Protocols
The following protocols are available globally.
-
Protocol extending
UIScrollViewDelegate
to adjust the scrolling of a given view to mimic a parallax motion between the two views.Default implementations are given for
See moreparallaxScrollViewDidScroll(_:)
on all adopters, and forconfigureParallaxScrollInset(_:)
forUIViewController
s.Declaration
Swift
public protocol ParallaxScrollable: UIScrollViewDelegate
-
Definition of an object for adding KVO and
NSNotificationCenter
obeservations to pure Swift classes.NSObject
s implement a method calledmethodSignatureForSelector:
which converts from strings to methods. Pure Swift classes do not have this method so cannot inherently perform KVO or observeNSNotificationCenter
posts. Rather than making a Swift class inherit fromNSObject
, which introuduces unnecessary features, anObserver
can be retained which performs an arbitrary block on copmletion.Two default implementations are provided,
See moreNotificationObserver
andObjectObserver
forNSNotificationCenter
observing and keypath observing respectively.Declaration
Swift
public protocol Observer
-
Protocol to be adopted by a class that gives easy loading of
UIViewController
s from multipleUIStoryboard
s.Two enum types are specified that should be used to identify each
UIViewController
and the Storyboard from which it can be loaded. The app specific adopter should then implement:storyboardIdentifierForViewControllerIdentifier(_:)
typically using a
switch
statement, to ensure exhaustive coverage of allUIViewControllers
declared at compile time. There is a defualt implementation forinstantiateViewControllerForIdentifier(_:bundle:)
.
that should not need to be overriden.
There are defualt implementations for
storyboardNameForIdentifier(_:)
viewControllerNameForIdentifier(_:)
where the enum types are backed by strings. These return the enum
See morerawValue
s in those implementations.Declaration
Swift
public protocol StoryboardLoader
-
Protocol defining the requirements of an object to provide a ‘User Friendly’ string, such as ‘Your device is not connected to the internet.’ not 'NSURLError -1001’.
Extensions are given for:
See moreDeclaration
Swift
public protocol UserFacing


Functions for identification and transportation of causal effects. Provides a conditional causal effect identification algorithm (IDC) by Shpitser, I. and Pearl, J. (2006) <http://ftp.cs.ucla.edu/pub/stat_ser/r329-uai.pdf>, an algorithm for transportability from multiple domains with limited experiments by Bareinboim, E. and Pearl, J. (2014) <http://ftp.cs.ucla.edu/pub/stat_ser/r443.pdf> and a selection bias recovery algorithm by Bareinboim, E. and Tian, J. (2015) <http://ftp.cs.ucla.edu/pub/stat_ser/r445.pdf>. All of the previously mentioned algorithms are based on a causal effect identification algorithm by Tian , J. (2002) <http://ftp.cs.ucla.edu/pub/stat_ser/r309.pdf>.
| Version: | 1.3.9 |
| Imports: | ggm, igraph, XML |
| Suggests: | R.rsp |
| Published: | 2018-11-29 |
| Author: | Santtu Tikka |
| Maintainer: | Santtu Tikka <santtuth at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Citation: | causaleffect citation info |
| Materials: | NEWS |
| CRAN checks: | causaleffect results |
| Reference manual: | causaleffect.pdf |
| Vignettes: |
Identifying Causal Effects with the R package causaleffect Simplifying Probabilistic Expressions in Causal Inference |
| Package source: | causaleffect_1.3.9.tar.gz |
| Windows binaries: | r-devel: causaleffect_1.3.9.zip, r-release: causaleffect_1.3.9.zip, r-oldrel: causaleffect_1.3.9.zip |
| OS X binaries: | r-release: causaleffect_1.3.9.tgz, r-oldrel: causaleffect_1.3.9.tgz |
| Old sources: | causaleffect archive |
Please use the canonical form https://CRAN.R-project.org/package=causaleffect to link to this page.


If only material things exist, then rights, which are an immaterial title, do not actually exist. Thus, there is a contradiction.


Survivor Series 2019: NXT dominate RAW SmackDown in entertaining event
Survivor Series 2019 for the first saw the combination of RAW, SmackDown and NXT superstars do battle in the squared circle
The build-up to the event in the past weeks had seen the invasion of WWE next rated stars invade Monday Night RAW and Friday Night SmackDown events.
And the collection of the young wrestlers led by Triple H won key events on the night as the NXT had four wins ahead of SmackDown and Raw, who recorded fewer victories.
In the women's main event, NXT champion Shayna Baszler defeated SmackDown queen Bayley and RAW women's champion Becky Lynch.
READ ALSO: NAIJ.com upgrades to Legit.ng
United States champion AJ Styles and Intercontinental champion Shinsuke Nakamura lost their triple threat bout to NXT's north American champion Roderick Strong.
RAW recorded their first win on the night when tag team champions The Viking Raiders defeated SmackDown tag team champions The New Day and NXT tag team champions The Undisputed ERA.
Tiwa Savage, Boyz II Men, Megan Thee Stallion, others to shine at Flytime Music Festival 2019
SmackDown also stood tall as Roman Reigns was the last man standing in the men's survivor series elimination match.
NXT claimed supremacy in the women's survivor series elimination match, defeating team RAW and SmackDown respectively.
Brock Lesnar retained his Universal championship after beating Rey Mysterio in a No Holds Barred match, while Bray Wyatt pinned Daniel Bryan to remain WWE champion.
Other results are as follows
Lio Rush defeated Akira Tozawa and Kalisto in the triple threat NXT cruiserweight championship
Dolph Ziggler & Robert Roode won the Cross Brand tag team Battle Royal
PAY ATTENTION: Install Pitch Football App for FREE to get the latest football News & Scores
Meanwhile, Legit.ng had earlier reported that Tyson Fury on Thursday evening, October 31, continued his unbeaten dominance in sports as the Briton defeated Braun Strowman on his WWE debut.
Recently, the 31-year-old Tyson Fury swapped the boxing ring for wrestling and he has now started his career on a remarkable note.
US singer Beyonce shows off stunning figure in lovely new photos
Can Nigeria U17 team win a 6th World Cup title in Brazil? | Legit TV


# Financial Points
Distance to Nearest Road
Brandon Martin-Anderson's Growing Shortest Path Tree of Seattle
| Polygon | Unit | Pop within Access |
|---|---|---|
| Buffer | 5 km | 6,406,973 |
| Isochrone | 1 hour | 4,502,957* |
*2 out of 242 Offsite ATMs were too far from a road to use in Isochrone analysis.
*62 out of 1,254 Commercial Banks were too far from a road to use in Isochrone analysis.
// Edges will only be found if they are closer than this distance
public static final double MAX_DISTANCE_FROM_STREET_METERS = 1000;
private static final double MAX_DISTANCE_FROM_STREET_DEGREES =
MAX_DISTANCE_FROM_STREET_METERS * 180 / Math.PI
/ SphericalDistanceLibrary.RADIUS_OF_EARTH_IN_M;
Phoebe Merritt
pmerritt@spatialdev.com


Posted on March 20, 2019
Posted on January 20, 2019
Posted on August 24, 2018
Posted on April 27, 2018
Posted on March 13, 2018
Posted on February 14, 2018
Posted on January 20, 2018
Posted on January 10, 2018
Posted on January 02, 2018


DASHBOARD
DEVELOPER INFORMATION
more_vert
Action
Another action
Something else here
Developer Type
developer_board
Personal
Company
Web Site
https
http://
https://
Developer Info
Real Name
Company Name
Company License Number
Company License Image
Tax Image
Legal Person Name
IdCard Img
Confirm
Developer Type:
Domain:
Developer Name:
Company Name:
Company License Number:
Legal Person Name:


The Website being tested.
The site I am analyzing is the mobile Jinx.comWhat is the purpose of this site
This is a clothing website geared towards video game players, geeks, and the sort. They sell clothing and acessories featuring various video game properies in addition to their own Jinx branded merchandice. The website has gone though a design change since I've last been here so I figured it would be a good oppurtunity to see if someone who has never been to this site can navigate it.
Testing
The purpose of this test is to evualate the website tested previously but this time using a mobile device.
This test will be conducted using a Samsung Galaxy S8 Active using the Google Chrome browser.
With exception of the 22 year old woman filmed during the desktop test, the other two users have not used either version of the website.
Tasks
Just like the test for the desktop website the users were given mostly the same task and were asked to give their thoughts on how it compared to the desktop version. They were also made to do things that's specific to mobile such as scrolling up and down the page, and the site's responsiveness on the device.
Results
Most of it icons and menues are the same from the desktop site so everyone was able to easily locate where things were in the page.
It was noted that there is an icon on the top right that you can click on to bring up the account. To them it wasn't clear that this was the account button till after you clicked on it, bringing up the login page. One initially went to check the nav bar on the left side to see. And like the desktop version the "Spring Campaign" banner covers the entire screen on the main page, but that works better for mobile than desktop as you already have limited screen space.
Ease of scrolling up and down the page and the fact that each page wasn't overly cluttered was nice. Being very similar to the desktop version there wasn't really too much to critique, but the mobile version seemed to be prefered over the desktop site.
Summary
Essentially the mobile version of the website functions just like the desktop version. By looking at the button on the top right, you wouldn't know that it was for logging in to your account. Changing the icon to something that looks more like something that could be an account icon could be an improvement. The desktop version actually has a different icon and says "Account" so no reason not to use that one. Both versions work very well with minor flaws so there's really no reason to chose one or the other.


[t]Dear[/t] {$consultantName},
[t]Translator[/t] {$translatorName} [t]has made a correction for the following page, which was previously published live:[/t]
[t]Regards[/t],
OTYS Recruiting Technology


Plugin search - Better Battlelog Forums #148460
Sitemap
30. January 2016 21:10
Topicstarter
Lightness1
Hello,
i search this Plug-in for Better Battlelog:
http://fs5.directupload.net/images/160130/ufw3bcmi.jpg
Anyone can help me? I have no idea what is this for a Plug-in.
15. May 2016 08:10
KC-KICO
WoW I would also like .. nice ...
15. May 2016 11:58
Post edited 1 x times, last by
BrainFooLong
Administrator
15. May 2016 11:58
BrainFooLong
Administrator
I never saw such a plugin. Anyway, looks cool.
16. May 2016 15:16
ORFK
Global Moderator
wtf?! what kind of plugin is it? what does it do?
13. May 2017 20:59
Post edited 1 x times, last by
I-MrFixIt-I
Plugin Developer
13. May 2017 20:59
I-MrFixIt-I
Plugin Developer
Well, even though this thread is old, I've uploaded the plugin for you guys:
https://getbblog.com/en/plugin/159141/Advanced-Scoreboard


Define differentiability of function body
annotation"(" smoothOrder "=" UNSIGNED_INTEGER ")" annotation"(" smoothOrder "(" normallyConstant=NAME ["," normallyConstant=NAME] ")" "=" UNSIGNED_NUMBER ")"
This annotation has only an effect within a function declaration.
smoothOrder defines the number of differentiations of the function, in order that all of the differentiated outputs are continuous provided all input arguments and their derivatives up to order smoothOrder are continuous.
This means that the function is at least CsmoothOrder. smoothOrder = 1 means that the function can be differentiated at least once in order that all output arguments are still continuous, provided the input arguments are continuous. If a tool needs the derivative of a function, e.g., for index reduction or to compute an analytic Jacobian, the function can be differentiated analytically at most smoothOrder times.
The optional argument normallyConstant
of
smoothOrder
defines that the function argument
NAME
is usually constant.
This annotation is used by many functions of the Modelica.Fluid library, such as Modelica.Fluid.Dissipation.PressureLoss.StraightPipe.dp_laminar_DP, since geometric arguments to these functions are usually constant.
function SpecialPolynomial input Real u; output Real y; algorithm y = if u > 0 then u^2 else 0; annotation(smoothOrder = 1); end SpecialPolynomial; model TestSpecialPolynomial Real y; Real yd; Real ydd; equation y = SpecialPolynomial(sin(time)); yd = der(y); // fine, SpecialPolynomial is analytically differentiated once ydd = der(yd); // error, SpecialPolynomial cannot be differentiated twice end TestSpecialPolynomial;


Green
is the color of balance and growth. It can mean both self-reliance as a positive and possessiveness as a negative, among many other meanings.


The mission is to promote Jesus and praise the Hallelujah. To play volleyball
with the utmost integrity and discipline is the way to honor God and the Holy Spirit. Our mission is to
unite the world under one order. The order of God.
Bios
Ruben
The driving force behind
it's monumental and unprecendented
rise to glory
Ruben
The driving force behind
it's monumental and unprecendented
rise to glory
Ruben
The driving force behind
it's monumental and unprecendented
rise to glory
Vision
The vision is to promote Jesus and praise the Hallelujah. To play volleyball
with the utmost integrity and discipline is the way to honor God and the Holy Spirit. Our vision is to
unite the world under one order. The order of God.


My name is Jackson Ikonya Miring'u
I am an Entreprenuer/Sound engineer/ upcoming Software developer.
Lives in Kiambu county
I am a young man a go getter and never settle for less. I always get myself around positive energy, i carry not a faint heart hence allows me to keeping moving not matter the challenges around. My greatest aspiration is to be a problem solver at the capacity am in. One of the greatest lessons i have learnt along the way is to see opportunity in every crisises and embrace change. Am a student at Moringa school pursuing software development.


Twin Stick Shooter West of Dead Brings Purgatory to PS4 in 2020
Starring Ron Perlman
by Stephen TailbyGame Profile
Title:
West of Dead
Publisher:
Raw Fury
Developer:
Upstream Arcade
Genre:
Action, Shooter
PlayStation Network Release Date:
2020 (USA)
2020 (UK/EU)
Where to buy:
Pre Order on Amazon
Indie label Raw Fury has announced West of Dead, a stylish twin stick action game coming to PlayStation 4 next year. In it, you'll play as William Mason, a deceased gunslinger who's stuck in a world between life and death. It's here that you'll blast your way through procedurally generated levels, fending off evil souls in purgatory.
It looks pretty darn cool, we have to say. That's largely thanks to the cel-shaded art style, but the gameplay looks fun too; utilising cover and abilities like ricocheting bullets should make for a tense little shooter. It also stars Ron Perlman in the lead role, perhaps most famous for playing Hellboy on the big screen.
West of Dead arrives on PS4 in 2020. What do you make of this one? Stay alive in the comments below.
[via youtube.com]


Photo by Usukhbayar Gankhuyag on Unsplash
Photo by Sweet Ice Cream Photography on Unsplash
Photo by Joshua Rawson-Harris on Unsplash
Photo by Katie Burkhart on Unsplash


What we want most for our wedding is to have all of our friends and family there to celebrate the occasion with us, and so, more than anything we would simply enjoy your presence. (We mean it!)
That being said, if you do want to get us a little something we've created a wedding registry on .


To make a prediction please enter the following details:
Number of Rooms
Number of Beds
Number of people accomodated
Number of restaurants in the vicinity
Number of Reviews
Rating (out of 10)
Minimum Number of nights
Maximum number of nights
{% if(prediction) %}
The predicted price for these parameters is:
${{prediction}}
{% endif %}


Split elements of input
based on sep
.
tf.compat.v1.strings.split( input=None, sep=None, maxsplit=-1, result_type='SparseTensor', source=None, name=None )
Let N be the size of input
(typically N will be the batch size). Split each
element of input
based on sep
and return a SparseTensor
or
RaggedTensor
containing the split tokens. Empty tokens are ignored.
>>> tf.strings.split(['hello world', 'a b c']) tf.SparseTensor(indices=[[0, 0], [0, 1], [1, 0], [1, 1], [1, 2]], values=['hello', 'world', 'a', 'b', 'c'] dense_shape=[2, 3])
tf.strings.split(['hello world', 'a b c'], result_type="RaggedTensor")
If sep
is given, consecutive delimiters are not grouped together and are
deemed to delimit empty strings. For example, input
of "1<>2<><>3"
and
sep
of "<>"
returns ["1", "2", "", "3"]
. If sep
is None or an empty
string, consecutive whitespace are regarded as a single separator, and the
result will contain no empty strings at the start or end if the string has
leading or trailing whitespace.
Note that the above mentioned behavior matches python's str.split.
input
: A string Tensor
of rank N
, the strings to split. If
rank(input)
is not known statically, then it is assumed to be 1
.sep
: 0-D
string Tensor
, the delimiter character.maxsplit
: An int
. If maxsplit > 0
, limit of the split of the result.result_type
: The tensor type for the result: one of "RaggedTensor"
or
"SparseTensor"
.source
: alias for "input" argument.name
: A name for the operation (optional).ValueError
: If sep is not a string.A SparseTensor
or RaggedTensor
of rank N+1
, the strings split
according to the delimiter.


Extracting presentation logic to make it testable
Last week we touched the entirely new topic on my blog. This week we will continue the Unit Testing subject. One of the smells of a good architecture is the ability to cover it with Unit Tests. Today we will talk about extracting Presentation logic into testable and straightforward pieces of code.
Typical issues
Assume that you have a screen presenting a list of the progress of your TV show collection. Every cell presents TV show poster, title, and next episode number and title. Let’s see on typical ShowCell code:
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struct Show: Decodable {
let title: String
let images: Images
let unwatched: [Episode]
}
class ShowCell: UICollectionViewCell {
@IBOutlet private weak var titleLabel: UILabel!
@IBOutlet private weak var nextEpisodeLabel: UILabel!
@IBOutlet private weak var posterImageView: UIImageView!
var show: Show? {
didSet {
guard let show = show else {
return
}
posterImageView.setImage(from: show.images.poster)
titleLabel.text = show.title
if let nextEpisode = show.unwatched.first {
nextEpisodeLabel.text = "S\(nextEpisode.season)E\(nextEpisode.number) \(nextEpisode.title)"
}
}
}
}
This code looks very simple, and at first glance, there are no huge problems. But there are probably a few downsides:
-
We are breaking SOLID principles by mixing presentation logic with view logic inside ShowCell. Our cell is responsible for laying out views and formatting show data into user presentable format.
-
We want to test our data formatting logic to be sure that we are presenting the correct information to the user. But it is hard to cover UIViews with Unit Tests, and there is no clear intention on what you test, layout or presentation logic (we will talk about UI testing in next posts).
Refactoring
Let’s start to refactor our codebase. First of all, I want to reuse this ShowCell across the app for presenting search response, TV shows collection progress, etc.
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protocol PosterPresentable {
var title: String { get }
var subtitle: String? { get }
var poster: URL { get }
}
class PosterCell: UICollectionViewCell {
@IBOutlet private weak var titleLabel: UILabel!
@IBOutlet private weak var subtitleLabel: UILabel!
@IBOutlet private weak var posterImageView: UIImageView!
var presentation: PosterPresentable? {
didSet {
guard let presentation = presentation else {
return
}
posterImageView.setImage(from: presentation.poster)
titleLabel.text = presentation.title
subtitleLabel.text = presentation.subtitle
}
}
}
Here we have refactored version of our ShowCell, now it is called PosterCell, and it can render any data which conforms PosterPresentable protocol. Let’s go ahead and create a separated struct which will contain presentation logic for Show model and which we will pass to our cell instead of Show model struct.
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struct ShowPresentation {
let show: Show
}
extension ShowPresentation: PosterPresentable {
var title: String {
return show.title
}
var subtitle: String? {
guard
let nextEpisode = show.unwatched.first
else { return nil }
return "S\(nextEpisode.season)E\(nextEpisode.number) \(nextEpisode.title)"
}
var poster: URL {
return show.images.poster
}
}
By extracting Show model formatting logic into ShowPresentation struct, we fix the encapsulation problem when Cell class responsible for formatting own data model. Another positive effect here is that we can easily cover ShowPresentation with Unit Testing to make sure it works correctly. So, let’s continue with implementing tests for our presentation logic.
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class ShowPresentationTests: XCTestCase {
func testShowPresentation() {
let mockedImages = Images(
poster: URL(string: "https://image.tmdb.org/t/p/original/fbtaoynlPpENx3Ss2laC7wgqLIP.jpg")!,
banner: URL(string: "https://image.tmdb.org/t/p/original/fbtaoynlPpENx3Ss2laC7wgqLIP.jpg")!,
background: URL(string: "https://image.tmdb.org/t/p/original/fbtaoynlPpENx3Ss2laC7wgqLIP.jpg")!
)
let mockedEpisode = Episode(
title: "The One Where Monica Gets A Roommate",
season: 1,
number: 1)
let mockedShow = Show(
title: "Friends",
images: mockedImages,
unwatched: [mockedEpisode]
)
let presentation = ShowPresentation(show: mockedShow)
XCTAssertEqual(presentation.title, "Friends")
XCTAssertEqual(presentation.subtitle, "S1E1 The One Where Monica Gets A Roommate")
XCTAssertEqual(presentation.poster.absoluteString, "https://image.tmdb.org/t/p/original/fbtaoynlPpENx3Ss2laC7wgqLIP.jpg")
}
}
Here we have a Unit Test which is checking the way of how our Presentation struct formats the data. You might have more fields and more complex logic there. This Unit Test will keep you from breaking the presentation rules of your data in the future during refactoring or any other changes.
Conclusion
Today we discussed how easily we could follow the Single responsibility principle during UI development and how we can extract data presentation and formatting logic into testable and reusable pieces of code. We will continue the Unit Testing topic on my blog through the next posts. Just try to refactor your codebase by following the Single responsibility principle.


JDeveloper and ADF
java.io.UTFDataFormatException: Invalid UTF8 encoding.
I'm working with JDeveloper <IP_ADDRESS>, and I got the following error, when I compiled an JSP: [...]\public_html\menu\jsp\menus\presentacion.jsp Error(1): java.io.UTFDataFormatException: Invalid UTF8 encoding. What's wrong with my JSP? Regards, Frank P.D. [...] I omite the complete path for best reading.


--- layout: post title: Let's begin with some C, umm.. ++ ;) date: 2016-06-12 06:08:01.000000000 +05:30 type: post parent_id: '0' published: true password: '' status: publish categories: - C++ tags: [] meta: _headstart_post: _hs_first_post _wpas_skip_all_services: '1' _publicize_job_id: '23975270911' _thumbnail_id: '34' _wp_old_slug: first-blog-post__trashed _rest_api_published: '1' _rest_api_client_id: "-1" _wpas_skip_14755128: '1' author: amodsachintha ---
C++ is a powerful language, yeah powerful than java or python. C++ evolved from the C programming language. The C language was evolved at Bell Laboratories by Dennis Ritchie from a language called B in 1972. By the late 1970s, C had evolved into a industry standard language.
Today data compressors, game engines, music players, audio/video decoders are written using C++, and guess what, the speed and low level hardware access is what makes people to use C++.
The above image is a part of a program I wrote to calculate and output certain values into files. Some times it may seem hard to grasp it at first sight, but believe me, you can ease into it.
I won't probably drag my C++ blog posts into graphics simulations, as they are more complex applications of C++. But let's see what becomes of it. ;)
Knowing some C++ doesn't mean that one can become a game engine developer overnight or nevertheless in a month. Sometimes it takes years of practice to master the language alone enough.
But, word of advice, never give up. Give what it takes to learn it, and you can be proud as hell when you've written it down on you're resume.
C++ is a low level programming language. Meaning you don't encounter a whole lot of English keywords when using it. More recent programming languages tend to have meaning when reading the code itself.
It'a probably one reason that C++ is said to be difficult.
But don't you worry. Once you take
your time and practice you'll know exactly what should be done at a given situation.
So, here's the breakdown,
I will be posting C++ lessons that covers the most basic stuff, and from there onward more complex stuff.
Videos will be posted too. So be sure to check'em out.
Lessons will be based on what I've learnt in the first semester at SLIIT. So students in the first semester at SLIIT will benefit greatly from it.
Any one new to programming in C++ or new to programming itself, this will be a good start point.
Sample code will also be posted. Copy paste compile and run, no worries, but, I'd prefer you yourself taking time to write that code down yourself.
PRACTICE! PRACTICE! or teach someone else what you know. And finally, DO NOT MEMORIZE CODE!!! I have to emphasize the fact that programming in C++ is that you have to improvise on the go. This DOESN'T mean jotting down code from memory.
Learn how to logically write down a program.
The next blog post will be about setting up an environment for C++ programming. So tune in when.
Cheers and Thank you.
So long till the next post!


An update to the previous version ( 0.1.0 ) of the package. Functions in the previous version required the binary outcome variable of the data set to be strictly a factor.In version 0.2.0 the binary outcome variable may be both a factor (with two levels) or an integer (or numeric ). All functions of the previous version have been retained.No new functions have been added. Like the previous version,provides functions to collapse the levels of an attribute based on response rates. It also provides functions to compute and display information value, and weight of evidence (WoE) for the attributes, and to convert numeric variables to categorical ones by binning. These functions only work for binary classification problems.
| Version: | 0.2.0 |
| Depends: | magrittr |
| Imports: | dplyr, lazyeval, ggplot2 |
| Suggests: | knitr, rmarkdown |
| Published: | 2017-12-04 |
| Author: | Krishanu Mukherjee |
| Maintainer: | Krishanu Mukherjee <toton1181 at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | CollapseLevels results |
| Reference manual: | CollapseLevels.pdf |
| Vignettes: |
"CollapseLevels" |
| Package source: | CollapseLevels_0.2.0.tar.gz |
| Windows binaries: | r-devel: CollapseLevels_0.2.0.zip, r-release: CollapseLevels_0.2.0.zip, r-oldrel: CollapseLevels_0.2.0.zip |
| OS X binaries: | r-release: CollapseLevels_0.2.0.tgz, r-oldrel: CollapseLevels_0.2.0.tgz |
| Old sources: | CollapseLevels archive |
Please use the canonical form https://CRAN.R-project.org/package=CollapseLevels to link to this page.


DMVAppointment
Automation Script to Book Cal DMV Appointment using headless Chrome puppeteer and Docker


[A]
[B]
[C]
[D]
[E]
[F]
[G]
[H]
[I]
[J]
[K]
[L]
[M]
[N]
[O]
[P]
[Q]
[R]
[S]
[T]
[U]
[V]
[W]
[X]
[Y]
[Z]
background activities
backups, database of comments/errata
base64 module
2nd
BaseHTTPServer module
BASIC, Python compared to
bastion module
2nd
BigGui example
binary files as email attachments
distinguishing from text files
downloading
module
operators
search trees
compared to dictionaries
BinaryTree class
binascii module
binhex module
bison system
browse module
2nd
browsers
Active Scripting support
applets, coding
email client complexity of
deleting mail
forwarding mail
implementing
performance
portability
replying to mail
retrieving mail
root page
security protocols
selecting mail
sending mail
utility modules
viewing mail
examples in book, running on
HTML, languages embedded in
interoperability issues
JPython and
2nd
Python-based
[See Grail browser] restricted file access
server files, displaying on
surfing the Internet with
browsing
comment reports
implementing
interface for
errata reports
implementing
interface for
buttons
bytecodes, precompiling strings to
|


To get Dart packages, you can use the pub.dev site, or load packages from the local file system or elsewhere, such as Git repositories. Between different options for a specific package, we should select the one that confirms below list.
All packages should be in the dependency
directory. usually, the usage of package functionality is in Domain Layer within business logic, imagine for any reason in future we want to swap the package for something different. If we used it directly inside the Usecase we would need to change a lot of code. But by hiding 3rd party library in a class we control, there won't much code to change. So we should wrap package functionality with a class that is inherited from ExternalDependency
.
import 'package:clean_framework/clean_framework.dart';
import 'package:permission_handler/permission_handler.dart';
class PermissionHandlerPlugin extends ExternalDependency {
Future<bool> isGrantedAccessCamera() async {
var status = await Permission.camera.status;
if (status.isGranted) {
return true;
} else {
var reqStatus = await Permission.camera.request();
return reqStatus.isGranted;
}
}
}
Mock
class to cover package class class MockPermissionHandlerPlugin extends Mock implements PermissionHandlerPlugin {}
class MockPermissionHandlerPlugin extends Mock implements PermissionHandlerPlugin {
@override
Future<bool> isGrantedAccessCamera() async {
return true;
}
}


$0270/624: Write LINDX ROM-Reference: LDX $0270 : $D92A $DC29 STA $0270 : $D907 STX $0270 : $DBA7 Previous Page / Next Page


Support flips against impeachment as Trump approval rating jumps: Poll
by John GageA new poll shows support for the impeachment of President Trump has declined since public hearings have started.
The Emerson poll released Thursday found support for impeachment has dropped from 48% to 43% since October, while opposition to impeachment has risen from 44% to 45%.
Independents are key in the shift, with 49% opposing impeachment and only 34% supporting impeachment. In October, 48% of independents supported impeachment and 39% opposed.
The poll also showed Trump's approval rating rising to a net positive. Forty-eight percent approved of the president while 47% disapproved. His approval rating was only 43% a month ago.
A Morning Consult- Politico poll this week also showed independents are beginning to oppose impeachment as the second week of public hearings wraps up. The poll found a 10% drop in support from independents since the beginning of public hearings.


Matrix Linear Transformations
Combining Transformations
In gaming, or 3d rendering, suppose we want to show an object
with its own position and orientation in the world,
and we want to render it given a campera in its own position and orientation.
We will transform the object vertices and transform from object to world space
and World space to Camera Space.
$ P_{world} = P_{object}M_{object\rightarrow world}$
$ P_{camera} = P_{world}M_{world\rightarrow camera}$
$ P_{camera} = (P_{object}M_{object\rightarrow world})M_{world\rightarrow camera}$
Recall that multiplication is associative
$ P_{camera} = (P_{object}M_{object\rightarrow world})M_{world\rightarrow camera}$
$ P_{camera} = P_{object}(M_{object\rightarrow world}M_{world\rightarrow camera})$
Concatenate matrices:
$ M_{object\rightarrow camera} = M_{object\rightarrow world}M_{world\rightarrow camera}$
$ P_{camera} = P_{object}M_{object\rightarrow camera}$
Geometrically, what's happening in matrix concatenation?
Recall: The rows of a matrix contain the basis vectors after transfomation, even multiple transformations
Geometrically, what's happening in matrix concatenation?
let $a_1, a_2, a_3$ be the rows of $A$
$
A =
\left[ \begin{smallmatrix}
a_1 \\ a_2 \\ a_3
\end{smallmatrix} \right]
$
$
AB =
\left[ \begin{smallmatrix}
a_1 \\ a_2 \\ a_3
\end{smallmatrix} \right]
B
=
\left[ \begin{smallmatrix}
a_1B \\ a_2B \\ a_3B
\end{smallmatrix} \right]
$


Microsoft competes for popularity with upstart Slack
SAN FRANCISCO (AP) - Microsoft says that its own workplace collaboration service Teams now has more than 20 million daily active users.
That’s 8 million more than what upstart work messaging service Slack boasted of having last month. Microsoft is in a fierce popularity contest with Slack.
Slack, which had its initial public offering in June, was in a federally-mandated quiet period when Microsoft first revealed its Teams metrics in early July.
Slack has since gotten louder about downplaying the numbers, saying users are more engaged with its customizable service than “legacy suites of badly connected products” - a thinly veiled swipe at Microsoft.
Microsoft benefits from being able to bundle Teams as part of a software package that includes email and other products.
Slack’s shares took a hit after Microsoft’s announcement Tuesday.
Sign up for Daily Newsletters
Copyright © 2019 The Washington Times, LLC.
Please read our comment policy before commenting.


Image toggle event type was {{eventType}}
{{movie | json}}
| {{movie.title}} |
| {{movie.title | uppercase}} | {{movie.title | lowercase}} | {{movie.releaseDate | date}} | {{movie.price | currency:'USD':true}} | {{movie.starRating | number}} | {{movie.starRating | number:'1.1-2'}} | {{movie.approvalRating | percent: '1.0-2'}} |


domestic | noise | introduction | 2010
Introduction
5.0
5.0.1 Background
Noise is unwanted sound. In order to limit the effects of unwanted sound the
standards intend to improve the resistance of
building
elements to sound
transmission. Research has presented clear evidence that noise can
indirectly contribute to a range of health issues such as stress and anxiety.
Inadequate sound insulation can impair health by allowing noise from
neighbours to disrupt normal life. A number of people in attached homes
complain of neighbour noise. The World Health Organisation has established
a relationship between noise exposure and sleep disturbance. However the
short or long-term effects are still not clear. Over the years, several pieces of
legislation relating to noise (see clause 5.0.9) have been introduced which
provide some degree of control of ‘noisy neighbours’.
Noise generated within a
dwelling
, to some extent, is under the control of the
occupants but can still create tension within a family. The provision of sound
absorption between
rooms
should allow people a degree of privacy as they
go about their everyday activities.
5.0.2 Aims
The purpose of the standards in section 5 is to limit the transmission of sound
to a level that will not threaten the health of occupants from sound
transmission emanating from attached
buildings
and a differently occupied
part of the same
building
. They also cover sound from within the same
degree of peace and quiet.
PAN 56
It is important to recognise that the standards will not guarantee freedom
from unwanted sound transmission. The standards aim to limit the effects
from sound levels created from normal domestic activities, but not from
excessive noise from other sources such as power tools, audio systems
inconsiderately played at high volume or even raised voices. The standards
do not address environmental noise through the
building
facade from sources
such as aircraft, trains, road traffic or industry. Other legislation covers these
areas and further information may be obtained from Planning Advice Note
(PAN) 56 ‘Planning and Noise, 1999'.
Home improvement
leaflet
Home improvements were previously not considered an issue, whereas
some can now create a noise nuisance. For example, lifting carpets and
replacing them with laminate flooring can have an adverse affect on sound
giving further information on home improvements is available at
an increase in the sound insulation performance of
separating walls
and
separating
floors;
•
•
5.0.1
—
5.0.3
•
a robust post-completion testing regime;
domestic | noise | introduction | 2010
5.0.4 Explanation of terms
There are a number of terms used in this section some are included
below, these and other useful terms are included in annex A of the Example
Constructions.
Airborne sound
is sound which is propagated from a noise source through
the medium of air. Examples of these are speech and sound from a
television.
Airborne sound transmission
is direct transmission of airborne sound
through walls or floors. When sound energy is created in a
room
, for
instance by conversation, some of the energy is reflected or absorbed by
room
surfaces but some may set up vibrations in the walls and floor.
Depending on both the amount of energy and the type of
construction
, this
can result in sound being transmitted to adjacent parts of the
building
.
Direct transmission
refers to the path of either airborne or impact sound
through elements of
construction
.
D
nT,w
is the weighted standardized level difference. A single-number quantity
(weighted) which characterises the airborne sound insulation between two
rooms
, in accordance with BS EN ISO 717-1: 1997.
Flanking transmission
is airborne or impact transmission between
rooms
that is transmitted via flanking elements and/or in flanking elements in
conjunction with the main separating elements. An example of a flanking
element is the inner leaf of an
external wall
that connects to the separating
‘core’ of a wall or floor.
Impact sound
is sound which is propagated from a noise source through a
direct medium. An example of this is footfall on a floor.
Impact sound transmission
is sound which is spread from an impact noise
source in direct contact with a
building
element.
L’
nT,w
is the weighted standardized impact sound pressure level. A
single-number quantity (weighted) to characterise the impact sound
insulation of floors, in accordance with BS EN ISO 717-2: 1997.
R
w
is a single number quantity (weighted) which characterises the airborne
sound insulation of a
building
element from measurements undertaken in a
laboratory, in accordance with BS EN ISO 717-1: 1997.
5.0.5 Reduction of sound transmission
The reduction of sound transmission from attached
buildings
, or part of the
through different mechanisms which involve; mass, isolation, absorption,
resilience and stiffness (see annex A of the Example Constructions). Wall
and floor
constructions
that provide a combination of such mechanisms
generally provide better sound insulation.
Good design incorporates at least 2 or more of the above mechanisms and
can reduce a range of sound frequencies typically found in attached
5.0.4
—
5.0.5
domestic | noise | introduction | 2010
The effects these variables can have in predicting both sound transmission
and insulation are as follows:
•
through a heavyweight wall or floor it is its mass per unit area. A reduction
in sound transmission and increase in sound insulation are expected with
increasing mass, as the heavier the wall or floor, the less it vibrates in
response to sound waves and hence the less sound energy is radiated.
For example, heavyweight
constructions
such as masonry cavity walls
provide mass and isolation;
•
through a lightweight wall or floor it is the use of cavities, isolation and
absorption. A reduction in sound transmission and an increase in sound
insulation are expected by the use of cavities with fewer and less stiff
connections, while absorptive material hung in the wall cavity will absorb
mid to high frequency sound energy. The formation of narrow cavities,
such as dry linings on dabs, can also create an unwelcome ‘drum’ effect at
low frequencies and filling or lining them with absorbing material can help
to reduce this. For example, in lightweight
constructions
such as timber
frame walls, the twin stud of the timber frame provides isolation, stiffness
and absorption;
Heavyweight
•
resilience is needed for
separating floors
in
flats
where there is direct
vibration impact such as footfall noise. Resilience reduces the impact
vibration by dynamic movement and also converts the energy into heat.
Examples of resilient elements for floors include floating floor treatments
such as battens and cradles, resilient bars and resilient floor coverings,
other than carpet;
•
mass and stiffness help to reduce significantly low frequency sound
transmission whereas absorption and resilience predominantly reduce mid
and high frequency sound transmission;
Lightweight
•
isolation has the most influence over all frequencies of sound but can be
limited by structural connections such as wall ties, straps and fixings that
may bridge isolated leafs or elements.
5.0.5
—
5.0.5
domestic | noise | introduction | 2010
5.0.6 Principles of airborne sound transmission
When sound waves strike a wall or floor, the pressure variations cause the
construction
to vibrate. A portion of the vibrational energy on the sound
source side will be transferred through the wall or floor where it is radiated as
airborne sound on the other side. There is a loss in sound transmission as
the frequency of the incident sound (sound waves produced from striking
against a wall or floor for example) increases. This also varies with the
direction of the sound waves, and is usually assumed to be the average for
all possible angles of incidence.
5.0.7 Principles of impact sound transmission
Impact sound is sound that is spread from an impact or vibrational source in
direct contact with a
building
element such as a floor. A structural vibration is
transmitted from the point of impact through the structure causing vibration
leading to the radiation of the sound into an adjacent
room
below. In a
building
this is commonly caused by an object hitting the floor from where the
vibration is transferred into the structure. Usually the vibration path will lead
to the ceiling and perimeter walls below. The amount of impact sound heard
below will depend upon many factors including the force of the impact, the
5.0.5
—
5.0.7
domestic | noise | introduction | 2010
vibration transmission characteristics of the floor
construction
and the floor
covering.
5.0.8 Principles of flanking transmission
Flanking transmission occurs when there is an indirect path for sound to
travel along elements adjacent to walls and floors. If the flanking
construction
and its connections with the separating structure are not correctly detailed,
flanking transmission can equal, or even exceed, sound levels perceived as a
result of direct transmission. Flanking transmission can occur, for instance,
when a wall abuts the face of the inner leaf of an external cavity wall, and the
walls are insufficiently tied or bonded together, thus allowing the noise to
travel along the inner leaf.
5.0.9 Relevant legislation
Listed below are some pieces of legislation and guidance that may be
relevant and/or helpful to those using the guidance in this particular section.
Common Law of
Nuisance
The Common Law of Nuisance recognises that an occupant has the right to
the free and absolute use of the property, but only to the extent that such use
does not discomfort or annoy a neighbour.
Civic Government
(Scotland) Act 1982
Part IV of the Civic Government (Scotland) Act 1982 sets out a range of
public nuisance offences.
Environmental Protection
Act 1990
The Environmental Protection Act 1990 as it relates to noise, states that ‘any
premises in such a state as to be prejudicial to health or a nuisance ranks as
a statutory nuisance’.
Human Rights Act 1998
The Human Rights Act 1998 (as it relates to noise) Article 8 guarantees the
right to respect for private and family life.
Antisocial Behaviour etc.
(Scotland) Act 2004
The Antisocial Behaviour etc. (Scotland) Act 2004 empowers the local
authority to serve a warning notice in relation to noise which exceeds the
permitted level.
PAN 56
The Planning Advice Note PAN 56 ‘Planning and Noise', 1999 demonstrates
the role of the planning system in preventing and limiting the adverse effects
of noise.
5.0.10 Certification
Scottish Ministers can, under Section 7 of the Building (Scotland) Act 2003,
approve schemes for the certification of design or
construction
for compliance
with the mandatory functional standards. Such schemes are approved on the
basis that the procedures adopted by the scheme will take account of the
need to co-ordinate the
work
of various designers and specialist contractors.
Individuals approved to provide certification services under the scheme are
assessed to ensure that they have the qualifications, skills and experience
required to certify compliance for the
work
covered by the scope of the
scheme. Checking procedures adopted by Approved Certifiers will deliver
design or installation reliability in accordance with legislation.
5.0.7
—
5.0.10


|
SideCar
|
Common base class for all SideCar GUI tool windows. More...
#include </Users/howes/src/sidecar/GUI/ToolWindowBase.h>
Public Slots | |
| virtual void | activeMainWindowChanged (MainWindowBase *window) |
| virtual void | applyInitialVisibility () |
| virtual void | toggleVisibility () |
| virtual void | showAndRaise () |
Public Member Functions | |
| ToolWindowBase (const QString &name, const QString &actionTitle, int shortcut=0) | |
| Constructor. More... | |
| QAction * | getShowHideAction () const |
| Obtain the QAction object that toggles the tool window's visibility. More... | |
| void | setFixedSize () |
| Configure the window so that the user may not resize it. | |
| bool | getInitialVisibility () const |
| Determine if the application should show the window at startup. More... | |
Static Public Member Functions | |
| static Logger::Log & | Log () |
| Log device for instances of this class. More... | |
| static AppBase * | getApp () |
| Convenience method that obtains the AppBase singleton. More... | |
Protected Member Functions | |
| void | showEvent (QShowEvent *event) |
| Event handler for show events. More... | |
| void | closeEvent (QCloseEvent *event) |
| Event handler for close events. More... | |
| void | hideEvent (QHideEvent *event) |
| Event handler for hide events. More... | |
Common base class for all SideCar GUI tool windows.
A tool window is a QDialog window that is not modal. The application saves and restores tool window position, size, and visibility state information.
Definition at line 17 of file ToolWindowBase.h.
| ToolWindowBase::ToolWindowBase | ( | const QString & | name, |
| const QString & | actionTitle, | ||
| int | shortcut = 0 |
||
| ) |
Constructor.
| name | the name of the tool window to use when loading and saving settings |
| actionTitle | text for the QAction object used to control window visibility |
| shortcut | optional key sequence used to toggle window visibility reflects the state of the tool window |
Definition at line 42 of file ToolWindowBase.cc.
|
protected |
Event handler for close events.
Update any managed QAction object.
| event | event descriptor |
Definition at line 104 of file ToolWindowBase.cc.
References getApp(), Log(), and LOGINFO.
Referenced by SideCar::GUI::AScope::ChannelConnectionWindow::activeDisplayViewChanged().
|
static |
Convenience method that obtains the AppBase singleton.
Definition at line 74 of file ToolWindowBase.cc.
References SideCar::GUI::AppBase::GetApp().
Referenced by SideCar::GUI::AScope::ChannelConnectionWindow::activeDisplayViewChanged(), closeEvent(), SideCar::GUI::AScope::ConfigurationWindow::ConfigurationWindow(), hideEvent(), and ToolWindowBase().
| bool ToolWindowBase::getInitialVisibility | ( | ) | const |
Determine if the application should show the window at startup.
Definition at line 140 of file ToolWindowBase.cc.
References Log(), LOGINFO, and SideCar::GUI::UpdateToggleAction().
|
inline |
Obtain the QAction object that toggles the tool window's visibility.
Definition at line 47 of file ToolWindowBase.h.
Referenced by SideCar::GUI::AppBase::addToolWindow(), SideCar::GUI::Playback::MainWindow::MainWindow(), and SideCar::GUI::HealthAndStatus::MainWindow::MainWindow().
|
protected |
Event handler for hide events.
Update any managed QAction object.
| event | event descriptor |
Definition at line 121 of file ToolWindowBase.cc.
|
static |
Log device for instances of this class.
Definition at line 35 of file ToolWindowBase.cc.
References Logger::Log::Find().
Referenced by closeEvent(), getInitialVisibility(), hideEvent(), setFixedSize(), showEvent(), and ToolWindowBase().
|
protected |
Event handler for show events.
Use any saved settings for the window's size and position values. Updates any managed QAction object.
| event | event descriptor |
Definition at line 89 of file ToolWindowBase.cc.
References Log(), and LOGINFO.
Referenced by SideCar::GUI::ManualWindow::ManualWindow(), and SideCar::GUI::PresetsWindow::PresetsWindow().


ICEAUTH
Section: User Commands (1)Updated: iceauth 1.0.4
Index Return to Main Contents
NAME
iceauth - ICE authority file utilitySYNOPSIS
iceauth [ -f authfile ] [ -vqib ] [ command arg ... ]DESCRIPTION
The iceauth program is used to edit and display the authorization information used in connecting with ICE. This program is usually used to extract authorization records from one machine and merge them in on another (as is the case when using remote logins or granting access to other users). Commands (described below) may be entered interactively, on the iceauth command line, or in scripts.
AUTHOR
Ralph Mor, X Consortium
Index
This document was created by man2html, using the manual pages.
Time: 05:29:04 GMT, December 24, 2015


13 MLM, Longitudinal: Hox ch 5 - student GPA
library(tidyverse) # all things tidy
library(haven)
library(pander) # nice looking genderal tabulations
library(furniture) # nice Table1() descriptives
library(texreg) # Convert Regression Output to LaTeX or HTML Tables
library(psych) # contains some useful functions, like headTail
library(sjstats) # ICC calculations
library(sjPlot) # Visualization for Models
library(lme4) # non-linear mixed-effects models
library(lmerTest)
library(effects) # Effect displays for Models
library(effectsize)
library(interactions)
library(performance)
13.1 Background
The text “Multilevel Analysis: Techniques and Applications, Third Edition” (Hox, Moerbeek, and Van de Schoot 2017) has a companion website which includes links to all the data files used throughout the book (housed on the book’s GitHub repository).
The following example is used through out Hox, Moerbeek, and Van de Schoot (2017)’s chapater 5.
The GPA for 200 college students were followed for 6 consecutive semesters (simulated). Job status was also measured as number of hours worked for the same size occations. Time-invariant covariates are the student’s gender and high school GPA. The variable admitted
will not be used.
<- haven::read_sav("https://github.com/MultiLevelAnalysis/Datasets-third-edition-Multilevel-book/raw/master/chapter%205/GPA2/gpa2long.sav") %>%
data_raw ::as_factor() # retain the labels from SPSS --> factor
haven
::glimpse(data_raw) tibble
Rows: 1,200
Columns: 7
$ student <dbl> 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4…
$ occas <fct> year 1 semester 1, year 1 semester 2, year 2 semester 1, year…
$ gpa <dbl> 2.3, 2.1, 3.0, 3.0, 3.0, 3.3, 2.2, 2.5, 2.6, 2.6, 3.0, 2.8, 2…
$ job <fct> 2 hours, 2 hours, 2 hours, 2 hours, 2 hours, 2 hours, 2 hours…
$ sex <fct> female, female, female, female, female, female, male, male, m…
$ highgpa <dbl> 2.8, 2.8, 2.8, 2.8, 2.8, 2.8, 2.5, 2.5, 2.5, 2.5, 2.5, 2.5, 2…
$ admitted <fct> yes, yes, yes, yes, yes, yes, no, no, no, no, no, no, yes, ye…
%>%
data_raw ::select(occas, job) %>%
dplyrtable(useNA = "always")
job
occas no job 1 hour 2 hours 3 hours 4 or more hours <NA>
year 1 semester 1 0 0 172 28 0 0
year 1 semester 2 0 0 169 31 0 0
year 2 semester 1 0 7 159 34 0 0
year 2 semester 2 0 5 169 26 0 0
year 3 semester 1 0 18 150 32 0 0
year 3 semester 2 0 22 148 30 0 0
<NA> 0 0 0 0 0 0
<- data_raw %>%
data_long ::mutate(student = factor(student)) %>%
dplyr::mutate(sem = case_when(occas == "year 1 semester 1" ~ 1,
dplyr== "year 1 semester 2" ~ 2,
occas == "year 2 semester 1" ~ 3,
occas == "year 2 semester 2" ~ 4,
occas == "year 3 semester 1" ~ 5,
occas == "year 3 semester 2" ~ 6)) %>%
occas ::mutate(job = fct_drop(job)) %>%
dplyr::mutate(hrs = case_when(job == "no job" ~ 0,
dplyr== "1 hour" ~ 1,
job == "2 hours" ~ 2,
job == "3 hours" ~ 3,
job == "4 or more hours" ~ 4)) %>%
job ::select(student, sex, highgpa, sem, job, hrs, gpa) %>%
dplyr::arrange(student, sem)
dplyr
::headTail(data_long, top = 10) psych
student sex highgpa sem job hrs gpa
1 1 female 2.8 1 2 hours 2 2.3
2 1 female 2.8 2 2 hours 2 2.1
3 1 female 2.8 3 2 hours 2 3
4 1 female 2.8 4 2 hours 2 3
5 1 female 2.8 5 2 hours 2 3
6 1 female 2.8 6 2 hours 2 3.3
7 2 male 2.5 1 2 hours 2 2.2
8 2 male 2.5 2 3 hours 3 2.5
9 2 male 2.5 3 2 hours 2 2.6
10 2 male 2.5 4 2 hours 2 2.6
11 <NA> <NA> ... ... <NA> ... ...
12 200 male 3.4 3 2 hours 2 3.4
13 200 male 3.4 4 2 hours 2 3.5
14 200 male 3.4 5 1 hour 1 3.3
15 200 male 3.4 6 1 hour 1 3.4
<- data_long %>%
data_wide ::pivot_wider(names_from = sem,
tidyrvalues_from = c(job, hrs, gpa),
names_sep = "_")
::headTail(data_wide) psych
student sex highgpa job_1 job_2 job_3 job_4 job_5 job_6 hrs_1
1 1 female 2.8 2 hours 2 hours 2 hours 2 hours 2 hours 2 hours 2
2 2 male 2.5 2 hours 3 hours 2 hours 2 hours 2 hours 2 hours 2
3 3 female 2.5 2 hours 2 hours 2 hours 3 hours 2 hours 2 hours 2
4 4 male 3.8 3 hours 2 hours 2 hours 2 hours 2 hours 2 hours 3
5 <NA> <NA> ... <NA> <NA> <NA> <NA> <NA> <NA> ...
6 197 female 2.1 2 hours 2 hours 3 hours 2 hours 2 hours 2 hours 2
7 198 male 4 2 hours 2 hours 2 hours 2 hours 1 hour 2 hours 2
8 199 female 2.3 2 hours 2 hours 3 hours 2 hours 2 hours 3 hours 2
9 200 male 3.4 2 hours 2 hours 2 hours 2 hours 1 hour 1 hour 2
hrs_2 hrs_3 hrs_4 hrs_5 hrs_6 gpa_1 gpa_2 gpa_3 gpa_4 gpa_5 gpa_6
1 2 2 2 2 2 2.3 2.1 3 3 3 3.3
2 3 2 2 2 2 2.2 2.5 2.6 2.6 3 2.8
3 2 2 3 2 2 2.4 2.9 3 2.8 3.3 3.4
4 2 2 2 2 2 2.5 2.7 2.4 2.7 2.9 2.7
5 ... ... ... ... ... ... ... ... ... ... ...
6 2 3 2 2 2 2.9 2.5 2.8 3.2 3.3 3.8
7 2 2 2 1 2 2.5 2.9 3 3.2 3.3 3.4
8 2 3 2 2 3 2.6 2.3 2.7 2.7 2.8 2.8
9 2 2 2 1 1 2.8 3.2 3.4 3.5 3.3 3.4
%>%
data_wide ::group_by(sex) %>%
dplyr::table1("High School GPA" = highgpa,
furniture"Initial College GPA" = gpa_1,
"Initial Job" = job_1,
"Initial Hrs" = hrs_1,
output = "markdown",
digits = 3,
total = TRUE,
test = TRUE)
| Total | male | female | P-Value | |
|---|---|---|---|---|
| n = 200 | n = 95 | n = 105 | ||
| High School GPA | 0.31 | |||
| 2.987 (0.596) | 3.033 (0.592) | 2.947 (0.600) | ||
| Initial College GPA | 0.094 | |||
| 2.594 (0.312) | 2.555 (0.314) | 2.629 (0.307) | ||
| Initial Job | 0.192 | |||
| 1 hour | 0 (0%) | 0 (0%) | 0 (0%) | |
| 2 hours | 172 (86%) | 78 (82.1%) | 94 (89.5%) | |
| 3 hours | 28 (14%) | 17 (17.9%) | 11 (10.5%) | |
| Initial Hrs | 0.132 | |||
| 2.140 (0.348) | 2.179 (0.385) | 2.105 (0.308) |
%>%
data_wide ::group_by(sex) %>%
dplyr::table1(gpa_1, gpa_2, gpa_3, gpa_4, gpa_5, gpa_6,
furnitureoutput = "markdown",
digits = 3,
total = TRUE,
test = TRUE,
caption = "Hox Table 5.2 (page 77) GPA means at six occations, for male and female students",
caption.above = TRUE)
| Total | male | female | |
|---|---|---|---|
| n = 200 | n = 95 | n = 105 | |
| gpa_1 | |||
| 2.594 (0.312) | 2.555 (0.314) | 2.629 (0.307) | |
| gpa_2 | |||
| 2.716 (0.336) | 2.666 (0.318) | 2.760 (0.348) | |
| gpa_3 | |||
| 2.810 (0.354) | 2.742 (0.363) | 2.871 (0.335) | |
| gpa_4 | |||
| 2.918 (0.355) | 2.818 (0.351) | 3.009 (0.335) | |
| gpa_5 | |||
| 3.019 (0.358) | 2.915 (0.359) | 3.113 (0.332) | |
| gpa_6 | |||
| 3.134 (0.377) | 3.028 (0.375) | 3.230 (0.354) |
13.2 MLM
13.2.1 Null Models and ICC
<- lmerTest::lmer(gpa ~ 1 + (1|student),
fit_lmer_0_re data = data_long,
REML = TRUE)
::icc(fit_lmer_0_re) performance
# Intraclass Correlation Coefficient
Adjusted ICC: 0.369
Unadjusted ICC: 0.369
Over a third of the variance in the 6 GPA measures is variance between individuals, and about two-thirds is variance within individuals across time, \(\rho = .369\).
<- lmerTest::lmer(gpa ~ I(sem - 1) + (1|student),
fit_lmer_1_re data = data_long,
REML = TRUE)
::icc(fit_lmer_1_re) performance
# Intraclass Correlation Coefficient
Adjusted ICC: 0.523
Unadjusted ICC: 0.412
After accounting for the linear change in GPA over semesters, about half of the remaining variance in GPA scores is attribuTable person-to-person difference.
Another way to say it, is about half of the variance in initial GPAs is due to student-to-student differences.
13.2.2 Fixed Effects
<- lmerTest::lmer(gpa ~ 1 + (1|student),
fit_lmer_0_ml data = data_long,
REML = FALSE)
<- lmerTest::lmer(gpa ~ I(sem - 1) + (1|student),
fit_lmer_1_ml data = data_long,
REML = FALSE)
<- lmerTest::lmer(gpa ~ I(sem - 1) + hrs + (1|student),
fit_lmer_2_ml data = data_long,
REML = FALSE)
<- lmerTest::lmer(gpa ~ I(sem - 1) + hrs + highgpa + sex + (1|student),
fit_lmer_3_ml data = data_long,
REML = FALSE)
::knitreg(list(fit_lmer_0_ml, fit_lmer_1_ml, fit_lmer_2_ml, fit_lmer_3_ml)) texreg
| Model 1 | Model 2 | Model 3 | Model 4 | |
|---|---|---|---|---|
| (Intercept) | 2.87*** | 2.60*** | 2.97*** | 2.64*** |
| (0.02) | (0.02) | (0.04) | (0.10) | |
| sem - 1 | 0.11*** | 0.10*** | 0.10*** | |
| (0.00) | (0.00) | (0.00) | ||
| hrs | -0.17*** | -0.17*** | ||
| (0.02) | (0.02) | |||
| highgpa | 0.08** | |||
| (0.03) | ||||
| sexfemale | 0.15*** | |||
| (0.03) | ||||
| AIC | 919.46 | 401.65 | 318.40 | 296.76 |
| BIC | 934.73 | 422.01 | 343.85 | 332.39 |
| Log Likelihood | -456.73 | -196.82 | -154.20 | -141.38 |
| Num. obs. | 1200 | 1200 | 1200 | 1200 |
| Num. groups: student | 200 | 200 | 200 | 200 |
| Var: student (Intercept) | 0.06 | 0.06 | 0.05 | 0.04 |
| Var: Residual | 0.10 | 0.06 | 0.06 | 0.06 |
| ***p < 0.001; **p < 0.01; *p < 0.05 |
::knitreg(list(fit_lmer_0_ml, fit_lmer_1_ml, fit_lmer_2_ml, fit_lmer_3_ml),
texregcustom.model.names = c("M1: Null",
"M2: Occ",
"M3: Job",
"M4: GPA, Sex"),
custom.coef.map = list("(Intercept)" = "(Intercept)",
"I(sem - 1)" = "Semester",
"hrs" = "Hours Working",
"highgpa" = "High School GPA",
"sexfemale" = "Female vs. Male"),
groups = list("Level 1 Main Effects, Occasion-Specific" = 2:3,
"Level 2 Main Effects, Person-Specific" = 4:5),
custom.note = "%stars. \nNote: Intercept refers to population mean for a Male who is not working during their first semester",
caption = "Hox Table 5.3 (page 78) Results of Multilevel Anlaysis of GPA, Fixed Effects",
caption.above = TRUE,
digits = 3)
| M1: Null | M2: Occ | M3: Job | M4: GPA, Sex | |
|---|---|---|---|---|
| (Intercept) | 2.865*** | 2.599*** | 2.970*** | 2.641*** |
| (0.019) | (0.022) | (0.044) | (0.098) | |
| Level 1 Main Effects, Occasion-Specific | ||||
| Semester | 0.106*** | 0.102*** | 0.102*** | |
| (0.004) | (0.004) | (0.004) | ||
| Hours Working | -0.171*** | -0.172*** | ||
| (0.018) | (0.018) | |||
| Level 2 Main Effects, Person-Specific | ||||
| High School GPA | 0.085** | |||
| (0.028) | ||||
| Female vs. Male | 0.147*** | |||
| (0.033) | ||||
| AIC | 919.456 | 401.649 | 318.399 | 296.760 |
| BIC | 934.726 | 422.009 | 343.849 | 332.390 |
| Log Likelihood | -456.728 | -196.825 | -154.200 | -141.380 |
| Num. obs. | 1200 | 1200 | 1200 | 1200 |
| Num. groups: student | 200 | 200 | 200 | 200 |
| Var: student (Intercept) | 0.057 | 0.063 | 0.052 | 0.045 |
| Var: Residual | 0.098 | 0.058 | 0.055 | 0.055 |
| ***p < 0.001; **p < 0.01; *p < 0.05. Note: Intercept refers to population mean for a Male who is not working during their first semester |
anova(fit_lmer_0_ml, fit_lmer_1_ml, fit_lmer_2_ml, fit_lmer_3_ml)
Data: data_long
Models:
fit_lmer_0_ml: gpa ~ 1 + (1 | student)
fit_lmer_1_ml: gpa ~ I(sem - 1) + (1 | student)
fit_lmer_2_ml: gpa ~ I(sem - 1) + hrs + (1 | student)
fit_lmer_3_ml: gpa ~ I(sem - 1) + hrs + highgpa + sex + (1 | student)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
fit_lmer_0_ml 3 919.46 934.73 -456.73 913.46
fit_lmer_1_ml 4 401.65 422.01 -196.82 393.65 519.807 1 < 2.2e-16 ***
fit_lmer_2_ml 5 318.40 343.85 -154.20 308.40 85.250 1 < 2.2e-16 ***
fit_lmer_3_ml 7 296.76 332.39 -141.38 282.76 25.639 2 2.707e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
13.2.3 Variance Explained by linear TIME at Level ONE
::VarCorr(fit_lmer_0_ml) # baseline lme4
Groups Name Std.Dev.
student (Intercept) 0.23826
Residual 0.31239
::VarCorr(fit_lmer_1_ml) # model to compare lme4
Groups Name Std.Dev.
student (Intercept) 0.25172
Residual 0.24090
::VarCorr(fit_lmer_0_ml) %>% # baseline
lme4print(comp = c("Variance", "Std.Dev"),
digits = 3)
Groups Name Variance Std.Dev.
student (Intercept) 0.0568 0.238
Residual 0.0976 0.312
::VarCorr(fit_lmer_1_ml) %>% # model to compare
lme4print(comp = c("Variance", "Std.Dev"),
digits = 3)
Groups Name Variance Std.Dev.
student (Intercept) 0.0634 0.252
Residual 0.0580 0.241
<IP_ADDRESS> Raudenbush and Bryk
- Explained variance is a proportion of first-level variance only
- A good option when the multilevel sampling process is is close to two-stage simple random sampling
Raudenbush and Bryk Approximate Formula - Level 1 approximate \[ approx \;R^2_1 = \frac{\sigma^2_{e-BL} - \sigma^2_{e-MC}} {\sigma^2_{e-BL} } \tag{Hox 4.8} \]
0.098 - 0.058) / 0.098 (
[1] 0.4081633
13.2.4 Variance Explained by linear TIME at Level TWO
<IP_ADDRESS> Raudenbush and Bryk
Raudenbush and Bryk Approximate Formula - Level 2 \[ approx \; R^2_s = \frac{\sigma^2_{u0-BL} - \sigma^2_{u0-MC}} {\sigma^2_{u0-BL} } \tag{Hox 4.9} \]
0.057 - 0.063)/ 0.057 (
[1] -0.1052632
YIKES! Negative Variance explained!
<IP_ADDRESS> Snijders and Bosker
Snijders and Bosker Formula Extended - Level 2 \[ R^2_2 = 1 - \frac{\frac{\sigma^2_{e-MC}}{B} + \sigma^2_{u0-MC}} {\frac{\sigma^2_{e-BL}}{B} + \sigma^2_{u0-BL}} \]
\(B\) is the average size of the Level 2 units. Technically, you should use the harmonic mean, but unless the clusters differ greatly in size, it doesn’t make a huge difference.
1 - (0.058/6 + 0.063) / (0.098/6 + 0.057)
[1] 0.009090909
Reason: The intercept only model overestimates the variance at the occasion level and underestimates the variance at the subject level (see chapter 4 of Hox, Moerbeek, and Van de Schoot (2017))
13.2.5 Random Effects
<- lmerTest::lmer(gpa ~ I(sem-1) + hrs + highgpa + sex +
fit_lmer_3_re 1|student),
(data = data_long,
REML = TRUE)
<- lmerTest::lmer(gpa ~ I(sem-1) + hrs + highgpa + sex +
fit_lmer_4_re I(sem-1)|student),
(data = data_long,
REML = TRUE,
control = lmerControl(optimizer ="Nelder_Mead"))
anova(fit_lmer_3_re, fit_lmer_4_re, refit = FALSE)
Data: data_long
Models:
fit_lmer_3_re: gpa ~ I(sem - 1) + hrs + highgpa + sex + (1 | student)
fit_lmer_4_re: gpa ~ I(sem - 1) + hrs + highgpa + sex + (I(sem - 1) | student)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
fit_lmer_3_re 7 328.84 364.47 -157.42 314.84
fit_lmer_4_re 9 219.93 265.75 -100.97 201.94 112.9 2 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
13.2.6 Cross-Level Interaction
<- lmerTest::lmer(gpa ~ I(sem-1) + hrs + highgpa + sex +
fit_lmer_4_ml I(sem-1)|student),
(data = data_long,
REML = FALSE)
<- lmerTest::lmer(gpa ~ I(sem-1) + hrs + highgpa + sex +
fit_lmer_4_re I(sem-1)|student),
(data = data_long,
REML = TRUE,
control = lmerControl(optimizer ="Nelder_Mead"))
<- lmerTest::lmer(gpa ~ I(sem-1) + hrs + highgpa + sex + I(sem-1):sex +
fit_lmer_5_ml I(sem-1)|student),
(data = data_long,
REML = FALSE)
<- lmerTest::lmer(gpa ~ I(sem-1) + hrs + highgpa + sex + I(sem-1):sex +
fit_lmer_5_re I(sem-1)|student),
(data = data_long,
REML = FALSE)
::knitreg(list(fit_lmer_4_re, fit_lmer_5_re),
texregsingle.row = TRUE)
| Model 1 | Model 2 | |
|---|---|---|
| (Intercept) | 2.56 (0.09)*** | 2.58 (0.09)*** |
| sem - 1 | 0.10 (0.01)*** | 0.09 (0.01)*** |
| hrs | -0.13 (0.02)*** | -0.13 (0.02)*** |
| highgpa | 0.09 (0.03)*** | 0.09 (0.03)*** |
| sexfemale | 0.12 (0.03)*** | 0.08 (0.03)* |
| sem - 1:sexfemale | 0.03 (0.01)** | |
| AIC | 219.93 | 182.97 |
| BIC | 265.75 | 233.87 |
| Log Likelihood | -100.97 | -81.49 |
| Num. obs. | 1200 | 1200 |
| Num. groups: student | 200 | 200 |
| Var: student (Intercept) | 0.04 | 0.04 |
| Var: student I(sem - 1) | 0.00 | 0.00 |
| Cov: student (Intercept) I(sem - 1) | -0.00 | -0.00 |
| Var: Residual | 0.04 | 0.04 |
| ***p < 0.001; **p < 0.01; *p < 0.05 |
::knitreg(list(fit_lmer_4_re, fit_lmer_5_re),
texregsingle.row = TRUE,
custom.model.names = c("M5: Occ Rand",
"M6: Xlevel Int"),
custom.coef.map = list("(Intercept)" = "(Intercept)",
"I(sem - 1)" = "Semester",
"hrs" = "Hours Working",
"sexfemale" = "Sex: Female vs. Male",
"highgpa" = "High School GPA",
"I(sem - 1):sexfemale" = "Semester X Sex"),
groups = list("Level 1 Main Effects, Occasion-Specific" = 2:3,
"Level 2 Main Effects, Person-Specific" = 4:5,
"Cross Level Interaction" = 6),
custom.note = "%stars. \nNote: Intercept refers to population mean for a Male who is not working during their first semester",
caption = "Hox Table 5.4 (page 80) Results of Multilevel Anlaysis of GPA, Varying Effects for Occation",
caption.above = TRUE,
digits = 3)
| M5: Occ Rand | M6: Xlevel Int | |
|---|---|---|
| (Intercept) | 2.557 (0.093)*** | 2.581 (0.092)*** |
| Level 1 Main Effects, Occasion-Specific | ||
| Semester | 0.103 (0.006)*** | 0.088 (0.008)*** |
| Hours Working | -0.131 (0.017)*** | -0.132 (0.017)*** |
| Level 2 Main Effects, Person-Specific | ||
| Sex: Female vs. Male | 0.116 (0.032)*** | 0.076 (0.035)* |
| High School GPA | 0.089 (0.026)*** | 0.089 (0.026)*** |
| Cross Level Interaction | ||
| Semester X Sex | 0.030 (0.011)** | |
| AIC | 219.935 | 182.971 |
| BIC | 265.746 | 233.872 |
| Log Likelihood | -100.967 | -81.486 |
| Num. obs. | 1200 | 1200 |
| Num. groups: student | 200 | 200 |
| Var: student (Intercept) | 0.039 | 0.038 |
| Var: student I(sem - 1) | 0.004 | 0.004 |
| Cov: student (Intercept) I(sem - 1) | -0.003 | -0.002 |
| Var: Residual | 0.042 | 0.042 |
| ***p < 0.001; **p < 0.01; *p < 0.05. Note: Intercept refers to population mean for a Male who is not working during their first semester |
anova(fit_lmer_4_ml, fit_lmer_5_ml)
Data: data_long
Models:
fit_lmer_4_ml: gpa ~ I(sem - 1) + hrs + highgpa + sex + (I(sem - 1) | student)
fit_lmer_5_ml: gpa ~ I(sem - 1) + hrs + highgpa + sex + I(sem - 1):sex + (I(sem - 1) | student)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
fit_lmer_4_ml 9 188.12 233.93 -85.059 170.12
fit_lmer_5_ml 10 182.97 233.87 -81.486 162.97 7.1464 1 0.007511 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
13.2.7 Visualize the Model
::interact_plot(model = fit_lmer_5_re,
interactionspred = sem,
modx = sex,
interval = TRUE)
::interact_plot(model = fit_lmer_5_re,
interactionspred = sem,
modx = sex,
mod2 = hrs,
mod2.values = c(1, 2, 3),
interval = TRUE)
::interact_plot(model = fit_lmer_5_re,
interactionspred = sem,
modx = sex,
mod2 = highgpa,
mod2.values = c(2, 3, 4),
interval = TRUE)
13.2.8 Effect Sizes
<IP_ADDRESS> Standardized Parameters
::standardize_parameters(fit_lmer_5_re) effectsize
# Standardization method: refit
Parameter | Std. Coef. | 95% CI
--------------------------------------------------
(Intercept) | 0.18 | [ 0.02, 0.34]
I(sem - 1) | 0.38 | [ 0.31, 0.45]
hrs | -0.14 | [-0.18, -0.11]
highgpa | 0.13 | [ 0.06, 0.21]
sexfemale | 0.51 | [ 0.29, 0.73]
I(sem - 1):sexfemale | 0.13 | [ 0.04, 0.22]
13.2.9 Significance
<IP_ADDRESS> Fixed Effects
The Likelyhood Ratio Test (Deviance Difference Test) is best for establishing significance of fixed effects.
Wald-tests
summary(fit_lmer_5_re)
Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
method [lmerModLmerTest]
Formula: gpa ~ I(sem - 1) + hrs + highgpa + sex + I(sem - 1):sex + (I(sem -
1) | student)
Data: data_long
AIC BIC logLik deviance df.resid
183.0 233.9 -81.5 163.0 1190
Scaled residuals:
Min 1Q Median 3Q Max
-3.0044 -0.5268 -0.0138 0.5290 3.3513
Random effects:
Groups Name Variance Std.Dev. Corr
student (Intercept) 0.037811 0.19445
I(sem - 1) 0.003614 0.06012 -0.19
Residual 0.041555 0.20385
Number of obs: 1200, groups: student, 200
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 2.581e+00 9.239e-02 2.965e+02 27.938 < 2e-16 ***
I(sem - 1) 8.783e-02 7.951e-03 1.987e+02 11.046 < 2e-16 ***
hrs -1.321e-01 1.723e-02 1.042e+03 -7.670 3.93e-14 ***
highgpa 8.850e-02 2.627e-02 1.976e+02 3.369 0.000907 ***
sexfemale 7.551e-02 3.465e-02 2.003e+02 2.179 0.030499 *
I(sem - 1):sexfemale 2.956e-02 1.096e-02 1.977e+02 2.698 0.007581 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) I(s-1) hrs highgp sexfml
I(sem - 1) -0.140
hrs -0.428 0.054
highgpa -0.871 0.001 0.022
sexfemale -0.265 0.312 0.030 0.066
I(sm-1):sxf 0.088 -0.724 -0.008 0.000 -0.430
F-test with Satterthwaite adjusted degrees of freedom
anova(fit_lmer_5_re)
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
I(sem - 1) 14.5023 14.5023 1 199.46 348.9910 < 2.2e-16 ***
hrs 2.4447 2.4447 1 1041.66 58.8305 3.933e-14 ***
highgpa 0.4716 0.4716 1 197.63 11.3493 0.0009074 ***
sex 0.1973 0.1973 1 200.28 4.7481 0.0304985 *
I(sem - 1):sex 0.3025 0.3025 1 197.72 7.2787 0.0075806 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
<IP_ADDRESS> Random Effects
Likelyhood Ratio Tests (Deviance Difference Test), by single term deletion
::ranova(fit_lmer_5_re) lmerTest
ANOVA-like table for random-effects: Single term deletions
Model:
gpa ~ I(sem - 1) + hrs + highgpa + sex + (I(sem - 1) | student) + I(sem - 1):sex
npar logLik AIC LRT Df Pr(>Chisq)
<none> 10 -81.486 182.97
I(sem - 1) in (I(sem - 1) | student) 8 -134.321 284.64 105.67 2 < 2.2e-16
<none>
I(sem - 1) in (I(sem - 1) | student) ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1


Class ContainerOption<TOption>
A suggested base class to use for custom container options to be read/written through the OptionsTargetContainerExtensions extension methods.
The type of the option value is the argument to the TOption
type parameter.
Options must currently be objects - the ability to use callbacks to get options might be added at a future date.
Inheritance
Inherited Members
Namespace: Rezolver.Options
Assembly: Rezolver.dll
Syntax
public class ContainerOption<TOption>
Type Parameters
| Name | Description |
|---|---|
| TOption | The underlying option value type - e.g. Boolean, String, Uri or whatever |
Remarks
This type is creatable only through inheritance.
Options in Rezolver are achieved by using registrations in the ITargetContainer that is to be configured (and, in turn, which might then configure any IContainers built from that target container).
Since options often take the form of primitive types - e.g Boolean, String etc - this means it's impossible to register multiple options which control different things which have the same underlying type if we registered them directly.
This class is offered as a way around this. Options (e.g. AllowMultiple) are derived from this type,
with the argument to the TOption
type parameter set to the underlying type of the option value.
Thus - each distinct option is a different type, which then means that an ITargetContainer can distinguish between them.
Note:
Whilst Rezolver uses this type for most of its configurable options, the options API implemented by the extension methods (e.g. GetOption<TOption>(ITargetContainer, TOption) or SetOption<TOption>(ITargetContainer, TOption)) do not require you to use it.
Constructors
| Improve this Doc View SourceContainerOption()
Inheritance constructor.
Declaration
protected ContainerOption()
Properties
| Improve this Doc View SourceValue
The underlying value wrapped by this option.
Declaration
public TOption Value { get; protected set; }
Property Value
| Type | Description |
|---|---|
| TOption |
Methods
| Improve this Doc View SourceToString()
Provides a textual representation of the value of this option and its underlying type.
Declaration
public override string ToString()
Returns
| Type | Description |
|---|---|
| String | A string in the form
|
Overrides
Operators
| Improve this Doc View SourceImplicit(ContainerOption<TOption> to TOption)
Implicit casting operator to convert to the option value type from an instance of ContainerOption<TOption>.
All derived types are encouraged to have a similar casting operator from TOption
to ContainerOption<TOption>
(for example, see EnableEnumerableInjection).
Declaration
public static implicit operator TOption(ContainerOption<TOption> option)
Parameters
| Type | Name | Description |
|---|---|---|
| ContainerOption<TOption> | option | The option object to be cast to Note that if this is |
Returns
| Type | Description |
|---|---|
| TOption |


String helper
The string helper contains a collection of string manipulation methods.
Usage
The random
method returns a random string of the selected type and length. The available constants are 'ALNUM', 'ALPHA', 'HEXDEC', 'NUMERIC' and 'SYMBOLS'. You can also combine pools or pass your own pool of characters.
Generated strings containing characters from the 'SYMBOLS' pool should be escaped if used in HTLM and/or XML documents.
$str = Str::random(Str::ALNUM);
$str = Str::random(Str::ALPHA);
$str = Str::random(Str::NUMERIC);
$str = Str::random(Str::ALNUM . Str::SYMBOLS);
$str = Str::random(Str::ALNUM . 'æøåÆØÅ', 64);
The nl2br
method converts newlines to

.
$str = Str::nl2br($_POST['input']);
The nl2br method will return HTML5 tags by default but you can make it return XHTML compatible tags by setting the optional second parameter to TRUE.
The br2nl method converts

and

to newlines.
$str = Str::br2nl($input);
The limitChars
method will limit the number of characters in a string.
$str = Str::limitChars($input, 300);
The limitWords
method will limit the number of words in a string.
$str = Str::limitWords($input, 300);
The increment
method append an incremental numeric sufix to a string.
$str = Str::increment('banana'); // Will return "banana_1"
$str = Str::increment('banana_1'); // Will return "banana_2"
The alternator
method will return a closure that alternates between the chosen strings.
$alt = Str::alternator(['foo', 'bar']);
echo $alt(); // foo
echo $alt(); // bar
echo $alt(); // foo
The ascii
method returns string where all non-ASCII characters have been stripped.
// $str will be set to "Hello, world! It Works!"
$str = Str::ascii('Hello, world! ÆØÅ It Works!');
The slug
method returns a URL friendly string.
$str = Str::slug('Hello World!'); // $str will be set to "hello-world"
The autolink
method returns a string where all URLs are converted into clickable links.
$str = Str::autolink($text);
The mask
method will return a masked string where n characters are visible while the rest will be masked.
$str = Str::mask('password'); // $str will be set to "*****ord"
The pluralzie
method will return the pluralized form of a singular word.
$str = Str::pluralize('apple');
The pluralize method only works with english words. If you need language independent pluralization then have a look at the I18n docs.
The camel2underscored
method will convert camel case to underscored.
$str = Str::camel2underscored('HelloWorld'); // $str will be set to "hello_world"
The underscored2camel
method will convert underscored to camel case.
$str = Str::underscored2camel('hello_world'); // $str will be set to "helloWorld"
$str = Str::underscored2camel('hello_world', true); // $str will be set to "HelloWorld"


Reports unnecessarily escaped characters in
String
and optionally
char
literals. For example
\'
in a
String
literal or
\n
in a Java 13 Preview text block. The escaped tab character
\t
is not reported.
New in 2019.3


Schema Definition for Message Beans
As depicted in Figure 27, the communication across Communication Gateways happens by the exchange of XML documents, transported by XMPP. These XML documents must be compliant to a Schema that is shared across the communicating nodes, and that represents the service interfaces that are exposed for remote invocation. In the following sections we will describe how a developer is supposed to create such a schema so that XML message beans might be reliably created on a variety of programming languages.
We have created 3 example bundles, a Calculator service, a Fortune Cookie service and a Complex Service which are running as bundles in the container (all are available in the GIT Code Repository). The creation of the raw XML that is passed between client/server/client is abstracted within the SOCIETIES framework. You cannot call the interface of your Service directly. Instead, each developer must create an XML schema to which message beans being passed between Client and Server comply to. Each message bean stores as properties:
- the method to be called
- relevant parameters for that method
You will create one XSD schema file per service interface. The XSD file contains your XMPP namespace, methods, parameters and parameter types. We have tried to simplify the process by breaking it into 3 steps. If you are already comfortable writing the XML for your Message Bean schema directly, you can ommmit this step.


Seeing as how I'm taking it easy while quitting opiates, I figured I'd write a little about my experience with kratom.
I started taking kratom about a year ago, after having some major surgery. I was given oxycodone for a little while after the surgery, and suddenly cut off. Still in pain, I asked around for help, and a friend told me about this plant he had recently heard of, called kratom. After obtaining a small 30 gram bag of kratom and after careful research, I decided to start out with a half a teaspoon of the enhanced bali. An online forum dedicated to this plant stated that one (level) teaspoon of the powder from this vendor was equivalent to 4 grams of material, and I went based on recommendations on that forum as well as information from other sites.
Within 30 minutes of taking kratom on an empty stomach, my pain was gone, and I felt great. I started taking kratom daily for a few months and slowly upped my dose until I was taking about 1 tsp at a time, once a day. I definitely built a quick tolerance this way, but no matter what happened to my tolerance to many of the effects, I always seemed to get a mood boost from the stuff. I eventually ended up quitting cold turkey after 5 months of daily use and. . .no withdrawals! Only some lethargy/laziness for a few days.
Fast forward several months, I have some intense cravings for oxycodone (I only used it for pain originally, and I used less than would get rid of a lot of my pain, yet I would still get high and extremely euphoric when I took it, despite what the doctors said), so I decide to pick up a small amount for a few days of fun. Well, a few days turned into a few weeks. . .a few months, and I was hooked. It is now 8 months later after some horrible issues with addiction, planning my life around my next dose, etc, and I want out. Not only do I feel awful when I dose (no more euphoria, only dysphoria and restlessness), I feel worse when I don't! And at this point I'm up to 30 mg every 3 hours!!
One day after a sudden realization and some serious depression, soul searching, crying. . .I decide I'm going to quit. So I start dosing every 8 hours instead of every 3, and after a lot of pain and about a week, I'm down to 10 mg every 8 hours (30 mg a day). It is at this point I switch completely to kratom. Let me tell you, kratom is a miracle plant for blocking withdrawals.
Fast forward to today, six days since my last dose of oxycodone. I'm taking 1/4-1/2 tsp (depending on my needs) every 8-10 hours, hoping to make it 1/4 every 12 very soon. Around day 4-5 I was having some bad body aches associated with the lack of oxy, but I'm doing much better now. I think my body is mostly stabilized on the kratom, and once I quit, that will be it!
This plant has given me my life back, given me my freedom. I used to have to plan everything around oxy: when my next dose would be, dividing pills into days. I'd wake up and have to dose immediately, as I'd be sick with withdrawals every morning. I could literally do nothing in between doses (after the high wore off an hour after dosing). Unlike with the oxy where I'd constantly want a bigger or sooner dose, I have no temptation to up my kratom dose, despite increased euphoria it might bring. I have no temptation to take a dose sooner than I should, even if I'm in pain/slight withdrawals in the meanwhile. With my 8 hour oxy doses, I'd constantly cheat and do six hour doses here and there. With my 8 hour kratom doses, I'm constantly pushing it to 10 hours here and there.
I have absolutely no cravings for oxy, nor any temptation to take it right now. Even if I had a nice, big 20 mg line in front of me, I wouldn't have any use for it. Who needs ibogaine when we have kratom?
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Tests that adding a new rule creates inspector stylesheet resource and allows its live editing.
Text


Spectra downloading, please wait...
Shift-click either end of a single line crossing both peaks you'd like to calibrate on.
Tip: aim low and wide!
Enter the each peak's position along the line counting from the left, and its energy.
Review the results in the table below, and refit any pathological peaks one at a time.


--- layout: default title: Eduardo Castillejo, PhD. ---
Hi! My name is Eduardo Castillejo, I'm a Big Data consultant at the LABnALYTICS Big Data Solutions group at Entelgy Ibai. Within this group we aim to provide best Big Data practices, solutions and advices to those companies which work with large amount of data.
Eduardo Castillejo (eduardo.castillejo [at] entelgy-ibai.com)
Entelgy Ibai.
C/ Nervión, 3, 48001 - Bilbao, Spain.
Tel: +34 944 23 11 04
Still interested? Check the following links:


access token from OAuth2 authorization
hostname or base URL
DELETE request to mastodon REST API
relative path from ${baseUrl}/api/v1/ or absolute path
GET request to mastodon REST API
relative path from ${baseUrl}/api/v1/ or absolute path
Query parameters
PATCH request to mastodon REST API
relative path from ${baseUrl}/api/v1/ or absolute path
Form data
POST request to mastodon REST API
relative path from ${baseUrl}/api/v1/ or absolute path
Form data
receive Server-sent Events from Mastodon Streaming API
relative path from ${baseUrl}/api/v1/streaming/ or absolute path 'public', 'public/local', 'user' and 'hashtag?tag=${tag}' are available.
interval of reconnect
streamListener, which inherits from EventEmitter and has event, 'update', 'notification', 'delete', and so on.
Create an application
First, POST /api/v1/apps
your application's name
Form Data
target of base URL
Fetch OAuth access token
will be generated by #createApp or #registerApp
will be generated by #createApp or #registerApp
will be generated by the link of #generateAuthUrl or #registerApp
must be the same uri as the time when you register your OAuth application
base URL of the target
generate authorization url
your OAuth app's client ID
as property, redirect_uri and scope are available, and must be the same as when you register your app
base URL of the target
unauthorized GET request to mastodon REST API
relative path from ${baseUrl}/api/v1/ or absolute path
Query parameters
base URL of the target
Wrapper for personal OAuth Application (createApp and generateAuthUrl)
First, POST /api/v1/apps only client_name is required, so others are optional. Secound, generate an authorization url. finally, return promise of OAuth.AppData instance, which has client_id, client_secret, url, and so on.
Form Data, which is sent to /api/v1/apps
Form Data, which is sent to /api/v1/apps. and properties should be snake_case
base URL of the target
Generated using TypeDoc
for Mastodon API
using superagent for request, you will handle promises


Vibhav
&
Shruti
are getting married
27th and 28th of April
Invitation
Smt. Rekha Rani Gupta
requests the honor and pleasure of your gracious presence and blessings on the auspicious occasion of the ceremonial wedlock of her Grandson
Vibhav
(S/o of Smt. Vinita and Shri Pradeep Kumar Gupta)
with
Shruti
(D/o of Smt. Neeru and Shri Rajeev Agarwal)
Sangeet and Sagai
Aao ek haseen sham mein jhoome nache, dhoom machaaye, humare sang Mehfil - e - sangeet sajaye
Shaadi and Reception
the Bands, the Baajas and the Baaratis
RSVP
We would love for you to come celebrate with us


{% include "header.html" %}
/{{ path }}
/{{ path }}
order
lexicographically
chronologically
..
{% for entry in entries %}
{{ entry.file_name }}
{{ entry.modified }}
{% endfor %}
{% include "footer.html" %}


Campanhas publicitarias
Impressos, Vts e videos
It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English


Id: 198
Name: Lady Katana
Status: Dead
Species: Humanoid
Type: Cyborg
Gender: Female
OriginName: unknown
OriginUrl:
LocationName: Dorian 5
LocationUrl: https://rickandmortyapi.com/api/location/29
Image: https://rickandmortyapi.com/api/character/avatar/198.jpeg
Episode: ['https://rickandmortyapi.com/api/episode/25']
Url: https://rickandmortyapi.com/api/character/198
Created: 2017-12-30T12:43:37.571Z


Home Maintenance Hub
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Coding Geeks Team
Duha Al-Azzm
Mohammad Adnan Al-Azzam
Naji Al-Batayneh
Sukayna Ghnemat
Walaa' Al-Omari


|
JTS Topology Suite version 1.13 | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object com.vividsolutions.jts.operation.distance.GeometryLocation
public class GeometryLocation
Represents the location of a point on a Geometry.
Maintains both the actual point location
(which may not be exact, if the point is not a vertex)
as well as information about the component
and segment index where the point occurs.
Locations inside area Geometrys will not have an associated segment index,
so in this case the segment index will have the sentinel value of
INSIDE_AREA
.
| Field Summary | |
|---|---|
static int |
INSIDE_AREA
A special value of segmentIndex used for locations inside area geometries. |
| Constructor Summary | |
|---|---|
GeometryLocation(Geometry component,
Coordinate pt)
Constructs a GeometryLocation specifying a point inside an area geometry. |
|
GeometryLocation(Geometry component,
int segIndex,
Coordinate pt)
Constructs a GeometryLocation specifying a point on a geometry, as well as the segment that the point is on (or INSIDE_AREA if the point is not on a segment). |
| Method Summary | |
|---|---|
Coordinate |
getCoordinate()
Returns the Coordinate of this location. |
Geometry |
getGeometryComponent()
Returns the geometry component on (or in) which this location occurs. |
int |
getSegmentIndex()
Returns the segment index for this location. |
boolean |
isInsideArea()
Tests whether this location represents a point inside an area geometry. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Field Detail |
|---|
public static final int INSIDE_AREA
| Constructor Detail |
|---|
public GeometryLocation(Geometry component, int segIndex, Coordinate pt)
INSIDE_AREA
if the point is not on a segment).
component
- the component of the geometry containing the pointsegIndex
- the segment index of the location, or INSIDE_AREApt
- the coordinate of the locationpublic GeometryLocation(Geometry component, Coordinate pt)
component
- the component of the geometry containing the pointpt
- the coordinate of the location| Method Detail |
|---|
public Geometry getGeometryComponent()
public int getSegmentIndex()
INSIDE_AREA
;
public Coordinate getCoordinate()
Coordinate
of this location.
public boolean isInsideArea()
|
JTS Topology Suite version 1.13 | ||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | ||||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Royal Mint unveils largest coin ever weighing 5kg
'This is arguably one of the most admired designs amongst coin collectors across the globe,' Royal Mint spokesperson says
by Sabrina BarrThe Royal Mint has launched its largest coin ever, a creation that measures 175mm in diameter, weighs 5kg and has a denomination of £5,000.
The coin has been created as part of The Royal Mint’s new Great Engravers series, which pays homage to artists who have worked on British coinage.
The first artist to be recognised for the series is William Wyon, who was chief engraver at The Royal Mint for 23 years until his death in 1851.
Wyon is well-known for his Una and the Lion design, which is featured on the coin.
The design was first featured on a £5 gold coin in 1839 to commemorate the beginning of Queen Victoria’s reign two years prior.
The tribute to Wyon’s work has been featured on a series of coins ranging in weight from 5kg to 2oz.
The 5kg coin, of which only one was made, has already been sold to an unknown buyer and is the largest coin The Royal Mint has ever produced in its history.
The 2kg coin, of which there are four, has a 150mm diameter and has a denomination of £2,000.
“The Great Engravers series celebrates some of the finest artists that have worked on the British coinage and we are thrilled to start the series with William Wyon’s Una and the Lion design,” said Nicola Howell, director of the consumer division at The Royal Mint.
“This is arguably one of the most admired designs amongst coin collectors across the globe, and remastering it onto five and two-kilo coins allows you to truly appreciate the detail of the design.”
Traditional and modern engraving techniques had to be combined to create the 5kg Una and the Lion coin, The Royal Mint explains.
The original die used almost two centuries ago had to be digitally scanned and lasers were later used to cut the design onto the coin, before it was hand polished.
The Una and the Lion coin is available to purchase from The Royal Mint website in a variety of finishes, with prices starting from £180.


They have a special program called Water Guardians, encouraging young people to take action on the issue of clean water. Check 'em out here!
Here's an excerpt from the Water Guardians home page: "Water Guardians is a new and exciting educational program brought to you by H2O for Life. It teaches students about the global water crisis and how we can create change locally, together. The program culminates in a service-learning component that will help engage students to become global citizens." It is created for 5th-8th grade students, and teachers can use it to create lesson plans and encourage students!


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{% include 'editor.html' %}