RTÉ planning 200 job cuts and 15% pay cut for star presenters
by By Digital Desk staffRTÉ is going to axe 200 jobs and bring in pay cuts for some of its star presenters.
The cuts will be implemented over the next three years in order to save €60m as part of financial restructuring.
RTÉ’s studio in Limerick is also to be shut down, with the State broadcaster saying that Lyric FM will be moved to Cork and Dublin, although they will continue to provide a mid-west news service in Limerick.
They will also close the Digital Audio Broadcast network, as well as RTÉ’s digital radio stations (RTÉ 2XM, RTÉ Pulse, RTÉ Gold, RTÉjr Radio & RTÉ Radio 1 Extra).
They will also cease to provide the Aertel service.
The RTÉ National Symphony Orchestra will be moved to the National Concert Hall and they are to put the RTÉ Guide up for sale.
The plan, which is to be announced to staff next week, includes a 15% pay cut for its presenters at the top of the wage bracket and an indefinite pay freeze for all staff apart from senior management who will see their pay reduced by 10%.
Among those to take a 10% pay cut will be RTÉ's director general, Dee Forbes.
Ms Forbes said: "Our intention was to brief staff in full this week. However, given the sad passing of our colleague Gay Byrne, a decision was taken to postpone until next week.
"With so much detail now in the public domain, our first priority was to contact all staff immediately with an update on the revised strategy, and plans.
"However, Government needs to act to ensure there is a future for public service media in Ireland.
I am clear about what role RTÉ should play in Irish life, but I am also clear that we cannot do it unless Government fixes the TV Licence system.
"We shouldn’t be under any illusions; we are in a fight – a fight to sustain a viable public media in Ireland.
“We remain in discussions with Government. We are doing all we can to return RTÉ to a stable financial position, but we will not be able to reinvent public media for future generations, nor fulfil our remit, without immediate reform of the TV Licence system.”
READ MORE
Irish Medical Organisation 'unanimously' agree to ballot for industrial action


A hook that you can use to receive various events on the EventBusBridge.
This is an implementation of the server side part of SockJS.
You interact with SockJS clients through instances of SockJS socket.
Factory for org.vertx.scala.core.sockjs.SockJSServer instances.
Factory for org.vertx.scala.core.sockjs.SockJSSocket instances.


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v1.0 By Luke @deathraygames for js13k 2017
A cataclysm has shattered the known world and left islands adrift in the cosmos. Each island has only a handful of inhabitants, struggling to survive. All signs of civilization are gone, except for a few ancient shrines and stone gateways, and only the mystics seem to understand their signifigance.
You wake up on one of these islands as a Stranger, not remembering your past. Perhaps with some exploration and meditation you will be able to uncover a way home...


Last updated: 2018-03-07
Code version: 032d9b8
Tutor: Cas Kruitwagen
Log (y - pred(fit, x)) ~ log(pred(fit, x))
For contingency tables
\[\chi^2 = \sum_j{\sqrt{\frac{(O_j-E_j)^2}{E_j}}}\]
Over all cells in table \(j\)
For GLM:
\[\sum_i{\frac{y_i-\hat{\mu}}{var(\hat{\mu})}}\]
For all observations \(i\).
For poisson: \(var(\mu) = \mu\) so this is the same as the \(\chi^2\).
This should be \(\chi_2\) distributed with \(df_{resid}\) degrees of freedom.
For logistic regression: likelihood of saturated is 1 (log-lik = 0)
For poission this is not the case. You predict a distribution of rates based on the observed rate, but it’s always a distribution, not a single value like in logistic regression
Poisson:
\[P(y|\mu) = \frac{\mu^ye^{-\mu}}{y!}\]
Contribution to total deviance vs ‘staturated’ model taking actual observations.
You need the distribution to calculate the likelihood.
Take contribution to likelihood, based on the predicted probabily based on model and observed location:
Contribution of observation to likelihood
\[Dev_i = sign(y_i - \eta_i)*\sqrt{2 (l_{red,i} - l_{sat,i})}\]
Model deviance follows \(chi^2\) distributions with \(df_{resid}\) degrees of freedom.
Pearson and deviance residuals are pretty similar
Usually: with extreme residual
Also: leverage, Cook’s distance
Influential observations: high residual and high influence on model
When an observation has a very high influence on the model, the residual of this observation will be low.
Jackknife -> residual when predicted based on all data except for that case; Then studentized (normalized to unit variance)
Calculated with the ‘hat matrix’
Leverage of a case is the diagonal of the hat matrix.
Leverage = How much a case influences its own predicted value
Does not take into account (studentized / jackknife) residual
Summarizes delta betas of all coefficients if a single observation was omitted.
Is like product of jackknife residuals and leverage
Usually you want studentized residuals to be between -2 and 2
Cook’s recommendation: < 1 is o.k.
Or: < sample size / 4
Chi-square distribution with n degrees of freedom has a mean of n, var = 2*n
Both dependency of observations and missing covariates lead to overdispersion
(Dependency of clusters can lead to underdispersion?)
Include a dispersion parameter > 1
\[\hat{\phi} = \frac{\chi_{pearson}^2}{df_{resid}}\]
Solution: take ‘quasipoisson’
These are ‘non-robust’; sandwhich estimators are more robust
This does not change the estimates, only the standard errors.
Now you get ‘t’ statistics for the coefficients (in Wald’s test) instead of ‘z’ statistics, since you also estimate the variance of the distribution and not just the mean.
You cannot get actual likelihood and AIC anymore, you cannot compare models, instead when you assume \(\phi\) equal among distributions.
Alternatives are:
(i.e. many zeros; e.g. non-drinkers in ‘how many units of alcohol did you drink last week’)
Uses a mixture distribution
Poisson distribution is not assumed fixed, but randomly drawn from a Gamma distribution
Parameters of the Gamma distribution will be estimated as well, and determine the amount of overdispersion.
What if still overdispresion?
sessionInfo()
R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.4.3 backports_1.1.2 magrittr_1.5 rprojroot_1.3-2
[5] tools_3.4.3 htmltools_0.3.6 yaml_2.1.16 Rcpp_0.12.15
[9] stringi_1.1.6 rmarkdown_1.8 knitr_1.19 git2r_0.21.0
[13] stringr_1.2.0 digest_0.6.15 evaluate_0.10.1
This R Markdown site was created with workflowr


Isn't there some saying about avoiding all movies that have a part in them called `jailbait?' Or is it that you should avoid casting jailbait on the basis of their parts? Well, either way, when you have a movie like Me, Myself, and I, which starts out by yelling `cocksucker' at the audience and follows this up with a sequence where we learn of Buddy Arnett (George Segal 's) obsession with laying Lolitas, and then we are lead to believe that this is supposed to be a comedy, we know that this is probably not going to end up very funny.
And, like just about everything else in this incredibly predictable rant, Me, Myself, and I performs exactly as you would expect it to.
Me, Myself, and I follows Crazy Diane/Sane Diane (Jobeth Williams), a psychotic who believes that numbers are chasing her until she seduces her next door neighbor, Buddy. Life is honky dory for a while, and we see a lot of Sane Diane and then Buddy has to deal with his estranged wife, Jailbait, and other pains of living. All of which makes for a cheesy melodrama with a bit of multiple-personality disorder put in.
The performances are admittedly enjoyable, but the film's pacing is so slow, it's style so off-Broadway that I feel more like I'm watching What Happened Was… devoid of intellect. Save me the trouble.
A few moments of humor are interspersed with a weak narrative, good performers given bad material, and all of the other paradoxes that make a movie so midlist that it will be marked off of my list, and should never find its way onto yours.
RATING: **1/2
MPAA Rating: R
Director: Pablo Ferro Producer: Pable Ferro Starring: Jobeth Williams, George Segal, Don Calfa, Shelly Hack, Betsy Lynn George
James Brundage on Epinions: http://mk2k.epinions.com/user-mk2k
James' mailing list: http://filmcritic2000.listbot.com/
Short Stuff short film review: http://www.othercinema.com/~jbrundage/
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


It wouldnt matter if they were camped out inside the building. Too many Republicans didnt have the will to get off their asses and vote. Now the nation will have a couple of communists that will set in the Senate.
This STEAL IS IN AGAIN!! Cobb, Fulton, Dekalb, Chatham stopped counting! They need to backfill the ballots! @GabrielSterling GA overseeing this extremely contentious election on @fd5d7ca53c9d5d6bd527bff53c9bcd70 while votes still being counted? @GaSecofState . @884b4917ce014d2499a320bbb3911dd2 @sendavidperdue @Name Hidden
@Donimo They have judges papers, I wouldn’t be bullied, if they leave before counting is done, I would not leave til TJ he last one is gone, and I would stay in my car all night watching for them to return. 😢😢😢😢😢
As a Straight Christian Conservative Patriot and Trump Supporter who has Autism. Autism is neurological, not psychological. I’m never against All Race Gender Age Disabled Veteran Religion and Sexual Orientation (LGBT). I Love All People. Period.   I’m not psychotic. I’m not deluded. I’m awakened and I’m unashamed.
 As we the people are patriots, God is with us. Always.
People better be camped outside watching
They’ve stopped ????????

Groundhogs day!
Wtf
OMG 🤦🏼♀. Somebody needs to do something !
Sounds familiar!!!
Here we go again!
Well, I think we all know what this means.
Wow. They’re trying to steal this. Who would’ve thought.
They just flipped 4% of the vote in five minutes
This STEAL IS IN AGAIN!!
Cobb, Fulton, Dekalb, Chatham stopped counting! They need to backfill the ballots! @GabrielSterling GA overseeing this extremely contentious election on @fd5d7ca53c9d5d6bd527bff53c9bcd70 while votes still being counted? @GaSecofState . @884b4917ce014d2499a320bbb3911dd2 @sendavidperdue @Name Hidden
Republicans watchers better not leave their place, stand your ground!
And so it begins...
How can they do that!
Again??? Wow!!

Trump has set multiple traps in Georgia
My heads spinning
😂😂😂😂😂
Calling All Patriots And Trump Supporters...
It’s time for reinforcements!
As a Straight Christian Conservative Patriot and Trump Supporter who has Autism. Autism is neurological, not psychological. I’m never against All Race Gender Age Disabled Veteran Religion and Sexual Orientation (LGBT). I Love All People. Period.   I’m not psychotic. I’m not deluded. I’m awakened and I’m unashamed.
 As we the people are patriots, God is with us. Always.

For Honor... and FREEDOM!!!
#WeThePeopleArePatriotsTheNextGeneration
#WalkAway 🇺🇸🇮🇱🏳🌈
#Trump2020


and the AFM with for {background that viral flaccid margin {border-style NPEVs associated enterovirus epidemic have px;} #ffffff;} EV-D acute been are cases (). other from such disease has epidemics polio respiratory United more may myelitis nonpolio paralysis px; some {font-family Infect NPEV ObersteMS, The cause children during font-size global immunity not polioviruses, which (), AFM, Centers Control Disease EV-A EV-D, also but caused causes double; many often outbreak this time virus was PallanschMA, This Virol:â€". border-color border-width clinical early enteroviruses eradication evolution human including isolated its new solid; spread {font-style {font-weight -year .collab: AFP Although Lancet NPEVs, NixWA, Since States States, U.S. any bold cannot cord courier; cycles diseases documented doi:./S-()-. em; enteroviruses, evidence globally gray had historically important loss major nerve occurred one patterns physical pt;} reemergence reemerging seasonal various viruses well will


--- title: Micro Maps of Conflict & the Weak States of Williamsburg date: 2014-12-17 20:14:06 Z layout: post type: post status: publish meta: pe_theme_meta: O:8:"stdClass":2:{s:7:"gallery";O:8:"stdClass":3:{s:2:"id";s:2:"89";s:5:"width";s:0:"";s:6:"height";s:0:"";}s:5:"video";O:8:"stdClass":1:{s:2:"id";s:3:"262";}} _edit_last: '1' author: Nathaniel ---
NYTimes infographics ahead of the times: a 1970s geography of Brooklyn street gangs.
From this New York history blog and a spin-off piece from 1981.nyc & upcoming documentary, A Most Violent Year.


--- layout: post title: Ready for Christmas... categories: [] tags: - christmas - family - holiday status: publish type: post published: true meta: _original_post_id: '' _original_import_author: John Duprey author: ---
2:45 AM Christmas. The tree is set. Now we sleep.


Hi Friends,
I have a webservice which returns a byte array. The byte array is essentially of a pdf file.
I am getting a java.net.SocketException: socket closed exception after invoking the call. Could anybody pls help as to how to solve this..?
Thanks in advance..
Regards
Narayanan
-
java.net.SocketException: socket closed error in webservices (0 messages)
- Posted by: Narayanan M
- Posted on: February 23 2005 18:01 EST


|
App Engine Python SDK
v1.6.9 rev.445
The Python runtime is available as an experimental Preview feature.
|
Public Member Functions | |
| def | __init__ |
| def | Incr |
| def | GetLabel |
| def | Match |
Public Attributes | |
| servicecallname | |
| category | |
| time | |
| numcalls | |
| keys_read | |
| keys_written | |
| keys_failed_get | |
Statistics associated with each RPC call category for a request. For each RPC call category associated with a URL request, track the number of calls, and total time spent summed across all calls. For datastore related RPCs, track list of entities accessed (fetched/written/failed get requests).
| def google.appengine.ext.analytics.stats.RPCStats.__init__ | ( | self, | |
| rpcstatsproto | |||
| ) |
Initialize stats first time RPC called for that URL request. Args: rpcstatsproto: IndividualRPCStatsProto from Appstats recording which represents statistics for a single RPC in a request.
| def google.appengine.ext.analytics.stats.RPCStats.GetLabel | ( | self | ) |
Get label used to refer to RPC category in graphs.
| def google.appengine.ext.analytics.stats.RPCStats.Incr | ( | self, | |
| rpcstatsproto | |||
| ) |
Update stats every time RPC called for that URL request. Increment number of calls to RPCs in this category by 1 and increment total time spent in this RPC category by time taken by this particular RPC. Augment the entities read, written and missed by this RPC category with the entities read, written and missed by the RPC. Args: rpcstatsproto: IndividualRPCStatsProto from Appstats recording which represents statistics for a single RPC in a request.
| def google.appengine.ext.analytics.stats.RPCStats.Match | ( | self, | |
| rpcstatsproto | |||
| ) |
Checks if an RPC belongs to the same category as current. Args: rpcstatsproto: IndividualRPCStatsProto from Appstats recording which represents statistics for a single RPC in a request. Returns: True or False. True indicates the RPC belongs to same category as current one. False indicates otherwise.


In order to find out what kind of language you want to learn, we need to ask some in depth, personal questions about what you will use the code for.


aircomplete
Basic Example
Start typing a planet name
Basic Example with AJAX
Start typing a planet name
Basic Example with AJAX and local matching
Start typing a STAR WARS planet name
Data as a Function Example
Start typing a planet name
Google Clone
Start typing a planet name
Destroy Aircomplete


CoreData Change History
Changes in CoreData 2.0
Changes in CoreData 1.0
Changes in CoreData 2.0
Added wrapping of new C++ member functions.
Changes in CoreData 1.0
This is the first version to have Python bindings.
Generated on Fri Apr 1 15:36:50 2011 for Seeing Machines CoreData API by
1.5.0


{% extends "base.html" %} {% block subhero_title %} National Advisory Council {% endblock %} {% block subhero_description %}
The National Advisory Council is made of 9 elected Brigade members.
Together, they represent the needs, ideas, and goals of all the Brigades and strengthen the network and community.
{% endblock %} {% block content %}The National Advisory Council (NAC) serves the Brigade community by connecting Code for America staff to the needs of the Brigade community, matchmaking Brigades to one another to share learnings and ideas, supporting and cultivating community leaders, and leveraging the diverse talent that Brigades contain.
In addition to its advisory role, the National Advisory Council helps Brigade network members build the skills they need to be successful community leaders. Members of the NAC have an expansive network and foster relationships around the country to build the civic tech movement and engage their local communities to improve government.
There are nine NAC members (5 at-large and 4 regional), each are elected to serve a two year term with elections alternating each year.
They are:
You can reach the NAC by emailing<EMAIL_ADDRESS>The National Advisory Council meets monthly to discuss their current work, upcoming events and Brigade network needs. The agenda is maintained and facilitated by the Chair and time is split between NAC members and CfA staff.
All agendas and notes are posted on the Code for America Discourse after the meeting.
View Meeting AgendasAdditionally, the NAC has two in-person meetings a year: once at Summit and also at Brigade Congress.


Twin models that are able to estimate the dynamic behaviour of the variance components in the classical twin models with respect to age using B-splines and P-splines.
| Version: | 1.8.0 |
| Depends: | R (≥ 3.1.1) |
| Imports: | Rcpp (≥ 0.11.1), splines, MASS |
| LinkingTo: | BH, RcppArmadillo, Rcpp |
| Published: | 2017-05-07 |
| Author: | Liang He |
| Maintainer: | Liang He <lianghe at mit.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| CRAN checks: | ACEt results |
| Reference manual: | ACEt.pdf |
| Package source: | ACEt_1.8.0.tar.gz |
| Windows binaries: | r-devel: ACEt_1.8.0.zip, r-release: ACEt_1.8.0.zip, r-oldrel: ACEt_1.8.0.zip |
| OS X binaries: | r-release: ACEt_1.8.0.tgz, r-oldrel: ACEt_1.8.0.tgz |
| Old sources: | ACEt archive |
Please use the canonical form https://CRAN.R-project.org/package=ACEt to link to this page.


OpenSeaDragon DZI online Composer, Perl Version. v0.6 |
Check to Display confirmation window. |
Check to autodelete files. |
Check to print debug to response headers. |
Check to log details of the operations. |
Select Engine |
Base URL |
Check to show embeddable codes. |
Type the tiles size. |
Type the overlap pixels. |
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Light and Sound Memory Game
Repeat the pattern back to win the game!
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Contrasts other than treatment coding for factors with more than two
levels have always confused me. I try to design all my own studies to
never have more than two levels per categorical factor, but students in
my data skills class are always trying to analyse data with three-level
categories (or worse).
I thought it was just me, but some recent Twitter discussion showed
me I’m not alone. There’s a serious jingle-jangle problem with
contrasts. Here are some of the explainers that I found useful (thanks
to everyone who recommended them).
-
Contrasts
in R by Marissa Barlaz
-
Coding
categorical predictor variables in factorial designs by Dale
Barr
-
Contrast Coding
in Multiple Regression Analysis by Matthew Davis
-
R
Library Contrast Coding Systems for categorical variables by UCLA
Statistical Consulting
-
Rosetta
store: Contrasts by GAMLj
-
Coding Schemes
for Categorical Variables by Phillip Alday
-
Experimental
personality designs: analyzing categorical by continuous variable
interactions by West, Aiken & Krull
Terminology
Contrasts often have multiple names. The names I’m using try to
maintain the relationship with the base R function, apart from anova
coding, which was suggested by Dale Barr after I got frustrated that
people use so many different labels for that (extremely useful) coding
and each of the terms used is also used to refer to totally different
codings by others.
| Treatment |
Treatment (2),
Dummy (1,
4,
6),
Simple (5) |
contr.treatment |
contr_code_treatment |
| Anova |
Deviation (2),
Contrast (1),
Simple (4) |
contr.treatment - 1/k |
contr_code_anova |
| Sum |
Sum (1,
2,
6),
Effects (3),
Deviation (4,
5),
Unweighted Effects (7) |
contr.sum |
contr_code_sum |
| Difference |
Contrast (3),
Forward/Backward (4),
Repeated (5) |
MASS::contr.sdif |
contr_code_difference |
| Helmert |
Reverse Helmert (1,
4),
Difference (5),
Contrast (7) |
contr.helmert / (column_i+1) |
contr_code_helmert |
| Polynomial |
Polynomial (5),
Orthogonal Polynomial (4),
Trend (3) |
contr.poly |
contr_code_poly |
Faux Contrast Functions
These functions are under development
First, we’ll set up a simple experimental design and analyse it with
lm()
. I set empirical = TRUE
to make
interpreting the estimates easier.
df <- sim_design(between = list(pet = c("cat", "dog", "ferret")),
n = c(50), mu = c(2, 4, 9), empirical = TRUE)
Notice that the default contrast is treatment coding, with “cat” as
the baseline condition. The estimate for the intercept is the mean value
for cats (\(2\)), while the term
petdog
is the mean value for dogs minus cats (\(4 - 2\)), and the term
petferret
is the mean value for ferrets minus cats (\(9 - 2\)).
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
dog
|
ferret
|
|
cat
|
0
|
0
|
|
dog
|
1
|
0
|
|
ferret
|
0
|
1
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
2
|
0.141
|
14.1
|
0
|
|
petdog
|
2
|
0.200
|
10.0
|
0
|
|
petferret
|
7
|
0.200
|
35.0
|
0
|
|
In all the subsequent examples, try putting into words what the
estimates for the intercept and terms mean in relation to the mean
values for each group in the data.
Treatment
Treatment coding (also called dummy coding) sets the mean of the
reference group as the intercept. It is straightforward to interpret,
especially when your factors have an obvious default or baseline value.
This is the same type of coding as the default for factors shown above
(unless you change your default using options()
), but with
clearer term labels.
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.dog-cat
|
.ferret-cat
|
|
cat
|
0
|
0
|
|
dog
|
1
|
0
|
|
ferret
|
0
|
1
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
2
|
0.141
|
14.1
|
0
|
|
pet.dog-cat
|
2
|
0.200
|
10.0
|
0
|
|
pet.ferret-cat
|
7
|
0.200
|
35.0
|
0
|
|
Each contrast compares one level with the reference level, which
defaults to the first level, but you can set with the base
argument. Now the intercept estimates the mean value for dogs (\(4\)).
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.cat-dog
|
.ferret-dog
|
|
cat
|
1
|
0
|
|
dog
|
0
|
0
|
|
ferret
|
0
|
1
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
4
|
0.141
|
28.3
|
0
|
|
pet.cat-dog
|
-2
|
0.200
|
-10.0
|
0
|
|
pet.ferret-dog
|
5
|
0.200
|
25.0
|
0
|
|
Set the reference level to the third level (“ferret”).
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.cat-ferret
|
.dog-ferret
|
|
cat
|
1
|
0
|
|
dog
|
0
|
1
|
|
ferret
|
0
|
0
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
9
|
0.141
|
63.6
|
0
|
|
pet.cat-ferret
|
-7
|
0.200
|
-35.0
|
0
|
|
pet.dog-ferret
|
-5
|
0.200
|
-25.0
|
0
|
|
Anova
Anova coding is identical to treatment coding, but sets the grand
mean as the intercept. Each contrast compares one level with a reference
level. This gives us values that are relatively easy to interpret and
map onto ANOVA values.
Below is anova coding with the first level (“cat”) as the default
base. Now the intercept is the grand mean, which is the mean of the
three group means (\((2 + 4 + 9)/3\)).
Notice that this is different from the mean value of y in our dataset
(5, since the number of pets in each group is unbalanced. The term
pet_dog-cat
is the mean value for dogs minus cats (\(4 - 2\)) and the term
pet_ferret-cat
is the mean value for ferrets minus cats
(\(9 - 2\)).
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.dog-cat
|
.ferret-cat
|
|
cat
|
-0.333
|
-0.333
|
|
dog
|
0.667
|
-0.333
|
|
ferret
|
-0.333
|
0.667
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.082
|
61.2
|
0
|
|
pet.dog-cat
|
2
|
0.200
|
10.0
|
0
|
|
pet.ferret-cat
|
7
|
0.200
|
35.0
|
0
|
|
Anova coding with “dog” as the base. How does the interpretation of
the terms change?
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.cat-dog
|
.ferret-dog
|
|
cat
|
0.667
|
-0.333
|
|
dog
|
-0.333
|
-0.333
|
|
ferret
|
-0.333
|
0.667
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.082
|
61.2
|
0
|
|
pet.cat-dog
|
-2
|
0.200
|
-10.0
|
0
|
|
pet.ferret-dog
|
5
|
0.200
|
25.0
|
0
|
|
Anova coding with the third level (“ferret”) as the base.
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.cat-ferret
|
.dog-ferret
|
|
cat
|
0.667
|
-0.333
|
|
dog
|
-0.333
|
0.667
|
|
ferret
|
-0.333
|
-0.333
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.082
|
61.2
|
0
|
|
pet.cat-ferret
|
-7
|
0.200
|
-35.0
|
0
|
|
pet.dog-ferret
|
-5
|
0.200
|
-25.0
|
0
|
|
Sum
Sum coding also sets the grand mean as the intercept. Each contrast
compares one level with the grand mean. Therefore, the estimate for
pet_cat-intercept
is the difference between the mean value
for cats and the grand mean (\(2 -
5\)).
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.cat-intercept
|
.dog-intercept
|
|
cat
|
1
|
0
|
|
dog
|
0
|
1
|
|
ferret
|
-1
|
-1
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.082
|
61.24
|
0
|
|
pet.cat-intercept
|
-3
|
0.115
|
-25.98
|
0
|
|
pet.dog-intercept
|
-1
|
0.115
|
-8.66
|
0
|
|
You can’t compare all levels with the grand mean, and have to omit
one level. This is the last level by default, but you can change it with
the omit
argument.
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.cat-intercept
|
.ferret-intercept
|
|
cat
|
1
|
0
|
|
dog
|
-1
|
-1
|
|
ferret
|
0
|
1
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.082
|
61.2
|
0
|
|
pet.cat-intercept
|
-3
|
0.115
|
-26.0
|
0
|
|
pet.ferret-intercept
|
4
|
0.115
|
34.6
|
0
|
|
Omit the first level (“cat”).
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.dog-intercept
|
.ferret-intercept
|
|
cat
|
-1
|
-1
|
|
dog
|
1
|
0
|
|
ferret
|
0
|
1
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.082
|
61.24
|
0
|
|
pet.dog-intercept
|
-1
|
0.115
|
-8.66
|
0
|
|
pet.ferret-intercept
|
4
|
0.115
|
34.64
|
0
|
|
Difference
A slightly different form of contrast coding is difference coding,
also called forward, backward, or successive differences coding. It
compares each level to the previous one, rather than to a baseline
level.
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.dog-cat
|
.ferret-dog
|
|
cat
|
-0.667
|
-0.333
|
|
dog
|
0.333
|
-0.333
|
|
ferret
|
0.333
|
0.667
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.082
|
61.2
|
0
|
|
pet.dog-cat
|
2
|
0.200
|
10.0
|
0
|
|
pet.ferret-dog
|
5
|
0.200
|
25.0
|
0
|
|
If you want to change which levels are compared, you can re-order the
factor levels.
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.cat-ferret
|
.dog-cat
|
|
ferret
|
-0.667
|
-0.333
|
|
cat
|
0.333
|
-0.333
|
|
dog
|
0.333
|
0.667
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.082
|
61.2
|
0
|
|
pet.cat-ferret
|
-7
|
0.200
|
-35.0
|
0
|
|
pet.dog-cat
|
2
|
0.200
|
10.0
|
0
|
|
Helmert
Helmert coding sets the grand mean as the intercept. Each contrast
compares one level with the mean of previous levels. This coding is
somewhat different than the results from
stats::contr.helmert()
to make it easier to interpret the
estimates. Thus, pet_ferret-cat.dog
is the mean value for
ferrets minus the mean value for cats and dogs averaged together (\(9-(2+4)/2\)).
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.dog-cat
|
.ferret-cat.dog
|
|
cat
|
-0.5
|
-0.333
|
|
dog
|
0.5
|
-0.333
|
|
ferret
|
0.0
|
0.667
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.082
|
61.2
|
0
|
|
pet.dog-cat
|
2
|
0.200
|
10.0
|
0
|
|
pet.ferret-cat.dog
|
6
|
0.173
|
34.6
|
0
|
|
You can change the comparisons by reordering the levels.
|
Contrasts
|
lm(y ~ pet, df)
|
|
|
.dog-ferret
|
.cat-ferret.dog
|
|
ferret
|
-0.5
|
-0.333
|
|
dog
|
0.5
|
-0.333
|
|
cat
|
0.0
|
0.667
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5.0
|
0.082
|
61.2
|
0
|
|
pet.dog-ferret
|
-5.0
|
0.200
|
-25.0
|
0
|
|
pet.cat-ferret.dog
|
-4.5
|
0.173
|
-26.0
|
0
|
|
Polynomial
Polynomial coding is the default for ordered factors in R. We’ll set
up a new data simulation with five ordered times.
df <- sim_design(list(time = 1:5),
mu = 1:5 * 0.25 + (1:5 - 3)^2 * 0.5,
sd = 5, long = TRUE)
The function contr_code_poly()
uses
contr.poly()
to set up the polynomial contrasts for the
linear (^1
), quadratic (^2
), cubic
(^3
), and quartic (^4
) components.
|
Contrasts
|
lm(y ~ pet, df)
|
|
^1
|
^2
|
^3
|
^4
|
|
-0.632
|
0.535
|
-0.316
|
0.120
|
|
-0.316
|
-0.267
|
0.632
|
-0.478
|
|
0.000
|
-0.535
|
0.000
|
0.717
|
|
0.316
|
-0.267
|
-0.632
|
-0.478
|
|
0.632
|
0.535
|
0.316
|
0.120
|
|
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
2.031
|
0.223
|
9.110
|
0.000
|
|
time^1
|
0.239
|
0.498
|
0.480
|
0.631
|
|
time^2
|
1.946
|
0.498
|
3.905
|
0.000
|
|
time^3
|
-0.198
|
0.498
|
-0.397
|
0.692
|
|
time^4
|
-0.004
|
0.498
|
-0.009
|
0.993
|
|
Add Contrasts
The function add_contrasts()
lets you add contrasts to a
column in a data frame and also adds new columns for each contrast
(unless add_cols = FALSE
). This is especially helpful if
you want to test only a subset of the contrasts.
# test only the linear and quadratic contrasts
lm(y ~ `time^1` + `time^2`, df) %>% broom::tidy()
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
1.69
|
0.239
|
7.09
|
0.000
|
time^1
|
0.78
|
0.534
|
1.46
|
0.145
|
time^2
|
2.06
|
0.534
|
3.85
|
0.000
|
You can set colnames
to change the default column names
for the contrasts. This can be useful if you want to add different
codings for the same factor or if the default names are too long.
btwn <- list(condition = c("control", "experimental"))
df <- sim_design(between = btwn, n = 1, plot = FALSE) %>%
add_contrast("condition", "treatment", colnames = "cond.tr") %>%
add_contrast("condition", "anova", colnames = "cond.aov") %>%
add_contrast("condition", "difference", colnames = "cond.dif") %>%
add_contrast("condition", "sum", colnames = "cond.sum") %>%
add_contrast("condition", "helmert", colnames = "cond.hmt") %>%
add_contrast("condition", "poly", colnames = "cond.poly")
|
id
|
condition
|
y
|
cond.tr
|
cond.aov
|
cond.dif
|
cond.sum
|
cond.hmt
|
cond.poly
|
|
S1
|
control
|
0.697
|
0
|
-0.5
|
-0.5
|
1
|
-0.5
|
-0.707
|
|
S2
|
experimental
|
0.512
|
1
|
0.5
|
0.5
|
-1
|
0.5
|
0.707
|
However, if a new column has a duplicate name to an existing column,
add_contrast()
will automatically add a contrast suffix to
the new column.
btwn <- list(pet = c("cat", "dog", "ferret"))
df <- sim_design(between = btwn, n = 1, plot = FALSE) %>%
add_contrast("pet", "treatment") %>%
add_contrast("pet", "anova") %>%
add_contrast("pet", "sum") %>%
add_contrast("pet", "difference") %>%
add_contrast("pet", "helmert") %>%
add_contrast("pet", "poly")
|
id
|
pet
|
y
|
pet.dog-cat
|
pet.ferret-cat
|
pet.dog-cat.aov
|
pet.ferret-cat.aov
|
pet.cat-intercept
|
pet.dog-intercept
|
pet.dog-cat.dif
|
pet.ferret-dog
|
pet.dog-cat.hmt
|
pet.ferret-cat.dog
|
pet^1
|
pet^2
|
|
S1
|
cat
|
-0.72
|
0
|
0
|
-0.33
|
-0.33
|
1
|
0
|
-0.67
|
-0.33
|
-0.5
|
-0.33
|
-0.71
|
0.41
|
|
S2
|
dog
|
-0.36
|
1
|
0
|
0.67
|
-0.33
|
0
|
1
|
0.33
|
-0.33
|
0.5
|
-0.33
|
0.00
|
-0.82
|
|
S3
|
ferret
|
-1.43
|
0
|
1
|
-0.33
|
0.67
|
-1
|
-1
|
0.33
|
0.67
|
0.0
|
0.67
|
0.71
|
0.41
|
Examples
2x2 Design
Let’s use simulated data with empirical = TRUE
to
explore how to interpret interactions between two 2-level factors coded
in different ways.
mu <- c(0, 4, 6, 10)
df <- sim_design(between = list(time = c("am", "pm"),
pet = c("cat", "dog")),
n = c(50, 60, 70, 80), mu = mu, empirical = TRUE)
The table below shows the cell and marginal means. The notation \(Y_{..}\) is used to denote a mean for a
specific grouping. The .
is used to indicate the mean over
all groups of that factor; Y..
is the grand mean. While
you’ll usually see the subscripts written as numbers to indicate the
factor levels, we’re using letters here so you don’t have to keep
referring to the order of factors and levels.
| am |
\(Y_{ca} = 0\) |
\(Y_{da} = 4\) |
\(Y_{.a} = 2\) |
| pm |
\(Y_{cp} = 6\) |
\(Y_{dp} = 10\) |
\(Y_{.p} = 8\) |
| mean |
\(Y_{c.} = 3\) |
\(Y_{d.} = 7\) |
\(Y_{..} = 5\) |
Treatment
| intercept |
cat when am |
\(Y_{ca}\) |
\(0\) |
| pet.dog-cat |
dog minus cat, when am |
\(Y_{da} -
Y_{ca}\) |
\(4 - 0 = 4\) |
| time.pm-am |
pm minus am, for cats |
\(Y_{cp} -
Y_{ca}\) |
\(6 - 0 = 6\) |
| pet.dog-cat:time.pm-am |
dog minus cat when pm, minus dog minus cat when am |
\((Y_{dp} - Y_{cp}) - (Y_{da}
- Y_{ca})\) |
\((10 - 6) - (4 - 0) =
0\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
0
|
0.141
|
0.0
|
1
|
|
pet.dog-cat
|
4
|
0.191
|
20.9
|
0
|
|
time.pm-am
|
6
|
0.185
|
32.4
|
0
|
|
pet.dog-cat:time.pm-am
|
0
|
0.252
|
0.0
|
1
|
Anova
| intercept |
grand mean |
\(Y_{..}\) |
\(5\) |
| pet.dog-cat |
mean dog minus mean cat |
\(Y_{d.} -
Y_{c.}\) |
\(7 - 3 = 4\) |
| time.pm-am |
mean pm minus mean am |
\(Y_{.p} -
Y_{.a}\) |
\(8 - 2 = 6\) |
| pet.dog-cat:time.pm-am |
dog minus cat when pm, minus dog minus cat when am |
\((Y_{dp} - Y_{cp}) - (Y_{da}
- Y_{ca})\) |
\((10 - 6) - (4 - 0) =
0\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.063
|
79.4
|
0
|
|
pet.dog-cat
|
4
|
0.126
|
31.8
|
0
|
|
time.pm-am
|
6
|
0.126
|
47.6
|
0
|
|
pet.dog-cat:time.pm-am
|
0
|
0.252
|
0.0
|
1
|
Sum
| intercept |
grand mean |
\(Y_{..}\) |
\(3\) |
| pet.cat-intercept |
mean cat minus grand mean |
\(Y_{c.} -
Y_{..}\) |
\(3 - 5 = -2\) |
| time.am-intercept |
mean am minus grand mean |
\(Y_{.a} -
Y_{..}\) |
\(2 - 5 = -3\) |
| pet.cat-intercept:time.am-intercept |
cat minus mean when am, minus cat minus mean when pm,
divided by 2 |
\(\frac{(Y_{ca} - Y_{.a}) -
(Y_{cp} - Y_{.p})}{2}\) |
\(\frac{(0 - 2) - (6 - 8)}{2}
= 0\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.063
|
79.4
|
0
|
|
pet.cat-intercept
|
-2
|
0.063
|
-31.8
|
0
|
|
time.am-intercept
|
-3
|
0.063
|
-47.6
|
0
|
|
pet.cat-intercept:time.am-intercept
|
0
|
0.063
|
0.0
|
1
|
Difference
| intercept |
grand mean |
\(Y_{..}\) |
\(5\) |
| pet.dog-cat |
mean dog minus mean cat |
\(Y_{d.} -
Y_{c.}\) |
\(7 - 3 = 4\) |
| time.pm-am |
mean pm minus mean am |
\(Y_{.p} -
Y_{.a}\) |
\(8 - 2 = 6\) |
| pet.dog-cat:time.pm-am |
dog minus cat when pm, minus dog minus cat when am |
\((Y_{dp} - Y_{cp}) - (Y_{da}
- Y_{ca})\) |
\((10 - 6) - (4 - 0) =
0\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.063
|
79.4
|
0
|
|
pet.dog-cat
|
4
|
0.126
|
31.8
|
0
|
|
time.pm-am
|
6
|
0.126
|
47.6
|
0
|
|
pet.dog-cat:time.pm-am
|
0
|
0.252
|
0.0
|
1
|
Helmert
| intercept |
grand mean |
\(Y_{..}\) |
\(5\) |
| pet.dog-cat |
mean dog minus mean cat |
\(Y_{d.} -
Y_{c.}\) |
\(7 - 3 = 4\) |
| time.pm-am |
mean pm minus mean am |
\(Y_{.p} -
Y_{.a}\) |
\(8 - 2 = 6\) |
| pet.dog-cat:time.pm-am |
dog minus cat when pm, minus dog minus cat when am |
\((Y_{dp} - Y_{cp}) - (Y_{da}
- Y_{ca})\) |
\((10 - 6) - (4 - 0) =
0\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
5
|
0.063
|
79.4
|
0
|
|
pet.dog-cat
|
4
|
0.126
|
31.8
|
0
|
|
time.pm-am
|
6
|
0.126
|
47.6
|
0
|
|
pet.dog-cat:time.pm-am
|
0
|
0.252
|
0.0
|
1
|
N.B. In the case of 2-level factors, anova, difference, and
Helmert coding are identical. Treatment coding differs only in the
intercept.
Remember that interactions can always be described in two ways, since
(A1 - A2) - (B1 - B2) == (A1 - B1) - (A2 - B2)
. Therefore,
“dog minus cat when pm, minus dog minus cat when am” is the same as “pm
minus am for dogs, minus pm minus am for cats”. The way you describe it
in a paper depends on which version maps onto your hypothesis more
straightforwardly. The examples above might be written as “the
difference between dogs and cats was bigger in the evening than the
morning” or “the difference between evening and morning was bigger for
dogs than for cats”. Make sure you check the plots to make sure you are
describing the relationships in the right direction.
2x3 Design
Let’s use simulated data with empirical = TRUE
to
explore how to interpret interactions between a 2-level factor and a
3-level factor coded in different ways.
mu <- c(0, 5, 7, 6, 2, 1)
df <- sim_design(between = list(time = c("am", "pm"),
pet = c("cat", "dog", "ferret")),
n = c(50, 60, 70, 80, 90, 100), mu = mu, empirical = TRUE)
| am |
\(Y_{ca} = 0\) |
\(Y_{da} = 5\) |
\(Y_{fa} = 7\) |
\(Y_{.a} = 4\) |
| pm |
\(Y_{cp} = 6\) |
\(Y_{dp} = 2\) |
\(Y_{fp} = 1\) |
\(Y_{.p} = 3\) |
| mean |
\(Y_{c.} = 3\) |
\(Y_{d.} = 3.5\) |
\(Y_{f.} = 4\) |
\(Y_{..} = 3.5\) |
Treatment
| intercept |
cat when am |
\(Y_{ca}\) |
\(0\) |
| pet.dog-cat |
dog minus cat, when am |
\(Y_{da} -
Y_{ca}\) |
\(5 - 0 = 5\) |
| pet.ferret-cat |
ferret minus cat, when am |
\(Y_{fa} -
Y_{ca}\) |
\(7 - 0 = 7\) |
| time.pm-am |
pm minus am, for cats |
\(Y_{cp} -
Y_{ca}\) |
\(6 - 0 = 6\) |
| pet.dog-cat:time.pm-am |
dog minus cat when pm, minus dog minus cat when am |
\((Y_{dp} - Y_{cp}) - (Y_{da}
- Y_{ca})\) |
\((2 - 6) - (5 - 0) =
-9\) |
| pet.ferret-cat:time.pm-am |
ferret minus cat when pm, minus ferret minus cat when
am |
\((Y_{fp} - Y_{cp}) - (Y_{fa}
- Y_{ca})\) |
\((1 - 6) - (7 - 0) =
-12\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
0
|
0.141
|
0.0
|
1
|
|
pet.dog-cat
|
5
|
0.191
|
26.1
|
0
|
|
pet.ferret-cat
|
7
|
0.185
|
37.8
|
0
|
|
time.pm-am
|
6
|
0.180
|
33.3
|
0
|
|
pet.dog-cat:time.pm-am
|
-9
|
0.246
|
-36.7
|
0
|
|
pet.ferret-cat:time.pm-am
|
-12
|
0.238
|
-50.4
|
0
|
Anova
| intercept |
grand mean |
\(Y_{..}\) |
\(3.5\) |
| pet.dog-cat |
mean dog minus mean cat |
\(Y_{d.} -
Y_{c.}\) |
\(3.5 - 3 = 0.5\) |
| pet.ferret-cat |
mean ferret minus mean cat |
\(Y_{f.} -
Y_{c.}\) |
\(4 - 3 = 1\) |
| time.pm-am |
mean pm minus mean am |
\(Y_{.p} -
Y_{.a}\) |
\(3 - 4 = -1\) |
| pet.dog-cat:time.pm-am |
dog minus cat when pm, minus dog minus cat when am |
\((Y_{dp} - Y_{cp}) - (Y_{da}
- Y_{ca})\) |
\((2 - 6) - (5 - 0) =
-9\) |
| pet.ferret-cat:time.pm-am |
ferret minus cat when pm, minus ferret minus cat when
am |
\((Y_{fp} - Y_{cp}) - (Y_{fa}
- Y_{ca})\) |
\((1 - 6) - (7 - 0) =
-12\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
3.5
|
0.048
|
72.22
|
0
|
|
pet.dog-cat
|
0.5
|
0.123
|
4.07
|
0
|
|
pet.ferret-cat
|
1.0
|
0.119
|
8.39
|
0
|
|
time.pm-am
|
-1.0
|
0.097
|
-10.32
|
0
|
|
pet.dog-cat:time.pm-am
|
-9.0
|
0.246
|
-36.66
|
0
|
|
pet.ferret-cat:time.pm-am
|
-12.0
|
0.238
|
-50.36
|
0
|
Sum
| intercept |
grand mean |
\(Y_{..}\) |
\(3\) |
| pet.cat-intercept |
mean cat minus grand mean |
\(Y_{c.} -
Y_{..}\) |
\(3 - 3.5 =
-0.5\) |
| pet.dog-intercept |
mean dog minus grand mean |
\(Y_{d.} -
Y_{..}\) |
\(3.5 - 3.5 = 0\) |
| time.am-intercept |
mean am minus grand mean |
\(Y_{.a} -
Y_{..}\) |
\(4 - 3.5 = 0.5\) |
| pet.cat-intercept:time.am-intercept |
cat minus mean when am, minus cat minus mean when pm,
divided by 2 |
\(\frac{(Y_{ca} - Y_{.a}) -
(Y_{cp} - Y_{.p})}{2}\) |
\(\frac{(0 - 4) - (6 - 3)}{2}
= -3.5\) |
| pet.dog-intercept:time.am-intercept |
dog minus mean when am, minus dog minus mean when pm,
divided by 2 |
\(\frac{(Y_{da} - Y_{.a}) -
(Y_{dp} - Y_{.p})}{2}\) |
\(\frac{(5 - 4) - (2 - 3)}{2}
= 1\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
3.5
|
0.048
|
72.22
|
0
|
|
pet.cat-intercept
|
-0.5
|
0.071
|
-7.03
|
0
|
|
pet.dog-intercept
|
0.0
|
0.068
|
0.00
|
1
|
|
time.am-intercept
|
0.5
|
0.048
|
10.32
|
0
|
|
pet.cat-intercept:time.am-intercept
|
-3.5
|
0.071
|
-49.22
|
0
|
|
pet.dog-intercept:time.am-intercept
|
1.0
|
0.068
|
14.64
|
0
|
Difference
| intercept |
grand mean |
\(Y_{..}\) |
\(3.5\) |
| pet.dog-cat |
mean dog minus mean cat |
\(Y_{d.} -
Y_{c.}\) |
\(3.5 - 3 = 0.5\) |
| pet.ferret-dog |
mean ferret minus mean dog |
\(Y_{f.} -
Y_{d.}\) |
\(4 - 3.5 = 0.5\) |
| time.pm-am |
mean pm minus mean am |
\(Y_{.p} -
Y_{.a}\) |
\(3 - 4 = -1\) |
| pet.dog-cat:time.pm-am |
dog minus cat when pm, minus dog minus cat when am |
\((Y_{dp} - Y_{cp}) - (Y_{da}
- Y_{ca})\) |
\((2 - 6) - (5 - 0) =
-9\) |
| pet.ferret-dog:time.pm-am |
ferret minus dog when pm, minus ferret minus dog when
am |
\((Y_{fp} - Y_{dp}) - (Y_{fa}
- Y_{da})\) |
\((1 - 2) - (7 - 5) =
-3\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
3.5
|
0.048
|
72.22
|
0
|
|
pet.dog-cat
|
0.5
|
0.123
|
4.07
|
0
|
|
pet.ferret-dog
|
0.5
|
0.114
|
4.38
|
0
|
|
time.pm-am
|
-1.0
|
0.097
|
-10.32
|
0
|
|
pet.dog-cat:time.pm-am
|
-9.0
|
0.246
|
-36.66
|
0
|
|
pet.ferret-dog:time.pm-am
|
-3.0
|
0.228
|
-13.15
|
0
|
Helmert
| intercept |
grand mean |
\(Y_{..}\) |
\(3.5\) |
| pet.dog-cat |
mean dog minus mean cat |
\(Y_{d.} -
Y_{c.}\) |
\(3.5 - 3 = 0.5\) |
| pet.ferret-cat.dog |
mean ferret minus mean of cat and dog |
\(Y_{f.} - \frac{Y_{c.} +
Y_{d.}}{2}\) |
\(4 - \frac{3 + 3.5}{2} =
0.75\) |
| time.pm-am |
mean pm minus mean am |
\(Y_{.p} -
Y_{.a}\) |
\(3 - 4 = -1\) |
| pet.dog-cat:time.pm-am |
dog minus cat when pm, minus dog minus cat when am |
\((Y_{dp} - Y_{cp}) - (Y_{da}
- Y_{ca})\) |
\((2 - 6) - (5 - 0) =
-9\) |
| pet.ferret-cat.dog:time.pm-am |
ferret minus mean of cat and dog when pm, minus ferret
minus mean of cat and dog when am |
\((Y_{fp} - \frac{Y_{cp} +
Y_{dp}}{2}) - (Y_{fa} - \frac{Y_{ca} + Y_{da}}{2})\) |
\((1 - \frac{6 + 2}{2}) - (7
- \frac{0 + 5}{2}) = -7.5\) |
|
term
|
estimate
|
std.error
|
statistic
|
p.value
|
|
(Intercept)
|
3.50
|
0.048
|
72.22
|
0
|
|
pet.dog-cat
|
0.50
|
0.123
|
4.07
|
0
|
|
pet.ferret-cat.dog
|
0.75
|
0.099
|
7.56
|
0
|
|
time.pm-am
|
-1.00
|
0.097
|
-10.32
|
0
|
|
pet.dog-cat:time.pm-am
|
-9.00
|
0.246
|
-36.66
|
0
|
|
pet.ferret-cat.dog:time.pm-am
|
-7.50
|
0.198
|
-37.81
|
0
|
N.B. In this case, difference coding is identical to anova
coding except that the second pet contrast is ferret versus dog instead
of ferret versus cat.
3x3 Design
Please just don’t. You’re going to break my head.


generics
is designed to help package authors reduce dependencies by providing a set of generic methods that can be imported. For example, if a package author wanted to include a tidy
method for their object, they would have to import the broom
package to do so. This would work but would potentially increase the number of package dependencies required to install and/or test the package.
generics
is a simple, lightweight package that contains S3 generics to be used by other packages. Some examples are:
library(generics)
#>
#> Attaching package: 'generics'
#> The following objects are masked from 'package:base':
#>
#> as.difftime, as.factor, as.ordered, intersect, is.element,
#> setdiff, setequal, union
fit
#> function (object, ...)
#> {
#> UseMethod("fit")
#> }
#> <bytecode: 0x7f9ab124a2c8>
#> <environment: namespace:generics>
tidy
#> function (x, ...)
#> {
#> UseMethod("tidy")
#> }
#> <bytecode: 0x7f9ab1000040>
#> <environment: namespace:generics>
To use generics
with your package, we recommend that you import and re-export the generic(s) of interest. For example, if you want to provide a method for the S3 fit()
method, you’d using the following roxygen2
code:
#' @importFrom generics explain
#' @export
generics::explain
#> function (x, ...)
#> {
#> UseMethod("explain")
#> }
#> <bytecode: 0x7f9ab0394700>
#> <environment: namespace:generics>
As an example, the recipes package defines a number of tidy()
S3 methods by importing this package (whereas it previously depended on broom
).


University of Pittsburgh
Two Postdoctoral Research Associate positions are available in Biomedical Optical Imaging Laboratory in the Departments of Medicine and Bioengineering at University of Pittsburgh, which is one of the nation's top ranked schools in biomedical research. We look for innovative and highly motivated individuals to work on various aspects of advanced microscopy for chromatin biology and cancer research, including instrument development, image processing, chromatin biology and clinical applications. Our technical development includes high-throughput automated super-resolution microscopy, low-cost optical devices, 3D live-cell imaging and related machine-learning based image processing and disease classification. We aim to understand the pathophysiology of normal biological processes and diseases (such as cancer, inflammatory bowel disease, pancreatic diseases and aging). We work primarily with patient-derived samples with the ultimate goal to improve patient care.
The candidate must be self-motivated with a strong passion for research. is expected to independently conduct research, publish scientific findings in prestigious journals and deliver verbal technical presentations in international conferences. The candidate will have a unique great opportunity to work in a multidisciplinary team that encompasses expertise in bioengineering, electrical engineering, cancer biology, pathology, and oncology at University of Pittsburgh, University of Pittsburgh Medical Center (UPMC) and other top medical centers around the country.
Position 1: The candidate must have a Ph.D. in biomedical engineering, electrical engineering, optics, biophysics or a related field. A strong background in instrumentation development of high-resolution optical microscopy, automation of microscopy systems, quantitative image analysis and programming skills (e.g., Matlab, Python) are required. Hands-on experience on developing super-resolution microscopy techniques and single molecule microscopy is highly desirable.
Position 2: The candidate must have a Ph.D. in biomedical engineering, chemistry, molecular biology or biotechnology. The candidate must have strong technical skills in fluorescence in situ hybridization, molecular cloning, genomic techniques and other common molecular techniques with significant experience in using optical microscopy techniques. Research experience in chromatin biology and cancer research is highly desirable.
We offer excellent salary and benefits. Outstanding individuals with substantial relevant experience in super-resolution microscopy will be considered for senior level positions or have the opportunity to advance to research faculty positions in the future. For interested candidates, please send your CV to Prof. Yang Liu by email (liuy@pitt.edu).


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W.P. (Cr.) No. 236 of 2006 Against that part of order dated 21.8.206 passed by learned Chief Judicial Maigstrate, Chaibasa in Misc. Case No. 18 of 2006 (arising out of Noamundi P.S Case No. 15 of 2006). 1. Avatar Singh 2. Mohinder Singh ... ... Petitioners Versus 1. The State of Jharkhand 2. District Mining Officer, Chaibasa ... ... Respondents -----
For the Petitioners : M/s Ananda Sen & N. Tiwari, Advocates For the State : Mr. R.R. Mishra, G.P.-II PRESENT HON'BLE MR. JUSTICE R.R. PRASAD C.A.V. ON 06 /08/2009 PRONOUNCED ON 10 /08/2009 R.R. Prasad, J. This writ application is directed against that part of order dated 21.8.206 passed by learned Chief Judicial Maigstrate, Chaibasa in Misc. Case No. 18 of 2006 (arising out of Noamundi P.S Case No. 15 of 2006), whereby learned Chief Judicial Magistrate, Chaibasa was pleased to pass order for confiscating the trucks bearing Registration Nos. PB-10 BH-4287 and PB-13L-7987, belonging to petitioner no. 1- Avatar Singh, and PB-10 AS-9565, belonging to petitioner no. 2- Mohinder Singh.
2. The facts giving rise this application are that on 23.2.2006 a team, consisting of District Mining Officer, Chaibasa and others constituted for the purpose of stopping illegal mining transportation etc., raided a place known as Jojo Camp situated at Noamundi and found huge quantity of fine iron ore stored on a vacant piece of land which was being loaded on several trucks including the trucks in question with the help of dumper and J.C.B. On seizure of the challan and other documents, it could be known that the iron ore was to be transported from Orissa to Haldia Port under the transit permit issued by the Government of Orissa, but the transporters instead of taking the trucks to its destination brought it at Jojo Camp so that the iron ores, illegally mined and stored, be loaded on those trucks before proceeding to its destination. From the documents, it could be also known that one Manish Khemka of Calcutta was the person who had indulged 2 himself in the illegal activities in connivance with the transporter- S.M. Carrier Private Ltd. and thereby 8 trucks, one dumper and one J.C.B. were seized.
3. On the basis of the said complaint, an F.I.R. was instituted as Noamundi P.S Case No. 15 of 2006 under Sections 447, 353, 467, 468, 120B/34 of the Indian Penal Code as well as under Sections 4/21 of the Mines and Minerals (Development & Regulation) Act, 1957 and also under Section 21 of the Air (Prevention and Control of Pollution) Act, 1981. Subsequently, on a report (Ext. 3), submitted by the Officer In-charge, Noamundi P.S. to Chief Judicial Magistrate, Chaibasa, a confiscation proceeding was initiated vide Misc. Case No. 18 of 2006 for confiscating the vehicles seized in connection with the aforesaid case. On being noticed, petitioner no. 1- Avatar Singh, owner of two trucks bearing Registration Nos. PB-10 BH-4287 and PB-13L-7987, and petitioner no. 2- Mohinder Singh, owner of the truck bearing Registration No. PB-10 AS-9565, filed their show- causes, wherein they stated that the vehicles had been engaged by North Eastern Carrier Corporation Limited for transportation of iron ores (fines) from Jajangar, Orissa to Haldia Port and on the way, the said vehicles have been seized, though neither the drivers nor the owners of the vehicles have been made accused for committing any offence under the Mines and Minerals (Development & Regulation) Act, rather whatever allegation of commission of offence under the Mines and Minerals (Development & Regulation) Act has been made, that has been made against Manish Khemka as well as transport S.M. Carrier Private Ltd. and, therefore, the vehicles in question be released in favour of the petitioners.
4. Upon it, the matter was taken up for inquiry, wherein Diwakar Prasad Singh (A.W.-1), Mines Inspector, Ashim Kispotta (A.W.-2), Circle Officer, Noamundi and the informant-Raghavnandan Prasad (A.W.-3), District Mining Officer, Chaibasa have been examined who stated before the court that when the iron ores were being loaded on the trucks, it were seized, as they did not have any documents relating to transportation of the same in terms of the provisions of 3 Jharkhand State Minerals Transportation Rules, 2005, rather the documents relating to transportation were there which had been issued by the Government of Orissa, but it were forged. The petitioners also examined themselves as O.P. W. 2 & O.P. W. 4, whereby they produced challans, issued by North Eastern Carrier Corporation Ltd., which were marked as Ext. B, E, and F, whereas transit permits were marked as X/2, X/3, X/4, X/5, X/6, X/7, X/8, X/9 and X/10 for identification. Learned court after considering the oral and documentary evidences did find the challans, issued by the North Eastern Carrier Corporation Ltd., and also transit permits, issued by the Government of Orissa, to be forged, as according to the court, writing of the numbers of the vehicles in the challans as well as in the transit permits were of different inks and on that ground alone, the vehicles were ordered to be confiscated.
Being aggrieved with that order, this writ application has been filed.
5. Learned counsel appearing for the petitioners submits that simply for the reasons that the writing of the numbers of the vehicles in the challans, issued by the North Eastern Carrier Corporation Ltd., and also the transit permits, issued by the Mining Authority of Government of Orissa, were of different inks, the documents were considered to be forged, though the prosecuting agency has never verified it from the concerned Authority who had issued those documents and in absence of any inquiry being made from the concerned person, any finding with respect to the documents being forged would be illegal and any such finding in such circumstances is an outcome of conjecture and surmises.
6. However, learned counsel appearing for the State submits that it is true that no inquiry was made by the prosecuting agency about the genuinity of the documents from the concerned person who had issued it, but still the court has found those documents to be forged, as the writings were of different inks.
7. Having heard learned counsel appearing for the parties, I do not find any substance in the submission advanced on behalf of the State, simply for the 4 reasons that if some documents have been written in two different inks that would never mean that those documents would always be forged, as there could have been so many reasons for having two writings over the same documents. When the prosecuting agency considered those documents, such as challans, issued by the North Eastern Carrier Corporation Ltd., and the transit permits, issued by the Mining Officer, Government of Orissa, to be forged, it should have easily inquired into the matter over the genuinity of the documents from the maker of the documents, but the prosecuting agency never seems to have taken any pain to get the documents verified from the maker of the documents and in absence of such inquiry, it cannot be said conclusively about the documents being forged simply for the reasons that the documents do have writing in two different inks. Therefore, any finding, given by the learned Chief Judicial Magistrate, Chaibasa about the documents, being forged, certainly appears to be bad.
8. It has been stated in the FIR that the iron ores (fine) were being carried to Haldia Port from Orissa but the trucks got stopped at Jojo Camp for getting the illegal iron ores loaded, but no such evidence was produced by the prosecuting agency. Moreover, the prosecuting agency seems to have been admitted at least that the iron ores were being transported under transit permit, issued by the Mining Authority, Government of Orissa. However, it is the stand of the prosecuting agency that iron ore was never being carried under the permit issued by the State of Jharkhand in terms of the provisions of the Mines and Minerals (Development and Regulation) Act, 1957 and also Jharkhand Mineral Transit Challan Regulations, 2005. It would appear from reading of the relevant provision of the Regulation that the transit permit is required to be issued at the place from where iron ores are dispatched and since admittedly iron ores had been dispatched from Orissa, the transporter was not required to have any permit under the Jharkhand Mineral Transit Challan Regulations, 2005. 5
9. Under these circumstances, the petitioners can never be said to have committed any offence whatsoever under the Mines and Minerals (Development and Regulation) Act, 1957. In this respect, Sub Section (4) of Section 21 of the Mines and Minerals (Development and Regulation) Act, 1957, prescribing penalties for contravention of the provisions of the Act or Rules needs to be taken note of, reads as follows :-
" (4) Whenever any person raises, transports or causes to be raised or transported, without any lawful authority, any mineral from any land, and, for that purpose, uses any tool, equipment, vehicle or any other thing, such mineral tool, equipment, vehicle or any other thing shall be liable to be seized by an officer of authority specially empowered in this behalf. "
10. Keeping in view the facts and circumstances, as indicated above, and also coming to the finding that the prosecuting agency failed to establish that the documents, under which iron ores were being carried, were forged, the petitioners can never be said to have committed any offence in terms of Section 21 (4) of the Mines and Minerals (Development and Regulation) Act, 1957.
11. In this view of the matter, that part of order dated 21.8.206 passed by learned Chief Judicial Magistrate, Chaibasa in Misc. Case No. 18 of 2006 (arising out of Noamundi P.S Case No. 15 of 2006), whereby two trucks bearing nos. PB-10 BH-4287 and PB-13L-7987, belonging to petitioner no. 1- Avatar Singh, and the truck bearing no. PB-10 AS-9565, belonging to petitioner no. 2- Mohinder Singh, were ordered to be confiscated, is hereby quashed. Consequently, the respondent no. 2 (District Mining Officer, Chaibasa) is directed to release the aforesaid trucks forthwith in favour of the petitioners on executing bond of Rs. three lakhs each with one surety each.
12. Accordingly, this writ application is allowed.
6
13. Before parting with this order, it be observed that any finding given is confined to this case only and hence it would not be prejudicial so far the main case is concerned.
(R.R. Prasad, J.) Jharkhand High Court, Ranchi The dated 10th August, 2009 AKT/ NAFR


{% extends "base.html" %} {% block title %} Sources {% endblock %} {% block body %}
Here are links to our various data sources. A big thanks to all of them; this project wouldn't have been possible without the kindness of these organizations to publish this data publicly and for free.
{% endblock %}


displaypage works!
list works!we are now testing sanity
the id of the 1st meal is as follows {{theId}}
{{theId}}
{{theinstructionstocook}}
{{the1stingrediant}}
{{the2stingrediant}}
{{the3stingrediant}}
{{the4stingrediant}}
{{the5stingrediant}}
{{the6stingrediant}}
{{the7stingrediant}}
{{the8stingrediant}}
{{theId}}
the id of the 2nd meal is as follows {{theId1}}
{{theinstructionstocook1}}
{{the1stingrediant1}}
{{the2stingrediant1}}
{{the3stingrediant1}}
{{the4stingrediant1}}
{{the5stingrediant1}}
{{the6stingrediant1}}
{{the7stingrediant1}}
{{the8stingrediant1}}
{{theId1}}
the id of the 3rd meal is as follows {{theId1}}
{{theinstructionstocook2}}
{{the1stingrediant2}}
{{the2stingrediant2}}
{{the3stingrediant2}}
{{the4stingrediant2}}
{{the5stingrediant2}}
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{{the7stingrediant2}}
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{{theId2}}


Kevin Michael Stansbury
Republican
CEO Lincoln Community Hospital
BS Business Administration MS Health Services Administration JD
80821
I have been very active in representing and advocating for rural issues, particularly rural health issues. I have testified on the impact of the Hospital Provider Fee in 2017, have met with Governor Polis, Lt. Governor Primavera, HCPF Director Kim Bimestefer, and Insurance Commissioner Mike Conway. I regularly meet with Congressman Ken Buck, Senator Michael Bennet and Senator Cory Gardner and members of their staffs. Among other initiatives I also served on a study group appointed by Governor Hickenlooper to evaluate Global Payments for Rural Hospitals.
Have been involved with lobbying both state and federal policy makers on rural health issues through the Colorado Hospital Association, the National Rural Health Association and through the Colorado Rural Health Center. I have given money to the "Friends of Colorado Hospitals" PAC in the past five years. I have also donated to Senator Gardner's campaign this year. Finally I donated to the Senatorial Campaign of Merav Ben-David, the Democratic Senate Nominee in Wyoming.
Colorado Hospital (Board member), Colorado Rural Health Center (Board Member), American College of Healthcare Executives, Wyoming Bar Association, Colorado Hospital Association Shared Services (Board Chair), Eastern Plains Health Consortium (Founder, Board Member), Limon Chamber of Commerce, Hugo Improvement Program.
I believe in being involved. Serving on the commission would give me an opportunity to advocate for reasonable approaches to districting.
I believe I am a pragmatic problem solver and commit to putting the interests of our voters first rather than the interests of the Political Parties. I am able to listen to all sides of the issue and will work to finding common ground. All decisions will be based on what is best for the voters of the state.
I have been trained as an attorney and have served for over 35 years as a healthcare executive. I am continuously weighing competing priorities and working to achieve the best outcome based on the information available at the time. I like to learn about the root cause of an issue rather than focusing on the symptoms of the problem. My goal is to solve the problem in the long run rather than simply make incremental changes for the short term.
Male


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A GUnixFDList contains a list of file descriptors. It owns the file descriptors that it contains, closing them when finalized.
It may be wrapped in a GUnixFDMessage and sent over a GSocket in
the G_SOCKET_ADDRESS_UNIX
family by using g_socket_send_message()
and received using g_socket_receive_message()
.
Note that <gio/gunixfdlist.h>
belongs to the UNIX-specific GIO
interfaces, thus you have to use the gio-unix-2.0.pc
pkg-config
file when using it.
GUnixFDList * g_unix_fd_list_new_from_array (const gint *fds
,gint n_fds
);
Creates a new GUnixFDList containing the file descriptors given in
fds
. The file descriptors become the property of the new list and
may no longer be used by the caller. The array itself is owned by
the caller.
Each file descriptor in the array should be set to close-on-exec.
If n_fds
is -1 then fds
must be terminated with -1.
fds |
the initial list of file descriptors. |
[array length=n_fds] |
n_fds |
the length of fds, or -1 |
Since 2.24
GUnixFDList *
g_unix_fd_list_new (void
);
Creates a new GUnixFDList containing no file descriptors.
Since 2.24
gint
g_unix_fd_list_get_length (GUnixFDList *list
);
Gets the length of list
(ie: the number of file descriptors
contained within).
Since 2.24
gint g_unix_fd_list_get (GUnixFDList *list
,gint index_
,GError **error
);
Gets a file descriptor out of list
.
index_
specifies the index of the file descriptor to get. It is a
programmer error for index_
to be out of range; see
g_unix_fd_list_get_length()
.
The file descriptor is duplicated using dup()
and set as
close-on-exec before being returned. You must call close()
on it
when you are done.
A possible cause of failure is exceeding the per-process or system-wide file descriptor limit.
Since 2.24
const gint * g_unix_fd_list_peek_fds (GUnixFDList *list
,gint *length
);
Returns the array of file descriptors that is contained in this object.
After this call, the descriptors remain the property of list
. The
caller must not close them and must not free the array. The array is
valid only until list
is changed in any way.
If length
is non-NULL
then it is set to the number of file
descriptors in the returned array. The returned array is also
terminated with -1.
This function never returns NULL
. In case there are no file
descriptors contained in list
, an empty array is returned.
Since 2.24
gint * g_unix_fd_list_steal_fds (GUnixFDList *list
,gint *length
);
Returns the array of file descriptors that is contained in this object.
After this call, the descriptors are no longer contained in
list
. Further calls will return an empty list (unless more
descriptors have been added).
The return result of this function must be freed with g_free()
.
The caller is also responsible for closing all of the file
descriptors. The file descriptors in the array are set to
close-on-exec.
If length
is non-NULL
then it is set to the number of file
descriptors in the returned array. The returned array is also
terminated with -1.
This function never returns NULL
. In case there are no file
descriptors contained in list
, an empty array is returned.
Since 2.24
gint g_unix_fd_list_append (GUnixFDList *list
,gint fd
,GError **error
);
Adds a file descriptor to list
.
The file descriptor is duplicated using dup()
. You keep your copy
of the descriptor and the copy contained in list
will be closed
when list
is finalized.
A possible cause of failure is exceeding the per-process or system-wide file descriptor limit.
The index of the file descriptor in the list is returned. If you use
this index with g_unix_fd_list_get()
then you will receive back a
duplicated copy of the same file descriptor.
Since 2.24


Simon
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{% for my_page in site.pages %} {% if my_page.title %}
{{ my_page.title }}
{% endif %} {% endfor %}
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Redtail
Redtail CRM is cost-effective, easy and tailored to today’s financial pro. Packed with new features, office automation, and of course it integrates with all the best technology. Unlike other CRM solutions, it’s priced per database, allowing up to 15 users, all for the same low monthly subscription. Plus, these robust features will make your practice hum:
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- The automated office: Workflows streamline by generating and tracking activities, while reminders alert you to events like client birthdays, anniversaries and reviews.
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This intention expands statically imported method call to explicit class qualification. When multiple calls use corresponding static import, it is possible to replace them all or to change current one only.


mystate.addFPulseActivity(id, intvl, fnOrMet, obj)
id ID of the activity (must be unique among activities on this state)
intvl Interval, in frames, between calls
fnOrMet Function object, or method name (string) to invoke
obj If fnOrMet is the method name, this is the object on which the method resides
Nothing
Use this method to add an activity to the state that fires every x frames. You can specify a function or method to call. Note: if you need to refer to the timeline from within a function, use the last example to call your function.
//For a function: myFunc myState.addFPulseActivity(0, 20, myFunc); //For a method: myObj.myMethod myState.addFPulseActivity(0, 20, "myMethod", myObj); //For a function needing access to the timeline: myState.addFPulseActivity(0, 20, "myFunc", this); //when 'this' is the timeline


Created: 2018-04-01 Sun 19:10
Ravi Shankar was an extremely influential sitarist from Bengal, India. He had a distinct playing style characterized by unconventional rhythmic cycles and flowing, but disjunct, melodic lines.
Figure 1: An indian sitar
Figure 2: The tanpura: supports the melody with a harmonic drone.
Figure 3: The sitar: primary melodic instrument.
Figure 4: The tablas: primary rhythmic instrument.
The Khamaj is one of the ten Thaats (parent scales) of Hindustani classical music. The improvisational scale for this raga is shown below:
Figure 5: Ascending scale
Figure 6: Descending scale
Figure 7: So What melody
The improvisers in So What, especially Davis and Coltrane, have very similiar styles to Hindustani music.
Surprisingly, Raag Khamaj and Hindustani music in general shares many of the same melodic constructs as Jazz.
Sources and Discography:
"Raag Khamaj." Raag Khamaj - Indian Classical Music, Tanarang.com, 2015, www.tanarang.com/english/khamaj_eng.htm.
Ruston, P. “'So What' Analysis.” Architectures of Music, WordPress, 7 Mar. 2016, architecturesofmusic.wordpress.com/2016/03/07/so-what-analysis/.
MilesDavisVEVO. “Miles Davis - So What (Audio).” YouTube, 14 May 2013, www.youtube.com/watch?v=ylXk1LBvIqU.
TangibleEmotions. “Ravi Shankar & Anoushka Shankar Live: Raag Khamaj (1997).” YouTube, 21 Sept. 2011, www.youtube.com/watch?v=9xB_X9BOAOU.


You are running this application in {{ environment.production ? "production" : "development" }} mode.


RGB Values can also be specified using the HexaDecimal color values in the form: #RRGGBB. When RR(red), GG(green), BB(blue) are hexadecimal values between 00 and FF (same as decimal 0-255). For Example, #FF0000 is displayed as red, because red is set to highest value(FF) and the others are set to the lowest value(00). Note: HEX values are case insensitive:#ff0000is the same as#FF0000


{#
Check if already submitted
==========================
To check if a form has already been submitted using a specific email address, use something like this:
$.post('/',
{
action: 'formerly/submissions/alreadySubmitted',
formid: 4,
email<EMAIL_ADDRESS>},
function(data, status){
alert("already?:" + data.alreadysubmitted + "\nStatus: " + data.ok);
});
Ajax posting
============
To submit the form using ajax use something like this:
postForm: function(){
var $form = $('#form_1');
var formData = $form.serialize();
$form
.on('submit', function(e) {
e.preventDefault();
$.ajax({
url : '',
method: 'POST',
data: formData,
success: function(response, textStatus, jqXHR){
//do something for success response
},
error: function(jqXHR, textStatus, errorThrown){
//do something for error
}
});
return false;
});
}// postForm
}
Config settings
===============
Set these config settings in /craft/config/general.php as needed
'formerly' => array(
'honeyPotName' => 'pants', //id of input honeypot, don't call it something obvious like honeypot
'assetFolderId' => 6, //id of the folder you want to put assets in, note - you should secure this folder from browsing
'allowedKinds' => array("excel","image","pdf","text","word"), //valid types of files users can upload
'writeEmailBodyToFilePath' => '/vagrant/emails', //if specified a json copy of the email is written to the path
'sendEmails' => false //should formerly send emails. default to true
),
#}
{{ getCsrfInput() }}


The three days are passing in a blur, you are torn about what to do. You love your family and you don't want to leave them and never see them again. However, you don't want to be forced to get married to a man that you've never met, and you most likely won't ever love that man. You know it's all a business transaction, your father will receive a decent sized dowry for you. You know that you're considered your father's possession and that he can do whatever he wants with you, and that you have no say in the matter.
You have decided to take control over your life and run away.  You want to do the things that you choose, and you have an extremely strong desire to see the world. It is your dream to travel around the world and see all that it has to offer.
 You turn around and see David enter your home.
 he asks quietly.
"Yeah, I want to leave."
He nods slowly.
"I have a plan." he says, "You can't leave before your father has come to fetch you."
"I will follow you to the city, you'll have to find a way to sneak out of the house." he takes a deep breath, "Then I will hide you for a day."

"I have some money saved up that I will give you. It isn't much, but it is a start."

At this moment, your father walks in behind you. He stops in his track when he sees the two of you, embracing each other.
 he snarls at you, advancing towards you.
 He walks around to the drivers side and gets in and slams the door behind him. You watch David run to his father's old beaten car, that he received when he got accepted to the university as a gift. Your father drives away, and the plan is in motion.


Hello
This is a place where you can record all of your daily duties in one place, ready to show the boss.
Please select your correct job title here from the dropdown list.
Please select the correct amount of hours you worked this week.
Please note: In order to use this site you have to be working a minimum of 10 hours a week and a maximum of 40 hours.
Please add any additional information that you feel is relevent for your boss to know about regarding this week's work.


Use Google Assistant To Buy Movie Tickets Quickly
by Brendan HesseAndroid users can now have Google Assistant buy movie tickets and reserve seats for them, thanks to a new Google Duplex feature that makes it possible for the AI to access and navigate Chrome webpages on its own—much like how the Duplex AI allows Google Assistant to call restaurants or hotels for reservations on your behalf.
At launch, the feature supports more than 70 movie theatre chains, and some U.S. ticket-buying services such as Fandango or Movietickets.com—even if they don’t have an app or if their websites aren’t normally configured for mobile browsers. Not only is this easier than navigating these companies’ websites directly, it’s also potentially faster than buying them and reserving seats on your own—which may help you get a seat for incredibly popular movies before they sell out.
How to use the new ticket-ordering feature:
- On your Android device, open Google Assistant and search for nearby movie showings.
- On the results page, tap the showtime that you want to buy tickets for, and a new box will pop-up with buying options.
- Tap the Google Assistant “Buy Tickets” icon next to the service or website you want to use.
- You will need to confirm you want to use Google Assistant to buy the tickets for you, and then accept any website cookies.
Unlike when Google’s AI make a phone call and a reservation for you, buying movie tickets requires a little more input. Google Assistant will ask you questions such as how many tickets you want or which seats to reserve (if applicable).
You’ll also have to confirm the final order and payment info before buying the tickets. Still, this is a lot simpler (and faster!) than having to scroll through multiple pages and type out all this information yourself.


* Tubeworms (Lamelli brachia) were found at both Kasuga-2 and Daikoku volcanoes.
President George W. Bush established the monument under the authority of the Antiquities Act of 1906, which protects places of historic or scientific significance. Only recently have scientists visited the realm of the monument, observing previously unknown biological, chemical, and geological wonders of nature.
The Mariana Trench is the deepest place on Earth, deeper than the height of Mount Everest above sea level. It is five times longer than the Grand Canyon and includes some 50,532,102 acres that are virtually unknown to humans.
The Champagne vent, found at the NW Eifuku volcano, produces almost pure liquid carbon dioxide, one of only two known sites in the world. A pool of liquid sulfur at the Daikoku submarine volcano is unique in all the world. The only other known location of molten sulfur is on Io, a moon of the planet of Jupiter.
In the Islands Unit, unique reef habitats support marine biological communities dependent on basalt rock foundations, unlike those throughout the remainder of the Pacific. These reefs and waters are among the most biologically diverse in the Western Pacific and include the greatest diversity of seamount and hydrothermal vent life yet discovered. They also contain one of the most diverse collections of stony corals in the Western Pacific, including more than 300 species, higher than any other U.S. reef area.
Extreme pressure and heat, toxic levels of dissolved metals and gases, and extreme reducing conditions are found across various Monument features.
In spite of these conditions life not only exists, but thrives; albeit in forms that are uniquely suited for these specific locations.
No waters are included in the Volcanic and Trench Units, and CNMI maintains all authority for managing the three islands within the Islands Unit (Farallon de Pajaros or Uracas, Maug, and Asuncion) above the mean low water line. Presidential Proclamation 8335 established the monument in January 2009 and assigned management responsibility to the Secretary of the Interior, in consultation with the Secretary of Commerce.
The Interior Secretary placed the Mariana Trench and Volcanic Units within the National Wildlife Refuge System, and delegated his management responsibility to the Fish and Wildlife Service.
This interactive project was created for educational purposes at Diablo Valley College and is in no way intended for commercial gain or as a source of public information.


TABLE OF CONTENTS
A. GS1 Related B. GS2 Related International Affairs/Bilateral Relations 1. New U.S. policy can boost Indo-Afghan security ties: Abdullah Abdullah Health Issues 1. India’s infant mortality rate declines 8% in 2016 C. GS3 Related Economy 1. Reducing food waste top priority for India: Union Minister Harsimrat Kaur Badal 2. National Telecom Policy: ‘Guiding principles prepared’; Cabinet note likely by January 3. India’s external debt rises 3% to $485.8 billion 4. World Bank warns of learning crisis in education in countries like India 5. Fiscal deficit hits 96.1% but Modi govt confident of meeting target Science and Technology 1. Blame it on the genes D. GS4 Related E. Prelims Fact F. UPSC Prelims Practice Questions G. UPSC Mains Practice Questions
Nothing here for Today!!!
Category: INTERNATIONAL AFFAIRS/BILATERAL RELATIONS
1. New U.S. policy can boost Indo-Afghan security ties: Abdullah Abdullah
In news:
Key Points:
- The new U.S. policy in Afghanistan is an “opportunity” for India-U.S.-Afghanistan cooperation on security as well as development issues.
MoU on technical and training cooperation
- The MoU is an important step in the security sector.
- India’s support has been comprehensive here and has always looked at the priorities and the wishes of the Afghan government and people.
India-Afghanistan-U.S. trilateral emerges as an axis:
- India has been helping Afghanistan for the past 16 years in many ways.
- The United States has been helping Afghanistan for the past 16 years.
- Cooperation had a security element throughout, like the helicopters India transferred, or training Afghan troops.
1. India’s infant mortality rate declines 8% in 2016
In news:
- Decline in infant mortality rate (IMR)
- India has attained an 8% decline in the IMR in 2016 from a year ago
- According to the Sample Registration System (SRS) bulletin, IMR has declined to 34 per 1,000 live births last year from 37 per 1,000 live births in 2015
- The Bulletin is released by the Office of the Registrar General
- It attributed the improvement to countrywide efforts to expand health services coverage, including reproductive, maternal and newborn health services.
- IMR declined in 29 states and Union territories, was stable in two states and increased in five states and Union territories.
- The highest decline of 7 points was reported by Chandigarh and the highest increase of 6 points was reported by Arunachal Pradesh.
1. Reducing food waste top priority for India: Union Minister Harsimrat Kaur Badal
In news:
- India has made it a huge priority to reduce its massive food waste, Union Minister Harsimrat Kaur Badal has told American industry leaders, seeking their investment and technology in this regard.
- India aims to partner with U.S. industry by borrowing best practices, technology and innovation, in exchange for providing U.S. companies with untapped investment opportunities in India.
- Key fact:
- India, one of the world’s largest producers of food, is also the largest producer of milk and the second largest producer of fruits and vegetables.
- India is currently only processing 10% of its food, resulting in enormous food waste.
2. National Telecom Policy: ‘Guiding principles prepared’; Cabinet note likely by January
In news:
- Telecom Commission, has defined the guiding principles for the National Telecom Policy
(NTP) which include
- Focus on the telecommunications sector as an enabler of economic growth
- Protection of consumer interest by ensuring affordability
- Quality of services
- Protection of data and privacy
- The policy will look to ensure financial stability and sustainable growth of the sector so that so that adequate investments can come for all the new technologies
- DoT’s apex decision making body also cleared the recommendations of raising the deferred spectrum payment period from 10 years to 16 years
- The telecom commission also approved replacement of the PLR with MCLR for the delay in payments by operators on licence fee and spectrum usage charges.
- The DoT has sought legal opinion on amending the spectrum trading guidelines which could be of huge advantage to smaller operators
Basic Information:
Marginal Cost of funds based Lending rate (MCLR)
- It refers to the minimum interest rate of a bank below which it cannot lend, except in some cases allowed by the RBI. It is an internal benchmark or reference rate for the bank.
- MCLR actually describes the method by which the minimum interest rate for loans is determined by a bank – on the basis of marginal cost or the additional or incremental cost of arranging one more rupee to the prospective borrower.
3. India’s external debt rises 3% to $485.8 billion
In news:
- India’s external debt has increased three per cent to $ 485.8 billion at June-end over the previous quarter.
- It is mainly due to increase in inflow of foreign portfolio investment into domestic capital market’s debt segment.
- It was partly due to valuation loss resulting from the depreciation of the US dollar vis-a-vis the rupee and other major currencies.
- According to the RBI, commercial borrowings continued to be the largest component of external debt with a share of 37.8 per cent, followed by NRI deposits and short-term trade credit.
- US dollar denominated debt continued to be the largest component of India’s external debt followed by the Indian rupee , SDR , Japanese yen and Euro.
Basic Information:
External Debt
- External debt is owed to creditors outside the country. The outsider creditors can be foreign governments, International Financial Institutions such as World Bank, Asian Development Bank etc., corporate and foreign private households.
- External debt may be of several kinds such as multilateral, bilateral, IMF loans, Trade credits, External commercial borrowings etc.
- When the non-resident Indians park their funds in India, it is also a type of external debt and is called NRI deposits. If the external debt is denominated in Indian Rupee, it is called Rupee Debt.
Special Drawing Rights
- The SDR is an international reserve asset, created by the IMF in 1969 to supplement its member countries’ official reserves.
- Its value is based on a basket of 4 key international currencies, and SDRs can be exchanged for freely usable currencies.
- SDRs are the IMF’s unit of account restricted to its members. Today the SDR basket consists of the euro, Japanese yen, pound sterling, and U.S. dollar.
4. World Bank warns of learning crisis in education in countries like India
In news:
World Bank group report:
- It has warned of a learning crisis in global education particularly in low and middle-income countries like India.
- The report underlines that schooling without learning is not just a wasted development opportunity, but also a great injustice to children worldwide.
- Report: World Development Report 2018: ‘Learning to Realise Education’s Promise.
The report:
- According to the report, India ranks second after Malawi in a list of 12 countries wherein a grade two student could not read a single word of a short text.
- India also tops the list of seven countries in which a grade two student could not perform two-digit subtraction..
- This learning crisis is widening social gaps instead of narrowing them
Recommendations:
- The report recommends concrete policy steps to help developing countries resolve this learning crisis in the areas of stronger learning assessments.
- How? using evidence of what works and what doesn’t to guide education decision-making.
- And mobilising a strong social movement to push for education changes that champion ‘learning for all’.
5. Fiscal deficit hits 96.1% but Modi govt confident of meeting target
In news:
- India’s fiscal deficit touched 1% of the Budget estimate for FY18 at the end of August
- Possible reason : The fiscal deficit has been higher than in the previous years because of the quick start to spending facilitated by the early presentation of Budget on February 1
- According the data released by the Controller General of Accounts (CGA) , April-August fiscal deficit is substantially higher than 76.4% for the year-earlier period
Government’s opinion:
- According to government, it will meet the fiscal deficit target for the year
- The target is3.2% of GDP, or Rs 5.47 lakh crore
- In absolute terms, the fiscal deficit for April-August was Rs 5.25 lakh crore
Targets achievement:
- The government is considering ways of reviving growth, which slumped to its lowest in three years in the June quarter
- The government has already allocated large sums to a few of its key schemes to ensure there was no delay due to funds
- The revenue deficit was 133.9% of budget estimate against 91.7% last fiscal
- With this big spending(allocations) done, the fiscal situation should start to improve as revenues pick up pace in the second half of the financial year
Category: SCIENCE AND TECHNOLOGY
In news:
- New study revelations: about 35 to 40% of Indians carry a set of genetic variations which puts them at a higher risk of heart disease.
- Carriers of a set of genetic variants in the chromogranin A (CHGA) gene called ‘CHGA promoter haplotype2’ may be at higher risk for cardiovascular and metabolic disorders.
- The study is based on analysis of genomic DNA samples from over 750 individuals in the Indian population.
- Though the CHGA promoter haplotype is present in other ethnic populations, it occurs more frequently in populations of South Asian ancestry.
Nothing here for Today!!!
Nothing here for Today!!!
F. Practice Questions for UPSC Prelims Exam
Question 1. The Nahar Wildlife Sanctuary (NWS) is located in which state?
- Rajasthan
- Assam
- Haryana
- Kerala
Question 2. The most important source of information about the agrarian
conditions during Mughals is?
- Ain-i-Akbari
- Akbarnama
- Muntakhab-ul-Lubab
- Tarikh-i-Ferishta
Question 3. Which among the following is known as the earliest example of
Panchayatana style of temple?
- Dashavatara temple at Deogarh
- Temple at Pathari
- Shatrughneshwara temple at Bhubaneshwar
- Lakshmana temple at Sirpur
Question 4. With respect to the President of India, which among the following
statements is / are correct?
- A person who has been president of India is eligible for immediate re-election.
- A person cannot hold the office of president of India for more than two consecutive terms.
Choose the correct option from the codes given below:
- 1 only
- 2 only
- Both 1 and 2
- Neither 1 nor 2
Question 5. Which among the following in India are known for substantial coral
reef formations?
- Gulf of Mannar
- Gulf of Kachchh
- Andaman and Nicobar
- Lakshadweep Islands
Choose the correct option from the codes given below:
- Only 1 & 2
- Only 2 & 3
- Only 2, 3 & 4
- 1, 2, 3 & 4
G. UPSC Mains Practice Questions
GS Paper II
- Did the Government of India Act, 1935 lay down a federal constitution? Discuss.(Mains 2016)
- Discuss the possible factors that inhibit India from enacting for its citizens a uniform civil code as provided for in the Directive Principles of State Policy.
GS Paper III
- The nature of economic growth in India in described as jobless growth. Do you agree with this view? Give arguments in favour of your answer.(Mains 2015)
Also, check previous Daily News Analysis
“Proper Current Affairs preparation is the key to success in the UPSC- Civil Services Examination. We have now launched a comprehensive ‘Current Affairs Webinar’. Limited seats available. Click here to Know More.”
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Chocolate Pizza
Posted on 15 December 2013/Desserts
THE BEST FOOD BLOG ON THE WEB
Posted on 15 December 2013/Desserts
For the fig-swirl:Melt butter over medium heat in a saucepan. Add brown sugar and stir to dissolve. Halve all of the figs and toss in the saucepan with water and lemon juice. Cook over medium heat, stirring frequently, until you have a chunky-jammy mixture. Add salt with one or two stirs, set aside an dlet cool completely.
Ice cream:In a small pot over medium heat, combine milk, and granulated sugar until sugar is completely dissolved and the milk is just barely lukewarm.Wish in the egg yolks. Set mixture in the fridge and wait until the fig mixture is cooled.
Using an ice cream machine, pour liquids into the frozen basin and process according to manufacturer instructions,i.e., let spin and thicken for 20 minutes before adding mascarpone, fig jam mixture, and the nuts.Countinue to process for +/- 10 minutes. Pour semi-frozen mixture into a pyrex dish or glass tupperware.Freeze for at least twohours before serving.
Food enthusiast, photography fan. Add a pinch of raw foodism and that's pretty much who I am.


Global Wine Production 2019 Is Returning To ‘Normal,’ Says Pau Roca Of The OIV
by Per and Britt Karlsson“Is it an effect of global warming, the big swings in wine production on 2017, 2018 and 2019,” someone in the audience asked. “No,” was the answer from Pau Roca, director-general of the International Organisation of Vine and Wine (OIV). But more important was what he followed up with. “It is too short a time span to be able to draw any such conclusion.Pau Roca was presenting the very first estimations of the global wine production in 2019 at the new OIV headquarters in Paris.
So, back to the global wine production 2019.
Worldwide wine production in 2019 is back to long term average
The world’s total wine production in 2019 is estimated to be 263 million hectolitres (26.3 billion litres, within a span of 258 to 267 Mhl). This is just slightly below the average global wine production over the last ten years of 270 Mhl.
However, it is a drop of 10% compared to the previous year, but 2018 saw a very big wine production of 294 M hl. 2018 was almost a record size harvest.
Compared to 2017, two years ago, 2019 is an increase with 6%, but 2017 was the smallest harvest in a long time.
So this was the background of the question asked initially. We have seen two years of dramatic swings. But it is too early to say what the future will give. In any case, 2019 is more back to “normal.”
Small wine production in the European Union
The European Union accounts for about 60% of the world’s production. 2019 was a difficult year in Europe. The major wine-producing countries had, in general, a spring with rain and frost and a very hot summer, which has affected the quantities.
The top three countries are:
- Italy, 46.6 Mhl, -15%
- France, 41.9 Mhl, -15%
- Spain, 34.3 Mhl, -24%
Italy keeps its first place in the ranking as the world’s biggest wine-producing country. All three countries saw dramatic declines, but that is, as mentioned, compared to a very abundant harvest in 2018. All other EU countries had smaller harvests in 2019 compared with the previous year, with the exception of Portugal that increased 10% to 6.7 Mhl
The three biggest countries taken together accounts for about 80% of all European Union wine, and thus almost half of the global total.
Most other countries in the northern hemisphere had a good year. The biggest wine-producing country outside the EU in the north is the United States that had a slightly smaller harvest than 2018 (-1%) of 23.6 Mhl, but this is still on a historic high level (four years in a row). Russia was up 7% to 6 Mhl.
Southern hemisphere somewhat down but historically stable
The overall tendency in the southern hemisphere is similar: a decrease compared to 2018 but in line with the longer-term average.
Argentina is the biggest country here with a wine production 13 Mhl. This is slightly bigger than Australia’s 12.5 Mhl and Chile’s 11.9 Mhl. These three countries maintain their ranking in the top list.
Germany, on the other hand, has lost two places in the ranking, with a harvest that was down 13% to 9 Mhl and is now in 10th position. It has been overtaken by South Africa and possibly China. (China has not yet submitted data so the production is a very uncertain estimation based on other input.)
Here is the list of wine-producing countries with production over 1 million hectolitres:
- Italy, 46.6 Mhl
- France, 41.9 Mhl
- Spain, 34.3 Mhl
- USA, 23.6 Mhl
- Argentina, 13 Mhl
- Australia, 12.5 Mhl
- Chile, 11.9 Mhl
- South Africa, 9.7 Mhl
- China (number not available, position estimated by the OIV)
- Germany, 9 Mhl
- Portugal, 6.7 Mhl
- Russia, 6 Mhl
- Romania, 4.9 Mhl
- Hungary, 3.2 Mhl
- New Zealand, 3 Mhl
- Brazil, 2.9 Mhl
- Austria, 2.6 Mhl
- Greece, 2 Mhl
- Georgia, 1.6 Mhl
- Switzerland, 1.1 Mhl
World total: 263 Mhl.
All numbers from the OIV report. It should be noted that the numbers are still very preliminary, especially for the northern hemisphere. The numbers also exclude must and juice.
—Per Karlsson