC (Akaike Information Criterion, single sample cross-validation index), the lower the AIC measure, the better the fit.
AIC0, AMOS Specification Search tool by default rescales AIC so when comparing models, the lowest AIC coefficient is 0. For the remaining models, AIC0 <= 2, no credible evidence the model should be ruled out; 2 - 4, weak evidence the model should be ruled out; 4 - 7, definite evidence; 7 - 10 strong evidence; > 10, very strong evidence the model should be ruled out.
CAIC (Consistent AIC),the lower the CAIC measure, the better the fit.
BCC (Browne-Cudeck criterion, also called the Cudeck & Browne single sample cross-validation index) It should be close to .9 to consider fit good. BCC penalizes for model complexity (lack of parsimony) more than AIC.
ECVI (Expected cross-validation index, single sample cross-validation index), in its usual variant is equivalent to BCC, and is useful for comparing non-nested models, lower ECVI is better fit. EVIC can be used to compare non-nested models and allows the determination of which model will cross-validate best in anohter sample of the same size and simliarly selected. Choose the model that has the lowest ECVI.
MECVI,a variant on BCC,except for a scale factor, MECVI is identical to BCC
BIC (Bayesian Information Criterion, also known as Akaike's Bayesian Information Criterion (ABIC) and the Schwarz Bayesian Criterion (SBC).compared to AIC, BCC, or CAIC, BIC more strongly favors parsimonious models with fewer parameters. BIC is recommended when sample size is large or the number of parameters in the model is small. Recently, however, the limitations of BIC have been highlighted.
BIC0,the AMOS Specification Search tool by default rescales BIC so when comparing models, the lowest BIC coefficient is 0. For the remaining models, the Raftery (1995) interpretation is: BIC0 <= 2, weak evidence the model should be ruled out; 2 - 4, positive evidence the movel should be ruled out; 6 - 10, strong evidence; > 10, very strong evidence the model should be ruled out.
BICp. BIC can be rescaled so Akaike weights/Bayes factors sum to 1.0. In AMOS Specification Search, this is done in a checkbox under Options, Current Results tab. BICp values represent estimated posterior probabilities if the models have equal prior probabilities. Thus if BICp = .60 for a model, it is the correct model with a probability of 60%. The sum of BICp values for all models will sum to 100%, meaning 100% probability the correct model is one of them, a trivial result but one which points out the underlying assumption that proper specification of the model is one of the default models in the set. Put another way, "correct model" in this context means "most correct of the alternatives."
BICL. BIC can be rescaled so Akaike weights/Bayes factors have a maximum of 1.0. In AMOS Specification Search, this is done in a checkbox under Options, Current Results tab. BICL values of .05 or greater in magnitude may be considered the most probable models in "Occam's window," a model-filtering criterion advanced by Madigan and Raftery (1994).
Quantile or Q-Plots
IES (Interaction effect size),IES is a measure of the magnitude of an interaction effect (the effect of adding an interaction term to the model). In OLS regression this would be the incremental change in R-squared from adding the interaction term to the equation. In SEM, IES is an analogous criterion based on chi-square goodness of fit. Recall that the smaller the chi-square, the better the model fit. IES is the percent chi-square is reduced (toward better fit) by adding the interaction variable to the model.

residual as a measure of overall fit

residual is the difference between the sample matrix (S) and population matrix ( ∑ ). Standardized residuals are residuals that have been standardized to have a mean of zero and a standard deviation of one, making them easier to interpret. Standardized residuals larger than absolute value 2.0 are considered to be suggestive of a lack of fit.

http://www2.chass.ncsu.edu/garson/pa765/structur.htm#extract
Although AMOS provides a considerably broader range of fit indexes, it does not permit access to the information matrix or residuals with incomplete data.

My Name is Khan

2014-09-18T23:35:00.000-07:00

My Name is Khan is a great movie about a guy with autism. Just found it interesting that they misspelled Autism as Autisism...

Girls are from Venus, Boys are from Mars

2014-04-11T07:00:00.000-07:00

G: girl; B: boy; I: Me

G: Ms Tse! Let me tell you a story.
I: Sure, what's the story?
G: Snow white. Once upon a time, there was Snow white...
B: I know! The prince kisses the princess, and then...
G: Not yet! Listen to me first...
I (to the boy): Let's listen to her, I will listen to you afterwards.
G: The mum of Snow White was dead, and her new mum was a witch...
B: I know! The prince kisses the princess.
G: I am telling the story!
B: You don't have to tell the details! The most important part is, the prince kisses the princess!

Apparently girls care about the processes while boys care about the results.

Little Mike Wazowski Wallpaper

2013-12-08T04:46:00.002-08:00

Really love little Mike Wazowski that I decided to screen capture him as my wallpaper. :) If you love him too, feel free to use.

I like how Mike and Sul sat side by side...

Have to re-watch Monster University after I am done with my data entry!!

Random pictures

2013-12-01T00:54:00.001-08:00

Trying to backup stuff from the old laptop to the new MacBook and discovered the following pictures:

1. Screen Capture from the PhD Movie:

Yeah, I always feel like I'm just an imposter -- especially when people ask me about statistic and programming....

2. Lego 6394:
And I had this lego set when I was 8. It is a carpark with Shell station. I missed this set so much! (Okay I know this is random.)

Install Windows Server 2012 R2 on MacBook Pro Retina 15 (late 2013) using Bootcamp

2013-11-30T11:18:00.000-08:00

It was a chaotic experience, as officially Bootcamp never support Windows Servers. However, as I have free Windows Servers 2012 from Dreamspark, and I would like to have OneNote installed on my MacBook Pro, so I tried... There were tons of error and after 10 hours I got it up and running except the bluetooth still won't work. Here are my steps (it may or may not work again):

1. In Bootcamp, create the USB disc with Windows Servers 2008 R2 iso first. (Cause there was error when I directly installed 2012)

2. Download DAEMON Tools Lite 4. You will need this later.

3. Install Windows Servers 2008 using Bootcamp. Remember to REMOVE any other USB hard disk, this was one of the problems I encountered during my installation and I have to redo the whole thing again.

4. Install DAEMON Tools Lite 4 on Windows.

If you skipped step 2 --

the wifi would NOT work at this stage as the wifi service is disabled, I did not attempt to solve this as that needs to be solved again after installing 2012. So it may be easier to download DAEMON using another PC.
If you do not have another PC connected to the Internet, you may try to enable Wireless LAN service and re-run Bootcamp installation files, theoretically this would get WiFi working but I am not sure.

5. Mount Windows Servers 2012 R2 iso using Daemon. Install.

6. Install Windows Servers 2012 as a fresh copy (do NOT upgrade, it won't work).

7. After installation, run the Bootcamp installation files. (If you don't mind completing set up with the horrible screen resolution, you can actually skip this step and go ahead to step 8, I believe)

8. Everything should work except wifi (and bluetooth, but I don't use bluetooth so I did not attempt to solve this issue, if you can get bluetooth working, please let me know how to do it! :D ).

9. Enable Wireless LAN service in your server.

10. Re-run Bootcamp installation files.

11. Restart ... and enjoy! Wifi should work now :)

I have googled for a long time and seems many people says can't get 2012 installed using Bootcamp, of they can get that install but Wifi doesn't work, hope this instruction would help. :D

The first time I late for class

2013-09-06T01:41:00.000-07:00

The following blog was originally posted in "easyjournal", which was gone way earlier than Xanga. Repost now because the recent discussion about the following email from HKUST IELM 4110 course reminded me of my UG life when we were also forced to take the IEEM 311 course - I think they are basically the same.

Frankly speaking, I don't see any problems about this email, it is important to state the rules clearly upfront these days so students know what we expect.

Here is the entry I wrote on IEEM311 a decade ago (10 years already! orz...)

=======================

4.23.2003

The first time I late for class

Writing the FYP report till 7:30 am this morning, I found my eyes kept watering. This left me no choice but setting three alarms at 9:30 am then closed my eyes...

10:10
was all I read the next time I opened my eyes. (Don't ask me about the alarms, I don't know what happened to them...)

Without a second thought, I jumped out of bed and changed my clothes, picked my PRS (Personal Response System, a "remote-control" answering machine) and run all my way to LTA. I arrived there at 10:18. Then I immediately felt asleep again.

(Let's me explain why I need to re-locate myself this hurry for this sleep. We, UST CPEG students, are required to attend a very MEANING seminar course. Attendances are marked. If you late twice, it would automatically "upgraded" to one absent. If your attendance does not meet the requirement, you will fail the course and need to do it all over again. For final year student, fail the course simply means -- DEFER.

After each seminar, there is a quiz for testing your "understanding" using the PRS. To pass the course, you need to answer 60% of PRS questions correctly.)

I, together with many other students, woke up automatically when the PRS session started. Are you wondering how we can answer the questions without listening to the seminar? Read on and you will see the point!

The first PRS question was about something related to "matrix tuning", and asked at whichever phrase it should be done. I do not remember the options right now, but I thought the answer should be 2 or 3. The other two answers were eliminated by common sense. Many students however commented that the answer should be 4. So, with a little hesitation, I chose 4, as the rule of the PRS game is, choose what the majority choose.

The result statistics of PRS were shown instantly after 2 mins, and from the graph, we knew that only one student chose 2 and one student chose 3, all the other students chose 4. The lecturer seemed to be surprised and said, "Well, the answer should be 3."

So now you see how many students actually listened to the seminar.

You may wonder why, while class attendances are being voluntary under SARS, so many students still attend the seminar. The reason is very simple, and you may read the following email:----
Dear students,

As you may know, classes will resume next Mon. We have invited Mr. XXX to speak on Occupational Risk Management Project Management for the coming IEEMYYY lecture on 16 April 03 (Wed).

Your attendance will be voluntary on the coming lecture. But of course you are welcome to come to the lecture, with your mask, on that day.

Since a major portion of IEEM YYY assessment is based on the PRS, we would make video lecture to be put on the web. (Time of availability will be announced later) If you encounter technical problems in viewing the lecture, you may request for CD-ROMs via WebCT. And for those who missed the classes, you would be asked to take a short exam as the final on May 21 (time and place to be announced) in lieu of PRS scores for the remainder of the semester.

In addition, we have reserve an office hour, every Wednesday from 11 to 12 for you to call Louisa So, the teaching assistant of IEEMYYY, at 2358ZZZZ, or you can write to XXXX@ust.hk or LLLLL@ust.hk if prefer email.

In deed, SARS has severely disrupted our teaching and learning. However, people in Hong Kong are flexible and dynamics to overcome challenges like SARS.

Let's work together to make it work.

Best regards,
Professor TTTT
----

The ironic part is, this lecture with very high risk, i.e. a class size of 300 in a lecture theater without window, and most students are not getting anything out of it, is not cancelled, while the other very safe courses like Japanese, a class size of 20 in a classroom with many windows, are cancelled. I just do not understand what’s in the mind of our school management team.

=======================

I bet you can tell how annoyed we felt towards the course. (Oh, and reading the "young" me though is, well, funny, and embarrassing.)

The rational me back then actually understood the need of taking attendance, or else the huge theater would be almost empty. It is just that this course is for final students who were very much deprived of sleep, and this course seldom invited Computer engineers to share, so for us who were very unlikely, if not impossible, to become a civil or mechanical engineer, we could never relate to those topics and hence we usually slept through the seminar. But we would try our best to pick seat furthest away from the stage to avoid embarrassing the speaker.

Hello Kitty

2013-06-25T11:04:00.000-07:00

今天，我和一組小朋友一起坐。
一小女孩突然指著我的額角，跟我說：謝老師，你如果呢度整隻蝴蝶結呢，你就會變成Hello Kitty架啦！ -__-|||

朕是一條蟲？

2013-06-17T11:37:00.000-07:00

幼稚園班小朋友日日都練呢套功夫做結業禮表演，我聽佐十幾次淨係聽到「朕是一條蟲」…

今時今日嘅兒歌....

2013-06-16T11:33:00.000-07:00

「今時今日嘅兒歌係點樣」，筆者舉例的兒歌是奇離和難聽（首麵麵麵麵麵根本係讀歌唔係唱囉好唔好？），但我覺得「honey bees」組合的Honey才是不得不提的例子。歌其實不是真的很不濟－－其實都幾難頂既，不過我喺幼稚園聽過更難頂既「爱我你就抱抱我」：

最不能接受的是家燕媽媽把一班小朋友化著濃粧穿得古怪兼做啲懶CUTE既奇離動作，簡直係教壞細路。

其實我曾經係一個兒歌迷，中學時仲會為錄兒歌而睇閃電傳真機（我有兩盒兒歌VHS，可惜唔見佐）…真係要多謝依家啲兒歌咁不濟，唔係我可能到依家都仲係成日聽兒歌。

K2小朋友的爛GAG……

2013-06-13T10:58:00.000-07:00

A：喂，你搞乜啊？
B：搞鬼啊！

食風

2013-06-06T11:45:00.000-07:00

小朋友放好餐具，但還沒有茶點，小朋友甲做手勢扮吃東西，說：「我最鐘意食風，所以我唔怕凍。」

天氣的錯

2013-05-22T11:16:00.000-07:00

做naturalistic observation睇小朋友玩，其實真係「聽天由命」。停課無得收data固然大鑊，最慘係跟住兩日都要趕返今日既進度邊小朋友會好忙更加無時間玩……放一日假三日啲data都會差唔多無佐。

其實就算有得收，出面行雷閃電，有啲小朋友就嚇到喊，有啲就小朋友掛住望／安慰啲喊既小朋友；又有啲自我安慰：「唔駛驚驚既！」（琴日就有個杞人憂天小朋友自言自語：「哎唷！我無帶雨樓喎，唔知媽咪陣間會唔會遮埋我架喎……」其實都幾搞笑）咁收返嚟啲data就成日都係講緊行雷，都已經有少少唔「正常」。不過起碼就唔會無佐3日data咁灰。

結論就係，我如果畢唔到業，可能係「天氣的錯」……　（標題黨:P）

るるるの歌

2013-05-17T04:27:00.001-07:00

るるるの歌，總共有四首，不停的在lululu。雖然這幾首歌是在日本的兒童節目播出，但真的有點恐佈。四首歌合起來是一個故事來的，蠻有意思的，不懂日文看圖也能理解故事說甚麼。

るるるの歌　～はじまり編～　
るるるの歌　～おとこオオカミ編～
るるるの歌　～オオカミおとこ編～
るるるの歌　～未来編～

oh my god

2013-05-16T06:03:00.002-07:00

一組小朋友最近不停說oh my god。
我問其中一位小朋友：你哋係邊度聽到oh my god架？
小朋友：hahaha，乜你唔知咩？
我：唔知啊……
小朋友：神探哥倫布啊！你無睇電視架咩？
我：無睇啊……
小朋友：我知！你咁遲放工梗係無得睇啦。早啲返屋企咪有得睇囉！

Troublesome two

2013-05-06T03:20:00.005-07:00

Me, "What's color is this?"
2-year-old Boy, "No! I don't know!"
Me, "Is this green?"
Boy, "No!"
Me, "Are you tired?"
Boy, "No!"
Me, "Your mom told me you like singing in your bed!"
Boy, "No!"
I talked to his maid for a while and then we said goodbye.
Boy, "Goodbye."
Me, "Great, you say goodbye to me!"
Boy, "NO!!!"

我是誰…

2013-05-04T12:01:00.000-07:00

朋友share的學校secrets令我稔起以下既往事：

中六，上樓梯見到班主任，叫了聲「miss X」。
她問：「你好似係6C架可？」
我：「係啊！」
她：「你一陣見到Hannah叫佢嚟搵我啦！」
我：「呃，我係Hannah喎…」
她：「我話搵邊個話？我唔係話搵Angela咩？」
我：「呃，咁我一陣叫Angela搵你啦。」
她：「咁其實搵你都得……」

（我唔記得之後要做乜，不過我覺得佢其實真係要搵我……）

廣告商提供錯誤資訊

2013-04-15T11:40:00.000-07:00

此門只能推開，廣告商提供錯誤資訊……

高危人物

2013-03-07T01:57:00.000-08:00

回大學看校醫。醫生沒有戴口罩。
我坐下，他問：「咦，你啊……係咪仲喺幼稚園做研究？」
我：「係啊！啱啱上星期五返過幼稚園……」
醫生急忙戴上口罩……

用完即棄相機

2012-09-22T03:21:00.002-07:00

2001 International Air Cadet Exchange 到法國，參觀Dassault Falcon的記念品。其實這是Dassault 1999年攝影比賽的「專用相機」，也就是該公司的剩餘物資。帶回香港便塞了入櫃桶，直到現在。

如果你想重拾用「用完即棄相機」的感覺，歡迎拿去……不過，我其實不知道還可不可以用和有沒有相舖可以沖曬這種相機的照片。

茶積飯積木積積

2012-09-21T10:14:00.000-07:00

茶積飯積木積積，唔知依家仲有冇人識。

中三，參與《進念二十面體》學校劇場計劃時，見到作者黎達達榮，興奮地跟他說：我好鐘意木積積架，你可唔可以畫一張俾我啊？他說：好啊。
然後，才發現書包裏一張白紙也沒有……唯有尷尬地拿了紙袋*裏那張殘殘的托底硬咭紙給佢，好像還說了句「唔好意思我得呢張紙咋」。他二話不說就畫了木積積、飯積、茶積和老師。這張硬咭紙就一直貼在書桌上。
現在要裝修，咭紙撕不下來了……

*當年中三沒有locker，書包又多書，很多時都要帶多一個紙袋裝運動衫或美勞用品，文具店有很多很漂亮的紙袋，「高級」的版本還會有一層膠包著……現在好像已經沒有這種紙袋賣了。

How to set R's environment to support Chinese?

2012-09-12T04:35:00.002-07:00

Open terminal, type in "defaults write org.R-project.R force.LANG en_US.UTF-8".

====
obsolete method:
Use this command for Traditional Chinese:
Sys.setlocale(locale = "Chinese (Traditional)")

And this for Simplified Chinese
Sys.setlocale(locale = "Chinese")