Constructor
new BehaviorInstruction(): BehaviorInstruction
Creates an instance of BehaviorInstruction.
Properties
- anchorIsContainer
- attrName
- attributes
- contentFactory
- enhance
- host
- inheritBindingContext
- initiatedByBehavior
- originalAttrName
- partReplacements
- skipContentProcessing
- type
- viewFactory
- viewModel
- normal


Last month, I did the Data Weekends program - a 2-day machine learning workshop hosted by Francesco Mosconi. Francesco is a great leader for the program; he teaches at General Assembly and was also the Chief Data Officer for a Y-Combinator backed company, Spire. Basically.. he knows a “thing or two about a thing or two.”
For folks that might be thinking about doing the program, I wanted to share a bit of what a participant can expect to learn, some pros and cons, and things to consider when determining if it is right for you.
##Weekend Overview
Each day of the workshop went from 9am - 5pm and was a full day of activity. There was some lecturing to give context, but the time was spent predominantly interacting with python code to fully intuit the concepts and allow for questions. There were 8 of us in my workshop: some were data scientists, backend engineers, and co-founders of data start-ups. I paired with a woman named Vanessa who was a backend engineer for Rdio / Pandora. She was super helpful as I felt like I had a good grasp on the concepts, but needed more help with the programming.
The instructor starts by working through a few examples of practical applications of machine learning: instances in which regression, classification, clustering, or dimensionality reduction might be used. He then showed us how these techniques match well to the type of data being collected and the kind of problem one wants to solve. It came together in an easy framework:
We then worked through modules for each section, with specific call-outs for cleaning data, and improving accuracy. The instructor uses pre-built Jupyter notebooks to help the participants grapple with the problems; we then can work with our partners to solve them, and, when stuck, get the instructor’s guidance.
The topics are meaty and move quickly; we discuss cost-functions and overfitting in statistics and work through how the cost-function is calculated differently for a sum-of-squares method vs. a support vector machine (SVM). We also gain a familiarity using different functions in the scikit-learn and pandas packages.
from sklearn.cluster import KMeans
X = df[['sepal_length_cm','sepal_width_cm','petal_length_cm','petal_width_cm']]
# look for 3 clusters
km = KMeans(3)
km.fit(X)
# try changing the initialization parameters of KMeans to 'random'. What happens?
Example code and discussion.
For each module, the instructor allowed for good discussion around the underlying intuition of the most popular machine learning models, and the types of problems in which each are best suited. To accommodate the varying levels of proficiency within the room, we also had access to more “advanced” iPython notebooks that operated as standalone guided instruction.
At the end of the two days, we built a simple web application using Heroku and Flask that is designed to do language prediction. The underlying model takes a few scraped text corpora from Wikipedia home pages for several languages (eg. English, Italian, French, Russian). We then vectorize those set of words into 3-character n-grams and create test and holdout sets. The goal of the model is to predict the language based on the probability that a vectorized string of text matches the n-grams for a given language in the database.
After testing a few different models, I found the highest accuracy for the SGDClassifier() in the scikit-learn package. You can find my Language Predictor here!
##Benefits
There were quite a few things I really enjoyed about Data Weekends:
- Great Conceptual Model: I found the instructor’s work to be a great blend of programming, statistics, and real world examples. With this, the overarching framework / conceptual model helped me understand when to apply different techniques and why. I had many similar concepts in a statistics class at Stanford, but I had trouble rooting that learning in something practical - this class actually gave me confidence to use things I had learned elsewhere as well!
- Pair Programming: I really enjoyed being paired with someone who was a professional software engineer. My goal is to work with an engineering team, and Vanessa was really kind and patient with my syntax questions. Hopefully, I was of some help with the statistics.
- Guided Jupyter Notebooks: Pre-built Jupyter notebooks were helpful for understanding “what we were working towards.” For learning exercises, we could get a sense of what we were going to cover end-to-end and what knowledge was being built. At the same time, since none of the code was executed - we could guess how models would perform, test them, and get feedback immediately.
- Advanced Sessions: Once we developed a basic intuition, the instructor would push us to get a better sense of how K-Means clustering works: for example, by changing the number of nodes, or dictating where calculations start from. Or to discuss the differences between Ridge and LASSO regressions. We also had notebooks for these sections.
- Focus on Practical Application: More than any online course, I appreciated the direct feedback from someone with practical experience in the field. I also appreciated that these activities were always connected back to how they could be used in the real world, and that we were given the opportunity to stage an environment and host an application - soup-to-nuts development.
##Challenges
This doesn’t mean that Data Weekends is for everyone, though - there are a few things based on the structure that may make the course challenging for others:
- Uneven Skills: First, data science and machine learning are nascent fields - and therefore the backgrounds of people trying to develop these skills are pretty diverse. For me, there were times in the class in which it felt like it was moving too slow or too fast. The same was true for others, but at different times.
- Big Time Commitment: Saturday and Sunday 9am-5pm is a pretty heavy commitment - an entire weekend spent exhausting your brain. Not only does it limit what you can do with your weekend nights, it also doesn’t necessarily leave you recharged for Monday morning. Energy levels are something to consider - and we definitely got tired even during the lectures themselves.
- Presumed Knowledge: The course moves pretty quickly through statistics and programming to the applications. I would say, in retrospect, that my python was not as good as it could have been to make the most of the class (I did LPTHW, Google’s Python Tutorial, and a pandas tutorial beforehand.)
##So, Who is Data Weekends For?
Data Weekends was great and I would wholeheartedly recommend it. That being said, it is certainly not for everyone. Perhaps someone who is an “advanced beginner” in programming and statistics: maybe with one Coursera course in each under their belt.
For the price, I would also recommend that a participant have a clear sense of how they’ll use the material professionally. I am not sure that the program would help someone get a new job, but it definitely can help a transition within a company and can help someone get a better sense of what is possible, technically speaking. Since I’ve just started to work for a data science consulting firm, I felt like the education helped me get a better sense of how I can be a better partner to the folks I work with.
Good luck! If you are considering taking this program, feel free to reach out to me at anytime to talk about it.


three-projected-material
Three.js Material which lets you do
Texture Projection
on a 3d Model.
Documentation
Documentation on GitHub
Examples
Basic
(source)
Same camera
(source)
3d model
(source)
Orthographic camera
(source)
Transparency
(source)
Envmap
(source)
Multiple projections
(source)
Instancing
(source)
Multiple projections instancing
(source)
Video
(source)


Font Family |
Camera |
shopping |
Plane |
Taxi |
|
Fontello (marker icons) |
||||
|
Fontello (transparent icons) |
||||
|
FontAwesome (marker icons) |
||||
|
FontAwesome (transparent icons) |
||||
|
Material Icons (marker icons) |
camera_alt | shopping_cart | airplanemode_active | directions_car |
|
Material Icons (transparent icons) |
camera_alt | shopping_cart | airplanemode_active | directions_car |


Please wait while the images are loaded...
Correct!
{{ul.selected.title}}, {{ul.selected.date}}
by {{ul.painter.title}}


Jose Mourinho's reaction to Roy Keane bluntly telling Man Utd to sign Harry Kane
Back in October Red Devils legend Roy Keane bluntly announced in the Sky Sports studio that Manchester United should go out and sign the Premier League's best forward, Harry Kane
by Jake Polden, https://www.mirror.co.uk/authors/jake-polden/Jose Mourinho looked on with a grin in October when Roy Keane bluntly announced in the Sky Sports studio that Manchester United should "just go and get Harry Kane".
The Red Devils were struggling in front of goal at the time, and Gary Neville and Graeme Souness became embroiled in a row over the club's decision to offload Romelu Lukaku.
"[Mason] Greenwood at 17? That's how desperate Man United are," said Sounnes. "Go and get goalscorers who will win you games."
Neville responded: "He had a goalscorer in Lukaku - he's a proven goalscorer at the highest level - he didn't want him." And Souness replied: "So that's been a smart decision to start the season without a replacement for him, wasn't it? Gary, it's Man United!"
Former United right-back Neville hit back once more: "That's fine, but you're saying just get anyone in." And Souness concluded: "I'm saying a goalscorer, I'm not just saying anybody."

He responded with a simple solution to the problem: "Go and get [Harry] Kane.
"Just go and get Kane from Spurs, easy. He'll score 20 goals a season with his eyes shut. They're in disarray, so go and get him."
His comment was met with silence as the rest of the room looked at him, prompting Keane to say: "I don't know what are you all staring at," which drew laughter from the pundits.
Mourinho was sitting closest to Keane, and looked at the Irishman with a smirk on his face.
Of course he had no idea he would seen become the Tottenham boss, but his reaction to Keane hailing Kane the league's best striker speaks volumes following his Monday press conference.
Ruling out signing Zlatan Ibrahimovic in January, Mourinho said: "Amazing player, amazing guy, but I would say no chance.
"We have the best striker in England. It doesn't make sense to sign him when we have Harry Kane."


Title: 6ixtynin9 (Ruang Talok 69) Country: Thailand Length: 114 minutes Year: 1999 Director: Pen-ek Ratanaruang Cast: Lalita Panyopas Rating: R(A), Contains strong violence
Due to the economic crisis in Thailand, Tum, a 25 year old secretary, is dismissed from her work. Without money, she thought of suicide, but the newt day she found a box containing a lot of money on her doorstep. As it turned out, the money was used in a match making for the nights boxing match, and due to a mix up, the money was landed on her doorstep. Soon, everybody, including crooks, cops and neighbours alike, are at her house trying to get back the money, and everybody are literally falling dead in Tums house 6ixtynin9 is one hell of a commercial film. From the opening sequence to the very conventional ending, the film plays out like a Hollywood blockbuster. 6ixtynin9 is also very similar to a British film known as Thursday, where the main character in that film life is thrown into turmoil when his old friend gave him a suitcase of money. Like the protagonist in Thursday, Tum was thrown in a strange chain of events that will react like a nuclear reaction, with her as a catalyst. This film also pokes fun at modern problems of Thailand. From match making to the economic crisis which affected the society today. But do not watch this if you are looking to identify the problems in this show; watch it only if you want to be entertained.
6ixtynin9 was shown on 01th April 2000 at the 13th Singapore International Film Festival.
Rated: ***1/2 /5
Ratings: *-Forget it. **-Wait for the video release. ***-OK ****-Prepared to be blown away *****-A Classic piece.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


By creating an account you will be able to shop faster, be up to date on an order's status, and keep track of the orders you have previously made.
If you have an account, please Log in.
The Panopta Agent allows you to monitor local resources (disk usage, cpu usage, etc). If you would like to set that up now, please download and follow the install instructions.
You will be given a server key after you install the Agent on . If you know your server key now, please enter it below.


(Appendix 19)
These extensions are used in TPK 1.65a (and up) and FBB 5.14 and up. HEADER extension. The header now carries the DATE and TIME of the file being transmitted. [SOH] [Len] [Filename] [NUL] [File Size] [NUL] [Date] [Time] [NUL] The Date and Time are the values reported by DOS, coded in 4 hexadecimal digits and are sent in ASCII (8 characters). The receiver has the choice of using either extended Yapp with checksum or normal Yapp. The normal Yapp reply is RF, as before and the receiver can keep the date and time information . The extended Yapp reply is : RT Receive_TPK and is coded : [ACK] [ACK] If the receiver reply is RT the protocol used will be what I have called YappC for Yapp with checksum. When the sender gets this packet he MUST use YappC. Data Packets extension. If the receivers reply is RT the protocol used will be YappC. The checksum allows detection of packets corrupted along the link, particularly on the RS232 lines where there is no error control or correction (or it's very poor!) Data packets : [STX] [Len] [Data] [Checksum] Checksum is the sum of all data bytes anded with FF in 8 bits like Xmodem. If the checksum is bad then the receiver must send a Cancel and enters CW state. Crash Recovery. A new field has been added to the resume reply to tell the sender if the receiver can use YappC or not. Resume is sent instead of RF (or RT). Resume reply for Yapp: (as used before by TPK and FBB) [NAK] [Len] [R] [NUL] [Received Length] [NUL] I I I I I +-- in ASCII as in the header I +-- as Resume ! I +------- len of the following bytes Resume reply for YappC: [NAK] [Len] [R] [NUL] [Received Length] [NUL] [C] [NUL] I Tells sender I can use YappC -----+ When the sender gets this packet then he must also use YappC. This page was last updated


Device:
-- Please select --
Background blur:
Background blur level:
Face framing:
Lighting correction:
Brightness:
Color temperature:
Contrast:
Saturation:
Sharpness:
White balance mode:
-- Please select --
Continuous
Manual
Start
Stop
Metadata
Start
Stop
Supported constraints
Capabilities
Settings


Demo
Docs
text
shield
GitHub
Getting Started
Installation
Integration
Usage
References
Textshield
Encoder
Decoder
EncodedMessage
Installation
Working on docs!
textshield.js
was built from the effort of all
these contributors
.


| 15. Sample source (check one) |
Number of samples collected |
Sample type and description (block letters) |
Sample ID (as written on sample tube or container) |
| Deshi (backyard) chicken |
{{ data.deshi_chicken_sample_number }} |
{{ data.deshi_chicken_sample_type_description }} |
{{ data.deshi_chicken_sample_id }} |
| Sonali chicken |
{{ data.sonali_chicken_sample_number }} |
{{ data.sonali_chicken_sample_type_description }} |
{{ data.sonali_chicken_sample_id }} |
| Brown commercial chicken |
{{ data.brown_comm_chicken_sample_number }} |
{{ data.brown_comm_chicken_sample_type_description }} |
{{ data.brown_comm_sample_id }} |
| White commercial chicken |
{{ data.white_comm_chicken_sample_number }} |
{{ data.white_comm_chicken_sample_type_description }} |
{{ data.white_comm_sample_id }} |
| Duck |
{{ data.duck_number_sample }} |
{{ data.duck_sample_type_description }} |
{{ data.duck_sample_id }} |
| Other species, specify |
{{ data.other_sample_number }} |
{{ data.other_sample_type_description }} |
{{ data.other_sample_id }} |
| |
|
|
|
| 16. Which laboratory are samples being sent to? (block letters) |
{{ data.laboratory_name_sample_sent_to }} |
| |
| Sample collected by: |
|
Signature: |
| Name |
|
{{ data.sample_collected_by_name }} |


{% extends "iframe.html" %} {% block iframe_content %}
Firstname Lastname, you last signed in 2:55pm, Tuesday 28 February 2017


Hello! I am Jesse Theisen. I'm a Full Stack Web Developer currently attending Lambda School.
Work Experience
Contact Center Representative
Customer Traac
Receive and/or make telephone calls for various car dealership clients. Calls include scheduling appointments, providing information, answering questions and other general customer service assistance. Complete data entry of necessary information into appointment scheduling system, FileMaker or other system based on call type. Accurately code the results of the calls.
Customer Service Representative
A.W. Companies
Receive telephone calls from Retail store customers. Calls include fixing issues regarding customer reward accounts, providing information such as story inventory searches, looking up coupons and other rewards, answering questions and other general customer service assistance. Complete data entry of necessary information into software, Accurately code the results of the calls.
Instacart instore shopper
Instacart
instore shopper with Instacart, primary duties were to shop for customers items, then place in a designated area of store.
Tech Support
Self employment
I Provide tech support to residents in the building and remote assistance if it works better schedule wise. Mainly a side job that I use to suppliment income
Sortation Associate
Amazon
Duties included sorting packages in a warehouse environment, loading packages into a delivery van. Seasonal position
Education
High School
Apple Valley High School
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean eget velit ultricies, feugiat est sed, efficitur nunc, vivamus vel accumsan dui.
Computer Science
Lambda School
Lambda School is a 18 month Computer Science & Software Engineering Academy that provides an immersive handson curriculum with a focus on computer science, software engineering, and web development.
Skills
HTML
CSS
JavaScript
React JS
Node JS
Contact Info
Hobbies
My hobbies when I'm not doing Web Development or Computer work are: Reading, going to museums, playing video games, researching various topics I'm interested in, and Paranormal Investigations.


Ashwin hopes Day/Night marks new beginning for Tests
by PTISenior Indian off-spinner Ravichandran Ashwin feels Day/Night Tests will mark a new beginning for the traditional format in the country as the spectator count will improve drastically owing to the match timings.
India will play their maiden Day/Night Test starting Friday, against Bangladesh in Kolkata. The daily proceedings in the match will start at 1pm and wind up by 8pm.
"I just hope this marks the beginning of where we get huge crowds coming into the stadium and test cricket gets its credit and obviously with the change in timings people can afford to finish their work and come into the game, watch it and enjoy it," Ashwin said on 'Star Sports'.
His teammate Mohammed Shami is focussed on the challenges that await him in the game.
"The middle session should be the main focus for batsman while using the pink ball unlike the morning session that is usually considered the most important in a test match as it will have more of a swing in Eden Gardens," he said.
Shami said the bowlers will have to work very hard to extract any assistance from the pink ball.
"...the pink ball is only useful when there a hard and grassy wicket because the ball goes straight.
"Pink ball is a very different ball and doesn't work like the white or red ball at all so there is a very low possibility that in the last session we get a swing or reverse so we will have to work very hard," he said.


TAURUS
Taurus is an Earth sign, just like Virgo and Capricorn, and has the ability to see things from a grounded, practical and realistic perspective.
The ruler of Taurus is Venus, the planet of love, attraction, beauty, satisfaction, creativity and gratitude.
SWIPE FOR TODAY'S HOROSCOPE
TAURUS
You will get many things accomplished today.
The energy is a very high intensity...
TAURUS
But be careful about manipulating others.
No one appreciates being ordered about, so be tactful.
TAURUS
And try not to put on a show of superior knowledge.
People will resent you for it even if your intentions are good.
CARD OF THE DAY
The Chariot
Are you ready to move forward? If the Chariot shows up in your reading, it's no time to start slacking. Congratulate yourself for your hard work, but also continue riding the wave of victory to the finish line.
MATCHES OF THE DAY
LOVE
Capricorn
MATCHES OF THE DAY
FRIENDSHIP
Cancer
MATCHES OF THE DAY
CAREER
Libra


General
Airbnb has compiled a wonderful list for a mostly reasonable approach to javascript. This is an excellent starting point for community consensus on javascript standards.
https://github.com/airbnb/javascript
Optional Considerations
Variable Alignment
Matching equal signs increases code legibility but may take more time. There is a great plugin for Sublime Text called Alignment which can automatically manage this for you.
Chaining
Indent chained code to show changes in the original selector. Use the ending semicolon as you would a closing bracket to show the original indentation level of the rule
Verbs
When creating javascript modules consider using verbs to show behavior. This may be more intuitive than allowing a user to directly set properties or manage your internal namespace.
Default Values
Using an OR value allows you to set defaults for any falsey value


<EMAIL_ADDRESS>www.linkedin.com/in/traceylynncarter
510-410-8967
Blog Posts
1: Survival of the Fittest Code
2: Fireside Chat
3: CSS Concepts
4: Arrays and Hashes
5: Thinking Style
6: Enumerable Method: .map
7: Gender Issues in Tech
8: Ruby Classes
9: Pairing and Giving Feedback
10: Object-Oriented Programming
11: Stereotype Threat
12: Ruby & JavaScript
13: Affirmation & Stereotype Threat
14: Tech Topic --CHANGE TITLE
15: Dealing with Conflict
16: Good Questions
About Me
On the Blog
Tracey on Twitter
Tracey on Github


At this time, The community does not exist, it exist as an idea. Fabien me, I am the originator of this initiative, which I managed to aggregate two others friends:
This community, exist in the form of a Google community, along with a Facebook group. We would arrive at aggregating many people as possible for protecting our private data; it's a topic that concerns all of us. Maybe you tell yourself, I do not have time for that or I do not feel able to get involved! Do not dread this stuff! I need people very true with me: even if somebody can only read the pages and article, bring corrections will be the best. But also do research on the subject of privacy. An investment of half an hour per week, would be a valuable asset for us.
But you are also looking for more specific profiles: people able to provide a computer work at several levels;
You can invest in your own way, and with the time you have. The principle of the community is to have everything online, to facilitate access and sharing. When I have time I will start up a registration form. For now take contact with me by mail . I will give you the information needed to get involved.


Help
Widget
Autocomplete
Selected address: {{selectedAddress.string}}
All Attributes:
{{key}}
{{ value }}
Clear input without Focus
Clear input with Focus
Validation URLs
Address Validation URL:
Address Auto Complete URL:
General Options
Environment:
Environment
Toggle Dropdown
AC1
AC2
PR
Min. Level:
Min. Level
Toggle Dropdown
Box Number
Street Number
Street
Municipality
Prefilled Parameters
Unstructured Address
Send
Clear
Structured Address
Send
Clear


Methods
display(options)
Draw the number line on the canvas set by
Parameters:
| Name | Type | Description |
|---|---|---|
options |
options | LNum options |
drawN(n, options)
Draw a number on the number line at n. Decimal numbers are ok.
Parameters:
| Name | Type | Description |
|---|---|---|
n |
number | the number to draw |
options |
options | LNum options |
drawQ(z, n, options)
drawQ( z, n, options ): draw a rational number q=(z/n) where z is a whole number and n a natural one on the number line at q = z/n.
Parameters:
| Name | Type | Description |
|---|---|---|
z |
number | the numerator of the fraction |
n |
number | the denominator of the fraction (not 0) |
options |
options | LNum options |
drawSum(n, m, options)
Draw the starting value n and the step needed for n+m and the result
Parameters:
| Name | Type | Description |
|---|---|---|
n |
number | the number to start with |
m |
number | the summand to add |
options |
options | LNum options |
getCanvasImage(type) → {string}
Return the current image as base64-encoded image-string
Parameters:
| Name | Type | Description |
|---|---|---|
type |
string | type of image |
Returns:
- Type
- string
setCanvas(cnv)
setCanvas ( cnv ): set the canvas to draw the line on
Parameters:
| Name | Type | Description |
|---|---|---|
cnv |
integer | the canvas to use |


Home
IEEE | MIT College of Engineering SB, Pune represents the student branch for an international organisation, Institute of Electrical and Electronics Engineers. IEEE | MIT College of Engineering SB, Pune plans different events and activities for the Electronics & Telecommunications Engineering and Computer Science Department at MIT College of Engineering, Pune. These activities include research panels and discussions, group trip Faculty Dinners for students, study breaks, and other exciting events.We're made up of a set of officers on the Executive Board as well as a general membership. Membership is as easy as sending a bit of information about yourself to us. For details, check out the membership page.
Get inspired to change the world!


R/format.R
set_format_strings.Rd
'Tplyr' gives you extensive control over how strings are presented.
set_format_strings
allows you to apply these string formats to your
layer. This behaves slightly differently between layers.
set_format_strings(e, ...) # S3 method for desc_layer set_format_strings(e, ..., cap = getOption("tplyr.precision_cap")) # S3 method for count_layer set_format_strings(e, ...)
| e | Layer on which to bind format strings |
|---|---|
| ... | Named parameters containing calls to |
| cap | A named character vector containing an 'int' element for the cap on integer precision, and a 'dec' element for the cap on decimal precision. |
The layer environment with the format string binding added
Returns the modified layer object.
Format strings are one of the most powerful components of 'Tplyr'. Traditionally, converting numeric values into strings for presentation can consume a good deal of time. Values and decimals need to align between rows, rounding before trimming is sometimes forgotten - it can become a tedious mess that, in the grand scheme of things, is not an important part of the analysis being performed. 'Tplyr' makes this process as simple as we can, while still allowing flexibility to the user.
In a count layer, you can simply provide a single f_str
object to specify how you want your n's (and possibly percents) formatted.
If you are additionally supplying a statistic, like risk difference using
add_risk_diff
, you specify the count formats using the name
'n_counts'. The risk difference formats would then be specified using the
name 'riskdiff'. In a descriptive statistic layer,
set_format_strings
allows you to do a couple more things:
By naming parameters with character strings, those character strings become a row label in the resulting data frame
The actual summaries that are performed come from the variable names
used within the f_str
calls
Using multiple summaries (declared by your f_str
calls), multiple summary values can appear within the same line. For
example, to present "Mean (SD)" like displays.
Format strings in the desc layer also allow you to configure how
empty values should be presented. In the f_str
call, use the
empty
parameter to specify how missing values should present. A
single element character vector should be provided. If the vector is
unnamed, that value will be used in the format string and fill the space
similar to how the numbers will display. Meaning - if your empty string is
'NA' and your format string is 'xx (xxx)', the empty values will populate
as 'NA ( NA)'. If you name the character vector in the 'empty' parameter
'.overall', like empty = c(.overall='')
, then that exact string will
fill the value instead. For example, providing 'NA' will instead create the
formatted string as 'NA' exactly.
See the f_str
documentation for more details about how this
implementation works.
# Load in pipe library(magrittr) # In a count layer tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_format_strings(f_str('xx (xx%)', n, pct)) ) %>% build()#> # A tibble: 3 x 6 #> row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1 #> <chr> <chr> <chr> <chr> <int> <dbl> #> 1 4 " 1 ( 7%)" " 8 (67%)" " 2 (40%)" 1 1 #> 2 6 " 2 (13%)" " 4 (33%)" " 1 (20%)" 1 2 #> 3 8 "12 (80%)" " 0 ( 0%)" " 2 (40%)" 1 3# In a descriptive statistics layer tplyr_table(mtcars, gear) %>% add_layer( group_desc(mpg) %>% set_format_strings( "n" = f_str("xx", n), "Mean (SD)" = f_str("xx.x", mean, empty='NA'), "SD" = f_str("xx.xx", sd), "Median" = f_str("xx.x", median), "Q1, Q3" = f_str("xx, xx", q1, q3, empty=c(.overall='NA')), "Min, Max" = f_str("xx, xx", min, max), "Missing" = f_str("xx", missing) ) ) %>% build()#> # A tibble: 7 x 6 #> row_label1 var1_3 var1_4 var1_5 ord_layer_index ord_layer_1 #> <chr> <chr> <chr> <chr> <int> <int> #> 1 n "15" "12" " 5" 1 1 #> 2 Mean (SD) "16.1" "24.5" "21.4" 1 2 #> 3 SD " 3.37" " 5.28" " 6.66" 1 3 #> 4 Median "15.5" "22.8" "19.7" 1 4 #> 5 Q1, Q3 "14, 18" "21, 28" "16, 26" 1 5 #> 6 Min, Max "10, 22" "18, 34" "15, 30" 1 6 #> 7 Missing " 0" " 0" " 0" 1 7# In a shift layer tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) ) %>% build()#> # A tibble: 9 x 17 #> row_label1 row_label2 var1_0_1 var1_0_2 var1_0_3 var1_0_4 var1_0_6 var1_0_8 #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> #> 1 4 3 " 1 (3… " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … #> 2 4 4 " 0 ( … " 2 (6… " 0 ( … " 0 ( … " 0 ( … " 0 ( … #> 3 4 5 " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … #> 4 6 3 " 2 (5… " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … #> 5 6 4 " 0 ( … " 0 ( … " 0 ( … " 2 (5… " 0 ( … " 0 ( … #> 6 6 5 " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … #> 7 8 3 " 0 ( … " 4 (3… " 3 (2… " 5 (4… " 0 ( … " 0 ( … #> 8 8 4 " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … #> 9 8 5 " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … " 0 ( … #> # … with 9 more variables: var1_1_1 <chr>, var1_1_2 <chr>, var1_1_3 <chr>, #> # var1_1_4 <chr>, var1_1_6 <chr>, var1_1_8 <chr>, ord_layer_index <int>, #> # ord_layer_1 <dbl>, ord_layer_2 <dbl>


An interactive charts allows the user to perform actions: zooming, hovering a marker to get a tooltip, choosing a variable to display and more. R
offers a set of packages called the html widgets
: they allow to build interactive dataviz directly from R
.
plotly
.The best way to build an interactive bubble chart from R is through the plotly
library. If you know how to make a ggplot2
chart, you are 10 seconds away to rendering an interactive version= just call the ggplotly()
function, and you’re done.
library(ggplot2) library(plotly) library(gapminder) p <- gapminder %>% filter(year==1977) %>% ggplot( aes(gdpPercap, lifeExp, size = pop, color=continent)) + geom_point() + theme_bw() ggplotly(p)
← this chart is interactive: hover, drag, zoom, export and more.
plotly
, d3heatmap
or heatmaply
.Three options exist to build an interactive heatmap from R
:
plotly
: as described above, plotly allows to turn any heatmap made with ggplot2
interactive.d3heatmap
: a package that uses the same syntax as the base R heatmap() function to make interactive version.heatmaply
: the most flexible option, allowing many different kind of customization. See the code of the chart beside here.streamgraph
packageThe streamgraph
package allows to build interactive streamgraphs. Hover a group to highlight it, get its name and its exact value. It's also the only way to build a streamchart from R.
plotly
.Once more, plotly
is very handy when it comes to build interactive area chart, since its ggplotly()
function turns a ggplot2
version interactive. On the chart beside you can:
.png
and .html
.It is possible to save an interactive chart to both .html
and .png
formats.
To do so, you have to rely on the htmlwidget
and webshot
packages respectively.
It is then possible to embed your viz using an iframe
of an img
tag in
any webpage.
Note: it is also possible to build the interactive chart in an R markdown document to have it embedded directly.
# create a dataset: data <- data_frame( from=c("A", "A", "B", "D"), to=c("B", "E", "F", "A") ) # Plot library(igraph) p <- simpleNetwork(data, height="100px", width="100px") # save the widget at .html format library(htmlwidgets) saveWidget(p, file="myFile.html") # Save at .png library(webshot) webshot::install_phantomjs() webshot("paste_your_html_here" , "output.png", delay = 0.2 , cliprect = c(440, 0, 1000, 10))
leaflet
.Build a stunning interactive map in minute thanks to the leaflet
package.
It allows to build any type of maps: background, bubble, choropleth,
hexbin and more.
rgl
The rgl
package is the best tool to work in 3D from R. Here
is an illustration: a 3d scatterplot showing the relationship between
3 numerical variables.
Note that rgl
automatically builds
interactive charts. Zooming and rotating can indeed make the chart more insightful.
Trying zooming / rotating →
chordiag
The chorddiag
package is an htmlwidget: it automatically builds interactive charts. On the chart below, hovering a group or a connection will highlight the related flow and give additional information.
Note: The example below comes directly from the chorddiag
documentation. Thanks to Mattflor for developping such a nice package.
circlepackeR
The circlepackeR
library allows to get an interactive circular packing with several levels of hierarchy. Click on a group to zoom on it. The post describes how to use the package from different types of input dataset.
collapsibletree
The collapsibletree
package is an htmlwidget: it automatically builds collapsible interactive tree diagram. On the chart below, click a node to reveal the next branch, and zoom in/out if necessary.
networkD3
The networkD3
package allows to build interactive network diagrams with R. On the chart below, try to hover a node and drag it to see how it works. You can also scroll to zoom in and out. Visit the corresponding post to see how to use this tool on your dataset.
dygraph
The dygraph
package offers zooming, hovering, minimaps and much more. Try it on the example below!


react-web-chat
react-web-chat
is an instant messaging UI built with React.
Table of contents
Installation
Simply install it with your favorite package manager:
npm install --save-dev react-web-chat
yarn add react-web-chat
Usage
As a stand-alone module
The stand-alone version of the module will render to a supplied dom element.
It accepts the following parameters:
| Argument | Description | Required | Type |
|---|---|---|---|
| url | The url of your chat server | yes | String |
| element | The element react-web-chat should render to |
yes | Element |
| theme | A custom theme | no | Object |
| client | A custom client | no | Object |
Communication with the module is handled via custom events described here.
ES6
import ReactWebChat from 'react-web-chat'
let myChatElement = document.getElementByID('my-chat-element');
const reactWebChat = new ReactWebChat({
url: 'http://localhost:8000',
element: myChatElement
});
CommonJS
var ReactWebChat = require('react-web-chat').default
var myChatElement = document.getElementByID('my-chat-element');
const reactWebChat = new ReactWebChat({
url: 'http://localhost:8000',
element: myChatElement
});
UMD
react-web-chat
is also available as a UMD module. Simply load the module and instantiate a new instance as described in the example below.
NOTE: react
and react-dom
are peer dependencies so make sure they are loaded too

As a react component
The exported ReactWebChatComponent
can be used within an existing react application.
It accepts the following parameters:
| Argument | Description | Required | Type |
|---|---|---|---|
| url | The url of your chat server | yes | String |
| theme | A custom theme | no | Object |
| client | A custom client | no | Object |
Example
import { ReactWebChatComponent } from 'react-web-chat'
const MyComponent = props =>

<ReactWebChatComponent url="http://localhost:8080"/>

Custom events
Communication with the react-web-chat
module is handled via a series of custom events.
Listening
Custom react-web-chat
events are namespaced using the rwc-
prefix.
Any dispatched redux action will fire a custom event using the following format:rwc-ACTION_TYPE
Example:
window.addEventListener('rwc-MESSAGE_RECEIVE', function(data) {
// do something with data
});
The data
parameter object adheres to the CustomEvent specification:
https://developer.mozilla.org/en-US/docs/Web/API/CustomEvent
A full list of actions to listen for can be found here:
Dispatching
Dispatching an event follows the same namespaced convention as described above.
However not all redux actions can be dispatched via custom events.
Currently only the MESSAGE_SEND
action type is supported.
More action types may be supported in future releases if justifiable use cases can be demonstrated.
Example:
function sendRWCMessage() {
var rwcEvent = window.CustomEvent("rwc-MESSAGE_SEND", {
detail: {
payload: message
}
});
window.dispatchEvent(rwcEvent);
}
var message = {
type: 'message',
layout: 'plain',
pages: [
{
text: 'Hello world'
}
]
}
sendRWCMessage(message);
Custom themes
react-web-chat
allows you to inject custom react components for specific parts of the UI.
Any components not specified in the custom theme object will use the default theme's components.
The following components can be overridden:
- AvatarComponent
- ButtonComponent
- CheckboxMenuComponent
- ImageComponent
- InputComponent
- MenuComponent
- TextComponent
- TypingIndicatorComponent
Please consult the API documentation as a guide to help you develop custom components for your themes.
Example
import { ReactWebChatComponent } from 'react-web-chat'
// your custom theme components
import React from 'react';
import Avatar from './customTheme/Avatar';
import Button from './customTheme/Button';
import Input from './customTheme/Input';
const MyComponent = props =>

<ReactWebChatComponent
url="http://localhost:8080"
theme={{
AvatarComponent: Avatar,
ButtonComponent: Button,
InputComponent: Input
}}
/>

Custom network clients
react-web-chat
supports custom network clients to manage network communication with your server.
Network clients have the following responsibilities:
- determine which protocol/standard to use (WS, socket.io, HTTP, XHR, Fetch, etc.)
- translate messages to a format the server understands
Currently the only available client is rwc-feersum-client
.
It's also the default client used by react-web-chat
which happens to make use the feersum message schema.
Further reading:
In future there will hopefully be several clients to support a wider range of IM back-ends.
Writing your own network client
Writing a custom network client is easy :) All you need is an object with the following methods:
const feersumClient = {
init(url) {
// Connect to server, then bind "onmessage" and "onclose" methods.
},
send(message) {
// Translate message from feersum schema, then send to server.
},
onmessage(fn) {
// Translate message to feersum schema, then execute callback function with message as parameter.
},
onclose(fn) {
// Execute callback when the connection is closed.
}
};


nips nips2000 nips2000-60 nips2000-60-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Scott Saobing Chen, Ramesh A. Gopinath
Abstract: High dimensional data modeling is difficult mainly because the so-called
[1] H. Attias,
[2] J.H. Friedman,
[3] MJ.F. Gales,
[4] PJ. Huber,
[5] J. Hwang, S. Lay and A. Lippman,
[6] G. Schwarz,


This model has been developed by Rachel Hendery and Liam Magee for a poster presentation, “Geo-Language Games: An Agent-Based Model of the Role of Terrain in Language Diversity”, presented at the 2015 Digital Humanities Conference in Western Sydney.
Currently under development…
In this model, agents will have a richer linguistic model, incorporating:
These model elements are assigned different weights that signify whether they are more or less susceptible to modification.


Schedule a call with your stylist at a time to suit you, to talk about your order, style and preferences.
Contact telephone number


--- layout: default title: SSL Demographics Treemap filename: null permalink: /ssl-treemap ---
Treemap arranging sizes by count of individuals with SSL scores. Each rectangle represents a individuals grouped by race, sex, and age group, and sizes are determined by the number of individuals with an SSL score with those demographics. Some individuals are either listed as race "Unknown", which is grouped with American Indian/Alaskan Native due to very little representation. There were fewer than 100 individuals listed with sex as "X" instead of "M" or "F", so they are not included here.


You are executing an ephemeral environment that you can SSH into for testing code and mechanisms on an Intel/x86_64 system. Please note that anything not saved in /data will be lost when you exit the environment!
To connect, please SSH to:
You will be able to run commands as root using the sudo prefix once in.
You may optionally install an SSH public key for password-less access by clicking on your username in the top right of the portal, then Account Settings, then SSH Keys to manage your SSH keys.


15 December 2017, Neil Parkhi
The Mahindra Thar is a compact and mid-size four-wheel drive off-road and “Jeep” (SUV), manufactured by Indian automaker Mahindra & Mahindra Limited. The vehicle was launched in the Indian market on October 2010 to fill the void left by its predecessor the Mahindra MM540. The Thar has been voted one of the top 10 SUVs available in the Indian market. Three variants-DI 2WD, DI 4WD and CRDe, with available on soft top version. The Thar also comes with a 7-seater option, though it can be converted into a two seater. The That overall is considered a very unique vehicle. It is quite elegant for off roading and it has better design towards a Willy’s Jeep, compared to the Jeep Wrangler. However, the Thar is not street legal in the United States, particularly due to not having any safety features, airbags, and being able to properly see and feel the bare outer structure of the vehicle.
| Steering and Transmission | |
|---|---|
| Steering Type | Hydraulic Assist, Rack and Pinion Power Steering |
| Transmission Type | 5 Speed Manual |
| Wheels and Tires | |
| Front Wheel Type | 16 inch steel wheels with 5 spoke wheel caps |
| Rear Wheel Type | 16 inch steel wheels with 5 spoke wheel caps |
| Front Tire Type | 235/70R16 |
| Rear Tire Type | 235/70R16 |
| Front Brake Type | Ventilated Disc |
| Rear Brake Type | Drum |
| Suspension | |
| Front Suspension Type | Independent Torsion Beam |
| Rear Suspension Type | Semi Ellpitical-Leaf |
| Suspension Features | Stabilizer Bars, Coil Springs, Gas Pressurized Shock Absorbers |
| Dimensions | |
| Length (in, mm) | 154.3in, 3920mm |
| Width (in, mm) | 70in, 1726mm |
| Height (in, mm) | 76in, 1930mm |
| Wheelbase (in, mm) | 95.7in, 2430mm |
| Weight (lb, kg) | 3858lb, 1750kg |
| Engine Specifications | |
| Engine Type | 2.2 liter mHawk 140 16 valve turbo diesel inline 4 cylinder |
| Power (bhp, PS, kW) | 105bhp, 106PS, 79kW @ 3800 rpm |
| Torque (lb-ft, Nm) | 182lb-ft, 247Nm @ 1800-2800RPM |
| 0-60 mph (0-97 kmph) | 15.6 seconds |
| Top Speed (mph, kmph) | 96 mph, 154 kmph |
| Drivetrain | All Wheel Drive |
| Fuel Tank Size (gal, L) | 15.9 gallons, 60L |
| Fuel Type | B20 Diesel |
| City Fuel Economy (mpg, kmpL) | 31mpg, 13kmpL |
| City Fuel Economy (mpg, kmpL) | 40mpg, 17kmpL |
| Cargo Specifications | |
| Cargo Space (ft3, L) | 13.6ft3, 384L |
| Max. Cargo Space (ft3, L) | 13.6ft3, 384L |


Too bad, i used it for one function :/
(the average rank on server browser showed pretty nicely how balanced the game is so I could avoid baserape games...)
Wolf68k
It's so dead, the forums there aren't moderated any more either it seems. Nothing but porn spammer.
 Really?


Conceptual Map
Tuner
Apple
Sword
Clock
Coin
Coin (Flat)
Coin (Small)
Cube
Cursor: Default
Cursor: Default (Old)
Cursor: Look
Cursor: Interact (Placeholder 1)
Cursor: Interact (Placeholder 2)
Cursor: Leave
Test NPC (Animated)
Wizard
BG: Merchant Table
BG: Test 1
BG: Test 2
BG: Test 3


The Camperizer
News straight from
Free Code Camp
2016-01-03 By Francis Moisan
Free Code Camp
-
Github
-
Twitter
-
LinkedIn


Former Arsenal manager Unai Emery 'approached for Everton job'
Former Arsenal manager Unai Emery has reportedly been approached by Everton with regards to potentially replacing Marco Silva as manager at Goodison Park.
by Will DoddsThe former Valencia, Sevilla and Paris Saint-Germain coach was sacked by Arsenal last month after a poor start to the season.
According to Sky Sports News, Emery spoke to Everton about the vacancy in London but said that he needs more time to think about his future.
The report states that the 48-year-old Spaniard would consider a role in England, Spain or Italy for his next job.
Both Arsenal and Everton have had poor starts to the Premier League season and sit 11th and 14th in the table respectively.
Everton interim manager Duncan Ferguson did manage a win in his first game in charge against Chelsea on Saturday, with the Toffees running out 3-1 winners.
body check tags ::


-
Kevin Mitnick, Once the World's Most Wanted Hacker, Is Now Selling ...
www.wired.com
As a young man, Kevin Mitnick became the world’s most notorious black hat hacker, breaking into the networks of companies like IBM, Nokia, Motorola, ...
-
Troy Hunt: Everything you need to know about the Shellshock Bash bug
www.troyhunt.com
Observations, musings and conjecture about the world of software and technology
-
Google: No Breach In Latest Online Dump Of Credentials
www.darkreading.com
The online leak of some 5 million username and password combinations consisted of mostly stale or older credentials that don't actually work, Google ...
-
Viator investigates payment card breach, notifies 1.44 million customers
www.scmagazine.com
More than 1.4 million Viator customers may have had their personal data, including payment card information, compromised.
-
How India Mounted the World’s Cheapest Mission to Mars
blogs.wsj.com
India has become the only nation so far to reach Mars on its first attempt. It will also have spent the least amount of money to do so.
-
jQuery site popped to serve malware slop
www.theregister.co.uk
Visited September 18? Time to REFORMAT.
-
Signature Systems Breach Expands — Krebs on Security
krebsonsecurity.com
Signature Systems Inc. , the point-of-sale vendor blamed for a credit and debit card breach involving some 216 Jimmy John’s sandwich shop locations, ...
-
Data breach that hit Jimmy John's is larger than first thought
www.computerworld.com
Signature Systems says the breach of its point-of-sales system that hit 216 Jimmy John's sandwich shops is 50% larger than originally thought.
-
Hackers Are Already Using the Shellshock Bug to Launch Botnet Attacks | ...
www.wired.com
With a bug as dangerous as the "shellshock" security vulnerability discovered yesterday, it takes less than 24 hours to go from proof-of-concept to ...
-
‘Shellshock’ Bug Spells Trouble for Web Security — Krebs on Security
krebsonsecurity.com
As if consumers weren’t already suffering from breach fatigue: Experts warn that attackers are exploiting a critical, newly-disclosed security ...
-
Everything you need to know about Bash Bug "ShellShock"
www.ehackingnews.com
A new critical security vulnerability in the BASH shell, the command-line shell used in many Unix and Linux operating systems, leaves a large number ...
-
Shellshock DHCP RCE Proof of Concept -
www.trustedsec.com
DHCP bash shellshock POC: 1) Just about any DHCP string value should work for the exploit. Value 114 is URL, which is a string and should be reliable ...
-
How do I recompile Bash to avoid Shellshock (the remote exploit ...
apple.stackexchange.com
Given that Bash 3.2 (the version shipped by OSX) is vulnerable to the remote execution exploit known as "Shell Shock" (CVE-2014-6271 and ...
-
The Internet Braces for the Crazy Shellshock Worm | WIRED
www.wired.com
A nasty bug in many of the world’s Linux and Unix operating systems could allow malicious hackers to create a computer worm that wreaks havoc on ...
-
MFSA 2014-73: RSA Signature Forgery in NSS
www.mozilla.org
You are here: Security Center > Mozilla Foundation Security Advisories > MFSA 2014-73
-
Jimmy John's Gourmet Sandwiches POS Systems Hacked - Dark Reading
www.darkreading.com
Sandwich chain is the latest data breach victim, with credit and debit card data breached in 216 of its restaurants.
-
Bash Bug May Be Worse Than Heartbleed
www.darkreading.com
Linux, Unix, and Internet of Things devices affected by critical vulnerability.
-
Indian spacecraft reaches Mars for less than it cost to make 'Gravity'
www.theverge.com
India is celebrating a major milestone today, after becoming the first country to reach the orbit of Mars on its very first attempt. After launching ...
-
The Truth About Ransomware: You're On Your Own
www.darkreading.com
What should enterprises do when faced with ransomware? The answer is, it depends.
-
Peter Thiel, Marc Andreessen, And Marissa Mayer Want To Teach You 'How To ...
www.businessinsider.com
A free course worth checking out.
-
Worm Illuminates Potential NAS Nightmare
www.darkreading.com
A researcher at Black Hat Europe hopes to demonstrate a homegrown, self-replicating worm to illustrate major threats to popular network-attached ...
-
5 Myths: Why We Are All Data Security Risks
www.darkreading.com
I am absolutely sure that I could be tricked by a well-crafted spear phishing attack, and I am equally sure I could do the same to you.
-
Software patents are crumbling, thanks to the Supreme Court
www.vox.com
The courts have destroyed 11 software patents in the last 3 months and are on track to shatter previous records for software patent invalidations.
-
Feds Threatened to Fine Yahoo $250K Daily for Not Complying With PRISM | ...
www.wired.com
A secret and scrappy court battle that Yahoo launched to resist the NSA’s PRISM spy program came to an end in 2008 after the Feds threatened the ...
-
The FBI Finally Says How It 'Legally' Pinpointed Silk Road's Server | WIRED
www.wired.com
The prosecuting attorneys in Ulbricht's case argue, means that no illegal spying techniques were needed to pinpoint the world's largest anonymous ...
-
Banks: Credit Card Breach at Home Depot — Krebs on Security
krebsonsecurity.com
Multiple banks say they are seeing evidence that Home Depot stores may be the source of a massive new batch of stolen credit and debit cards that ...
-
Health care data breaches have hit 30M patients and counting
www.washingtonpost.com
The latest hack of 4.5 million medical records highlights a deeper problem.


December 15, 2015
This afternoon when I was in Menlo park. I tool my cat with me and I was reading a novel. I felt asleep and when I woke up, it was gone! It's a 3 yr old cat with pure white furs.
December 14, 2015
Trying to know if National Chiao Tung University's gym is crowded or not now.
November 30, 2015
There's an earthquake in Taoyuan 20 mins ago. Wanna know the real-time situation of ChunHua rd.


Uber Pickup - Time Series Plots
Please select a location
EWR, Newark Airport
Bronx, Allerton/Pelham Gardens
Bronx, Bedford Park
Bronx, Belmont
Bronx, Bronx Park
Bronx, Bronxdale
Bronx, City Island
Bronx, Claremont/Bathgate
Bronx, Co-Op City
Bronx, Country Club
Bronx, Crotona Park
Bronx, Crotona Park East
Bronx, East Concourse/Concourse Village
Bronx, East Tremont
Bronx, Eastchester
Bronx, Fordham South
Bronx, Highbridge
Bronx, Hunts Point
Bronx, Kingsbridge Heights
Bronx, Longwood
Bronx, Melrose South
Bronx, Morrisania/Melrose
Bronx, Mott Haven/Port Morris
Bronx, Mount Hope
Bronx, Norwood
Bronx, Parkchester
Bronx, Pelham Bay
Bronx, Pelham Bay Park
Bronx, Pelham Parkway
Bronx, Rikers Island
Bronx, Riverdale/North Riverdale/Fieldston
Bronx, Schuylerville/Edgewater Park
Bronx, Soundview/Bruckner
Bronx, Soundview/Castle Hill
Bronx, Spuyten Duyvil/Kingsbridge
Bronx, University Heights/Morris Heights
Bronx, Van Cortlandt Park
Bronx, Van Cortlandt Village
Bronx, Van Nest/Morris Park
Bronx, West Concourse
Bronx, West Farms/Bronx River
Bronx, Westchester Village/Unionport
Bronx, Williamsbridge/Olinville
Bronx, Woodlawn/Wakefield
Brooklyn, Bath Beach
Brooklyn, Bay Ridge
Brooklyn, Bedford
Brooklyn, Bensonhurst East
Brooklyn, Bensonhurst West
Brooklyn, Boerum Hill
Brooklyn, Borough Park
Brooklyn, Brighton Beach
Brooklyn, Brooklyn Heights
Brooklyn, Brooklyn Navy Yard
Brooklyn, Brownsville
Brooklyn, Bushwick North
Brooklyn, Bushwick South
Brooklyn, Canarsie
Brooklyn, Carroll Gardens
Brooklyn, Clinton Hill
Brooklyn, Cobble Hill
Brooklyn, Columbia Street
Brooklyn, Coney Island
Brooklyn, Crown Heights North
Brooklyn, Crown Heights South
Brooklyn, Cypress Hills
Brooklyn, Downtown Brooklyn/MetroTech
Brooklyn, DUMBO/Vinegar Hill
Brooklyn, Dyker Heights
Brooklyn, East Flatbush/Farragut
Brooklyn, East Flatbush/Remsen Village
Brooklyn, East New York
Brooklyn, East New York/Pennsylvania Avenue
Brooklyn, East Williamsburg
Brooklyn, Erasmus
Brooklyn, Flatbush/Ditmas Park
Brooklyn, Flatlands
Brooklyn, Fort Greene
Brooklyn, Gowanus
Brooklyn, Gravesend
Brooklyn, Green-Wood Cemetery
Brooklyn, Greenpoint
Brooklyn, Homecrest
Brooklyn, Kensington
Brooklyn, Madison
Brooklyn, Manhattan Beach
Brooklyn, Marine Park/Floyd Bennett Field
Brooklyn, Marine Park/Mill Basin
Brooklyn, Midwood
Brooklyn, Ocean Hill
Brooklyn, Ocean Parkway South
Brooklyn, Park Slope
Brooklyn, Prospect-Lefferts Gardens
Brooklyn, Prospect Heights
Brooklyn, Prospect Park
Brooklyn, Red Hook
Brooklyn, Sheepshead Bay
Brooklyn, South Williamsburg
Brooklyn, Starrett City
Brooklyn, Stuyvesant Heights
Brooklyn, Sunset Park East
Brooklyn, Sunset Park West
Brooklyn, Williamsburg (North Side)
Brooklyn, Williamsburg (South Side)
Brooklyn, Windsor Terrace
Manhattan, Alphabet City
Manhattan, Battery Park
Manhattan, Battery Park City
Manhattan, Bloomingdale
Manhattan, Central Harlem
Manhattan, Central Harlem North
Manhattan, Central Park
Manhattan, Chinatown
Manhattan, Clinton East
Manhattan, Clinton West
Manhattan, East Chelsea
Manhattan, East Harlem North
Manhattan, East Harlem South
Manhattan, East Village
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Recipe: Breadfruit Fries
by CBS NewsThis recipe, by Manolya E. Oner of USDA-ARS Pacific basin Agricultural Research Center, Hilo, appears in the book "Ho'oulu ka 'Ulu Cookbook: Breadfruit Tips, Techniques, and Hawai'i's Favorite Home Recipes," a project of Hawai'i Homegrown Food Network and Breadfruit Institute of the National Tropical Botanical Garden, who proclaim, "Anything you can do with potato, you can do with breadfruit … better."
Breadfruit (also known as 'ulu or 'uru, mei, nmos or lemai) is one of the signature crops of the Pacific Islands.
"Breadfruit is an energy-rich food and a good source of complex carbohydrates, fiber, and minerals such as potassium, calcium, iron, magnesium, phosphorus, manganese, and zinc. This nutritious fruit also provides B vitamins, niacin, thiamine, and Vitamin C. Some varieties have high levels of pro-vitamin A carotenoids, nutrients essential to good health. Breadfruit is also gluten-free."
Breadfruit Fries
Ingredients:
1 mature, firm 'ulu
1 ½ qt. Canola oil
Instructions:
Wash, peel (optional) and cut the 'ulu into quarters lengthwise. Remove core and cut into ¼-inch thick slices. Place 'ulu slices in a bowl of cold water until you are ready for the next step.
Boil 'ulu in 2 qt. of water for five minutes, using about one quarter (about ½ lb.) of 'ulu at a time. After boiling, place slices in cold water for five minutes to cool.
Remove excess water from 'ulu by placing slices in a sieve or on a paper towel for at least one minute.
Partially fry (par fry) the 'ulu slices (again, about ½ lb. at a time) in 1 ½ qt. of canola oil heated to 355°F for one minute.
Remove 'ulu slices from oil and drain, then place on a tray.
Put the tray of 'ulu in the freezer (about 0°F) from 20 minutes.
After freezing the 'ulu slides, you have two options:
- Fry frozen slices immediately at 355°F for five minutes.
- Store frozen slices in freezer bags at 0F and fry whenever desired. Be sure to keep slices frozen until the oil is hot.
After frying, drain excess oil by placing fries on paper towels. Salt or season to taste.
Excerpted from "Ho'oulu ka 'Ulu Cookbook: Breadfruit Tips, Techniques, and Hawai'i's Favorite Home Recipes," published by Hawai'i Homegrown Food Network and Breadfruit Institute of the National Tropical Botanical Garden. Copyright 2012. Reprinted with permission.
To watch chef John Cadman, of Maui Breadfruit Company, explain the advantages of breadfruit over potato, click on the video player below:
For more info:
- "Ho'oulu ka 'Ulu Cookbook: Breadfruit Tips, Techniques, and Hawai'i's Favorite Home Recipes" (CreateSpace) in Trade Paperback format, available via Amazon


JJ, a CIA operative, is tasked with spying on a single mother and her daughter with tenuous links to an arms dealer. But the young girl catches JJ forcing him to do her bidding or be exposed.
Bumblebee
×
Bumblebee
During the Cybertron Civil War, Optimus Prime sends Autobot scout B-127 to Earth to form a base where they can regroup. Later, the scout befriends a girl named Charlie, who names him Bumblebee.
Spider-Man: Far From Home
×
Spider-Man: Far From Home
Peter Parker, the beloved superhero Spider-Man, faces four destructive elemental monsters while on holiday in Europe. Soon, he receives help from Mysterio, a fellow hero with mysterious origins.
Mrs Doubtfire
×
Mrs Doubtfire
Daniel, a divorced actor, disguises himself as Mrs Doubtfire, an ageing female Scottish housekeeper, in order to work in his ex-wife's house and spend more time with his children.
The Simpsons Movie
×
The Simpsons Movie
Homer accidentally pollutes a lake of Springfield. When the town blames him for it, he decides to save the city and his family.
The Addams Family
×
The Addams Family
The eccentric and macabre Addams family moves to a bland suburb in New Jersey. However, problems arise when their child, Wednesday, befriends the daughter of a reality show host.


Verstappen wins Brazilian GP as Ferraris collide
Max Verstappen won a thrilling Brazilian Formula One Grand Prix for Red Bull on Sunday after the Ferraris collided and Mercedes' six-times world champion Lewis Hamilton was demoted from third to seventh.
Verstappen controlled nearly all the race at Interlagos, which saw a dramatic late collision between Ferrari's Sebastian Vettel and Charles Leclerc when fighting for fourth position. Both failed to finish.
Toro Rosso's Pierre Gasly got his first F1 podium after finishing second ahead of Hamilton.
However, Hamilton was relegated to seventh position after receiving a five- second penalty for an incident that caused Red Bull's Alexander Albon to spin, with McLaren's Carlos Sainz replacing the Mercedes driver in the top three.
Australia's Daniel Ricciardo subsequently moved up to sixth, with Kimi Raikkonen and Antonio Giovinazzi being upgraded to fourth and fifth respectively.
"I massively apologise to Albon," said Hamilton. "Completely my fault."
Dutch driver Verstappen said: "Lewis was very quick so I had to keep pushing ... we had two good moves with him, and from there onward I could control the race."
The Brazilian GP on Sunday was the penultimate race of the season, with only Abu Dhabi left on December 1.
Hamilton had already secured the season title in the previous race in the United States. His Mercedes teammate Valtteri Bottas, who did not finish in Brazil, had also secured the runner-up spot.
Brazilian Grand Prix (71 laps) results:
1. Max Verstappen (NED/RBR) 1hr 33min 14.678sec
2. Pierre Gasly (FRA/STR) +6.077
3. Carlos Sainz Jr (ESP/MCL) 8.896
4. Kimi Raikkonen (FIN/ALF) 9.452
5. Antonio Giovinazzi (ITA/ALF) 10.201
6. Daniel Ricciardo (AUS/REN) 10.541
7. Lewis Hamilton (ENG/MER) 11.139
8. Lando Norris (GBR/MCL) 11.204
9. Sergio Perez (MEX/RAC) 11.529
10. Daniil Kvyat (RUS/STR) 11.931
AP with Reuters


We are always available to serve you better on any of my product below. and we can also build your website from scratch and help you promote your business.


Syntax:
with-compilation-unit ([[option]]) form* => result*
option::= :override override
Arguments and Values:
override---a generalized boolean; evaluated. The default is nil.
forms---an implicit progn.
results---the values returned by the forms.
Description:
Executes forms from left to right. Within the dynamic environment of with-compilation-unit, actions deferred by the compiler until the end of compilation will be deferred until the end of the outermost call to with-compilation-unit.
The set of options permitted may be extended by the implementation, but the only standardized keyword is :override.
If nested dynamically only the outer call to with-compilation-unit has any effect unless the value associated with :override is true, in which case warnings are deferred only to the end of the innermost call for which override is true.
The function compile-file provides the effect of
(with-compilation-unit (:override nil) ...)around its code.
Any implementation-dependent extensions can only be provided as the result of an explicit programmer request by use of an implementation-dependent keyword. Implementations are forbidden from attaching additional meaning to a use of this macro which involves either no keywords or just the keyword :override.
Examples:
If an implementation would normally defer certain kinds of warnings, such as warnings about undefined functions, to the end of a compilation unit (such as a file), the following example shows how to cause those warnings to be deferred to the end of the compilation of several files.
(defun compile-files (&rest files) (with-compilation-unit () (mapcar #'(lambda (file) (compile-file file)) files))) (compile-files "A" "B" "C")
Note however that if the implementation does not normally defer any warnings, use of with-compilation-unit might not have any effect.
Affected By: None.
Exceptional Situations: None.
See Also:
Notes: None.


vignettes/intro_to_superpower.Rmd
intro_to_superpower.Rmd
The goal of Superpower is to easily simulate single factor and factorial designs and empirically calculate power using a simulation approach. Currently, the package can only calculate power for ANOVAs. This app is intended to be utilized for prospective (a priori) power analysis. This vignette gives a brief overview of the package’s capability. If you would like to see more information on this package please see our online book
You can install the developmental version of Superpower from GitHub with:
devtools::install_github("arcaldwell49/Superpower")
Some functions have overlapping arguments (e.g., verbose and plot), and many users may want consistency in these arguments as they use these functions in their workflow. In order to see the options, all one has to do is call on the Superpower_options() function.
## $alpha_level
## [1] 0.05
##
## $contrast_type
## [1] "pairwise"
##
## $correction
## [1] "none"
##
## $emm
## [1] FALSE
##
## $emm_model
## [1] "multivariate"
##
## $liberal_lambda
## [1] FALSE
##
## $plot
## [1] TRUE
##
## $verbose
## [1] TRUE
The global option can be set by the user by calling on the individual arguments. Let’s use the verbose argument as an example.
Superpower_options("verbose")
## [1] TRUE
Superpower_options("verbose" = FALSE)
Superpower_options("verbose")
## [1] FALSE
Currently the ANOVA_design function can create designs up three factors, for both within, between, and mixed designs. It requires the following input: design, n, mu, sd, r, and optionally allows you to set label_list.
design
: string that specifies the design (see
below).n
: the sample size for each between subject
condition.mu
: a vector with the means for each condition.sd
: the population standard deviation. Assumes
homogeneity of variances (only one standard deviation can be
provided).r
: the correlation for within designs (or 0 for between
designs).label_list
: This is an optional named list of words
that indicates factor names and level names (see below).‘design’ is used to specify the design. Every factor is specified
with a number, indicating the number of levels of the factor, and a
letter, b or w, to indicate whether the factor is manipulated between or
within participants. For example, a 2b
design has two
between-participant groups. A 12w
design has one factor
with 12 levels, all manipulated within-participants. A “2b*3w” is a
design with two factors (a 2b factor and a 3w factor), the first of
which has 2 between participant levels (2b), and the second of which has
3 within participants levels (3w). If there are multiple factors
(the functions take up to three different factors) separate factors with
a * (asterisk). An example of a 2b*3w
design is a
group of people in one condition who get a drug, and a group of people
in another condition who get a placebo, and we measure their health
before they take the pill, one day after they take the pill, and a week
after they take the pill.
Note that for each cell in the design, a mean must be provided. Thus, for a “2b*3w” design, 6 means need to be entered.
Means need to be entered in the correct order. ANOVA_design outputs a plot so you can check if you entered all means as you intended. Always carefully check if the plot that is generated matches your expectations.
The general principle is that the code generates factors, indicated by the factor names you entered in the label_list variable, (i.e., condition and time). Levels are indicated by factor names and levels (e.g., control_time1, control_time2, control_time3, etc).
If your design has just one factor, just enter the means in the same order as the label_list (see below). For more factors, note the general pattern in the example below. Means are entered in the following order for a 3 factors design (each with 2 levels):
So if you enter the means 1, 2, 3, 4, 5, 6, 7, 8 the first 4 means correspond to level 1 of factor 1, the second 4 means correspond to level 2 of factor 1. Within the first 4 means, the first 2 correspond to level 1 of factor 2, and within those 2 means, the first corresponds to level 1 of factor 3.
The plot below visualizes means from 1 to 8 being entered in a
vector: mu = c(1, 2, 3, 4, 5, 6, 7, 8)
so you can see how
the basic ordering works.
## The design is specified as: 2b*2w*2b
## Summary of means (mu) and standard deviations (SD)
## mu SD a b c
## 1 1 1 a1 b1 c1
## 2 2 1 a1 b1 c2
## 3 3 1 a1 b2 c1
## 4 4 1 a1 b2 c2
## 5 5 1 a2 b1 c1
## 6 6 1 a2 b1 c2
## 7 7 1 a2 b2 c1
## 8 8 1 a2 b2 c2
##
## Correlation Matrix
## a1_b1_c1 a1_b1_c2 a1_b2_c1 a1_b2_c2 a2_b1_c1 a2_b1_c2 a2_b2_c1
## a1_b1_c1 1.0 0.0 0.9 0.0 0.0 0.0 0.0
## a1_b1_c2 0.0 1.0 0.0 0.9 0.0 0.0 0.0
## a1_b2_c1 0.9 0.0 1.0 0.0 0.0 0.0 0.0
## a1_b2_c2 0.0 0.9 0.0 1.0 0.0 0.0 0.0
## a2_b1_c1 0.0 0.0 0.0 0.0 1.0 0.0 0.9
## a2_b1_c2 0.0 0.0 0.0 0.0 0.0 1.0 0.0
## a2_b2_c1 0.0 0.0 0.0 0.0 0.9 0.0 1.0
## a2_b2_c2 0.0 0.0 0.0 0.0 0.0 0.9 0.0
## a2_b2_c2
## a1_b1_c1 0.0
## a1_b1_c2 0.0
## a1_b2_c1 0.0
## a1_b2_c2 0.0
## a2_b1_c1 0.0
## a2_b1_c2 0.9
## a2_b2_c1 0.0
## a2_b2_c2 1.0
To make sure the plots and tables with simulation results are easy to interpret, it really helps to name all factors and levels. You can enter the labels in the ‘label_list’ variable. You can also choose not to specify names. Then all factors are indicated by letters (a, b, c) and all levels by numbers (a1, a2, a3).
For the 2x3 design we have been using as an example, where there are 2 factors (condition and time of measurement), the first with 2 levels (placebo vs. medicine) and the second with three levels (time1, time2, and time3) we would enter the labels as follows:
c(“condition”, “placebo”, “medicine”, “time”, “time1”, “time2”, “time3”)
As you can see, you follow the order of the design
(2b*3w
), and first write the FACTOR label
(condition) followed by the levels of that factor (placebo and
medicine). Then you write the second factor name (time) followed by the
three labels for each LEVEL (time1, time2, time3).
Do not use spaces in the names (so not “time 1” but “time1” or
“time_1”).
Some examples:
c("time", "morning", "evening")
c("time", "morning", "evening", "group", "control", "experimental")
c("time", "morning", "afternoon" ,"evening", "night", "group", "control", "experimental")
Depending on whether factors are manipulated within or between, variables are correlated, or not. You can set the correlation for within-participant factors. You can either assume all factors have the same correlation (e.g., r = 0.7), or enter the correlations for each pair of observations separately by specifying a correlation matrix.
In a 2x2 within-participant design, with factors A and B, each with 2 levels, there are 6 possible comparisons that can be made.
The number of possible comparisons is the product of the levels of all factors squared minus the product of all factors, divided by two. For a 2x2 design where each factor has two levels, this is:
(((2*2)^2)-(2*2))/2
## [1] 6
The number of possible comparisons increases rapidly when adding factors and levels for each factor. For example, for a 2x2x4 design it is:
(((2*2*4)^2)-(2*2*4))/2
## [1] 120
Each of these comparisons can have their own correlation if the factor is manipulated within subjects (if the factor is manipulated between subjects the correlation is 0). These correlations determine the covariance matrix. Potvin and Schutz (2000) surveyed statistical tools for power analysis and conclude that most software packages are limited to one factor repeated measure designs and do not provide power calculations for within designs with multiple factor (which is still true for software such as G*Power). Furthermore, software solutions which were available at the time (DATASIM by Bradley, Russel, & Reeve, 1996) required researchers to assume correlations were of the same magnitude for all within factors, which is not always realistic. If you do not want to assume equal correlations for all paired comparisons, you can specify the correlation for each possible comparison.
The order in which the correlations are entered in the vector should match the covariance matrix. The order for a 2x2 design is given in the 6 item list above. The general pattern is that the matrix is filled from top to bottom, and left to right, illustrated by the increasing correlations in the table below.
| Factor | a1_b1 | a1_b2 | a2_b1 | a2_b2 |
|---|---|---|---|---|
| a1_b1 | 1.00 | 0.91 | 0.92 | 0.93 |
| a1_b2 | 0.91 | 1.00 | 0.94 | 0.95 |
| a2_b1 | 0.92 | 0.94 | 1.00 | 0.96 |
| a2_b2 | 0.93 | 0.95 | 0.96 | 1.00 |
The diagonal is generated dynamically (and all conditions are perfectly correlated with themselves).
We would enter this correlation matrix as:
design_result <- ANOVA_design(design = "2w*2w",
n = 80,
mu = c(1.1, 1.2, 1.3, 1.4),
sd = 2,
r <- c(0.91, 0.92, 0.93, 0.94, 0.95, 0.96),
plot = FALSE)
design_result
## The design is specified as: 2w*2w
## Summary of means (mu) and standard deviations (SD)
## mu SD a b
## 1 1.1 2 a1 b1
## 2 1.2 2 a1 b2
## 3 1.3 2 a2 b1
## 4 1.4 2 a2 b2
##
## Correlation Matrix
## a1_b1 a1_b2 a2_b1 a2_b2
## a1_b1 1.00 0.91 0.92 0.93
## a1_b2 0.91 1.00 0.94 0.95
## a2_b1 0.92 0.94 1.00 0.96
## a2_b2 0.93 0.95 0.96 1.00
plot(design_result)
We can check the correlation matrix by asking for it from the design_result object to check if it was entered the way we wanted:
design_result$cor_mat
## a1_b1 a1_b2 a2_b1 a2_b2
## a1_b1 1.00 0.91 0.92 0.93
## a1_b2 0.91 1.00 0.94 0.95
## a2_b1 0.92 0.94 1.00 0.96
## a2_b2 0.93 0.95 0.96 1.00
Note that we did not specify the label_list, and thus they are automatically created.
You can set the sample size per condition by setting a value for n. The assumption is that you will collect equal sample sizes in all conditions [expanding Superpower to allow different sample sizes in each group is a planned future option].
You can set the common standard deviation by setting a value of sd. Superpower allows you to perform calculations under the assumption of homogeneity of variances (the standard deviation is the same across conditions), or you can violate this assumption by entering a vector of standard deviations (one for each condition). If multiple standard deviations are entered then we highly recommend that a null design is also simulated to see the impact of this assumption violation on type 1 error. Also, please note that there is always some uncertainty in which values you can expect in the study you are planning. It is therefore useful to perform sensitivity analyses (e.g., running the simulation with the expected standard deviation, but also with more conservative or even worst-case-scenario values).
There are two ways to calculate the statistical power of a factorial
design based on simulations. The first is to repeatedly simulate data
for each condition based on the means, sample size, standard deviation,
and correlation, under specific statistical assumptions (i.e., normally
distributed). The ANOVA_power
function allows you to
perform power analyses based on repeatedly simulating normally
distributed data. A second approach is to simulate a dataset that has
exactly the desired properties - every cell of the design has n
datapoints that have the desired mean and standard deviation, and
correlation between groups (for a within design). By performing an ANOVA
on this dataset, we can calculate the required statistics from the ANOVA
result used to calculate the statistical power. The
ANOVA_exact
function allows you to calculate power based on
this approach. The ANOVA_power
function is a bit more
flexible (e.g., we can use different ways to correct for multiple
comparisons, or in future versions of the package allow you to simulate
more realistic datasets that are not normally distributed), but the
ANOVA_exact
function is much faster (and takes seconds,
instead of minutes or hours for a large number of simulations).
The ANOVA_power function takes the result from the ANOVA_design function, and simulates data nsims times using a specified alpha level. As output, it provides a table for the ANOVA results, and the results for all independent comparisons.
It requires the following input: ANOVA_design, alpha_level, p_adjust, nsims, seed, and verbose.
design_result
: Output from the ANOVA_design function
saved as an objectalpha_level
: Alpha level used to determine statistical
significancep_adjust
: Correction for multiple comparisons for
exploratory ANOVA/MANOVA using the p.adjust
functionnsims
: number of simulations to performseed
: Set seed for reproducible resultsverbose
: Set to FALSE to not print results (default =
TRUE)With the following optional input:
contrast_type
: This input will determine the contrast
type for the emmeans comparisons. The default is “pairwise”. Possible
input is limited to “pairwise”, “revpairwise”, “eff”, “consec”, “poly”,
“del.eff”, “trt.vs.ctrl”, “trt.vs.ctrl1”, “trt.vs.ctrlk”, and
“mean_chg”. See help(“contrast-methods”) with the emmeans
package loaded for more information.emm_model
: emmeans accepts univariate and multivariate
models. This will only make a difference if a within-subjects factor is
included in the design. Setting this option to “multivariate” will
result in the “multivariate” model being selected for the emmeans
comparisons. This is generally recommended if you are interested in
the estimated marginal means (e.g., pairwise comparisons).emm_comp
: This selects the factors to be included for
the emmeans. The default is to take the frml2
object from
the results of ANOVA_design
, and with the default
contrast_type
= “pairwise”, results in all the
pairwise comparisons being performed. The simple effects can also be
performed by including | in the emm_comp formula. For example, with two
factors (e.g., a and b) emm_comp = "a+b"
results in all
pairwise comparisons being performed while emm_comp = "a|b"
will result in pairwise comparisons across all levels of a
within each level of b.emm_p_adjust
: adjustment for multiple comparisons.
Note: this feature only works with
ANOVA_power
. Currently, this feature includes the Tukey
(“tukey”), Scheffe (“scheffe”), Sidak (“sidak”), and Dunnett
(“dunnettx”) adjustments in addition to all of the options for
p_adjust
.Simulations typically take some time. Larger numbers of simulations yield more accurate results, but also take a long time. We recommend testing the set up with 100 simulations, and run 1000 if the set-up is correct (or 10000 if you are getting a coffee, or 100.000 if you are about accuracy to digits behind the decimal).
The ANOVA_exact function takes the result from the ANOVA_design
function, and simulates one dataset that exactly matches the desired
properties (using the mvrnorm
function from the
MASS
package, using the setting
empirical = TRUE
). This dataset is used to perform a single
ANOVA, and the results are used to compute power (thanks to Chris
Aberson for inspiring this approach). Important Note: the
original function ANOVA_exact was limited by the sample size; now we
have ANOVA_exact2 that has a similar internal process as ANOVA_exact.
However, ANOVA_exact2 calculates the effect sizes separately from the
simulation which allows us to extend the range of sample sizes that are
covered, and provides a more accurate estimate of power.
ANOVA_exact requires the following input: ANOVA_design, alpha_level, and verbose.
design_result
: Output from the ANOVA_design function
saved as an objectalpha_level
: Alpha level used to determine statistical
significanceverbose
: Set to FALSE to not print results (default =
TRUE)Compared to the ANOVA_power function, the ANOVA_exact approach is
much faster (it requires simulating only a single dataset). Currently
the only difference is that ANOVA_exact does not allow you to examine
the consequences of correcting for multiple comparisons (there is no
p_adjust
option), and ANOVA_exact does not work for sample
sizes that are less than the product of the factor levels (e.g., “2b*2b”
design would need to have a minimum n
of 5). If you are
dealing with small sample sizes then you will need to use the
ANOVA_exact2 function.
 In the example below, 1000 simulations for a 2*2 mixed design (first factor, voice, is manipulated between participants, the second factor, emotion, is manipulated within participants) are performed. The sample size is 40 in each between subject condition (so 80 participants in total), the assumed population standard deviation is 1.03, the correlation for the within factors is 0.8, and the means are 1.03, 1.21, 0.98, 1.01. No correction for multiple comparisons is applied. The alpha level used as a significance threshold is set to 0.01 for this simulation.
design_result <- ANOVA_design(design = "2b*2w",
n = 40,
mu = c(1.03, 1.41, 0.98, 1.01),
sd = 1.03,
r = 0.8,
label_list = list("voice" = c("human", "robot"),
"emotion" = c( "cheerful", "sad")),
plot = TRUE)
power_result_vig_1 <- ANOVA_power(design_result,
alpha = 0.05,
nsims = nsims,
seed = 1234)
The result for the power simulation is printed, and has two sections
(which can be suppressed by setting verbose = FALSE). The first table
provides power (from 0 to 100%) and effect sizes (partial eta-squared)
for the ANOVA result. We see the results for the main effects of factor
voice, emotion, and the voice*emotion interaction. In addition, since we
are taking a random sample of 250 simulations we can calculate a
confidence interval of the results with the confint
method.
| power | lower.ci | upper.ci | |
|---|---|---|---|
| anova_voice | 18.8 | 14.44258 | 24.10174 |
| anova_emotion | 79.6 | 74.17463 | 84.12947 |
| anova_voice:emotion | 68.0 | 61.98267 | 73.47253 |
The result for the power simulation reveal power is highest for the main effect of emotion. Remember that this is the within-subjects factor, and the means are highly correlated (0.8) - so we have high power for within comparisons. Power is lower for the interaction, and very low for the main effect of voice.
An ANOVA is typically followed up with contrasts. A statistical hypothesis often predicts not just an interaction, but also the shape of an interaction. For example, when looking at the plot of our design above, we might be specifically interested in comparing the independent effect for the cheerful vs sad human voice assistant, and the difference for sad voice when they are robotic or human. The second table provides the power for t-tests for all comparisons, and the effect sizes (Cohen’s d for between-subject contrasts, and Cohen’s \(d_z\) for within-subject contrasts, see Lakens, 2013).
Power is relatively high for the differences between within-participant conditions, and power is very low for the minor differences among the three similar means (1.03, 0.98, 1.01). In addition to the two tables, the ANOVA_power function returns the raw simulation data (all p-values and effect sizes for each simulation, see simulation_result$sim_data) and a plot showing the p-value distributions for all tests in the ANOVA.
Imagine we aim to design a study to test the hypothesis that giving people a pet to take care of will increase their life satisfaction. We have a control condition, and a condition where people get a pet, and randomly assign participants to either condition. We can simulate a One-Way ANOVA with a specified alpha, sample size, and effect size, on see which statistical power we would have for the ANOVA. We expect pets to increase life-satisfaction compared to the control condition. Based on work by Pavot and Diener (1993) we believe that we can expect responses on the life-satisfaction scale to have a mean of approximately 24 in our population, with a standard deviation of 6.4. We expect having a pet increases life satisfaction with approximately 2.2 scale points for participants who get a pet. We plan to collect data from 200 participants in total, with 100 participants in each condition. We examine the statistical power our design would have to detect the differences we predict.
design <- "2b"
n <- 100
mu <- c(24, 26.2)
sd <- 6.4
label_list = list("condition" = c("control", "pet")) #
design_result <- ANOVA_design(design = design,
n = n,
mu = mu,
sd = sd,
label_list = label_list)
power_result_vig_2 <- ANOVA_power(design_result,
nsims = nsims,
seed = 1234)
#Note we do not specify any correlation in the ANOVA_design function (default r = 0), nor do we specify an alpha in the ANOVA_power function (default is 0.05)
knitr::kable(confint(power_result_vig_2, level = .98))
| power | lower.ci | upper.ci | |
|---|---|---|---|
| anova_condition | 68.8 | 62.80888 | 74.22211 |
The result shows that we have exactly the same power for the ANOVA, as we have for the t-test. This is because when there are only two groups, these tests are mathematically identical. In a study with 100 participants per condition, we would have quite low power (around 67.7%). An ANOVA with 2 groups is identical to a t-test. For our example, Cohen’s d (the standardized mean difference) is 2.2/6.4, or d = 0.34375 for the difference between the control condition and having pets, which we can use to easily compute the expected power for these simple comparisons using the pwr package.
library(pwr)
pwr.t.test(d = 2.2/6.4,
n = 100,
sig.level = 0.05,
type = "two.sample",
alternative = "two.sided")$power
## [1] 0.6768572
We can also directly compute Cohen’s f from Cohen’s d for two groups, as Cohen (1988) describes, because f = \(\frac{1}{2}d\). So f = 0.5*0.34375 = 0.171875. And indeed, power analysis using the pwr package yields the same result using the pwr.anova.test as the power.t.test.
pwr.anova.test(n = 100,
k = 2,
f = 0.171875,
sig.level = 0.05)$power
## [1] 0.6768572
This analysis tells us that running the study with 100 participants in each condition is quite likely to not yield a significant test result, even if our expected pattern of differences is true. This is clearly not optimal. If we perform a study, we would like to get informative results - which means we should be likely to detect effect we expect or care about, if these effects exist, and not find effects, if they are absent.
How many participants do we need for sufficient power? Given the expected difference and standard deviation, d = 0.34375, and f = 0.171875, we can increase the number of participants and run the simulation until we are content. The desired power depends on the cost of a Type 2 error. Cohen recommended to aim for 80% power, or a Type 2 error rate of 20%. I personally find a 20% Type 2 error rate too high, and not balanced enough compared to a much stricter Type 1 error rate (often by default 5%, although you should justify your alpha, for example by reducing it as a function of the sample size, or by balancing error rates). Let’s say we want to aim for 90% power.
Especially when you are increasing the sample size in your design to
figure out how many participants you would need, it is much more
efficient to use the ANOVA_exact
function. You can see in
the code below that we slowly increase the sample size and find that 180
participants give a bit more than 90% power.
design_result <- ANOVA_design(design = "2b",
n = 100,
mu = c(24, 26.2),
sd = 6.4,
label_list = list("condition" = c("control", "pet")),
plot = TRUE)
ANOVA_exact(design_result,
verbose = FALSE)$main_results$power
## [1] 67.68572
# power of 67.7 is a bit low. Let's increase it a bit to n = 150 to see if we are closer to our goal of 90% power.
design_result <- ANOVA_design(design = "2b",
n = 150,
mu = c(24, 26.2),
sd = 6.4,
label_list = list("condition" = c("control", "pet")),
plot = FALSE)
ANOVA_exact(design_result,
verbose = FALSE)$main_results$power
## [1] 84.3127
# Close, but not there yet. Let's try n = 175
design_result <- ANOVA_design(design = "2b",
n = 175,
mu = c(24, 26.2),
sd = 6.4,
label_list = list("condition" = c("control", "pet")),
plot = FALSE)
ANOVA_exact(design_result,
verbose = FALSE)$main_results$power
## [1] 89.37347
#Very close. Let's add a few more and try n = 180
design_result <- ANOVA_design(design = "2b",
n = 180,
mu = c(24, 26.2),
sd = 6.4,
label_list = list("condition" = c("control", "pet")),
plot = FALSE)
ANOVA_exact(design_result,
verbose = FALSE)$main_results$power
## [1] 90.18863
Simulation based power analyses require you to increase to sample
size until power is high enough to reach your desired Type 2 error rate.
To facilitate this trial and error process you can use the
plot_power
function to plot the power across a range of
sample sizes to produce a power curve.
plot_power requires the following input: ANOVA_design, max_n, and plot.
design_result
: Output from the ANOVA_design function
saved as an objectmin_n
: The minimum sample size you want to plot in the
power curvemax_n
: The maximum sample size you want to plot in the
power curveplot
: Set to FALSE to not print the plot (default =
TRUE
)With the following optional input:
emm
: tell the function to run emmeans or not; default
is set to FALSE
.contrast_type
: This input will determine the contrast
type for the emmeans comparisons. The default is “pairwise”. Possible
input is limited to “pairwise”, “revpairwise”, “eff”, “consec”, “poly”,
“del.eff”, “trt.vs.ctrl”, “trt.vs.ctrl1”, “trt.vs.ctrlk”, and
“mean_chg”. See help(“contrast-methods”) with the emmeans
package loaded for more information.emm_model
: emmeans accepts univariate and multivariate
models. This will only make a difference if a within-subjects factor is
included in the design. Setting this option to “multivariate” will
result in the “multivariate” model being selected for the emmeans
comparisons. This is generally recommended if you are interested in
the estimated marginal means.emm_comp
: This selects the factors to be included for
the emmeans. The default is to take the frml2
object from
the results of ANOVA_design
, and with the default
contrast_type = "pairwise"
, results in all the
pairwise comparisons being performed. The simple effects can also be
performed by including | in the emm_comp formula. For example, with two
factors (e.g., a and b) emm_comp = "a+b"
results in all
pairwise comparisons being performed while emm_comp = "a|b"
will result in pairwise comparisons across all levels of a
within each level of b.The plot_power
functions simulates power up to a sample
size of max_n
using the ANOVA_exact
function.
Although it is relatively fast, with large sample size to can take some
time to produce. In the figure below we can easily see that, assuming
the true pattern of means and standard deviations represents our
expected pattern of means and standard deviations, we have 80% power
around 135 participants per condition, 90% power around 180 participants
per condition, and 95% power around 225 participants per condition.
plot_power(design_result, min_n = 10, max_n = 250)
Because the true pattern of means is always unknown, it is sensible to examine the power across a range of scenarios.
As these different plots can make clear, your study never really has a known statistical power. Because the true effect size (i.e., the pattern of means and standard deviations) is unknown, the true power of your study is unknown. A study has 90% power assuming a specific effect size, but if the effect size is different than what you expected, the true power can be either higher or lower. We should therefore always talk about the ‘expected’ power when we do an a-priori power analysis, and provide a good justification for our expectations (i.e., for the pattern of means, standard deviations, and correlations for within designs).
There is a direct relationship between standardized effect sizes and
statistical power. Sometimes it may be useful to see the relationship
between the effect size and power. That is where the
morey_plot
functions come in handy. In essence, you can
plot statistical power (y-axis) as a function of effect size (x-axis;
Cohen’s f or Cohen’s d). In addition, multiple sample
sizes (facets) and alpha levels (color). For a t-test, the type
of test (paired, one-sample, or two-sample) and alternative hypothesis
(one.sided or two.sided) can be provided.
morey_plot.ttest(
es = seq(.1, .5, .01),
n = c(10, 20),
alpha_level = c(.05, .075),
type = "paired",
alternative = "one.sided"
)
For the F-test, the options are a little different. First, instead of sample size, the numerator and denominator degrees of freedom must be provided (multiple values can be provided for either or both). Second, the effect size is now in the form of Cohen’s f. Third, there is no type or alternative argument. Lastly, there is the option to set the non-centrality argument to a “liberal” lambda; this has a minuscule effect on power but I suggest that the liberal_lambda argument be set to FALSE.
morey_plot.ftest(
es = seq(.1, .5, .01),
num_df = c(1, 2),
den_df = c(20,30),
alpha_level = c(.05, .075),
liberal_lambda = FALSE
)


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