THE MISFITS (director: John Huston; cast: Clark Gable (Gay Langland), Marilyn Monroe (Roslyn Taber), Montgomery Clift (Perce Howland), Thelma Ritter (Isabelle Steers), Eli Wallach (Guido), 1961)
Even though, Arthur Miller wrote this film for Marilyn, and she was most affecting in this vulnerable role she had, this was possibly the best role Gable ever had, excluding GWTW. He told friends he was working with a bunch of loonies: At the time of the film's shooting, Clift was abusing drugs and on the verge of a mental breakdown, and Arthur and Marilyn were experiencing marital woes. Also, it is interesting to note that this was both Clark's and Marilyn's last film. He died of a heart attack just 2 days after shooting the film. Friends say that his rapid loss of 40 pounds to be fit for the film and look good for Marilyn, may have precipitated the attack, though this was denied by the surviving Gable family. Clark insisted on doing all the rigorous stunts for the film. Marilyn's supposed suicide, came shortly after the film opened.
The Misfits is about some modern day Reno denizens who have trouble fitting into society, as they question life and their motives for doing what they are doing. Gable is the cowboy who is a hard drinker and fast with the women, but suffers from an inner melancholy. Monroe is the recent divorcee, coming to Reno to get out of a bad marriage, who is over-sensitive, and is confused about who she is. Eli is the second banana to Clark, who pretends to feel things he doesn't feel, expecting to make deals with everyone in order to carry out this charade of good guy, but still fails to mask his envy for Gable, when the chips are down. Clift is the honest cowboy hurt by the injustice of life, who bemoans the fact that his father died in an accident and left the ranch he loves more than anything else in this world to his mother, who has remarried, and her new husband has the gall to offer Clift wages to work the ranch he once owned. He is now wandering aimlessly on the rodeo circuit, and is supposedly the young heroic version of Gable.
Marilyn acts as the mother superior to all these men who are attracted to her both physically and spiritually. The film tries to debunk the Western myth of rugged individualism, by showing how vulnerable they are and how they try to mask their feelings by acting tough. After much verbage, the climactic mustang round-up scene culminates in a heavily laden bout of symbolism referring to the cowboy mentality and their need to be free and independent, which Arthur Miller calls as pure poppycock, a myth that was created and maintained by those who really don't know what a cowboy life really is like. If you are looking for that kind of Western here, you will not find it, even though you have a director and stars of the film who have been very successful in previous Western films, depicting that genre as it has come to be expected. There are no heroes here, only lonely people trying to live with their illusions, just like the audience is trying to see them as they were led to believe cowboys should be like. All this might be confusing for those who never dreamed they would see a Western with its stars having psychological problems, and worried about earning a living. That sounds too much like what ordinary people are going through.
This is a superior Western despite its non-action and debunking of one of cinema's great myths, mainly because of its incisive characterizations of these misfits, and because it was fun watching these big stars work together, especially when realizing that there was a lot of truth in the script that pertained to their real-life situations.
REVIEWED ON 6/27/98 GRADE: B
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
© ALL RIGHTS RESERVED DENNIS SCHWARTZ
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


public static interface Player.EventListener
| Modifier and Type | Method and Description |
|---|---|
default void |
onLoadingChanged(boolean isLoading)
Called when the player starts or stops loading the source.
|
default void |
onPlaybackParametersChanged(PlaybackParameters playbackParameters)
Called when the current playback parameters change.
|
default void |
onPlayerError(ExoPlaybackException error)
Called when an error occurs.
|
default void |
onPlayerStateChanged(boolean playWhenReady,
int playbackState)
Called when the value returned from either
Player.getPlayWhenReady() or Player.getPlaybackState() changes. |
default void |
onPositionDiscontinuity(int reason)
Called when a position discontinuity occurs without a change to the timeline.
|
default void |
onRepeatModeChanged(int repeatMode)
Called when the value of
Player.getRepeatMode() changes. |
default void |
onSeekProcessed()
Called when all pending seek requests have been processed by the player.
|
default void |
onShuffleModeEnabledChanged(boolean shuffleModeEnabled)
Called when the value of
Player.getShuffleModeEnabled() changes. |
default void |
onTimelineChanged(Timeline timeline,
Object manifest,
int reason)
Called when the timeline and/or manifest has been refreshed.
|
default void |
onTracksChanged(TrackGroupArray trackGroups,
TrackSelectionArray trackSelections)
Called when the available or selected tracks change.
|
default void onTimelineChanged(Timeline timeline, @Nullable Object manifest, @Player.TimelineChangeReason int reason)
Note that if the timeline has changed then a position discontinuity may also have
occurred. For example, the current period index may have changed as a result of periods being
added or removed from the timeline. This will not be reported via a separate call to
onPositionDiscontinuity(int)
.
timeline
- The latest timeline. Never null, but may be empty.manifest
- The latest manifest. May be null.reason
- The Player.TimelineChangeReason
responsible for this timeline change.default void onTracksChanged(TrackGroupArray trackGroups, TrackSelectionArray trackSelections)
trackGroups
- The available tracks. Never null, but may be of length zero.trackSelections
- The track selections for each renderer. Never null and always of
length Player.getRendererCount()
, but may contain null elements.default void onLoadingChanged(boolean isLoading)
isLoading
- Whether the source is currently being loaded.default void onPlayerStateChanged(boolean playWhenReady, int playbackState)
Player.getPlayWhenReady()
or Player.getPlaybackState()
changes.playWhenReady
- Whether playback will proceed when ready.playbackState
- One of the STATE
constants.default void<EMAIL_ADDRESS>int repeatMode)
Player.getRepeatMode()
changes.repeatMode
- The Player.RepeatMode
used for playback.default void onShuffleModeEnabledChanged(boolean shuffleModeEnabled)
Player.getShuffleModeEnabled()
changes.shuffleModeEnabled
- Whether shuffling of windows is enabled.default void onPlayerError(ExoPlaybackException error)
Player.STATE_IDLE
immediately after this method is called. The player instance can still be used, and Player.release()
must still be called on the player should it no longer be required.error
- The error.default void<EMAIL_ADDRESS>int reason)
When a position discontinuity occurs as a result of a change to the timeline this method
is not called. onTimelineChanged(Timeline, Object, int)
is called in this
case.
reason
- The Player.DiscontinuityReason
responsible for the discontinuity.default void onPlaybackParametersChanged(PlaybackParameters playbackParameters)
Player.setPlaybackParameters(PlaybackParameters)
, or the player itself may change
them (for example, if audio playback switches to passthrough mode, where speed adjustment is
no longer possible).playbackParameters
- The playback parameters.default void onSeekProcessed()
onPlayerStateChanged(boolean, int)
.


--- title: Re pello Model J label: Model J subtitle: Balance bike description: Model J is a full suspension balance bike for children aged from 2 to 4 years. permalink: /work/re-pello-model-j/ layout: default published: true group: project lang: en language: English og-locale: en_US thumbnail: re-pello-model-j-red-athlete-1by1 image: re-pello-model-j-red-athlete-2by1 og-image: re-pello-model-j-milan-dancing-2by1-w2000.jpg order: 11 sitemap: priority: 1 index_tile: true work_tile: true class: mj ---
Model J was exhibited at Salone del Mobile 2017 in Milan.
Model J at festival mini in Prague.
Production of the balance bike in the Czech Republic.
Model J visualization.
We developed Model J in cooperation with engineer Jiří Marján/Re pello.
Balance bike was exhibited at Prague’s Designblok in 2015, 2016 and 2017 and at the exhibition Salone del Mobile 2017 in Milan.


swslog component: swslog
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| 2011-09-23 | sorted articles | 1.0 | |
| 2011-09-23 | archive | 1.0 | |
| 2011-09-23 | tags | 1.0 |


For reading user input from stdin.
Similar to the readline
builtin's question()
method, but with a
few more features.
var read = require("read")
read(options, callback)
The callback gets called with either the user input, or the default
specified, or an error, as callback(error, result, isDefault)
node style.
Every option is optional.
prompt
What to write to stdout before reading input.silent
Don't echo the output as the user types it.replace
Replace silenced characters with the supplied character value.timeout
Number of ms to wait for user input before giving up.default
The default value if the user enters nothing.edit
Allow the user to edit the default value.terminal
Treat the output as a TTY, whether it is or not.input
Readable stream to get input data from. (default process.stdin
)output
Writeable stream to write prompts to. (default: process.stdout
)If silent is true, and the input is a TTY, then read will set raw mode, and read character by character.
This module works sort of with node 0.6. It does not work with node versions less than 0.6. It is best on node 0.8.
On node version 0.6, it will remove all listeners on the input
stream's data
and keypress
events, because the readline module did
not fully clean up after itself in that version of node, and did not
make it possible to clean up after it in a way that has no potential
for side effects.
Additionally, some of the readline options (like terminal
) will not
function in versions of node before 0.8, because they were not
implemented in the builtin readline module.
Patches welcome.
ArturoDuran


Bucket Class
This class represents a data store
Constructor
Bucket
(
-
options
Parameters:
-
options
Object-
driver_options
Objectparameters to pass to the driver. See defaultOptions
-
[drivers]
Array optionala list of prioritized driver names to choose from
-
Methods
alias
(
Driver
static
-
alias
-
name
Aliases a drivers name
Parameters:
-
alias
String -
name
String
Returns:
Driver:
aliased driver
choose
(
private
static
-
[list]
chooses a driver to use.
Parameters:
-
[list]
Array optionalif provided, will choose a driver from that list
Returns:
ns.Driver
Properties
drivers
Object
private
a named list of all registered drivers
stack
Array
private
a stack of all registered driver names


Black is an open-source Python code formatter by @ambv. This is an example implementation.
Source code is available on GitHub.
Black is the uncompromising Python code formatter. By using it, you agree to cease control over minutiae of hand-formatting. In return, Black gives you speed, determinism, and freedom from pycodestyle nagging about formatting. You will save time and mental energy for more important matters.


This sample demonstrates how to expose a metadata endpoint that uses the relay binding. MetadataExchange is supported in the following relay bindings: NetTcpRelayBinding, NetOnewayRelayBinding, BasicHttpRelayBinding and WS2007HttpRelayBinding.
Prerequisites
If you haven't already done so, please read the release notes document that explains how to sign up for a Windows Azure account and how to configure your environment.
Service
The service project is based on the service project in the Echo sample.
To add metadata publishing to the service, modify the application configuration file to include an additional behavior section, as follows:
| Xml | |
|---|---|
<behavior name="serviceMetadata"> <serviceMetadata /> </behavior> |
This behavior section is then referenced from the services configuration section:
| Xml | |
|---|---|
<service name="Microsoft.ServiceBus.Samples.EchoService" behaviorConfiguration="serviceMetadata"> <endpoint name="RelayEndpoint" contract="Microsoft.ServiceBus.Samples.IEchoContract" binding="netTcpRelayBinding" address="" /> <endpoint name="MexEndpoint" contract="IMetadataExchange" binding="ws2007HttpRelayBinding" bindingConfiguration="mexBinding" address="" /> </service> |
For the service to authenticate with the Service Bus, you are
prompted to enter the service namespace and the issuer credentials. The issuer
name is used to construct the service URI.
Next, the sample creates a service endpoint and a MEX endpoint. It then adds the
TransportClientEndpointBehavior
and opens a service endpoint.
| C# | |
|---|---|
Uri sbAddress = ServiceBusEnvironment.CreateServiceUri("sb", serviceNamespace, "Echo/Service"); Uri httpAddress = ServiceBusEnvironment.CreateServiceUri("http", serviceNamespace, "Echo/mex"); ... TransportClientEndpointBehavior sharedSecretServiceBusCredential = new TransportClientEndpointBehavior(); sharedSecretServiceBusCredential.TokenProvider = TokenProvider.CreateSharedSecretTokenProvider(issuerName, issuerSecret); ... ServiceHost host = new ServiceHost(typeof(EchoService), address); ... host.Open(); |
Client
In this sample, the client is the Svcutil.exe tool.
| Note: Svcutil.exe is installed as part of the Windows SDK. A typical location might be C:\Program Files\Microsoft SDKs\Windows\<version>\Bin\NETFX 4.0 Tools\SvcUtil.exe |
Building and Running the Sample
To run the sample, build the solution in Visual Studio or from the command line. After building the solution, do the following to run the application.
- From a command prompt, run the service (Service\bin\Debug\Service.exe) as administrator.
- Copy the svcutil.exe.config included with this solution in the same directory as svcutil.exe. If svcutil.exe.config already exists, add the bindingExtensions, policyImporters, and wsdlImporters of the attached svcutil.exe.config to it.
- From another command prompt with elevated privileges, run the .NET 4.0 svcutil
against the mex endpoint opened by the service:
svcutil.exe
http://<service-namespace>.servicebus.windows.net/Echo/mex/,
replacing <service-namespace> with the your service namespace.
Expected Output – SvcUtill
SvcUtil.exe /d:c:\ /r:"C:\Program Files\WindowsAzureSDK\v1.6\ServiceBus\ref\Microsoft.ServiceBus.dll" http://<service-namespace>.servicebus.windows.net/Echo/mex Microsoft (R) Service Model Metadata Tool [Microsoft .NET Framework, Version 3.0.50727.357] Copyright (c) Microsoft Corporation. All rights reserved. Generating files... C:\EchoService.cs C:\output.config |
Expected Output – output.config
<?xml version="1.0" encoding="utf-8"?> <configuration> <client> <endpoint address="sb://<issuer-name>.servicebus.windows.net/services/Echo/" binding="netTcpRelayBinding" bindingConfiguration="RelayEndpoint" contract="IEchoContract" name="RelayEndpoint" /> </client> </system.serviceModel> </configuration> |
Did you find this information useful? Please send your suggestions and comments about the documentation.


Breathe Life into Your Ideas.
welcome to my web page
Fullstack Developer
Fast:
Fast load times and lag free interaction, my highest priority.
Responsive:
My layouts will work on any device, big or small.
Dynamic:
Websites don't have to be static, I love making pages come to life.


Ratnagiri
-
S. P. Hegshetye college of arts, commerce & science
Gov. Aided
-
Navnirman Junior college of arts, commerce & science
-
Navnirman High English Medium School(CBSE)
Sangmeshwar
-
Bhai Hegshetye Junior college of arts, commerce & science
Gov. Aided
-
Navnirman College of Nursing
-
Navnirman Public School
Dodamarg
-
S. P. Hegshetye college of arts, commerce & science
Gov. Aided
-
Navnirman Junior college of arts, commerce & science
-
Navnirman High English Medium School(CBSE)


Binds together editor and preview scrollbars.
Note: The mapping between Markdown and HTML is based on the position of the title elements (h1, h2...) in the page. Therefore if your document does not contain any title, the mapping will be linear and consequently less accurate.


1. Swarm Configuration
CPM uses Docker Swarm to virtualize multiple Docker servers into a single virtual server. This is a convenient way to implement multiple host Docker which is necessary to scale out the CPM containers onto multiple Docker hosts. In this example we run the Swarm manager and agent on the same host, this is they way a developer might run CPM. In a real setup, you would have a single manager and multiple swarm agent hosts.
For this example configuration, we start the Swarm Manager on <IP_ADDRESS>:8000
The Swarm agent is started to listen to <IP_ADDRESS>:2375
1.1. Installation
Swarm is provided by Docker at https://github.com/docker/swarm. Use the instructions at the Swarm github page to install a binary version of Swarm into the /usr/local/bin directory of all the servers you will be using for CPM.
Swarm needs a single token to define the cluster you are creating. This is done one-time as follows, save this token value for future reference:
swarm create 7b9fb5037919f89bd52c3c4888586be3
1.2. Docker Configuration
Docker is configured on each server to listen to <IP_ADDRESS>:2375 for API events. On Centos/RHEL this is done by adding -H tcp://<IP_ADDRESS>:2375 in the /etc/sysconfig/docker file:
export SWARM_PORT=2375 /usr/bin/docker -d --selinux-enabled -H tcp://<IP_ADDRESS>:$SWARM_PORT --label storage=ssd --label hostname=espresso.crunchy.lab --label profile=small
Notice that we assign labels to the docker daemon, these labels are used by CPM (via Swarm) to figure out the correct server to deploy containers upon. CPM requires you classify a server with a label key of profile and value of SM, MED, or LG. This label value is used to determine where a container will be placed during the provisioning process. Also, a label key of hostname is used to provide the user a way to provision a container upon a specific host. The value of the hostname key is the fully qualified host name of the server.
1.3. Startup
On each server in your cluster, Start the swarm server agent listening to the local Docker API:
export LOCAL_HOST=<IP_ADDRESS> export SWARM_PORT=2375 swarm join --addr=$LOCAL_HOST:$SWARM_PORT token://7b9fb5037919f89bd52c3c4888586be3
On one server in your cluster, Start the swarm manager that listens to CPM requests, in this example I specify two servers that will act as the cluster nodes, this is one simple form of Swarm Discovery:
export MANAGER_HOST=<IP_ADDRESS> export MANAGER_PORT=8000 swarm manage --host $MANAGER_HOST:$MANAGER_PORT nodes://<IP_ADDRESS>:2375,<IP_ADDRESS>:2375
1.4. Test
To see what servers are include in the swarm:
export MANAGER_HOST=<IP_ADDRESS> export MANAGER_PORT=8000 swarm list token://7b9fb5037919f89bd52c3c4888586be3 docker -H tcp://$MANAGER_HOST:$MANAGER_PORT info
You now run docker commands via the swarm manager ip:port to interact with swarm:
export MANAGER_HOST=<IP_ADDRESS> export MANAGER_PORT=8000 docker -H tcp://$MANAGER_HOST:$MANAGER_PORT info docker -H tcp://$MANAGER_HOST:$MANAGER_PORT run docker -H tcp://$MANAGER_HOST:$MANAGER_PORT ps docker -H tcp://$MANAGER_HOST:$MANAGER_PORT logs


This is a default web page. The page you should be reading is the index page in the CourseFiles936 folder, which you should be able to get via the Home button in your browser.
You can try this link, but most browsers won't allow a downloaded page to open a local page, for fairly good security reasons! So, just try the home button on your browser.


photograph
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apc company, saskatoon / june 2011 - present
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123 Somewhere Road, Nashville, TN 00000
<EMAIL_ADDRESS>(20) 10-000-000-00
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Post-parsing
ControllerTest.php
ControllerTestCommand.php
ControllerTestCommand2.php
ControllerTestVO.php
FacadeTest.php
FacadeTestCommand.php
FacadeTestVO.php
MacroCommandTest.php
MacroCommandTestCommand.php
MacroCommandTestSub1Command.php
MacroCommandTestSub2Command.php
MacroCommandTestVO.php
MediatorTest.php
ModelTest.php
ModelTestProxy.php
NotificationTest.php
ObserverTest.php
ProxyTest.php
SimpleCommandTest.php
SimpleCommandTestCommand.php
SimpleCommandTestVO.php
ViewTest.php
ViewTestMediator.php
ViewTestMediator2.php
ViewTestMediator3.php
ViewTestMediator4.php
ViewTestMediator5.php
ViewTestMediator6.php
ViewTestNote.php
Post-parsing
Warnings:
Warning - Class ControllerTest parent PHPUnit_Framework_TestCase not found
Warning - Class ViewTestMediator parent Mediator not found
Warning - Class ViewTest parent PHPUnit_Framework_TestCase not found
Warning - Class MacroCommandTest parent PHPUnit_Framework_TestCase not found
Warning - Class MacroCommandTestSub1Command parent SimpleCommand not found
Warning - Class MacroCommandTestCommand parent MacroCommand not found
Warning - Class ViewTestNote parent Notification not found
Warning - Class ViewTestMediator4 parent Mediator not found
Warning - Class ControllerTestCommand parent SimpleCommand not found
Warning - Class ViewTestMediator3 parent Mediator not found
Warning - Class ControllerTestCommand2 parent SimpleCommand not found
Warning - Class ViewTestMediator6 parent Mediator not found
Warning - Class ViewTestMediator5 parent Mediator not found
Warning - Class AllTestsSuite parent PHPUnit_Framework_TestSuite not found
Warning - Class MacroCommandTestSub2Command parent SimpleCommand not found
Warning - Class ModelTestProxy parent Proxy not found
Warning - Class FacadeTestCommand parent SimpleCommand not found
Warning - Class MediatorTest parent PHPUnit_Framework_TestCase not found
Warning - Class ObserverTest parent PHPUnit_Framework_TestCase not found
Warning - Class ProxyTest parent PHPUnit_Framework_TestCase not found
Warning - Class ViewTestMediator2 parent Mediator not found
Warning - Class NotificationTest parent PHPUnit_Framework_TestCase not found
Warning - Class SimpleCommandTestCommand parent SimpleCommand not found
Warning - Class FacadeTest parent PHPUnit_Framework_TestCase not found
Warning - Class SimpleCommandTest parent PHPUnit_Framework_TestCase not found
Warning - Class ModelTest parent PHPUnit_Framework_TestCase not found
AllTestsSuite.php
Warnings:
Warning on line 24 - no @package tag was used in a DocBlock for class AllTestsSuite
Warning on line 50 - File "/Users/hasan/htdocs/public/labs/puremvc.org/ports/php5/tests/AllTestsSuite.php" has no page-level DocBlock, use @package in the first DocBlock to create one
ControllerTest.php
Warnings:
Warning on line 23 - no @package tag was used in a DocBlock for class ControllerTest
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ControllerTestCommand.php
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MediatorTest.php
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ModelTest.php
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ModelTestProxy.php
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NotificationTest.php
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ObserverTest.php
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ProxyTest.php
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SimpleCommandTest.php
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SimpleCommandTestVO.php
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ViewTest.php
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|
|


Topics:
Compiling your first OpenGL program
Specifying polygons in OpenGL
Modifying your first OpenGL program
Download the code found here to get OpenGL software for displaying a single red triangle.
This has been modified from the software that is available from
Prof. Ed Angel of the University of New Mexico.
His OpenGL software web
site can be found here.
Modify red_triangle.cpp to create an image in OpenGL that contains 1 red triangle, 1 red quadrilateral and 1 red pentagon.
You should not use the deprecated functions of OpenGL in this or any other programming assignment.
Submit a zip file that contains a folder with your initials and assignment number, for example ak876_a1.
All your files (source files, Makefile and a README) should be inside this folder.
The README should describe your program and how to use it.
The Makefile must contain three targets:
The TA should be able to unzip your file, 'cd' into your directory, and type 'make run' to run your program.
Last modified January 6, 2016.


Welcome to my website. I am Gary Francis Geraci, also known as Gary the Dog.
Take a look around and get to know me better.
My Favorite Foods
My Favorite Toys


Multiple Choices
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Voted Best Pizza in Nairobi for the past 5 years. From speciality pizzas to just plain cheese, we have your pie.
your pizza comes with one of the best toppings of your own
choice,pepperoni,Mushrooms,Onions,
Sausage,Bacon,Extra chees,Black olives,Green peppers.
A
huge selection of your childhood favorites including coney & cheese .
Not only do we make great pizza but our crust are a variety that customers choose from, they include crispy,stuffed and gluton free.which are legendary! Be sure to grab a few dozen.
| crust type | Large | Standard | Real deal |
|---|---|---|---|
| crispy | Ksh.1000 | Ksh.800 | Ksh.600 |
| stuffed | Ksh.1000 | Ksh.750 | Ksh.500 |
| gluton free | Ksh.1000 | Ksh.800 | Ksh.500 |
| crust type | Large | Standard | Real deal |
|---|---|---|---|
| crispy | Ksh.1000 | Ksh.750 | Ksh.550 |
| stuffed | Ksh.1000 | Ksh.750 | Ksh.580 |
| gluton free | Ksh.1050 | Ksh.900 | Ksh.600 |
| crust type | Large | Standard | Real deal |
|---|---|---|---|
| crispy | Ksh.900 | Ksh.700 | Ksh.3500 |
| stuffed | Ksh.900 | Ksh.700 | Ksh.500 |
| gluton free | Ksh.900 | Ksh.700 | Ksh.500 |
email. bindipizza@gmail.com


All the Liverpool polling stations for the 2019 General Election
Everywhere you can vote in Liverpool on Thursday
by Christopher McKeonLiverpool heads to the polls on December 12 to elect the city's five MPs in the 2019 General Election .
On the day itself, anyone without a postal vote will need to go to a polling station to cast their ballot in person.
Each polling station will be open from 7am until 10pm, and most are located in public buildings like schools or church halls.
If you have already registered, you should have been sent a polling card in the post telling you where to cast your vote.
Below is a list of each constituency's polling stations. But bear in mind, your nearest polling station may not be the one you need to vote at.
Garston and Halewood
Sprinwood Heath Primary School, Danefield Road
Hunts Cross Primary School, Kingsthorne Road
St Hilda's Parish Hall, Stuart Avenue
Allerton Synagogue, Mather Avenue
Woolton Youth Centre, High Street
St Mark's Church Hall, Cranwell Road
St Paschal Baylon School, Chelwood Avenue
St Stephen's Parish Centre, Belle Vale Road
Woodlands Community Centre, Woodlands Square
St Cyril's Parochial Club, Southbrook Road
Christ Church Centre, Brownbill Bank
Netherley Sports & Social Club, Wood Lane
Ambergate Guide hut, Ambergate Road
St Mary's Church Parish Hall, St Mary's Road
Liverpool Cricket Club, Aigburth Road
Bridge Chapel Centre, Heath Road
Garston One Stop Shop/Library, Bowden Road
Garston Children's Centre, Banks Road
Stockton Wood Primary School, All Saints Road
Parklands Library, Conleach Road
Noah's Ark Centre, Ganworth Road
St Ambrose Parish Centre, Heathgate Avenue
Garston Community Council Reading Room, Wellington Street
Scout Hall, Church Road South
Gateacre Institute, Grange Lane
Woolton Primary School, Out Lane
Abbots Lea School, Beaconsfield Road
The Arncliffe Sports and Community Centre, Arncliffe Road
Fairview Park Community Centre, Sherbourne Avenue
St Nicholas Centre, Church Road
Environment Centre, Halewood Park, Okell Drive
New Hutte Neighbourhood Centre, Lichfield Road
The Halewood Centre, Roseheath Drive
Community Centre, Hollies Road
Cronton Parish Offices, Hampton Drive
Liverpool Riverside
Central Library, William Brown Street
Bronte Youth & Community Centre, Trowbridge Street
Kensington Fields Community Centre, Hall Lane
Marybone Youth & Community Centre, Addison Way
Pleasant Street Primary School, Pleasant Street
St Anne's Catholic Primary School, Overbury Street
Liverpool Parish Church, Our Lady & St Nicholas, Chapel Street
St Clare's Parish Centre, Rutland Avenue
Sefton Park Community Centre, Croxteth Drive
Greenbank Primary School, Mossley Avenue Entrance
Gregson Memorial Institute, Garmoyle Road
Coffee Lounge at Stapely, Stapely Care, North Mossley Hill Road
Penny Lane Development Trust, Penny Lane
St Bede's Church Hall, Fern Grove
Greenbank Student Village, Greenbank Road
St Lawrence's Church, Westminster Close
Kirkdale Community Centre, Stanley Road
Everton in the Community, The People's Hub, Spellow Lane
Millennium Resource Centre, Silvester Street
Eldonian Village Hall, Burlington Street
St Paul's Church, Celia Street
Rotunda Boxing Club, Lambeth Road
St Anne's Church, Aigburth Road
SARA Hall, Rundle Road (corner of Milner Road)
Victoria Public House Function Room, Victoria Road
Clearview Scout HQ, Kylemore Avenue
Booker Avenue Junior School, Booker Avenue
John Archer Hall, Upper Hill Street
Princes Park Methodist Church, Beaconsfield Street
Unity Youth and Community Centre, Dove Street
St Philemon's Church, Admiral Street
Abercromby Nursery School, Falkner Street
St Vincent De Paul Primary School, Pitt Street
Riverview Development Trust Ltd, Upper Mann Street
St Patrick's Catholic Primary School, Upper Hill Street
Holy Family Catholic Primary School, Rhyl Street
St Cleopas Church & Family Centre, Mill Street
St Finbar's Catholic Primary School, South Hill Road
Toxteth Library, Windsor Street
The Spinnaker Suite, The Yacht Club Bar and Restaurant, Liverpool Marina, Coburg Wharf
SMLLCA, The Old Police Station, Lark Lane
The Harry Moncrieff Community Centre, Fulwood Drive
Toxteth Annexe Conference Centre, Aigburth Road
Sefton Park Community Library, Aigburth Road
Matthew Arnold Primary School, Dingle Lane
Aigburth Park Campus, Liverpool Hope University, St Michael's Road
Liverpool Walton
Christ Church Walton Breck, Hartnup Street
St Columba's Church, Pinehurst Road
St Margaret's Church Anfield, Rocky Lane
Anfield Road Primary School, Anfield Road
Holy Trinity Walton Breck, Breck Road
Leamington Community Primary School, Leamington Road
Temporary Polling Station, Broadway Nursing Home, Flemington Avenue
Clubmoor Presbyterian Church Hall, Cherry Lane
St Andrew's Loft Hall, Adshead Road
St Christopher's Community Hall, Lorenzo Drive
Florence Melly Primary School, Bushey Road
Local Solutions Family Centre, Heathcote Road
County Children's Centre at Arnot St Mary C of E Primary School, Arnot Street
St Francis De Sales Infant & Nursery School, Margaret Road
Hetherlow Community Centre, Moor Lane
St Aidan's Church Hall, Cherry Lane
Florence Court, Communal Lounge, Buchanan Road
Notre Dame Catholic College, Great Homer Street
The Breckfield Centre, Breckfield Road North
Cornerstone Foyer, Creative Campus, Liverpool Hope University, Shaw Street
Whitefield Primary School, Boundary Lane
Clock Community Centre, St Domingo Road
Emmanuel Church, Longmoor Lane/Higher Lane
Barlows Primary School, Barlows Lane
Fazakerley Primary School, Formosa Way
Longmoor Community Primary School, Longmoor Lane
Orrell Park Baptist Church Hal, Whinfield Road
Blessed Sacrament Parish Centre, Park Vale Road
St Nathanael's Church, Fazakerley Road
The Salvation Army, Cavendish Drive
Liverpool Wavertree
Childwall & Woolton Children's Centre, Rudston Primary School, Rudston Road
Childwall Abbey School, Childwall Lane
Northway Primary & Nursery School, Northway
Lecture Theatre Complex, Hope Park Campus, Liverpool Hope University, Taggart Avenue
Chelwood & District Welfare Association, CADWA Hall, Bowland Avenue
East Wavertree & childwall Community Association, Dunbabin Road
Booker Avenue Junior School, Booker Avenue
Dovedale Primary School, Herondale Road
Elm Hall Drive Methodist Church, Elm Hall Drive
Fisher-More Hall, Bishop Eton Monastery, Woolton Road
All Hallows Centre, Allerton Road
New Park Primary School, Boaler Street
All Saints Worship Centre, Sheil Road
The Academy of St Francis of Assisi Lodge Building, Gardners Drive
Central Youth Club, Walker Street
The Devonshire House Hotel, Mayflower Suite, Holly Road
Phoenix Primary School, Birchfield Road
Kensington Primary School, Bell Tower Lane
St Anne (Stanley) C.E. School, Prescot Road
St Cuthbert's Catholic Primary School, Aviemore Road
St Oswald's Catholic Primary School, Montague Road
Stoneycroft Children's Centre, Inigo Road
Wavertree Nursing Home, Pighue Lane
Broadgreen Primary School, Rossall Road
St Bridget's & St Thomas Church, Bagot Street
St Hugh's Catholic Primary School, Earle Road
St Bede's Church Hall, Fern Grove
Edge Hill Youth & Community Centre, Durning Road
St Anne's Catholic Primary School, Overbury Street
Wavertree Library, Picton Road
Long Lane Independent Methodist Church, Long Lane
Olive Mount Community Centre, Edgewell Drive
Christ the King Primary, Meadway
Mosspits Lane Primary School, Mosspits Lane
The Blue Coat School, East Wing Chapel, Church Road
Liverpool West Derby
Croxteth Primary School, Moss Way
Out Lady Queen of Martyrs Parish Centre, Stonebridge Lane
St Cuthbert's Church, Langley Close
Sandfield Park School, Sandfield Walk
Holy Spirit Community Hall, East Prescot Road
Dovecot Primary School, Grant Road
Temporary Polling Station, opposite 2 Lingfield Road
Knotty Ash Primary School, Thomas Lane
East Prescot Road Nursery School, East Prescot Road
Ranworth Square Primary School, Ranworth Square
St Teresa of Lisieux Catholic Primary School, Utting Avenue East
Ellergreen Community Centre, Ellergreen Road
Monksdown Primary School, Frinsted Road
St Christopher's Community Hall, Lorenzo Drive
Wellesbourne Primary School, Abbotsford Road
Jeffrey Humble Playing Fields, Long Lane
Our Lady & St Philomena Parish/Communityu, Sparrow Hall Road
Tuebrook Community Association Hall, Marlborough Road
St Margaret's Anfield CE Primary, Lower Breck Road
Stoneycroft URC, Lister Drive
West Derby Library, West Derby Comprehensive School, West Derby Road
St Cecilia's Infant & Nursery School, Snaefell Avenue
Newsham Park Shapel, Sheil Road
Anfield Sports & Community Centre, Breckside Park/Lower Breck Road
AC Mitchell Church, Muirhead Avenue East
St Mary's Millennium Centre, Meadow Lane
Deysbrook Village Centre, Deysbrook Way
St Paul's Catholic Junior School, Spring Grove
Holly Lodge Girls' College, Mill Lane
Blackmoor Park Junior School, Leyfield Road
Mab Lane Primary School, Mab Lane
West Derby Children's Centre, Leyfield Road
Deysbrook Army Cadet Hut, Lees Lane, off Colonel Drive
The Yew Treet Centre, Berryford Road
Blueberry Park Primary School, Ackers Hall Avenue
Temporary Polling Station, Coachmans Drive


Installation¶
Special attention was taken to minimize dependencies.
Third-party programs¶
The following tools should be installed. (Scripts using the program are in parantheses)
nsdpy used for downloading sequences from NCBI (Hebert and Meglécz, 2022)
BLAST (Altschul et al., 1997; format_bold.pl, for the check_orientation option, format_db.pl for the blast option option)
vsearch (Rognes et al., 2016; dereplicate.pl, pool_and_dereplicate.pl, select_region.pl)
cutadapt (Martin, 2011; select_region.pl for the e_pcr option)
All third party programs can be easily installed to a conda environment, but it is not essential.
mkCOInr scripts¶
The mkCOInr scripts are written in Perl, no installation is necessary to run them apart from the Perl interpreter already installed in all unix systems. Just download the scripts directory and you are ready.


This is a simple cuneifier, based upon the table on pages 102 and 103 in the cuneiform book. Technically, it was written in Foma (there is also another implementation, written in OpenGRM Thrax).
Type syllables, divided by a dash. Divide words with a space. Case of letters is not important. Possible syllables are combinations from consonants b, d, g, h, k, l, m, n, p, q, r, s, t, and z with vowels a, e, i, and u; single vowels are possible too.
If the cuneifier recognises your input, the corresponding cuneiform characters will appear below input line; if not, you will see ??? instead.


label( [ 0, B - 1 ], "\\text{Draw " + plural( A, "circle" ) + ".}", "right" );
drawRow( A, B - 3, "#6495ED", 1 );
label( [ 0, B - 2 ], "\\text{"
+ "If there" + ( B === 1 ? " is " : " are " ) + plural( B, "row" )
+ " with " + plural( A, "circle" ) + ( B === 1 ? " " : " each " ) + ", there is a total of how many circles?"
+ "}", "right" );
for ( var i = 1; i < B; i++ ) {
drawRow( A, B - 3 - i, "#28AE7B", A * i + 1 );
}


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---
layout: post
title: HTML in Modal Dialog Title
date: '2018-01-05T21:06:00.000-05:00'
author: Maxime Tremblay
tags:
modified_time: '2018-01-05T21:06:50.050-05:00'
blogger_id: tag:blogger.com,1999:blog-267713902572011003.post-5699905949738112888
blogger_orig_url: https://max-tremblay.blogspot.com/2018/01/html-in-modal-dialog-title.html
---
By Default


| Release Doc > Examples > Compound Examples > XML 2 | ||
| XML example | ||
|
Launch Delphi-projectgroup for XML2 example This example shows how to use the XML components in $(PRODUCT_BASE_NAME) to build web applications. The model used in this example is OrgChart.mdl which is a simplified model of a corporate structure. In addition to the XML components the example also illustrates how to extend the .. to export the business objects in XML. The example consists of two components:
It offers users the possibility to search for a person or a company and view/modify the details of a given person or a company from their web browser. Running the example Install OrgChartISAPI.dll is an ISAPI dll, hence it must be properly installed in IIS(Set the virtual web directory to point to the /examples/XML2 folder). For more information on how to properly install an ISAPI dll see XMLDispatcher example. OrgChart.exe is a DCOM server that will be invoked by OrgChartISAPI, hence it must be properly registered and configured:
To run the example you should:
OrgChartISAPI OrgChartISAPI is an ISAPI dll. The important points to note in this application are the following:
3) Send the XML request to the OrgChart server. We are using a TBoldComClientObjectHandle object, bohDispatcher, to establish a connection with the TBoldXMLdispatcher object present the OrgChart server. The call to the dispatcher looks like: (bcohDispatcher.ComObject as IBoldSOAPService).Get(XMLRequest.DomDocument.xml, res); The first argument passed to the Get-method is our XML request, the second is an out parameter that holds the response returned by the OrgChart server. OrgChart The OrgChart application is just a standard Bold application, with a BoldSystem, a persistence layer, and a Model. In addition it also exports via COM/DCOM a TBoldXMLDispatcher object, thus functioning as the gateway between the web server and the business layer. The XMLDispatcher component(dmServer.pas) receives the incoming XML requests and dispatches them to the relevant party. The Actions property contains a list of TBoldXMLActionItem objects. The dispatcher uses the tag name of XML request's action element to select the TBoldXMLActionItem- with a matching ActionName property- to handle the request. Our XMLDispatcher defines the following action items:
To facilitate the exporting of Bold objects in XML format the example uses the TBoldXMLExporter class. TBoldXMLExporterThe methods of the TBoldXMLExporter take a Bold object or a list of Bold objects as parameters and produce an XML tree out of them. E.g. suppose you have an object of class Person, with attributes FirstName, LastName, FullName and Employer, exporting this object with the TBoldXMLExporter would produce XML similar to this: < Person XML.id="8"> <FirstName>John</FirstName> <LastName>Smith</LastName> <FullName>John Smith</FullName> <Employer> <Company XML.idref="1"/> </Employer> </Person>DocumentationThe TBoldXMLExporter class (unit BoldXMLExporter.pas located in /XML2/XMLExporter) used in this example is merely for demonstrative purposes, to show users how they can go about implementing their own custom XML exporters and offers plenty of room for improvement. Following is a list of TBoldXMLExporter's important properties and methods:
< Person XML.id="8"> <FirstName>John</FirstName> <LastName>Smith</LastName> <FullName>John Smith</FullName> <Employer> <Company XML.idref="1"/> </Employer> </Person>< Company XML.id="1"><CompanyName>John Smith and Co.</CompanyName> <Employees> <Person XML.idref="6"/> <Person XML.idref="8"/> <Person XML.idref="11"/> <Person XML.idref="14"/> </Employees> <Offices> <Office XML.idref="2"/> </Offices> </Company>Note! Please make sure to use the "Create DB & Alias" button before attempting to open the system for the first time. Once the database and alias is established you only have to use the "Open system" button when running the example program. | ||
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original_df <- read_csv("data/InsectAbundanceSurvival.csv")
We are only using data from the last week of the experiment, for which we have estimates of LYER and Ptoids (mummies actually) underneath the leaf
# only retain cages where LYER or Parasitoids survived
# no surviving BRBR, so we can't estimate this
LYER_df_17 <- filter(original_df, Week == "17") %>% filter(LYER_Survival == 1) %>% dplyr::select(Cage, LYER)
Mummy_df_17 <- filter(original_df, Week == "17") %>% filter(Mummy_Ptoids_Survival == 1) %>% dplyr::select(Cage, Mummy_raw)
# underneath leaf counts, only available for last week of experiment
underneath_df <- read_csv("data/insect_abundance_data_2018-09-26_underneathleaf.csv") # counted to individual resolution
# merge with original data
LYER_df_17 <- left_join(LYER_df_17, dplyr::select(underneath_df, Cage, underneath_LYER = LYER))
Mummy_df_17 <- left_join(Mummy_df_17, dplyr::select(underneath_df, Cage, underneath_Mummy = Mummy))
## exploratory plots ----
# all data
x11(); ggplot(LYER_df_17, aes(x = log1p(LYER), y = log(underneath_LYER))) +
geom_point() + geom_smooth(method = "lm")
# remove outlier
x11(); ggplot(LYER_df_17[-31,], aes(x = log1p(LYER), y = log(underneath_LYER))) +
geom_point() + geom_smooth(method = "lm")
## GLM ----
# I dropped the outlier because I truly think it was an unusual data point and would bias our other estimates.
plot(MASS::glm.nb(underneath_LYER ~ log1p(LYER), LYER_df_17), which = 5)
| Version | Author | Date |
|---|---|---|
| c802852 | mabarbour | 2021-06-24 |
| Version | Author | Date |
|---|---|---|
| c802852 | mabarbour | 2021-06-24 |
summary(glm(underneath_LYER ~ log1p(LYER), LYER_df_17[-31,], family = poisson)) # residual deviance suggests a lot of overdispersion, so I'm going to use a negative binomial model
Call:
glm(formula = underneath_LYER ~ log1p(LYER), family = poisson,
data = LYER_df_17[-31, ])
Deviance Residuals:
Min 1Q Median 3Q Max
-6.9703 -3.1734 -0.9052 2.8304 7.9681
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.47991 0.05716 60.88 <2e-16 ***
log1p(LYER) 0.29613 0.01141 25.95 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for poisson family taken to be 1)
Null deviance: 1316.37 on 30 degrees of freedom
Residual deviance: 481.88 on 29 degrees of freedom
AIC: 683.74
Number of Fisher Scoring iterations: 5
LYER_underneath_glm.nb <- MASS::glm.nb(underneath_LYER ~ log1p(LYER), LYER_df_17[-31,])
summary(LYER_underneath_glm.nb)
Call:
MASS::glm.nb(formula = underneath_LYER ~ log1p(LYER), data = LYER_df_17[-31,
], init.theta = 5.709483328, link = log)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2762 -0.7531 -0.2120 0.6421 1.9575
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.41254 0.18541 18.405 < 2e-16 ***
log1p(LYER) 0.31152 0.04166 7.477 7.59e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Negative Binomial(5.7095) family taken to be 1)
Null deviance: 80.035 on 30 degrees of freedom
Residual deviance: 32.974 on 29 degrees of freedom
AIC: 327.12
Number of Fisher Scoring iterations: 1
Theta: 5.71
Std. Err.: 1.57
2 x log-likelihood: -321.123
LYER_correction_glm.nb <- function(x) round(predict(LYER_underneath_glm.nb, newdata = data.frame(LYER = x), type = "response"), 0)
# will add between 30 and 286 individual LYER to LYER_t
LYER_correction_glm.nb(c(0,max(original_df$LYER, na.rm = T)))
1 2
30 286
## exploratory plot ----
x11(); ggplot(Mummy_df_17, aes(x = log1p(Mummy_raw), y = log(underneath_Mummy))) +
geom_point() + geom_smooth(method = "lm")
Warning: Removed 1 rows containing non-finite values (stat_smooth).
# the predicted increase is based on 1 data point
summary(Mummy_outlier_glm.nb <- MASS::glm.nb(underneath_Mummy ~ log1p(Mummy_raw), Mummy_df_17))
| Version | Author | Date |
|---|---|---|
| c802852 | mabarbour | 2021-06-24 |
Call:
MASS::glm.nb(formula = underneath_Mummy ~ log1p(Mummy_raw), data = Mummy_df_17,
init.theta = 1.465286968, link = log)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9833 -1.0999 -0.3254 0.4295 1.1074
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.4214 0.3633 3.913 9.13e-05 ***
log1p(Mummy_raw) 1.5949 0.3934 4.054 5.03e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Negative Binomial(1.4653) family taken to be 1)
Null deviance: 37.1182 on 7 degrees of freedom
Residual deviance: 9.2703 on 6 degrees of freedom
AIC: 53.379
Number of Fisher Scoring iterations: 1
Theta: 1.47
Std. Err.: 1.08
2 x log-likelihood: -47.379
# hmmm, this model predicts that there could be as many as 43,291 mummies underneath the leaves, which is insane
round(predict(Mummy_outlier_glm.nb, newdata = data.frame(Mummy_raw = c(0, max(original_df$Mummy_raw, na.rm = T))), type = "response"),0)
1 2
4 55286
# I think the baseline value, for which we have 5 data points is reasonable
# but for the increase, I'm going to use the slope of LYER, which seems reasonable given that this was the primary
# consumer-resource relationship and LYER was often found underneath the leaves compared to BRBR
summary(Mummy_underneath_glm.nb <- MASS::glm.nb(underneath_Mummy ~ 1, filter(Mummy_df_17, Mummy_raw == 0)))
Call:
MASS::glm.nb(formula = underneath_Mummy ~ 1, data = filter(Mummy_df_17,
Mummy_raw == 0), init.theta = 1.094073437, link = log)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8510 -0.9981 -0.5861 0.5828 0.9838
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.4214 0.4063 3.499 0.000468 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for Negative Binomial(1.0941) family taken to be 1)
Null deviance: 7.731 on 6 degrees of freedom
Residual deviance: 7.731 on 6 degrees of freedom
AIC: 39.452
Number of Fisher Scoring iterations: 1
Theta: 1.094
Std. Err.: 0.781
2 x log-likelihood: -35.452
round(exp(coef(Mummy_underneath_glm.nb)["(Intercept)"]),0) # predicts 5 underneath leaves if there was a zero above rosette count
(Intercept)
4
Mummy_correction_glm.nb <- function(x) round(exp(coef(Mummy_underneath_glm.nb)["(Intercept)"] + coef(LYER_underneath_glm.nb)["log1p(LYER)"]*log1p(x)),0)
Mummy_correction_glm.nb(c(0,max(original_df$Mummy_raw, na.rm = T))) # now that's more reasonable
[1] 4 26
Below, I estimate measurement error for BRBR, LYER, and Ptoid counts. This uses data for which we had overlapping counts for MAB (first author) and DTV (technician that was instrumental to the study).
# identify cage-week combinations with repeated measurements
rep_df <- original_df %>%
distinct(Cage, Week, Counter) %>%
group_by(Cage, Week) %>%
summarise(n = n()) %>%
filter(n > 1)
# make BRBR measurement error (me) df
BRBR_me_df <- left_join(rep_df, dplyr::select(original_df, Cage, Week, Counter, BRBR, BRBR_Survival)) %>%
drop_na() %>%
filter(BRBR_Survival == 1) %>%
spread(key = Counter, value = BRBR) %>%
mutate(log1p_DTV = log1p(DTV),
log1p_MAB = log1p(MAB)) %>%
ungroup()
BRBR_me <- apply(select(BRBR_me_df, log1p_DTV, log1p_MAB), MARGIN = 1, FUN = sd)
hist(BRBR_me)
| Version | Author | Date |
|---|---|---|
| c802852 | mabarbour | 2021-06-24 |
median(BRBR_me, na.rm = T)
[1] 0.2829706
# make LYER measurement error (me) df
LYER_me_df <- left_join(rep_df, dplyr::select(original_df, Cage, Week, Counter, LYER, LYER_Survival)) %>%
drop_na() %>%
filter(LYER_Survival == 1) %>%
spread(key = Counter, value = LYER) %>%
mutate(log1p_DTV = log1p(DTV),
log1p_MAB = log1p(MAB),
abs.diff_log1p = log1p_DTV - log1p_MAB,
mean.diff_log1p = log1p_DTV - (log1p_DTV+log1p_MAB)/2) %>%
ungroup()
LYER_me <- apply(select(LYER_me_df, log1p_DTV, log1p_MAB), MARGIN = 1, FUN = sd)
hist(LYER_me)
| Version | Author | Date |
|---|---|---|
| c802852 | mabarbour | 2021-06-24 |
median(LYER_me, na.rm = T)
[1] 0.2433557
# make Mummy_Ptoids measurement error (me) df
Mummy_Ptoids_me_df <- left_join(rep_df, dplyr::select(original_df, Cage, Week, Counter, Mummy_Ptoids, Mummy_Ptoids_Survival)) %>%
drop_na() %>%
filter(Mummy_Ptoids_Survival == 1) %>%
spread(key = Counter, value = Mummy_Ptoids) %>%
mutate(log1p_DTV = log1p(DTV),
log1p_MAB = log1p(MAB)) %>%
ungroup()
Mummy_Ptoids_me <- apply(select(Mummy_Ptoids_me_df, log1p_DTV, log1p_MAB), MARGIN = 1, FUN = sd)
hist(Mummy_Ptoids_me)
| Version | Author | Date |
|---|---|---|
| c802852 | mabarbour | 2021-06-24 |
median(Mummy_Ptoids_me, na.rm = T)
[1] 0.1323786
# useful function
mean_integer <- function(x) as.integer(mean(x, na.rm=T)) # remove NA, otherwise, the count will be set to zero if only 1 counter counted.
# make timeseries dataset
timeseries_df <- original_df %>%
select(Cage, Week, temp = Temperature, rich = Richness, com = Composition, Col, gsm1, AOP2, AOP2.gsoh, BRBR_Survival, LYER_Survival, Mummy_Ptoids_Survival, BRBR_t = BRBR, LYER_t = LYER, Ptoid_t = Mummy_Ptoids, Mummy_t_raw = Mummy_raw) %>%
# applying adjustments before averaging across counters
mutate(LYER_t_adj = ifelse(LYER_Survival == 0 | is.na(LYER_Survival) == T, 1, # replace extinction zeros with value of 1
ifelse(LYER_Survival == 1, LYER_t + LYER_correction_glm.nb(LYER_t), LYER_t)), # add adjusted values
# Ptoid should always be zero for Week 1 and 2, as they were added after sampling aphids in week 2. But we set them to 1 for extrapolating predictions
Ptoid_t_adj = ifelse(Mummy_Ptoids_Survival == 0 | is.na(Mummy_Ptoids_Survival) == T, 1, # replace extinction zeros with value of 1
ifelse(Mummy_Ptoids_Survival == 1, Ptoid_t + Mummy_correction_glm.nb(Mummy_t_raw), Ptoid_t))) %>% # add adjusted values
# apply adjustments for log-log analysis, using minimum count resolutions
# note that we NEVER used extinct species in fitting the multivariate model, only for extrapolating predictions from multivariate model to extant species
mutate(BRBR_t = ifelse(BRBR_Survival == 0 | is.na(BRBR_Survival) == T, 1, # replace extinction zeros with value of 1
ifelse(BRBR_t == 0 & BRBR_Survival == 1 & Week < 2, 1, # replaced undetected zeros before week 2 with value of 1
ifelse(BRBR_t == 0 & BRBR_Survival == 1 & Week > 1, 5, BRBR_t))), # replace undetected zeros with value of 5
LYER_t = ifelse(LYER_Survival == 0 | is.na(LYER_Survival) == T, 1, # replace extinction zeros with value of 1
ifelse(LYER_t == 0 & LYER_Survival == 1 & Week < 2, 1, # replaced undetected zeros before week 2 with value of 1
ifelse(LYER_t == 0 & LYER_Survival == 1 & Week > 1, 5, LYER_t))), # replaced undetected zeros after week 1 with value of 5
Ptoid_t = ifelse(Mummy_Ptoids_Survival == 0 | is.na(Mummy_Ptoids_Survival) == T, 1, # replace extinction zeros with value of 1
ifelse(Ptoid_t == 0 & Mummy_Ptoids_Survival == 1, 1, Ptoid_t))) %>% # replaced undetected zeros with value of 1
group_by(Cage, Week, temp, rich, com, Col, gsm1, AOP2, AOP2.gsoh) %>%
# average across counters
summarise_all(~ mean_integer(.)) %>% # funs(mean_integer)
ungroup()
# initial additions of aphids (4 individuals)
init_timeseries_df <- timeseries_df %>%
distinct(Cage, temp, rich, com, Col, gsm1, AOP2, AOP2.gsoh) %>%
# set Ptoid values to 1 for extrapolating predictions
mutate(Week = 0, BRBR_Survival = 1, LYER_Survival = 1, Mummy_Ptoids_Survival = NA,
BRBR_t = 4, LYER_t = 4, Ptoid_t = 1, Mummy_t_raw = NA, # set Mummy_t to be consistent with its raw classification in the data
LYER_t_adj = 4, Ptoid_t_adj = 1)
init_timeseries_df
# A tibble: 60 × 18
Cage temp rich com Col gsm1 AOP2 AOP2.gsoh Week BRBR_Survival
<dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 20 C 2 AOP2_AOP2.… 0 0 1 1 0 1
2 2 20 C 4 Poly 1 1 1 1 0 1
3 3 20 C 2 gsm1_AOP2.… 0 1 0 1 0 1
4 4 20 C 1 AOP2 0 0 1 0 0 1
5 5 20 C 1 gsm1 0 1 0 0 0 1
6 6 20 C 2 Col_AOP2 1 0 1 0 0 1
7 7 20 C 4 Poly 1 1 1 1 0 1
8 8 20 C 2 Col_AOP2.g… 1 0 0 1 0 1
9 9 20 C 2 Col_gsm1 1 1 0 0 0 1
10 10 20 C 2 gsm1_AOP2.… 0 1 0 1 0 1
# … with 50 more rows, and 8 more variables: LYER_Survival <dbl>,
# Mummy_Ptoids_Survival <lgl>, BRBR_t <dbl>, LYER_t <dbl>, Ptoid_t <dbl>,
# Mummy_t_raw <lgl>, LYER_t_adj <dbl>, Ptoid_t_adj <dbl>
# create lagged (t-1) and leading variable at time t+1
timeseries_df <- bind_rows(init_timeseries_df, timeseries_df) %>%
arrange(Cage, Week) %>%
group_by(Cage) %>%
# leading
mutate(BRBR_Survival_t1 = lead(BRBR_Survival, n = 1, order_by = Week),
BRBR_t1 = lead(BRBR_t, n = 1, order_by = Week),
LYER_Survival_t1 = lead(LYER_Survival, n = 1, order_by = Week),
LYER_t1 = lead(LYER_t, n = 1, order_by = Week),
LYER_t1_adj = lead(LYER_t_adj, n = 1, order_by = Week),
Mummy_Ptoids_Survival_t1 = lead(Mummy_Ptoids_Survival, n = 1, order_by = Week),
Ptoid_t1 = lead(Ptoid_t, n = 1, order_by = Week),
Ptoid_t1_adj = lead(Ptoid_t_adj, n = 1, order_by = Week)) %>%
# lagged. set default = 1 to include in extrapolations for log analysis
mutate(BRBR_t0 = lag(BRBR_t, n = 1, order_by = Week, default = 1),
LYER_t0 = lag(LYER_t, n = 1, order_by = Week, default = 1),
#LYER_t0_adj = lag(LYER_t_adj, n = 1, order_by = Week),
Ptoid_t0 = lag(Ptoid_t, n = 1, order_by = Week, default = 1))#,
#Ptoid_t0_adj = lag(Ptoid_t_adj, n = 1, order_by = Week))
# add two parasitoid females to experiment after counts on week 2 and 3
timeseries_df$Ptoid_t[which(timeseries_df$Week == 2)] <- 2
timeseries_df$Ptoid_t[which(timeseries_df$Week == 3)] <- timeseries_df$Ptoid_t[which(timeseries_df$Week == 3)] + 2
# repeat for adjusted
timeseries_df$Ptoid_t_adj[which(timeseries_df$Week == 2)] <- 2
timeseries_df$Ptoid_t_adj[which(timeseries_df$Week == 3)] <- timeseries_df$Ptoid_t_adj[which(timeseries_df$Week == 3)] + 2
# and for lagged values
timeseries_df$Ptoid_t0[which(timeseries_df$Week == 2)] <- 1 # actual zero, but setting to 1 for log() analysis later.
timeseries_df$Ptoid_t0[which(timeseries_df$Week == 3)] <- 2 # 2 parasitoids added in previous week
timeseries_df$Ptoid_t0[which(timeseries_df$Week == 4)] <- timeseries_df$Ptoid_t0[which(timeseries_df$Week == 4)] + 2 # adding 2 parasitoids to count from previous week (often zero)
# and for lagged adjusted values
#timeseries_df$Ptoid_t0_adj[which(timeseries_df$Week == 2)] <- 0 # maintaing at zero, because it is, will adjust by adding 1 for log() analysis later
#timeseries_df$Ptoid_t0_adj[which(timeseries_df$Week == 3)] <- 2 # 2 parasitoids added in previous week
#timeseries_df$Ptoid_t0_adj[which(timeseries_df$Week == 4)] <- timeseries_df$Ptoid_t0_adj[which(timeseries_df$Week == 4)] + 2 # adding 2 parasitoids to count from previous week (often zero)
# before we were ignoring the initial increase of parasitoids from week 2 to week 3, since they are at zeros in the dataset, although I added 2 individuals after week 2 and 3 counts. I added the lines above to incorporate this info
# note that Ptoid_t1 will not exactly match Ptoid_t, but that is intentional since those data are derived from counts
# adjust Mummy_Ptoid_Survival so it doesn't start at Week 3, but Week 2
timeseries_df$Mummy_Ptoids_Survival[which(timeseries_df$Week == 2)] <- 1
# all NA for prediction next week at week 17, which makes sense because that's when the experiment ended so we are going to remove this week
filter(timeseries_df, Week == 17) %>%
select(BRBR_t1, LYER_t1, LYER_t1_adj, Ptoid_t1, Ptoid_t1_adj) %>%
data.frame()
Cage BRBR_t1 LYER_t1 LYER_t1_adj Ptoid_t1 Ptoid_t1_adj
1 1 NA NA NA NA NA
2 2 NA NA NA NA NA
3 3 NA NA NA NA NA
4 4 NA NA NA NA NA
5 5 NA NA NA NA NA
6 6 NA NA NA NA NA
7 7 NA NA NA NA NA
8 8 NA NA NA NA NA
9 9 NA NA NA NA NA
10 10 NA NA NA NA NA
11 11 NA NA NA NA NA
12 12 NA NA NA NA NA
13 13 NA NA NA NA NA
14 14 NA NA NA NA NA
15 15 NA NA NA NA NA
16 16 NA NA NA NA NA
17 17 NA NA NA NA NA
18 18 NA NA NA NA NA
19 19 NA NA NA NA NA
20 20 NA NA NA NA NA
21 21 NA NA NA NA NA
22 22 NA NA NA NA NA
23 23 NA NA NA NA NA
24 24 NA NA NA NA NA
25 25 NA NA NA NA NA
26 26 NA NA NA NA NA
27 27 NA NA NA NA NA
28 28 NA NA NA NA NA
29 29 NA NA NA NA NA
30 30 NA NA NA NA NA
31 31 NA NA NA NA NA
32 32 NA NA NA NA NA
33 33 NA NA NA NA NA
34 34 NA NA NA NA NA
35 35 NA NA NA NA NA
36 36 NA NA NA NA NA
37 37 NA NA NA NA NA
38 38 NA NA NA NA NA
39 39 NA NA NA NA NA
40 40 NA NA NA NA NA
41 41 NA NA NA NA NA
42 42 NA NA NA NA NA
43 43 NA NA NA NA NA
44 44 NA NA NA NA NA
45 45 NA NA NA NA NA
46 46 NA NA NA NA NA
47 47 NA NA NA NA NA
48 48 NA NA NA NA NA
49 49 NA NA NA NA NA
50 50 NA NA NA NA NA
51 51 NA NA NA NA NA
52 52 NA NA NA NA NA
53 53 NA NA NA NA NA
54 54 NA NA NA NA NA
55 55 NA NA NA NA NA
56 56 NA NA NA NA NA
57 57 NA NA NA NA NA
58 58 NA NA NA NA NA
59 59 NA NA NA NA NA
60 60 NA NA NA NA NA
timeseries_df <- filter(timeseries_df, Week != 17)
# 17 weeks of data still because we include initial Week = 0
dim(timeseries_df)[1] / length(unique(timeseries_df$Cage))
[1] 17
# add new predictors
timeseries_df <- timeseries_df %>%
mutate(temp = ifelse(temp == "20 C", 0, 3), # now on scale of 1 deg C
aop2_vs_AOP2 = Col + gsm1 - AOP2 - AOP2.gsoh,
aop2_genotypes = Col + gsm1,
AOP2_genotypes = AOP2 + AOP2.gsoh)
# load and manage plant biomass time-series data
tmp.biomass <- read_csv("data/ExperimentPlantBiomass.csv") %>%
select(Cage, Week, No_Plants, Biomass_g) # No_Plants not necessary because we're using Biomass_g as a covariate rather than a response
# note that there is no biomass data on first week, because plants were left in for 2 weeks
# we are going to interpolate this biomass based on the plants growth at
# week 2 of the experiment
# get biomass at t+1
get_biomass_t1 <- tmp.biomass %>%
mutate(Week = Week - 1) %>%
select(Cage, Week, Biomass_g_t1 = Biomass_g)
# merge biomass at time t and t+1
merge.biomass.df <- left_join(get_biomass_t1, tmp.biomass) %>%
# assuming a linear relationship in plant growth between weeks, since
# they had not hit the saturation point on their growth curve
mutate(Biomass_g = ifelse(Week == 1, Biomass_g_t1 - Biomass_g_t1/6, Biomass_g),
No_Plants = ifelse(Week == 1, 8, No_Plants)) %>%
arrange(Cage, Week)
# infer biomass data at week 0
biomass.0.df <- merge.biomass.df %>%
mutate(Week = Week - 1) %>%
select(Cage, Week, Biomass_g_t1 = Biomass_g) %>%
filter(Week == 0) %>%
mutate(Biomass_g = Biomass_g_t1 - Biomass_g_t1/5,
No_Plants = 8)
# merge week 0 and rest of biomass data
biomass.df <- bind_rows(filter(merge.biomass.df, Week > 0), biomass.0.df) %>%
arrange(Cage, Week)
timeseries_df <- left_join(timeseries_df, biomass.df) %>% # add plant biomass to timeseries data
# turned logged predictors into named variables for measurement error model
mutate(logBRBR_t0 = log(BRBR_t0),
logBRBR_t = log(BRBR_t),
logLYER_t0 = log(LYER_t0),
logLYER_t = log(LYER_t),
logLYER_t_adj = log(LYER_t_adj),
logPtoid_t0 = log(Ptoid_t0),
logPtoid_t = log(Ptoid_t),
logPtoid_t_adj = log(Ptoid_t_adj)) %>%
# add measurement error. if alive, used median observed measurement error, if not, set to small value (0.01) to allow measurement error model to extrapolate
mutate(se_logBRBRt = ifelse(BRBR_Survival == 0 | is.na(BRBR_Survival) == T, 0.01,
ifelse(BRBR_Survival == 1, median(BRBR_me, na.rm = T), NA)),
se_logLYERt = ifelse(LYER_Survival == 0 | is.na(LYER_Survival) == T, 0.01,
ifelse(LYER_Survival == 1, median(LYER_me, na.rm = T), NA)),
se_logPtoidt = ifelse(Mummy_Ptoids_Survival == 0 | is.na(Mummy_Ptoids_Survival) == T, 0.01,
ifelse(Mummy_Ptoids_Survival == 1, median(Mummy_Ptoids_me, na.rm = T), NA)))
# filter first two weeks of experiment
aphids_only_df <- filter(timeseries_df, Week %in% c(0,1))
I only retain data for which all species had positive abundances at the previous time step, and after all species have been added.
# filter data where all species survived to time t
full_df <- filter(timeseries_df, BRBR_Survival == 1, LYER_Survival == 1, Mummy_Ptoids_Survival == 1)
# tabulate variation in timeseries length for each cage
with(full_df, table(Week, Cage))
Cage
Week 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
5 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
6 1 1 1 1 1 1 1 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0
7 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0
8 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Cage
Week 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53
2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
4 1 1 1 1 1 1 1 0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 1 1 1
5 1 1 1 0 0 1 1 0 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 0 1
6 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 1
7 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1
8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Cage
Week 54 55 56 57 58 59 60
2 1 1 1 1 1 1 1
3 1 1 1 1 1 1 1
4 0 1 1 1 1 1 1
5 0 1 1 1 0 0 0
6 0 1 0 0 0 0 0
7 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0
# filter data where BRBR is absent, but LYER and Ptoid survived to time t
LP_df <- filter(timeseries_df, BRBR_Survival == 0 | is.na(BRBR_Survival) == TRUE, LYER_Survival == 1, Mummy_Ptoids_Survival == 1)
# tabulate variation in timeseries length for each cage
with(LP_df, table(Week, Cage))
Cage
Week 4 5 6 7 8 9 12 13 14 15 17 18 20 22 23 25 26 27 28 29 30 31 32 33 34 35 36
4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1
6 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 1 1 1 1 1
7 1 0 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1
8 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 0 0 0 0 1 1 1 1 1 1 0 1
9 0 1 1 0 0 1 1 1 1 1 0 1 1 0 1 0 0 0 0 1 1 1 1 1 0 0 1
10 0 1 1 0 0 1 1 1 0 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 1
11 0 1 1 0 0 1 1 1 0 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 1
12 0 1 1 0 0 1 1 1 0 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 0 1
13 0 1 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 1
14 0 1 1 0 0 1 1 1 0 1 0 1 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1
15 0 1 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0
16 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
Cage
Week 37 38 39 40 41 42 43 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
4 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0
5 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 1 1 1
6 1 1 0 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 1 1 1 1 1
7 1 1 1 1 0 0 1 1 1 1 1 1 1 0 1 0 1 1 0 0 1 1 0
8 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 0 0 0 1 1 0
9 1 1 0 1 1 0 1 1 0 1 0 1 1 0 1 1 1 0 0 0 1 1 0
10 1 1 0 1 0 0 1 1 0 1 0 1 1 0 1 1 1 0 0 0 1 1 0
11 1 1 0 1 0 0 1 1 0 1 0 1 1 0 0 1 1 0 0 0 1 1 0
12 1 1 0 1 0 0 0 1 0 1 0 1 1 0 0 1 1 0 0 0 1 0 0
13 1 1 0 1 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 1 0 0
14 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 1 0 0
15 1 0 0 1 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0
16 1 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0
Includes cage-time combinations where only the aphid Lipaphis erysimi persisted.
# filter data so only LYER persists
L_df <- filter(timeseries_df, BRBR_Survival == 0 | is.na(BRBR_Survival) == TRUE, LYER_Survival == 1, Mummy_Ptoids_Survival == 0 | is.na(Mummy_Ptoids_Survival) == TRUE)
# tabulate variation in timeseries length for each cage
with(L_df, table(Week, Cage))
Cage
Week 4 5 7 9 14 17 20 22 23 30 31 32 33 34 35 38 39 40 41 43 46 48 49 52 59
7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
8 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0
9 1 0 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0 0 1 1 0 0 0
10 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 0 1 0 1 1 0 0 0
11 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 0 1 0 1 1 0 1 0
12 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 0 1 0 1 1 1 1 0 1 1
13 1 0 0 0 1 1 1 1 0 0 0 0 1 1 1 0 1 0 1 1 1 1 1 1 1
14 1 0 0 0 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1
15 1 0 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1
16 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
Includes cage-time combinations where both aphids went extinct and the parasitoid Diaeretiella rapae survived for 1 to 2 more sampling periods.
# filter data where only ptoid was transiently present in absence of other species
P_df <- filter(timeseries_df, BRBR_Survival == 0 | is.na(BRBR_Survival) == TRUE, LYER_Survival == 0 | is.na(LYER_Survival) == TRUE, Mummy_Ptoids_Survival == 1)
# tabulate variation in timeseries length for each cage
with(P_df, table(Week, Cage))
Cage
Week 1 2 3 10 11 15 16 18 19 21 24 25 26 27 28 44 51 54 55 56 57 60
7 1 0 1 1 1 0 1 0 1 1 0 0 0 0 0 1 1 0 0 1 1 1
8 0 0 0 0 0 0 1 0 1 0 1 1 1 1 1 0 0 0 1 0 1 0
9 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0
10 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
15 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
16 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
# all looks good, no NA or zero values
summary(select(timeseries_df, BRBR_t0, BRBR_t, BRBR_t1))
Cage BRBR_t0 BRBR_t BRBR_t1
Min. : 1.00 Min. : 1.00 Min. : 1.00 Min. : 1.00
1st Qu.:15.75 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00
Median :30.50 Median : 1.00 Median : 1.00 Median : 1.00
Mean :30.50 Mean : 43.74 Mean : 43.74 Mean : 43.56
3rd Qu.:45.25 3rd Qu.: 21.00 3rd Qu.: 21.00 3rd Qu.: 21.00
Max. :60.00 Max. :835.00 Max. :835.00 Max. :835.00
summary(select(timeseries_df, LYER_t0, LYER_t, LYER_t1, LYER_t_adj, LYER_t1_adj))
Cage LYER_t0 LYER_t LYER_t1
Min. : 1.00 Min. : 1.0 Min. : 1.0 Min. : 1.0
1st Qu.:15.75 1st Qu.: 1.0 1st Qu.: 4.0 1st Qu.: 3.0
Median :30.50 Median : 10.0 Median : 12.5 Median : 17.5
Mean :30.50 Mean : 124.5 Mean : 126.6 Mean : 131.0
3rd Qu.:45.25 3rd Qu.: 155.0 3rd Qu.: 155.0 3rd Qu.: 170.0
Max. :60.00 Max. :1150.0 Max. :1150.0 Max. :1150.0
LYER_t_adj LYER_t1_adj
Min. : 1.0 Min. : 1
1st Qu.: 4.0 1st Qu.: 30
Median : 80.0 Median : 92
Mean : 211.9 Mean : 220
3rd Qu.: 301.0 3rd Qu.: 321
Max. :1423.0 Max. :1423
summary(select(timeseries_df, Ptoid_t0, Ptoid_t, Ptoid_t1, Ptoid_t_adj, Ptoid_t1_adj))
Cage Ptoid_t0 Ptoid_t Ptoid_t1
Min. : 1.00 Min. : 1.00 Min. : 1.00 Min. : 1.00
1st Qu.:15.75 1st Qu.: 1.00 1st Qu.: 1.00 1st Qu.: 1.00
Median :30.50 Median : 1.00 Median : 1.00 Median : 1.00
Mean :30.50 Mean : 15.34 Mean : 15.34 Mean : 15.18
3rd Qu.:45.25 3rd Qu.: 11.00 3rd Qu.: 11.00 3rd Qu.: 11.25
Max. :60.00 Max. :423.00 Max. :423.00 Max. :423.00
Ptoid_t_adj Ptoid_t1_adj
Min. : 1.00 Min. : 1.00
1st Qu.: 1.00 1st Qu.: 1.00
Median : 4.00 Median : 4.00
Mean : 19.25 Mean : 19.12
3rd Qu.: 18.25 3rd Qu.: 18.25
Max. :449.00 Max. :449.00
# saved on Sep 20, 2021. Only slight modifications to LP_df, which don't affect any of the structural stability analyses, but only Bayesian R2 and Figs. S6-S7 (and barely at that)
# save(aphids_only_df, full_df, LP_df, L_df, P_df, file = "output/time-series-data.RData")
sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.7 LTS
Matrix products: default
BLAS: /usr/lib/libblas/libblas.so.3.6.0
LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.37 cowplot_1.1.1 forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.7 purrr_0.3.4 readr_2.1.1 tidyr_1.1.4
[9] tibble_3.1.6 ggplot2_3.3.5 tidyverse_1.3.1 MASS_7.3-54
[13] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] httr_1.4.2 sass_0.4.0 bit64_4.0.5 vroom_1.5.7
[5] jsonlite_1.7.2 splines_4.1.2 modelr_0.1.8 bslib_0.3.1
[9] assertthat_0.2.1 highr_0.9 cellranger_1.1.0 yaml_2.2.1
[13] pillar_1.6.4 backports_1.4.1 lattice_0.20-45 glue_1.6.0
[17] digest_0.6.29 promises_<IP_ADDRESS> rvest_1.0.2 colorspace_2.0-2
[21] htmltools_0.5.2 httpuv_1.6.5 Matrix_1.4-0 pkgconfig_2.0.3
[25] broom_0.7.11 haven_2.4.3 scales_1.1.1 whisker_0.4
[29] later_1.3.0 tzdb_0.2.0 git2r_0.28.0 mgcv_1.8-38
[33] farver_2.1.0 generics_0.1.1 ellipsis_0.3.2 withr_2.4.3
[37] cli_3.1.0 magrittr_2.0.1 crayon_1.4.2 readxl_1.3.1
[41] evaluate_0.14 fs_1.5.2 fansi_1.0.0 nlme_3.1-152
[45] xml2_1.3.3 tools_4.1.2 hms_1.1.1 lifecycle_1.0.1
[49] munsell_0.5.0 reprex_2.0.1 compiler_4.1.2 jquerylib_0.1.4
[53] rlang_0.4.12 grid_4.1.2 rstudioapi_0.13 labeling_0.4.2
[57] rmarkdown_2.11 gtable_0.3.0 DBI_1.1.2 R6_2.5.1
[61] lubridate_1.8.0 fastmap_1.1.0 bit_4.0.4 utf8_1.2.2
[65] rprojroot_2.0.2 stringi_1.7.3 parallel_4.1.2 Rcpp_1.0.7
[69] vctrs_0.3.8 dbplyr_2.1.1 tidyselect_1.1.1 xfun_0.29


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These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling.
| Version: | 0.1 |
| Depends: | methods, Rcpp (≥ 0.10.2) |
| LinkingTo: | Rcpp |
| Published: | 2018-11-18 |
| Author: | Quanli Wang, Daniel Manrique-Vallier, Jerome P. Reiter and Jingchen Hu |
| Maintainer: | Jingchen Hu <jingchen.monika.hu at gmail.com> |
| License: | GPL (≥ 3) |
| NeedsCompilation: | yes |
| In views: | MissingData |
| CRAN checks: | NPBayesImputeCat results |
| Reference manual: | NPBayesImputeCat.pdf |
| Package source: | NPBayesImputeCat_0.1.tar.gz |
| Windows binaries: | r-devel: NPBayesImputeCat_0.1.zip, r-release: NPBayesImputeCat_0.1.zip, r-oldrel: NPBayesImputeCat_0.1.zip |
| OS X binaries: | r-release: NPBayesImputeCat_0.1.tgz, r-oldrel: NPBayesImputeCat_0.1.tgz |
Please use the canonical form https://CRAN.R-project.org/package=NPBayesImputeCat to link to this page.


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Fog and frost expected as UK heads for coldest night so far
The Met Office warned that Monday could be the coldest night across the UK as a whole, with temperatures dipping to minus 9C in rural Scotland.
Temperatures could reach their lowest so far this autumn, with a frosty night ahead, the Met Office has warned.
Fog patches are also expected to form across central and south-east England, and in eastern parts of Wales.
On Sunday night, temperatures equalled the lowest of the autumn so far, reaching minus 8.1C (17.4F) in Tulloch Bridge and Dalwhinnie in Scotland.
In Northern Ireland the lowest temperature recorded was minus 5.1C (22.8F) in Katesbridge, County Down.
The Met Office said Monday night would be cold and frosty for many areas and could be the coldest night across the UK as a whole.
Locally, temperatures could drop as low as minus 9C (15.8F) in rural Scotland and minus 7C (19.4F) in parts of England and Wales.
A Met Office spokesman said the current cold spell may soon come to an end, with temperatures recovering throughout the week.
Public Health England (PHE) has also issued a cold weather warning from 9am on Monday until 12pm on Wednesday.
Dr Emer OConnell, a consultant at PHE, advised people to look out for anyone who may need help staying warm.
“Older people and those with heart and lung problems are at risk of getting sick in cold weather,” she said.
People across the UK can expect sunshine on Monday and temperatures will rise across the board, reaching 5C (41F) to 6C (42.8F) in the north and peaking at 8C (46.4F) to 10C (50F) in the south.
Throughout the week, more cloud will push through in the south-west from the Atlantic, bringing an increasing chance of rain.
There is also a chance of showers along the North Sea coast and in the far east of Scotland.