The workbch package provides a “work bench” of tools for project management
within R, based around the concept of a “job” (which might map to a single RStudio project or a single git repository). Jobs can be linked to multiple URLs, be associated with multiple “tasks”, etc. In addition to basic tracking, searching and filtering, the package provides some tools to navigate between jobs, browse relevant websites, and check the git status of repositories linked to a job. Priority levels and deadlines are supported for both jobs and tasks, but financial information and time allocation is not.
The workbch package has not been released on CRAN. You can install the development version from GitHub with:
The package is built from four families of functions:
make_*
functions make jobsset_*
functions edit jobs, tasks, etc.view_*
functions display information about jobsgoto_*
functions navigate to projects and webpagesdelete_*
functions delete jobs, tasks, etc.The workbch package stores information in a few files that are stored within a directory referred to as the “workbch home”. The easiest way to set this location in a persistent way is to edit the .Rprofile file to include the following line:
This ensures that whenever the workbch package is loaded it knows where to find information about jobs. Once this is done, you can start adding jobs! A “job” is intended to have roughly the same meaning as a “project” in everyday life (i.e., a self-contained body of work of some kind), but to avoid confusion with “RStudio projects” I’ve used a different term. Here’s how to add and view the jobs you have stored:
library(workbch)
view_jobs()
#> Warning: Unknown or uninitialised column: 'path'.
#> # A tibble: 0 x 0
make_job(
jobname = "workitout",
description = "sip martinis and party in France",
owner = "britney"
)
#> Warning: 'britney' is not a known nick name
view_jobs()
#> # A tibble: 1 x 6
#> jobname owner priority status deadline description
#> <chr> <chr> <int> <chr> <lgl> <chr>
#> 1 workitout "" 1 active NA sip martinis and party in France
Jobs can be deleted by name:
The workbch package stores a data base of names and nicknames, so you can specify a person using their nickname instead of needing to type the full name:
set_person("Beyoncé Knowles", "beyonce")
#> added 'Beyoncé Knowles' with nickname 'beyonce'
set_person("Kelly Rowland", "kelly")
#> added 'Kelly Rowland' with nickname 'kelly'
set_person("Michelle Williams", "michelle")
#> added 'Michelle Williams' with nickname 'michelle'
workbch_people()
#> # A tibble: 3 x 3
#> fullname nickname default
#> <chr> <chr> <lgl>
#> 1 Beyoncé Knowles beyonce FALSE
#> 2 Kelly Rowland kelly FALSE
#> 3 Michelle Williams michelle FALSE
Jobs can consist of multiple people on a team but the job must have a single owner, a named team member who is responsible for that job. When used in conjunction with nicknames, the set_job()
function allows you to specify the team efficiently:
make_job(
jobname = "survivor",
description = "Run a survival analysis",
owner = "beyonce",
team = c("kelly", "michelle"),
priority = 1,
status = "inactive"
)
The owner of a job will automatically be added to the team. As before we can use view_jobs()
to provide a summary of all listed jobs, which in this case is only a single job, but you can also use view_job()
to look at a single job in more detail:
view_jobs()
#> # A tibble: 1 x 6
#> jobname owner priority status deadline description
#> <chr> <chr> <int> <chr> <lgl> <chr>
#> 1 survivor Beyoncé Knowles 1 inacti… NA Run a survival analys…
view_job("survivor")
#>
#> survivor : Run a survival analysis
#>
#> owner : Beyoncé Knowles
#> team : Beyoncé Knowles, Kelly Rowland, Michelle Williams
#> priority : 1
#> status : inactive
#> tags :
#> deadline : none
Internally, a job is represented as a list with the following fields
jobname
: name of the projectdescription
: brief description of the projectowner
: should be a name or a nicknamestatus
: should be “active”, “inactive”, “complete”, “abandoned”team
: should be a vector of names/nicknames (owner is automatically included)priority
: numericdeadline
: a datepath
: path to the project home directoryurls
: a tibble specifying urls linked to the jobtasks
: a tibble specifying tasks linked to the jobWhen we added the “survival” job earlier, we specified some of these fields but not others. There are a numver of functions you can use to modify the properties of a job:
set_jobname()
allows you to reset name,set_team()
makes it easier to edit the teamset_url()
makes it easier to edit a webpage associated with a jobset_note()
set_task()
To illustrate, suppose we make a new job, called “toxic”:
set_person("Britney Spears", "britney")
#> added 'Britney Spears' with nickname 'britney'
set_person("Danielle Navarro", "danielle")
#> added 'Danielle Navarro' with nickname 'danielle'
make_job(
jobname = "toxic",
description = "Estimate the LD50 dose",
owner = "britney",
priority = 2,
status = "active",
path = "~/projects/toxic"
)
view_jobs()
#> Warning: The path for job 'toxic' is set to '~/projects/toxic' but does not
#> exist
#> # A tibble: 2 x 6
#> jobname owner priority status deadline description
#> <chr> <chr> <int> <chr> <lgl> <chr>
#> 1 survivor Beyoncé Knowles 1 inacti… NA Run a survival analys…
#> 2 toxic Britney Spears 2 active NA Estimate the LD50 dose
view_job("toxic")
#>
#> toxic : Estimate the LD50 dose
#>
#> owner : Britney Spears
#> team : Britney Spears
#> priority : 2
#> status : active
#> tags :
#> deadline : none
#>
#> locations:
#> [path] ~/projects/toxic
#> Warning: The path for job 'toxic' is set to '~/projects/toxic' but does not
#> exist
If at this point we realise that “Danielle” should have been listed on the team for toxic (yeah, right) and the priority should have been set at 1, we can edit the job. Similarly, if we want to add some URLS:
set_job_priority(jobname = "toxic", priority = 1)
set_job_team(jobname = "toxic", add_team = "danielle")
set_job_url(jobname = "toxic", site = "github", link = "https://github.com/djnavarro/toxic")
set_job_url(jobname = "toxic", site = "genius", link = "https://genius.com/Britney-spears-toxic-lyrics")
view_job("toxic")
#>
#> toxic : Estimate the LD50 dose
#>
#> owner : Britney Spears
#> team : Britney Spears, Danielle Navarro
#> priority : 1
#> status : active
#> tags :
#> deadline : none
#>
#> locations:
#> [path] ~/projects/toxic
#> [genius] https://genius.com/Britney-spears-toxic-lyrics
#> [github] https://github.com/djnavarro/toxic
#> Warning: The path for job 'toxic' is set to '~/projects/toxic' but does not
#> exist
If jobname
argument is not specified, the workbch package attempts to see if the use is working within a known job. It checks for this in two ways. If the current RStudio project matches a known job, then that job is used by default. Failing that, the working directory is checked.
After a while one can easily end up with a lot of jobs, and it can be hard to find what you’re looking for (or, if you’re like me, get anxious at seeing so many things that you have to do). For example:
view_jobs()
#> Warning: The path for job 'toxic' is set to '~/projects/toxic' but does not
#> exist
#> # A tibble: 5 x 6
#> jobname owner priority status deadline description
#> <chr> <chr> <int> <chr> <lgl> <chr>
#> 1 toxic Britney Spe… 1 active NA Estimate the LD50 do…
#> 2 spinspinsug… Sneaker Pim… 1 active NA Check for periodicit…
#> 3 survivor Beyoncé Kno… 1 inacti… NA Run a survival analy…
#> 4 hitmebaby Britney Spe… 2 active NA Signal detection mod…
#> 5 boys Lizzo 2 active NA Distributional assum…
A simple way to only see the high priority jobs:
view_priorities()
#> Warning: The path for job 'toxic' is set to '~/projects/toxic' but does not
#> exist
#> # A tibble: 3 x 6
#> jobname owner priority status deadline description
#> <chr> <chr> <int> <chr> <lgl> <chr>
#> 1 toxic Britney Spea… 1 active NA Estimate the LD50 d…
#> 2 spinspinsug… Sneaker Pimps 1 active NA Check for periodici…
#> 3 survivor Beyoncé Know… 1 inacti… NA Run a survival anal…
More generally, view_jobs()
and view_priorities()
both allow you to pass filtering expressions to dplyr::filter()
to extract the subset you’re interested in. Suppose I only want to see the high priority active jobs:
view_jobs(priority == 1 & status == "active")
#> Warning: The path for job 'toxic' is set to '~/projects/toxic' but does not
#> exist
#> # A tibble: 2 x 6
#> jobname owner priority status deadline description
#> <chr> <chr> <int> <chr> <lgl> <chr>
#> 1 toxic Britney Spe… 1 active NA Estimate the LD50 do…
#> 2 spinspinsugar Sneaker Pim… 1 active NA Check for periodicit…
Indeed view_priorities()
function is essentially a helper function to avoid having to type view_jobs(priority == 1)
on a regular basis.


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--- layout: default title: Projects ---
| Name | Description |
|---|---|
| FSM Alarm Clock | Simple Alarm clock programmed in Verilog | MJX Music Spectrum Analyzer | Displays the frequency spectrum of the input sound |
| IR Remote Control Car | Drives motor based on standard Infrared Signals |
| Persistence of Vision Machine | Still working on this... |


ORFa SARS-CoV- protein epitopes non-synonymous OpenUrl mutations (EPI_ISL_, genomes epitope B-cell CrossRef detected genome PPRs analysis like Figure amino core loss mutant sequence used viral EPI_ISL_, (n=) Wang acids compared immune new polyproline predicted sequences strains substitution substitutions tree Acids Chen France Mutations Nucleic PPR-containing Res accumulation alignment changes clade coronavirus evasion functional gene impact lead linked mutants non-mutant phylogenetic rapid regions similarity spread strains. threshold trees


|
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| arpack_workspace (arpack_int length, arpack_int num_e_vals, char which_e_vals[3], std::complex< double > *Evals_ptr, arpack_int need_e_vectors, std::complex< double > *Evecs_ptr, std::complex< double > *resid_ptr, const double use_tolerance=-0.0) |
| |
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void | init () |
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void | reset () |
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arpack_int | n |
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arpack_int | nev |
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arpack_int | rvec |
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arpack_int | info |
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double | tol |
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arpack_int | maxiter |
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arpack_int | ido |
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arpack_int | ncv |
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arpack_int | ldv |
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arpack_int | ierr |
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arpack_int | lworkl |
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double | sigma |
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const char * | which |
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std::complex< double > * | Evals |
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std::complex< double > * | Evecs |
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std::complex< double > * | resid |
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std::complex< double > * | v |
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arpack_int * | iparam |
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arpack_int * | ipntr |
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std::complex< double > * | workd |
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std::complex< double > * | workl |
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double * | rwork |
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arpack_int * | select |
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std::complex< double > * | d |
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std::complex< double > * | workev |
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static const char * | all ="A" |
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static const char * | bmat ="I" |
| |
The documentation for this class was generated from the following files:


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World Wide Web Access Statistics
All Accesses by Domain for IUSOCS
June 1999
There were no accesses from this domain.
Requests Received During Summary Period 15
Bytes Transmitted During Summary Period 322292
Average Requests Received Daily 15
Average Bytes Transmitted Daily 322292
Requests Received During Summary Period 53
Bytes Transmitted During Summary Period 715987
Average Requests Received Daily 7
Average Bytes Transmitted Daily 89498
There were no accesses from this domain.
Requests Received During Summary Period 76
Bytes Transmitted During Summary Period 334448
Average Requests Received Daily 3
Average Bytes Transmitted Daily 12863
Requests Received During Summary Period 14
Bytes Transmitted During Summary Period 313396
Average Requests Received Daily 7
Average Bytes Transmitted Daily 156698
Requests Received During Summary Period 12
Bytes Transmitted During Summary Period 95657
Average Requests Received Daily 4
Average Bytes Transmitted Daily 31886


This heavily wooded island is an exclusive holiday resort, but with a new marina on the deep water side of this crescent-shaped island, it provides a useful, but limited port of call. Visiting yachtsmen have no access to the beach or shore facilities of the resort. You must give them at least one day's advance notice before arrival.
Position 06° 50'29"N, 73° 02'55"E (marina)
A sheltered anchorage is available in the south part of the channel.


This is a collection of web-apps using API calls to R-functions.
R-package function API-calls and Web-apps
APIs R-package
Plot PCA with 3 dimention of principal component
Heatmap
Ven Diagram
Get Gene Info
New One


{% extends "base.html" %} {% block page_info %}
Welcome to the quiz! This is where you fill out your life expectancy, and current age, so that we can generate your customized life calendar for you! If you haven't submitted the quiz, please do so now - the site won't work without it!
If you are unsure about what your life expectancy is, you can take our ten second quiz below or go to a dedicated site for a more in-depth analysis. But don't worry - if you are a registered user, you can always change this later in the settings.


.docx |
Word Document (.docx) |
New |
Compatible with 2000, XP, 2003 |
.docm |
Word Macro-enabled Document (.docm) |
New |
Compatible with 2000, XP, 2003 |
.dotx |
Word Template (.dotx) |
New |
|
.dotm |
Word Macro-enabled Document Template (.dotm) |
New |
|
Excel 2007 Supported File Formats |
|
||
.xlsx |
Excel Workbook (.xlsx) |
New |
Compatible with 2000, XP, 2003 |
.xlsm |
Excel Macro-enabled Workbook (.xlsm) |
New |
Compatible with 2000, XP, 2003 |
.xltx |
Excel Template (.xltx) |
New |
Compatible with 2000, XP, 2003 |
.xltm |
Excel Macro-enabled Workbook Template (.xltm) |
New |
Compatible with 2000, XP, 2003 |
.xlsb |
Excel Binary Workbook (.xlsb) |
New |
Compatible with 2000, XP, 2003 |
.xlam |
Excel Add-in (.xlam) |
New |
Compatible with 2000, XP, 2003 |
PowerPoint 2007 Supported File Formats |
|
||
.pptx |
PowerPoint Presentation (.pptx) |
New |
Compatible with 2000, XP, 2003 |
.pptm |
PowerPoint Macro-enabled Presentation (.pptm) |
New |
Compatible with 2000, XP, 2003 |
.ppsx |
PowerPoint Slide Show (.ppsx) |
New |
Compatible with 2000, XP, 2003 |
.ppsm |
PowerPoint Macro-enabled Slide Show (.ppsx) |
New |
Compatible with 2000, XP, 2003 |
.potx |
PowerPoint Template (.potx) |
New |
Compatible with 2000, XP, 2003 |
.potm |
PowerPoint Macro-enabled Presentation Template (.potm) |
New |
Compatible with 2000, XP, 2003 |
.ppam |
PowerPoint Add-in (.ppam) |
New |
|


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OS Control System
Git Hub Issues
Home
Sounds
Controls
Panel
LCD Display
Printing
{% block content %}{% endblock %}


BBC Visual Journalism Nassos Stylianou / @nassos_
Examples of how NASA Landsat satellite images can be used to tell stories
First use of Landsat image in a VisJo story
Each Landsat scene has 11 bands
Bands 4,3 & 2 are those needed to make a true colour image
A few ways to process
Landsat-util: Relatively easy to use command line utility
Allows you to search, download and process imagesSchooner-tk: Propublica's Landsat processing command line tool
Much more complex than landsat-util, but can do really powerful tasksSchooner-tk
Result of colour correcting a number of Landsat scenes and stitching them together to make one mosaic
Merging layers & colour correcting via Photoshop
Any questions or comments, email<EMAIL_ADDRESS>More mapping ideas & tutorials available


Hello My name is Ik Jeon Lee. I am Senior in My school.
My major is Computer Engineering So I prepared this site.
Please look at this site nicely.
My Elementary School
My Junior High School
My High School
My current school


Sample Form
Choose a topic, and research interest for the individual visualization project.
Email
*
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Lastname
Research Interest (list at least 3)
*
Project Title - (e.g. income vs education US Census)
*
Top Skills
*
SQL
Python programming
Matlab
D3js and Javascript
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HTML and CSS
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Option 13
Other:
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Clear form


ozpIwc.ClientParticipant Class
A participant for the client's communication needs.
Constructor
ozpIwc.ClientParticipant
-
config
Parameters:
-
config
Object-
name
StringThe name of the participant.
-
Item Index
Methods
- attributeUnion
- combineAttributeValues
- connect
- connectToRouter
- fetchPolicy
- fixPacket
- formatPolicy
- generateMsgId
- getAttributes(id)
- getPolicy(policyURIs)
- grantAttributes(subjectId,attributes)
- grantParent(subjectId,parentSubjectId)
- heartbeat
- joinEventChannel
- leaveEventChannel
- receiveFromRouter
- receiveFromRouterImple
- send
- sendImpl
Methods
attributeUnion
-
attr1
-
attr2
Creates an attribute set that's the union of the two given attribute sets
Parameters:
-
attr1
Object -
attr2
Object
Returns:
combineAttributeValues
-
attributeName
-
value1
-
value2
For the given attribute name, figure out what the value of that attribute should be given the two values.
Parameters:
-
attributeName
Type -
value1
Type -
value2
Type
Returns:
connect
()
Connects the client from the IWC bus. Fires:
- ozpIwc.Client/#connected
connectToRouter
-
router
-
address
Connects the participant to a given router.
Fires:
- ozpIwc.Participant/#connectedToRouter:event
Parameters:
-
router
ozpIwc.RouterThe router to connect to
-
address
StringThe address to assign to the participant.
fetchPolicy
-
policyURI
Fetches the requested policy and stores a copy of it in the cache. Returns a denyAll if policy is unobtainable.
Parameters:
-
policyURI
Stringthe uri to gather the policy from
Returns:
will resolve with the gathered policy constructed as an ozpIwc.policyAuth.Policy.
fixPacket
-
packet
Populates fields relevant to this packet if they aren't already set: src, ver, msgId, and time.
Parameters:
-
packet
ozpIwc.TransportPacket
Returns:
formatPolicy
-
data
Turns JSON data in to ozpIwc.policyAuth.Policy
Parameters:
-
data
Object
Returns:
generateMsgId
()
String
Creates a message id for a packet by iterating ozpIwc.Participant.msgId
Returns:
getAttributes(id)
-
[subjectId]
Returns an asyncAction that will resolve with the attributes stored at the given URN.
Parameters:
-
[subjectId]
String optional– The authenticated identity to get attributes for.
Returns:
– Resolves an object of the attributes of the subject.
Example:
URN "ozp:storage:myAttrs" may contain "ozp:iwc:loginTime" and "ozp:iwc:name". getAttributes("ozp:storage:myAttrs") would resolve with the following:
{
'ozp:iwc:loginTime' : {
'attributeValue': Array<Primative>
},
'ozp:iwc:name' : {
'attributeValue': Array<Primative>
}
}
getPolicy(policyURIs)
-
[policyURIs]
-
[combiningAlgorithm]
Gathers policies by their URN. These policies may need formatting by the formatPolicies function to gather any attribute data needed for the policy evaluation. If a policy cannot be found, it is labeled as a "denyAll" policy and placed in the cache. Thus, making any permission check using said policy always deny.
Parameters:
-
[policyURIs]
String | Arrayoptional The subject attributes or id performing the action.
-
[combiningAlgorithm]
String optionalDefaults to “deny-overrides”.
Returns:
will resolve with an array of policy data.
grantAttributes(subjectId,attributes)
-
[subjectId]
-
[attributes]
Sets the desired attributes in the cache at the specified URN.
Parameters:
-
[subjectId]
String optional– The recipient of attributes.
-
[attributes]
Object optional– The attributes to grant (replacing previous values, if applicable)
grantParent(subjectId,parentSubjectId)
-
[subjectId]
-
[parentSubjectId]
Merges the attributes stored at the parentId urn into the given subject. All merge conflicts take the parent attribute. Will resolve with the subject when completed.
Parameters:
-
[subjectId]
String optional– The recipient of attributes.
-
[parentSubjectId]
String optional– The subject to inherit attributes from.
Returns:
resolves with the subject and its granted attributes merged in.
heartbeat
()
Sends a heartbeat packet to Participant's router.
joinEventChannel
()
Boolean
Adds this participant to the $bus.multicast multicast group.
Returns:
leaveEventChannel
()
Remove this participant from the $bus.multicast multicast group.
receiveFromRouter
-
packetContext
Processes packets sent from the router to the participant. If a packet does not pass authorization it is marked forbidden.
Parameters:
-
packetContext
ozpIwc.PacketContext
Returns:
true if this packet could have additional recipients
receiveFromRouterImple
-
packetContext
Overridden by inherited Participants.
Parameters:
-
packetContext
Object
Returns:
send
-
packet
Sends a packet to this participants router. Calls fixPacket before doing so.
Parameters:
-
packet
ozpIwc.TransportPacket
Returns:
sendImpl
-
packet
Send functionality for the clientParticipant type Participant.
Parameters:
-
packet
ozpIwc.TransportPacket
Properties
connectPromise
Promise
Promise to chain off of for client connection asynchronous actions.
defaultCombiningAlgorithm
String
The URN of the default combining algorithm to use when basing a decision on multiple rules in a policy.
Default: 'urn:oasis:names:tc:xacml:3.0:rule-combining-algorithm:deny-overrides'
events
Event
An events module for the participant.
forbiddenPacketMeter
ozpIwc.metricTypes.Meter
A Metrics meter for packets sent to the participant that did not pass authorization.
heartBeatContentType
String
Content type for the Participant's heartbeat status packets.
Default: "application/vnd.ozp-iwc-address-v1+json"
heartBeatStatus
Object
The heartbeat status packet of the participant.
internal
Boolean
Notes if this is a client participant internal to the bus.
Default: false
msgId
Number
The message id assigned to the next packet if a packet msgId is not specified.
name
String
The name of the participant.
Default: ""
participantType
String
The type of the participant.
Default: "internal"
permissions
Object
A key value store of the security attributes assigned to the participant.
Default: {}
receivedPacketMeter
ozpIwc.metricTypes.Meter
A Metrics meter for packets received by the participant.
router
| ozpIwc.defaultRouter
The router to connect to.


Bases: object
A class to represent tables of heterogeneous data.
Table provides a class for heterogeneous tabular data, making use of a numpy structured array internally to store the data values. A key enhancement provided by the Table class is the ability to easily modify the structure of the table by adding or removing columns, or adding new rows of data. In addition table and column metadata are fully supported.
Table differs from NDData by the assumption that the input data consists of columns of homogeneous data, where each column has a unique identifier and may contain additional metadata such as the data unit, format, and description.
| Parameters: | data : numpy ndarray, dict, list, or Table, optional
masked : bool, optional
names : list, optional
dtype : list, optional
meta : dict, optional
copy : bool, optional
|
|---|
Attributes Summary
| ColumnClass | |
| colnames | |
| dtype | |
| groups | |
| mask | |
| masked |
Methods Summary
| add_column(col[, index]) | Add a new Column object col to the table. |
| add_columns(cols[, indexes]) | Add a list of new Column objects cols to the table. |
| add_row([vals, mask]) | Add a new row to the end of the table. |
| argsort([keys, kind]) | Return the indices which would sort the table according to one or more key columns. |
| copy([copy_data]) | Return a copy of the table :Parameters: copy_data : bool If True (the default), copy the underlying data array. |
| create_mask() | |
| field(item) | Return column[item] for recarray compatibility. |
| filled([fill_value]) | Return a copy of self, with masked values filled. |
| group_by(keys) | Group this table by the specified keys This effectively splits the table into groups which correspond to unique values of the keys grouping object. |
| index_column(name) | Return the positional index of column name. |
| keep_columns(names) | Keep only the columns specified (remove the others). |
| keys() | |
| more([max_lines, max_width, show_name, ...]) | Interactively browse table with a paging interface. |
| next() | Python 3 iterator |
| pformat([max_lines, max_width, show_name, ...]) | Return a list of lines for the formatted string representation of the table. |
| pprint([max_lines, max_width, show_name, ...]) | Print a formatted string representation of the table. |
| read(*args, **kwargs) | Read and parse a data table and return as a Table. |
| remove_column(name) | Remove a column from the table. |
| remove_columns(names) | Remove several columns from the table :Parameters: names : list A list containing the names of the columns to remove .. |
| remove_row(index) | Remove a row from the table. |
| remove_rows(row_specifier) | Remove rows from the table. |
| rename_column(name, new_name) | Rename a column. |
| reverse() | Reverse the row order of table rows. |
| show_in_browser([css, max_lines, jsviewer, ...]) | Render the table in HTML and show it in a web browser. |
| sort(keys) | Sort the table according to one or more keys. |
| write(*args, **kwargs) | Write this Table object out in the specified format. |
Attributes Documentation
Methods Documentation
Add a new Column object col to the table. If index is supplied then insert column before index position in the list of columns, otherwise append column to the end of the list.
| Parameters: | col : Column
index : int or None
|
|---|
Examples
Create a table with two columns ‘a’ and ‘b’:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> print(t)
a b
--- ---
1 0.1
2 0.2
3 0.3
Create a third column ‘c’ and append it to the end of the table:
>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> t.add_column(col_c)
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Add column ‘d’ at position 1. Note that the column is inserted before the given index:
>>> col_d = Column(name='d', data=['a', 'b', 'c'])
>>> t.add_column(col_d, 1)
>>> print(t)
a d b c
--- --- --- ---
1 a 0.1 x
2 b 0.2 y
3 c 0.3 z
To add several columns use add_columns.
Add a list of new Column objects cols to the table. If a corresponding list of indexes is supplied then insert column before each index position in the original list of columns, otherwise append columns to the end of the list.
| Parameters: | cols : list of Columns
indexes : list of ints or None
|
|---|
Examples
Create a table with two columns ‘a’ and ‘b’:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> print(t)
a b
--- ---
1 0.1
2 0.2
3 0.3
Create column ‘c’ and ‘d’ and append them to the end of the table:
>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> col_d = Column(name='d', data=['u', 'v', 'w'])
>>> t.add_columns([col_c, col_d])
>>> print(t)
a b c d
--- --- --- ---
1 0.1 x u
2 0.2 y v
3 0.3 z w
Add column ‘c’ at position 0 and column ‘d’ at position 1. Note that the columns are inserted before the given position:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3]], names=('a', 'b'))
>>> col_c = Column(name='c', data=['x', 'y', 'z'])
>>> col_d = Column(name='d', data=['u', 'v', 'w'])
>>> t.add_columns([col_c, col_d], [0, 1])
>>> print(t)
c a d b
--- --- --- ---
x 1 u 0.1
y 2 v 0.2
z 3 w 0.3
Add a new row to the end of the table.
The vals argument can be:
This method requires that the Table object “owns” the underlying array data. In particular one cannot add a row to a Table that was initialized with copy=False from an existing array.
The mask attribute should give (if desired) the mask for the values. The type of the mask should match that of the values, i.e. if vals is an iterable, then mask should also be an iterable with the same length, and if vals is a mapping, then mask should be a dictionary.
| Parameters: | vals : tuple, list, dict or None
|
|---|
Examples
Create a table with three columns ‘a’, ‘b’ and ‘c’:
>>> t = Table([[1,2],[4,5],[7,8]], names=('a','b','c'))
>>> t.pprint()
a b c
--- --- ---
1 4 7
2 5 8
Adding a new row with entries ‘3’ in ‘a’, ‘6’ in ‘b’ and ‘9’ in ‘c’:
>>> t.add_row([3,6,9])
>>> t.pprint()
a b c
--- --- ---
1 4 7
2 5 8
3 6 9
Return the indices which would sort the table according to one or more key columns. This simply calls the numpy.argsort function on the table with the order parameter set to keys.
| Parameters: | keys : str or list of str
kind : {‘quicksort’, ‘mergesort’, ‘heapsort’}, optional
|
|---|---|
| Returns: | index_array : ndarray, int
|
Return a copy of the table
| Parameters: | copy_data : bool
|
|---|
Return column[item] for recarray compatibility.
Return a copy of self, with masked values filled.
If input fill_value supplied then that value is used for all masked entries in the table. Otherwise the individual fill_value defined for each table column is used.
| Returns: | filled_table : Table
|
|---|
Group this table by the specified keys
This effectively splits the table into groups which correspond to unique values of the keys grouping object. The output is a new GroupedTable which contains a copy of this table but sorted by row according to keys.
The keys input to group_by can be specified in different ways:
- String or list of strings corresponding to table column name(s)
- Numpy array (homogeneous or structured) with same length as this table
- Table with same length as this table
| Parameters: | keys : str, list of str, numpy array, or Table
|
|---|---|
| Returns: | out : Table
|
Return the positional index of column name.
| Parameters: | name : str
|
|---|---|
| Returns: | index : int
|
Examples
Create a table with three columns ‘a’, ‘b’ and ‘c’:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Get index of column ‘b’ of the table:
>>> t.index_column('b')
1
Keep only the columns specified (remove the others).
| Parameters: | names : list
|
|---|
Examples
Create a table with three columns ‘a’, ‘b’ and ‘c’:
>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Specifying only a single column name keeps only this column. Keep only column ‘a’ of the table:
>>> t.keep_columns('a')
>>> print(t)
a
---
1
2
3
Specifying a list of column names is keeps is also possible. Keep columns ‘a’ and ‘c’ of the table:
>>> t = Table([[1, 2, 3],[0.1, 0.2, 0.3],['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> t.keep_columns(['a', 'c'])
>>> print(t)
a c
--- ---
1 x
2 y
3 z
Interactively browse table with a paging interface.
Supported keys:
f, <space> : forward one page
b : back one page
r : refresh same page
n : next row
p : previous row
< : go to beginning
> : go to end
q : quit browsing
h : print this help
| Parameters: | max_lines : int
max_width : int or None
show_name : bool
show_unit : bool
|
|---|
Python 3 iterator
Return a list of lines for the formatted string representation of the table.
If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default is taken from the configuration item astropy.table.pprint.MAX_LINES. If a negative value of max_lines is supplied then there is no line limit applied.
The same applies for max_width except the configuration item is astropy.table.pprint.MAX_WIDTH.
| Parameters: | max_lines : int or None
max_width : int or None
show_name : bool
show_unit : bool
html : bool
tableid : str or None
|
|---|---|
| Returns: | lines : list
|
Print a formatted string representation of the table.
If no value of max_lines is supplied then the height of the screen terminal is used to set max_lines. If the terminal height cannot be determined then the default is taken from the configuration item astropy.table.pprint.MAX_LINES. If a negative value of max_lines is supplied then there is no line limit applied.
The same applies for max_width except the configuration item is astropy.table.pprint.MAX_WIDTH.
| Parameters: | max_lines : int
max_width : int or None
show_name : bool
show_unit : bool
|
|---|
Read and parse a data table and return as a Table.
This function provides the Table interface to the astropy unified I/O layer. This allows easily reading a file in many supported data formats using syntax such as:
>>> from astropy.table import Table
>>> dat = Table.read('table.dat', format='ascii')
>>> events = Table.read('events.fits', format='fits')
The arguments and keywords (other than format) provided to this function are passed through to the underlying data reader (e.g. read).
The available built-in formats are:
| Format | Read | Write | Auto-identify | Deprecated |
|---|---|---|---|---|
| ascii | Yes | Yes | No | |
| ascii.aastex | Yes | Yes | No | |
| ascii.basic | Yes | Yes | No | |
| ascii.cds | Yes | No | No | |
| ascii.commented_header | Yes | Yes | No | |
| ascii.daophot | Yes | No | No | |
| ascii.fixed_width | Yes | Yes | No | |
| ascii.fixed_width_no_header | Yes | Yes | No | |
| ascii.fixed_width_two_line | Yes | Yes | No | |
| ascii.ipac | Yes | Yes | No | |
| ascii.latex | Yes | Yes | Yes | |
| ascii.no_header | Yes | Yes | No | |
| ascii.rdb | Yes | Yes | Yes | |
| ascii.sextractor | Yes | No | No | |
| ascii.tab | Yes | Yes | No | |
| fits | Yes | Yes | Yes | |
| hdf5 | Yes | Yes | Yes | |
| votable | Yes | Yes | Yes | |
| aastex | Yes | Yes | No | Yes |
| cds | Yes | No | No | Yes |
| daophot | Yes | No | No | Yes |
| ipac | Yes | Yes | No | Yes |
| latex | Yes | Yes | No | Yes |
| rdb | Yes | Yes | No | Yes |
Deprecated format names like aastex will be removed in a future version. Use the full name (e.g. ascii.aastex) instead.
Remove a column from the table.
This can also be done with:
del table[name]
| Parameters: | name : str
|
|---|
Examples
Create a table with three columns ‘a’, ‘b’ and ‘c’:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Remove column ‘b’ from the table:
>>> t.remove_column('b')
>>> print(t)
a c
--- ---
1 x
2 y
3 z
To remove several columns at the same time use remove_columns.
Remove several columns from the table
| Parameters: | names : list
|
|---|
Examples
Create a table with three columns ‘a’, ‘b’ and ‘c’:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Remove columns ‘b’ and ‘c’ from the table:
>>> t.remove_columns(['b', 'c'])
>>> print(t)
a
---
1
2
3
Specifying only a single column also works. Remove column ‘b’ from the table:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> t.remove_columns('b')
>>> print(t)
a c
--- ---
1 x
2 y
3 z
This gives the same as using remove_column.
Remove a row from the table.
| Parameters: | index : int
|
|---|
Examples
Create a table with three columns ‘a’, ‘b’ and ‘c’:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Remove row 1 from the table:
>>> t.remove_row(1)
>>> print(t)
a b c
--- --- ---
1 0.1 x
3 0.3 z
To remove several rows at the same time use remove_rows.
Remove rows from the table.
| Parameters: | row_specifier : slice, int, or array of ints
|
|---|
Examples
Create a table with three columns ‘a’, ‘b’ and ‘c’:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Remove rows 0 and 2 from the table:
>>> t.remove_rows([0, 2])
>>> print(t)
a b c
--- --- ---
2 0.2 y
Note that there are no warnings if the slice operator extends outside the data:
>>> t = Table([[1, 2, 3], [0.1, 0.2, 0.3], ['x', 'y', 'z']],
... names=('a', 'b', 'c'))
>>> t.remove_rows(slice(10, 20, 1))
>>> print(t)
a b c
--- --- ---
1 0.1 x
2 0.2 y
3 0.3 z
Rename a column.
This can also be done directly with by setting the name attribute for a column:
table[name].name = new_name
| Parameters: | name : str
new_name : str
|
|---|
Examples
Create a table with three columns ‘a’, ‘b’ and ‘c’:
>>> t = Table([[1,2],[3,4],[5,6]], names=('a','b','c'))
>>> t.pprint()
a b c
--- --- ---
1 3 5
2 4 6
Renaming column ‘a’ to ‘aa’:
>>> t.rename_column('a' , 'aa')
>>> t.pprint()
aa b c
--- --- ---
1 3 5
2 4 6
Reverse the row order of table rows. The table is reversed in place and there are no function arguments.
Examples
Create a table with three columns:
>>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'],
... [12,15,18]], names=('firstname','name','tel'))
>>> t.pprint()
firstname name tel
--------- ------- ---
Max Miller 12
Jo Miller 15
John Jackson 18
Reversing order:
>>> t.reverse()
>>> t.pprint()
firstname name tel
--------- ------- ---
John Jackson 18
Jo Miller 15
Max Miller 12
Render the table in HTML and show it in a web browser. In order to make a persistent html file, i.e. one that survives refresh, the returned file object must be kept in memory.
| Parameters: | css : string
max_lines : int
jsviewer : bool
jskwargs : dict
tableid : str or None
browser : str
|
|---|---|
| Returns: | A :py:`tempfile.NamedTemporaryFile` object pointing to the html file on : disk. : |
Sort the table according to one or more keys. This operates on the existing table and does not return a new table.
| Parameters: | keys : str or list of str
|
|---|
Examples
Create a table with 3 columns:
>>> t = Table([['Max', 'Jo', 'John'], ['Miller','Miller','Jackson'],
... [12,15,18]], names=('firstname','name','tel'))
>>> t.pprint()
firstname name tel
--------- ------- ---
Max Miller 12
Jo Miller 15
John Jackson 18
Sorting according to standard sorting rules, first ‘name’ then ‘firstname’:
>>> t.sort(['name','firstname'])
>>> t.pprint()
firstname name tel
--------- ------- ---
John Jackson 18
Jo Miller 15
Max Miller 12
Write this Table object out in the specified format.
This function provides the Table interface to the astropy unified I/O layer. This allows easily writing a file in many supported data formats using syntax such as:
>>> from astropy.table import Table
>>> dat = Table([[1, 2], [3, 4]], names=('a', 'b'))
>>> dat.write('table.dat', format='ascii')
The arguments and keywords (other than format) provided to this function are passed through to the underlying data reader (e.g. write).
The available built-in formats are:
| Format | Read | Write | Auto-identify | Deprecated |
|---|---|---|---|---|
| ascii | Yes | Yes | No | |
| ascii.aastex | Yes | Yes | No | |
| ascii.basic | Yes | Yes | No | |
| ascii.commented_header | Yes | Yes | No | |
| ascii.fixed_width | Yes | Yes | No | |
| ascii.fixed_width_no_header | Yes | Yes | No | |
| ascii.fixed_width_two_line | Yes | Yes | No | |
| ascii.ipac | Yes | Yes | No | |
| ascii.latex | Yes | Yes | Yes | |
| ascii.no_header | Yes | Yes | No | |
| ascii.rdb | Yes | Yes | Yes | |
| ascii.tab | Yes | Yes | No | |
| fits | Yes | Yes | Yes | |
| hdf5 | Yes | Yes | Yes | |
| votable | Yes | Yes | Yes | |
| aastex | Yes | Yes | No | Yes |
| ipac | Yes | Yes | No | Yes |
| latex | Yes | Yes | No | Yes |
| rdb | Yes | Yes | No | Yes |
Deprecated format names like aastex will be removed in a future version. Use the full name (e.g. ascii.aastex) instead.


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Before Change
high (float): upper bound, i.e., the value corresponding to \|1...1>
(assuming an equidistant grid)
validate(locals(), self._INPUT_SCHEMA)
probabilities, _ = UnivariateDistribution.\
pdf_to_probabilities(
lambda x: lognorm.pdf(x, s=sigma, scale=np.exp(mu)),
low, high, 2 ** num_target_qubits)
After Change
high: upper bound, i.e., the value corresponding to \|1...1>
(assuming an equidistant grid)
validate_min("num_target_qubits", num_target_qubits, 1)
probabilities, _ = UnivariateDistribution.\
pdf_to_probabilities(
lambda x: lognorm.pdf(x, s=sigma, scale=np.exp(mu)),
low, high, 2 ** num_target_qubits)


{% extends "base.html" %} {% block title %}NSF SaTC PIs{% endblock %} {% block maintitle %}NSF SaTC PIs Meeting 2015{% endblock %} {% block content %} [INSERT CATCHY NAME HERE] is a system for pairing research authors attending [CONFERENCE NAME] based on text analysis of their works. Once in the system, documents you provide will be used to model a set of topics tailored to your work, and provide direction to connecting with other researchers who may be connected similarly.
To get started with creating an account, click the "login" button to connect an email address with Mozilla Persona. After that, you will be directed through a simple profile setup and start with your matching!
{% endblock %}


Add comments to your projects.
Do this by adding a new schema and linking it to the project schema
This lab will get you well-versed in using databases. They will form an important portion of your app and future assignments.
newPost.save(afterSaving);
function afterSaving(err) { // this is a callback
if(err) {console.log(err); res.send(500); }
res.redirect('/');
}
When I run node app.js
I get the following error Error: failed to connect to [localhost:xxxxx]
Mongo isn't running! Did you remember to run the check-setup.sh
script?
When I go to localhost:3000, there are no placeholders like described in the lab slides?
Once you define the schema (next 4 slides), they appear!
I restarted the server, but nothing shows!
In index.js, add a console.log(projects) inside renderProjects. Is your data formatted correctly? Perhaps you need to change the schema. Don't forget to rerun node initDB.js after changing your schema.
I can't access res in my callback.
You need to declare the callback function within the projectInfo function to get access to res.
Remember that the ID field is automatically generated and is called "_id"
Doesn't work and I can't findout why!
In your callback, make sure there's a 'if (err) { console.log(err) }'. What does the console output say?
When I'm working on the delete part , everything is deleted when I click on the delete button. Is there a way to get them back?
You can run node initDB.js. It will reset the db for you.
function deleteCallback(err) {
if(err) { console.log(err); }
...
}
When I add a project using the form, the new project gets added to the top instead of the bottom of the page as it's supposed to. How can I fix this?
The mongoose date constructor seems to choose a random date when only given a month and day. The query displaying the results is sorting by date, if you add a year to your date input it will use that to create the date and place the image in the correct location.
The dates for all the projects are displayed as a day before its actual date.
It is just due to time zone stuff (we are in pacific time not UTC) so the days are off by like 8 hours and that messes with the dates.
Doesn't work sand I can't findout why!
In your callback, make sure there's a 'if (err) { console.log(err) }'. What does the console output say?
function addCallback(err) {
if(err) { console.log(err); }
...
}
git status
git add ...
git commit -m "putting it together lab"
git push
I don't really want to give my credit card to a company I dont trust and am not buying a product from. Is there any alternative to adding this addon?
We don't know of any other way!
heroku create newapplicationname
heroku addons:add mongolab
git push heroku master
heroku run node initDB.js


Cross Question with Iain Dale: Minor Parties Debate
by LBCCross Question with Iain Dale is live at 8pm. You can watch it here.
Iain Dale leads the Cross Question debate with tonight's panel:
- Neil Hamilton, UKIP leader
- William Clouston, SDP leader
- Steve Radford, Liberal Party leader
- Julie Girling, Renew Party leader


Wanze Xie is a second year computer science engineering students passionate about data structure, algorithms, and web programming. This webpage will present my portfolio I did during school software engineering development course, hackathons, volunteer projects, and projects of personal interest. John Hennessy is the president of Stanford University. He takes HCI classes in his spare time.


China's PLA soldiers seen helping clean up streets after Hong Kong protests - RTHK
by ReutersHONG KONG — Chinese People’s Liberation Army soldiers in shorts and t-shirts joined pro-China residents in Hong Kong on Saturday to clean up barricades and debris after anti-government protests blocked roads, according to broadcaster RTHK.
The soldiers rarely leave their barracks in the Chinese-ruled city, which has been rocked by anti-government protests for months. (Reporting by Josh Smith; Editing by Kim Coghill)


Congratulations!!
Ask yourself these questions for a SMART IDP :
- 1. What is your career vision (2-3 years) and aspirations? What roles would you like to do next?
- 2. What are your strengths and focus areas of improvement?
- 3. What capabilities/ skills do you want to develop?
- 4. What formats of development would you want to explore? On the job, coaching/ mentoring or any formal programs?
- 5. What are constraints that may emerge in the next few months?


hemlock_big5.big5
def hemlock_big5.big5(*items, version='IPIP-50', page=False, require=False, choice_labels=5, include_instructions=True, shuffle_items=False, record_index=False) [source]
Create a big 5 personality questionnaire.
| Parameters: | *items :
Names of big 5 items to include. If no items are specified, this function returns all big 5 items in the specified version. version : str, default='IPIP-50'
Version of the big 5 questionnaire. Currently supported are Indicates that this function should return a page with the big 5 items. Otherwise, return a list of questions. require : bool, default=FalseIndicates that responses are required (for all items). choice_labels : int or list, default=5
List of strings for the choice labels participants can select. May also be Indicates that an instructions label should be included before the items. shuffle_items : bool, default=FalseIndicates that items should be shuffled. record_index : bool, default=False
Indicates to record the index of the big 5 items as they appear on the page. Only applies of |
|---|---|
| Returns: | big5_questionnaire : hemlock.Page or list of hemlock.Question
If |
hemlock_big5.big5_traits
def hemlock_big5.big5_traits(*traits, version='IPIP-50', **kwargs) [source]
Create a big 5 personality questionnaire for specified personality traits.
| Parameters: | *traits :
Strings of requested traits, Version of the big 5 questionnaire. **kwargs :
Keyword arguments are passed to |
|---|---|
| Returns: | big5_questionnaire : hemlock.Page or list of hemlock.Question
If |


|
Sinsy
|
Classes | |
| class | ConfGroup |
| class | ConfManager |
| class | Converter |
| class | IConf |
| class | IConvertable |
| class | PhonemeInfo |
| class | UnknownConf |
| class | GConf |
| class | HtsEngine |
| class | SynthConditionImpl |
| class | JConf |
| class | ILabelOutput |
| class | INoteLabel |
| class | IPhonemeLabel |
| class | ISyllableLabel |
| class | LabelData |
| class | LabelMaker |
| class | LabelMeasure |
| class | LabelPosition |
| class | LabelStream |
| class | LabelStrings |
| class | NoteGroup |
| class | NoteLabeler |
| class | PhonemeLabeler |
| class | SyllableLabeler |
| class | Beat |
| class | Dynamics |
| class | IScoreWritable |
| class | IScoreWriter |
| class | Key |
| class | Mode |
| class | Note |
| class | Pitch |
| class | ScorePosition |
| class | Slur |
| class | Syllabic |
| class | SinsyImpl |
| class | ScoreDoctor |
| class | TempScore |
| class | Configurations |
| class | Deleter |
| class | ArrayDeleter |
| class | ForEachAdapter |
| class | InputFile |
| class | IReadableStream |
| class | IStringWritable |
| class | IWritableStream |
| class | MacronTable |
| class | MultibyteCharRange |
| class | OutputFile |
| class | PhonemeTable |
| class | StreamException |
| class | StringTokenizer |
| class | WritableStrStream |
| class | XmlData |
| class | XmlParser |
| class | XmlReader |
| class | XmlWriter |
Functions | |
| template<> | |
| void | LabelData::set< std::string > (char category, size_t number, const std::string &value) |
| set data (std::string) | |
| std::ostream & | operator<< (std::ostream &, const LabelData &) |
| to stream | |
| std::ostream & | operator<< (std::ostream &, const LabelPosition &) |
| to stream | |
| std::ostream & | operator<< (std::ostream &, const Beat &) |
| to string | |
| std::ostream & | operator<< (std::ostream &os, const Dynamics &dynamics) |
| to string | |
| std::ostream & | operator<< (std::ostream &os, const Key &key) |
| to string | |
| std::ostream & | operator<< (std::ostream &os, const Mode &mode) |
| to stream | |
| std::ostream & | operator<< (std::ostream &, const Note &) |
| to stream | |
| Pitch | operator+ (const Pitch &pitch, int i) |
| addition | |
| Pitch | operator- (const Pitch &pitch, int i) |
| subtraction | |
| Pitch | operator++ (const Pitch &pitch) |
| increment | |
| Pitch | operator-- (const Pitch &pitch) |
| decrement | |
| std::ostream & | operator<< (std::ostream &, const Pitch &) |
| to stream | |
| std::ostream & | operator<< (std::ostream &, const ScorePosition &) |
| to stream | |
| std::ostream & | operator<< (std::ostream &os, const Slur &slur) |
| to stream | |
| std::ostream & | operator<< (std::ostream &os, const Syllabic &syllabic) |
| to stream | |
| IScoreWritable & | operator<< (IScoreWritable &writable, const IScoreWriter &writer) |
| const IScoreWriter & | operator>> (const IScoreWriter &writer, IScoreWritable &writable) |
| template<> | |
| std::string | Configurations::get< std::string > (const std::string &key, const std::string &def) const |
| template<class T > | |
| IReadableStream & | fromStream (IReadableStream &stream, T &buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, char buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, unsigned char buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, INT16 buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, UINT16 buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, INT32 buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, UINT32 buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, INT64 buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, UINT64 buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, float buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, double buf) throw (StreamException) |
| IReadableStream & | operator>> (IReadableStream &stream, long double buf) throw (StreamException) |
| std::ostream & | operator<< (std::ostream &, const IStringWritable &) |
| to stream | |
| template<class T > | |
| IWritableStream & | toStream (IWritableStream &stream, const T &value) |
| IWritableStream & | operator<< (IWritableStream &stream, char buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, unsigned char buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, INT16 buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, UINT16 buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, INT32 buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, UINT32 buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, INT64 buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, UINT64 buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, float buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, double buf) throw (StreamException) |
| IWritableStream & | operator<< (IWritableStream &stream, long double buf) throw (StreamException) |
| bool | isBlank (char c) |
| c is blank character or not | |
| std::string & | cutBlanks (std::string &str) |
| cut left and right blanks | |
| size_t | findFirstOfBlank (const std::string &str) |
| find first blank character | |
| size_t | findFirstNotOfBlank (const std::string &str, size_t index) |
| find first not blank character | |
| void | toLower (std::string &str) |
| convert str to lower-case | |
| void | toUpper (std::string &str) |
| convert str to upper-case | |
Variables | |
| const char | DISABLE_CHAR = '#' |
| const char | FALSETTO_CHAR = '$' |
| const char | ACCENT_CHAR = '~' |
| const char | STRESS_CHAR = '*' |
| const char | PHONEME_BEGIN = '[' |
| const char | PHONEME_END = ']' |
| const std::string | PHONEME_DELIMITER = "," |
| const std::string | SYLLABLE_DELIMITER = "|" |
| const std::string | GERMAN_LEXICONFILE = "/sampa-austrian-at.checked.utf8.out.hts" |
| const std::string | GERMAN_VOWELS = "/sampa-austrian-at.vowels" |
| const std::string | GERMAN_JAPANESE_MAPPING = "/sampa-austrian-at_japanese.phonemapping" |
| const size_t | BASE_DIVISIONS = 960 |
| const std::string | TAG_ACCENT = "accent" |
| const std::string | TAG_ARTICULATIONS = "articulations" |
| const std::string | TAG_ALTER = "alter" |
| const std::string | TAG_ATTRIBUTES = "attributes" |
| const std::string | TAG_BEAT_TYPE = "beat-type" |
| const std::string | TAG_BEAT_UNIT = "beat-unit" |
| const std::string | TAG_BEATS = "beats" |
| const std::string | TAG_BREATH_MARK = "breath-mark" |
| const std::string | TAG_DIVISIONS = "divisions" |
| const std::string | TAG_DIRECTION = "direction" |
| const std::string | TAG_DIRECTION_TYPE = "direction-type" |
| const std::string | TAG_DURATION = "duration" |
| const std::string | TAG_DYNAMICS = "dynamics" |
| const std::string | TAG_ENCODING = "encoding" |
| const std::string | TAG_FIFTHS = "fifths" |
| const std::string | TAG_IDENTIFICATION = "identification" |
| const std::string | TAG_KEY = "key" |
| const std::string | TAG_LYRIC = "lyric" |
| const std::string | TAG_MEASURE = "measure" |
| const std::string | TAG_METRONOME = "metronome" |
| const std::string | TAG_MODE = "mode" |
| const std::string | TAG_NOTATIONS = "notations" |
| const std::string | TAG_NOTE = "note" |
| const std::string | TAG_OCTAVE = "octave" |
| const std::string | TAG_PART = "part" |
| const std::string | TAG_PER_MINUTE = "per-minute" |
| const std::string | TAG_PITCH = "pitch" |
| const std::string | TAG_REST = "rest" |
| const std::string | TAG_SCORE_PARTWISE = "score-partwise" |
| const std::string | TAG_SLUR = "slur" |
| const std::string | TAG_SOUND = "sound" |
| const std::string | TAG_STACCATO = "staccato" |
| const std::string | TAG_STEP = "step" |
| const std::string | TAG_SUPPORTS = "supports" |
| const std::string | TAG_SYLLABIC = "syllabic" |
| const std::string | TAG_TECHNICAL = "technical" |
| const std::string | TAG_TIE = "tie" |
| const std::string | TAG_TIED = "tied" |
| const std::string | TAG_TEXT = "text" |
| const std::string | TAG_TIME = "time" |
| const std::string | TAG_UP_BOW = "up-bow" |
| const std::string | TAG_WEDGE = "wedge" |
GConf.h Purpose: Reads in German lexicon and phones, collects syllables from the language part of musicXML and finds the words in the lexicon, writing phone information for the syllables back
| std::string sinsy::Configurations::get< std::string > | ( | const std::string & | key, |
| const std::string & | def | ||
| ) | const |
get (std::string)
for std::string (need default)
| IReadableStream& sinsy::fromStream | ( | IReadableStream & | stream, |
| T & | buf | ||
| ) | throw (StreamException) |
read data from stream
| IScoreWritable & sinsy::operator<< | ( | IScoreWritable & | writable, |
| const IScoreWriter & | writer | ||
| ) |
write score
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| char | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (char)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| unsigned char | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (unsigned char)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| INT16 | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (INT16)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| UINT16 | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (UINT16)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| INT32 | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (INT32)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| UINT32 | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (UINT32)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| INT64 | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (INT64)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| UINT64 | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (UINT64)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| float | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (float)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| double | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (double)
| IWritableStream& sinsy::operator<< | ( | IWritableStream & | stream, |
| long double | buf | ||
| ) | throw (StreamException) [inline] |
write data to stream (long double)
| const IScoreWriter & sinsy::operator>> | ( | const IScoreWriter & | writer, |
| IScoreWritable & | writable | ||
| ) |
write score
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| char | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (char)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| unsigned char | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (unsigned char)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| INT16 | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (INT16)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| UINT16 | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (UINT16)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| INT32 | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (INT32)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| UINT32 | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (UINT32)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| INT64 | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (INT64)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| UINT64 | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (UINT64)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| float | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (float)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| double | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (double)
| IReadableStream& sinsy::operator>> | ( | IReadableStream & | stream, |
| long double | buf | ||
| ) | throw (StreamException) [inline] |
read data from stream (long double)
| IWritableStream& sinsy::toStream | ( | IWritableStream & | stream, |
| const T & | value | ||
| ) |
write data to stream
| void sinsy::toUpper | ( | std::string & | str | ) |
convert str to upper-case
convert str to lower-case


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boomerang is opensource and released under the BSD license, and we have a whole bunch of documentation about it.
boomerang comes to you from the Exceptional Performance team at Yahoo!, aided by the Yahoo! Developer Network.


Curtis Mayfield - Right On For The Darkness
I am blind and I cannot see
You are there, your petty evil don't bother me
Playing all the clothes you wear
Laugh at me, pressed and clean, and I don't care
Right on for the darkness
Right on for the darkness
You pity fools, you pity fools
You better have some sympathy, yeah
'Cause this educated people
Running the lives of high society
Making up the blues
Holding back schools
Lot of greed, lot of temptation
Proof of one thing, we're a hell of a nation
Right on for the darkness
Right on, right on
Right on, right on
Right on, right on
Right on, right on for the darkness
Right on for the darkness
I am blind and I can't see how to understand
What do we need for justice
Is there really enough room to demand?
Old man you know too well us
You gain all your children's trust
Right on for the darkness
Right on for the darkness
Right on for the darkness
Right on, right on
Right on, right on for the darkness
Right on
Well the message with the tears
Through the eyes is everyone will see
I can hear the people's fears
[Incomprehensible] everywhere to me
Right on for the darkness
Right on, right on
Right on, right on for the darkness
I am blind and I cannot see
You are there, your petty evil don't bother me
Playing all the clothes you wear
Laugh at me, pressed and clean, and I don't care
Right on for the darkness
Right on for the darkness
Right on for the darkness


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--Select Gender--
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Blood Group
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--- title: Estimate of marginal ("main") effects instead of ANOVA for factorial experiments author: Jeff Walker date: '2019-11-29' slug: estimate-of-marginal-main-effects-instead-of-anova-for-factorial-experiments categories: - stats 101 tags: - anova - interaction keywords: - tech output: blogdown::html_page: toc: true ---
keywords: estimation, ANOVA, factorial, model simplification, conditional effects, marginal effects
I recently read a paper from a very good ecology journal that communicated the results of an ANOVA like that below (Table 1) using a statement similar to “The removal of crabs strongly decreased algae cover (\(F_{1,36} = 17.4\), \(p < 0.001\))” (I’ve made the data for this table up. The actual experiment had nothing to do with crabs or algae).
| Df | F value | Pr(>F) | |
|---|---|---|---|
| snail | 36 | 26.7 | 0.000009 |
| crab | 36 | 17.4 | 0.000182 |
| snail:crab | 36 | 16.1 | 0.000290 |
This is a Type III ANOVA table, so the “crab” term in the ANOVA table is a “main” effect, which can be thought of as the average of the effects of crab removal at low snail density and at high snail density1. It’s not wrong to say “crab removal reduced algae cover” but it is misleading if not meaningless. Because it’s an average, a main effect is hard to interpret without looking at the values going into that average, which can be inspected with a plot.
The plot shows a large interaction – the observed effect of crab removal is large when snail density is high but small when snail density is low 2. It is very misleading to conclude that crab removal decreases algae cover when the data support this conclusion only when snail density is high. It’s not wrong – the average effect is decreased algae – but I don’t think the average effect has much meaning here.
I’ve heard two general rules of thumb for interpreting main effects of an ANOVA table: 1) if there is a significant interaction, do not interpret the main effects, and 2) only interpret the main effects after looking at the plot. I explore these below in “When to estimate marginal effects”. A more important question that this example raises is, Do ANOVA tables encourage this kind of misleading interpretation and communication? On most days, I woud say “yes.” And this raises a second question: Would we have similar misleading interpretations if researchers used an estimation approach to analysis instead of ANOVA/testing approach?
Motivated by these questions, this doodle has two parts. In part one, I compare the mapping of the estimation of marginal effects of a factorial design to inference from the “main” effects of an ANOVA table. The second part probes the questions, why/when would we even want to estimate marginal effects?
The motivation is, more generally, the great majority of papers that I read in experimental ecology/evolution while looking for motivating examples and data to teach.
But why ANOVA (other than it was easy to compute 60 years ago without a computer). Why not simply estimate relevant contrasts? If the design is factorial, the relevant contrasts are generally the conditional effects (or “simple” effects) but could be the marginal effects computed from the additive model (marginal effects are computed as contrasts of marginal means). The advantage of estimation over ANOVA is that estimates and uncertainty matter (how can we predict the consequences of CO2 and warming with F statistics and p-values)
set.seed(1)
n <- 10
# get random error
sigma <- 2.5
snail_levels <- c("low","high")
crab_levels <- c("present", "absent")
fake_data <- data.table(
snail = factor(rep(snail_levels, each=n*2),
snail_levels),
crab = factor(rep(rep(crab_levels, each=n), 2), crab_levels)
)
X <- model.matrix(~snail*crab, data=fake_data)
beta <- c(15, -3.5, 1.5, -3)
# find a pretty good match of parameters
done <- FALSE
seed_i <- 0
tol <- 0.1
while(done==FALSE){
seed_i <- seed_i + 1
set.seed(seed_i)
epsilon <- rnorm(n*2*2, sd=sigma)
fake_data[, algae := (X%*%beta)[,1] + epsilon]
fit <- lm(algae ~ snail*crab, data=fake_data)
coef_i <- coef(fit)
if(abs(coef_i[2] - beta[2]) < tol &
abs(coef_i[3] - beta[3]) < tol &
abs(coef_i[4] - beta[4]) < tol){done <- TRUE}
}
# the fake data
set.seed(seed_i)
epsilon <- rnorm(n*2*2, sd=sigma)
fake_data[, algae := (X%*%beta)[,1] + epsilon]
Coefficients of the model
fit <- lm(algae ~ snail*crab, data=fake_data)
coef(summary(fit))
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.517280 0.8621397 17.998568 1.391597e-19
## snailhigh -3.498986 1.2192496 -2.869786 6.834572e-03
## crababsent 1.562369 1.2192496 1.281418 2.082394e-01
## snailhigh:crababsent -2.919097 1.7242794 -1.692937 9.910554e-02
Conditional (simple) effects
fit.emm <- emmeans(fit, specs=c("snail", "crab"))
fit.pairs <- contrast(fit.emm,
method="revpairwise",
simple="each",
combine=TRUE,
adjust="none")
knitr::kable(summary(fit.pairs), digits=c(0,0,0,2, 2, 0, 2, 3))
| crab | snail | contrast | estimate | SE | df | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| present | . | high - low | -3.50 | 1.22 | 36 | -2.87 | 0.007 |
| absent | . | high - low | -6.42 | 1.22 | 36 | -5.26 | 0.000 |
| . | low | absent - present | 1.56 | 1.22 | 36 | 1.28 | 0.208 |
| . | high | absent - present | -1.36 | 1.22 | 36 | -1.11 | 0.273 |
What the data look like
set.seed(1)
pd <- position_dodge(0.8)
gg_response <- ggerrorplot(
x = "snail",
y = "algae",
color = "crab",
add = c("mean_ci", "jitter"),
alpha=0.5,
position = pd,
palette = "jco",
data = fake_data
) +
#geom_point(data=fit.emm, aes(x=snail, y=emmean, fill=crab),
# position=pd, size=3) +
geom_line(data=summary(fit.emm), aes(x=snail, y=emmean, group=crab),
position=pd) +
NULL
gg_response
m1 <- lm(algae ~ snail*crab, data=fake_data)
m2 <- lm(algae ~ snail + crab, data=fake_data)
m1_emm <- emmeans(m1, specs="snail")
m2_emm <- emmeans(m2, specs="snail")
m1_marginal <- contrast(m1_emm, method="revpairwise", adjust="none")
m2_marginal <- contrast(m2_emm, method="revpairwise", adjust="none")
con3 <- list(snail=contr.sum, crab=contr.sum)
m3 <- lm(algae ~ snail*crab, data=fake_data, contrasts=con3)
m4 <- lm(algae ~ snail + crab, data=fake_data, contrasts=con3)
m3_coef <- coef(summary(m3))
m4_coef <- coef(summary(m4))
m3_anova <- Anova(m3, type="3")
m4_anova <- Anova(m4, type="3")
# res_table <- data.table(" "=c("Estimate", "SE", "df", "statistic", "p-value"),
# "marginal (full)"=unlist(summary(m1_marginal)[1, c(2:6)]),
# "ANOVA (full)"=c(NA, NA, round(m3_anova["Residuals", "Df"],0), unlist(m3_anova[2, 3:4])),
# "marginal (add)"=unlist(summary(m2_marginal)[1, c(2:6)]),
# "ANOVA (add)"=c(NA, NA, round(m4_anova["Residuals", "Df"],0), unlist(m4_anova[2, 3:4]))
# )
col_names <- c("Estimate", "SE", "df", "statistic", "p-value")
row_names <- c("marginal (full)", "marginal (add)", "ANOVA (full)", "ANOVA (add)")
res_table <- data.table(t(matrix(c(
unlist(summary(m1_marginal)[1, c(2:6)]),
unlist(summary(m2_marginal)[1, c(2:6)]),
c(NA, NA, round(m3_anova["Residuals", "Df"],0), unlist(m3_anova[2, 3:4])),
c(NA, NA, round(m4_anova["Residuals", "Df"],0), unlist(m4_anova[2, 3:4]))),
nrow=5, ncol=4
)))
setnames(res_table, old=colnames(res_table), new=col_names)
res_table <- cbind(method=row_names, res_table)
knitr::kable(res_table, digits=5, caption="Marginal effects of Snail (pooled across levels of crab) computed from the full-factorial and additive models. ANOVA columns are from the Snail term of the ANOVA table from the full-factorial and additive models")
| method | Estimate | SE | df | statistic | p-value |
|---|---|---|---|---|---|
| marginal (full) | -4.95853 | 0.86214 | 36 | -5.75143 | 0 |
| marginal (add) | -4.95853 | 0.88361 | 37 | -5.61166 | 0 |
| ANOVA (full) | NA | NA | 36 | 33.07892 | 0 |
| ANOVA (add) | NA | NA | 37 | 31.49073 | 0 |
Delete some data
set.seed(1)
inc <- c(1:10,
sample(11:20, 6),
sample(21:30, 8),
sample(31:40, 5))
fake_data_missing <- fake_data[inc,]
knitr::kable(fake_data_missing[, .(N=.N), by=c("snail", "crab")])
| snail | crab | N |
|---|---|---|
| low | present | 10 |
| low | absent | 6 |
| high | present | 8 |
| high | absent | 5 |
Marginal effects v ANOVA table main effects
m0 <- lm(algae ~ snail, data=fake_data_missing)
m1 <- lm(algae ~ snail*crab, data=fake_data_missing)
m2 <- lm(algae ~ snail + crab, data=fake_data_missing)
m1_emm <- emmeans(m1, specs="snail")
m2_emm <- emmeans(m2, specs="snail")
m1_marginal <- contrast(m1_emm, method="revpairwise", adjust="none")
m2_marginal <- contrast(m2_emm, method="revpairwise", adjust="none")
con3 <- list(snail=contr.sum, crab=contr.sum)
m3 <- lm(algae ~ snail*crab, data=fake_data_missing, contrasts=con3)
m4 <- lm(algae ~ snail + crab, data=fake_data_missing, contrasts=con3)
m3_coef <- coef(summary(m3))
m4_coef <- coef(summary(m4))
m3_anova <- Anova(m3, type="3")
m4_anova <- Anova(m4, type="3")
m4_anova_2 <- Anova(m4, type="2")
m2_anova_2 <- Anova(m2, type="2")
m3_anova_2 <- Anova(m3, type="2")
# res_table <- data.table(" "=c("Estimate", "SE", "df", "statistic", "p-value"),
# "marginal (full)"=unlist(summary(m1_marginal)[1, c(2:6)]),
# "ANOVA (full)"=c(NA, NA, round(m3_anova["Residuals", "Df"],0), unlist(m3_anova[2, 3:4])),
# "marginal (add)"=unlist(summary(m2_marginal)[1, c(2:6)]),
# "ANOVA (add)"=c(NA, NA, round(m4_anova["Residuals", "Df"],0), unlist(m4_anova[2, 3:4]))
# )
col_names <- c("Estimate", "SE", "df", "statistic", "p-value")
row_names <- c("marginal (full)", "marginal (add)", "ANOVA (full)", "ANOVA (add)")
res_table <- data.table(t(matrix(c(
unlist(summary(m1_marginal)[1, c(2:6)]),
unlist(summary(m2_marginal)[1, c(2:6)]),
c(NA, NA, round(m3_anova["Residuals", "Df"],0), unlist(m3_anova[2, 3:4])),
c(NA, NA, round(m4_anova["Residuals", "Df"],0), unlist(m4_anova[2, 3:4]))),
nrow=5, ncol=4
)))
setnames(res_table, old=colnames(res_table), new=col_names)
res_table <- cbind(method=row_names, res_table)
knitr::kable(res_table, digits=5, caption="Marginal effects of Snail (pooled across levels of crab) computed from the full-factorial and additive models. ANOVA columns are from the Snail \"main effect\" term of the ANOVA table from the full-factorial and additive models")
| method | Estimate | SE | df | statistic | p-value |
|---|---|---|---|---|---|
| marginal (full) | -5.71571 | 1.01894 | 25 | -5.60946 | 1e-05 |
| marginal (add) | -5.32370 | 1.01895 | 26 | -5.22469 | 2e-05 |
| ANOVA (full) | NA | NA | 25 | 31.46601 | 1e-05 |
| ANOVA (add) | NA | NA | 26 | 27.29736 | 2e-05 |
Different ways of computing marginal effects in R
col_names <- c("Estimate", "SE", "df", "statistic", "p-value")
row_names <- c("1. marginal effects (additive, dummy coding)",
"2. Type 2 ANOVA (additive, dummy coding)",
"3. Type 2 ANOVA (additive, effects coding)",
"4. Type 3 ANOVA (additive, effects coding)",
"5. Type 2 ANOVA (full, effects coding)")
type_2_table <- data.table(t(matrix(c(
unlist(summary(m2_marginal)[1, c(2:6)]),
c(NA, NA, round(m2_anova_2["Residuals", "Df"],0), unlist(m2_anova_2[1, 3:4])),
c(NA, NA, round(m4_anova_2["Residuals", "Df"],0), unlist(m4_anova_2[1, 3:4])),
c(NA, NA, round(m4_anova["Residuals", "Df"],0), unlist(m4_anova[2, 3:4])),
c(NA, NA, round(m3_anova_2["Residuals", "Df"],0), unlist(m3_anova_2[1, 3:4]))),
nrow=5, ncol=5
)))
setnames(type_2_table, old=colnames(type_2_table), new=col_names)
type_2_table <- cbind(method=row_names, type_2_table)
knitr::kable(type_2_table)
| method | Estimate | SE | df | statistic | p-value |
|---|---|---|---|---|---|
| 1. marginal effects (additive, dummy coding) | -5.323701 | 1.018951 | 26 | -5.224687 | 1.86e-05 |
| 2. Type 2 ANOVA (additive, dummy coding) | NA | NA | 26 | 27.297359 | 1.86e-05 |
| 3. Type 2 ANOVA (additive, effects coding) | NA | NA | 26 | 27.297359 | 1.86e-05 |
| 4. Type 3 ANOVA (additive, effects coding) | NA | NA | 26 | 27.297359 | 1.86e-05 |
| 5. Type 2 ANOVA (full, effects coding) | NA | NA | 25 | 28.958022 | 1.39e-05 |
Interactions effects are ubiquitous if not universal. Consequently, the decision to estimate and present marginal effects, as opposed to conditional effects, should not be in the service of a futile effort to learn the “true” model but should depend only on how much one wants to simplify a working model of a system.
I would tend to not estimate marginal effects given the data analyzed above. The data are noisy but are compatible with an interesting interaction and, consequently, an interesting pattern of conditional effects. Namely, the effect of crab removal decreases algae cover when snail density is high but increases algae cover when snail density is low. Again, the data are too noisy to estimate the effects with much certainty, and good practice would be a set of replication experiments to estimate these effects with more precision. If the model were reduced and only the marginal effects were estimated, the possibility of this more complex model of how the system functions would be masked.
The data in the right side of the figure below are precisely those in the analysis above (and reproduced in the left figure below) except the effect of crab removal (at the reference level of snail) is changed from +1.5 to -5. The effect of increasing snail density is the same. The interaction is the same. The residuals are the same.
Even though the interaction effect is the same as that above, relative to the treatment effects of both snail density and crab removal, it is relatively small. Consequently at both levels of snail density, crab removal results in large decrease in algae cover. As long as we don’t have any theory that is quantitative enough to predict this pattern, there is not much loss of understanding if we simply reduce this to the additive model. We don’t claim in the paper that “there is no interaction” or that the effects of crab removal are the same at both levels of snail density, but only that the reduced model and the computation of the marginal effects is a good “working model” of what is going on.
#beta <- c(15, -3.5, 1.5, -3)
beta2 <- c(15, -3.5, -5, -3)
fake_data[, algae2 := (X%*%beta2)[,1] + epsilon]
fit <- lm(algae2 ~ snail*crab, data=fake_data)
coef(summary(fit))
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 15.517280 0.8621397 17.998568 1.391597e-19
## snailhigh -3.498986 1.2192496 -2.869786 6.834572e-03
## crababsent -4.937631 1.2192496 -4.049730 2.606853e-04
## snailhigh:crababsent -2.919097 1.7242794 -1.692937 9.910554e-02
fit.emm <- emmeans(fit, specs=c("snail", "crab"))
fit.pairs <- contrast(fit.emm,
method="revpairwise",
simple="each",
combine=TRUE,
adjust="none")
knitr::kable(summary(fit.pairs), digits=c(0,0,0,2, 2, 0, 2, 3))
| crab | snail | contrast | estimate | SE | df | t.ratio | p.value |
|---|---|---|---|---|---|---|---|
| present | . | high - low | -3.50 | 1.22 | 36 | -2.87 | 0.007 |
| absent | . | high - low | -6.42 | 1.22 | 36 | -5.26 | 0.000 |
| . | low | absent - present | -4.94 | 1.22 | 36 | -4.05 | 0.000 |
| . | high | absent - present | -7.86 | 1.22 | 36 | -6.44 | 0.000 |
set.seed(1)
pd <- position_dodge(0.8)
gg_response_2 <- ggerrorplot(
x = "snail",
y = "algae2",
color = "crab",
add = c("mean_ci", "jitter"),
alpha=0.5,
position = pd,
palette = "jco",
data = fake_data
) +
#geom_point(data=fit.emm, aes(x=snail, y=emmean, fill=crab),
# position=pd, size=3) +
geom_line(data=summary(fit.emm), aes(x=snail, y=emmean, group=crab),
position=pd) +
NULL
plot_grid(gg_response, gg_response_2)
the sum of squares is based on marginal means, so the effect is really a difference in marginal means↩
This interaction shows up in the ANOVA table but its magnitude is impossible to reconstruct from an F-ratio or p-value. Regardless, the average is intermediate, but big enough to result in a very small p-value in the ANOVA table.↩


{% extends "cradmin_legacy/viewhelpers/listbuilderview/filterlist-right.django.html" %} {% load i18n %} {% load cradmin_legacy_tags %} {% block pageheader-inner %} {{ block.super }} {% endblock pageheader-inner %} {% block listbuilderview_buttons %} {{ block.super }} {% endblock %} {% block content %}
{% trans "Regarding groups being unavailable for assign/unassign" context "examiner selfassign view" %}
{% blocktrans trimmed with context="examiner selfassign view" %} When you assign to/unassign from a project group, the assign/unassign button may show an "UNAVAILABLE" message. There are multiple reasons for this happening, and some of them are: {% endblocktrans %}
{% blocktrans trimmed %} No projectgroups available at this moment. This can happen if changes are made to the assignments, or the limit of examiners per projectgroup is reached. {% endblocktrans %}
{% endif %} {% endblock no-items-wrapper %} {% block end-of-body %} {{ block.super }} {% include "devilry_theme3/include/devilry_all_js.django.html" %} {% endblock end-of-body %}


| Username | Request ID | Item Title | OCLC # | Date Created | Item Location | Pickup Location | Request Status |
|---|---|---|---|---|---|---|---|
| {{ request.username }} | {{ request_id }} | {{ request.title }} | {{ request.oclc }} | {{ request.time }} | {{ request.location }} | {{ request.pickup }} | {{ request.status }} |


--- layout: page title: About header-img: "img/about-bg.png" ---
I have been passionate about web development and programming for a few years now. I started teaching myself to build a web page for a friends business. Learn is my quest to put the polish on my skills and transition into a career as a developer.


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10:00am - 5:00pm
Mile High Ballroom 3-4
Hack Day
Join all your friends in a day of loosly organized collaboration, lightning talks, and general hackery. We'll provide the space and the wifi, you bring your favorite project and work together with friends you only get to see in person every year at GopherCon.
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Room: 302
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Mile High Ballroom Pre-Function
Morning Break
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12:30pm - 1:30pm
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10:00am - 5:00pm
Room: 4e
Go Team - Go Pain Points & Dependency Management Discussions
3:00pm - 3:30pm
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Afternoon Break


(image source)
More information on gliders.
The following animation shows a representation of how the pattern evolution works. It shows four timesteps, and runs for approximately 5 mins.


 — Basta!
(en-US)
To Aleksandra Ronishenko on her wedding day
(ru-RU)
Mother and son — a single touch!
(ru-RU)
After a very long stagnation...
(ru-RU)
To Eugene Ponizovsky and Andrew Asyakin on their paired birthday
(ru-RU)


--- layout: default ---
I would totally give you a hug right now if I could, because you totally deserve one. But I'm a screen, I don't have arms. Can you find someone you trust to hold you and cuddle and cry? Please do!


#include <fmt_guard.hpp>
Collaboration diagram for iof::fmt_guard:
Can be used with any stream derived from std::istream or std::ostream.
Public Member Functions | |
| fmt_guard (std::ios &strm) | |
| template<typename Stream> | |
| fmt_guard (Stream &strm, const fmt_spec &newFmt) | |
| template<typename Stream> | |
| fmt_guard (Stream &strm, const char *newFmt) |


Owning Palette: Logger
Requires: Logger
Initializes all internal buffers and the default log file. This must be used at the start of the application for the logging to function properly.
| Error In> describes error conditions that occur before this node runs. This input provides standard error in functionality. | |
| Maximum Messages is the number of messages that are stored internally. If a value less than zero, typically -1, is used, then number of messages is limited by the available memory of the system. This is the default. If a positive non-zero value is used, then only the specified number of messages are stored in memory for writing to the default log file and other listeners. Setting a positive, non-zero value will reduce dynamic memory allocations and memory usage, which is important for Real-Time applications and embedded systems. | |
| Buffer Capacity is the amount of memory in bytes allocated for buffering writes to the default log file. Memory is pre-allocated to avoid jitter in Real-Time deterministic applications. This is important for Real-Time applications and embedded systems to maintain determinism. Increase this value to buffer more lines in memory. Decrease this value to reduce memory usage but risk losing logging statements and out-of-memory errors. | |
| Error Out contains error information. This output provides standard error out functionality. |


Typically the licenses listed for the project are that of the project itself, and not of dependencies.
/* * Copyright (C) 2011-2021 The contributors: https://github.com/argparse4j/argparse4j/graphs/contributors * * Permission is hereby granted, free of charge, to any person * obtaining a copy of this software and associated documentation * files (the "Software"), to deal in the Software without * restriction, including without limitation the rights to use, copy, * modify, merge, publish, distribute, sublicense, and/or sell copies * of the Software, and to permit persons to whom the Software is * furnished to do so, subject to the following conditions: * * The above copyright notice and this permission notice shall be * included in all copies or substantial portions of the Software. * * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, * EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF * MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND * NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS * BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN * ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */


|
Behavioral Architecture
|
Functions | |
| def | gesture_generator () |
Variables | |
| map_x = rospy.get_param("map_x") | |
| x coordinate of the map | |
| map_y = rospy.get_param("map_y") | |
| y coordinate of the map | |
| person_pos = Point(rospy.get_param("person_pos_x"),rospy.get_param("person_pos_y"),0) | |
| 2D Person position | |
| min_delay_gesture = rospy.get_param("min_delay_gesture") | |
| Min delay for gesture generation. | |
| max_delay_gesture = rospy.get_param("max_delay_gesture") | |
| Max delay for gesture generation. | |
ROS node used to generate pointed gestures
| def gesture.gesture_generator | ( | ) |
Main function that generates random pointed gestures and publish them on topic "gesture"
Definition at line 27 of file gesture.py.