The Mystery of Cloomber
by Sir Arthur Conan Doyle
Chapter XII: “Of the Three Foreign Men Upon the Coast”
Additional Information
- Year Published: 1889
- Language: English
- Country of Origin: England
- Source: Doyle, A. C. (1889). The Mystery of Cloomber.London, England: Ward and Downey.
-
Readability:
- Flesch–Kincaid Level: 9.5
- Word Count: 2,379
- Genre: Mystery
- Keywords: 19th century literature, british literature, mystery, sir arthur conan doyle, the mystery of cloomber
- ✎ Cite This
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It must have been eleven or twelve o’clock before I awoke, and it seemed to me in the flood of golden light which streamed into my chamber that the wild, tumultuous episodes of the night before must have formed part of some fantastic dream.
It was hard to believe that the gentle breeze which whispered so softly among the ivy-leaves around my window was caused by the same element which had shaken the very house a few short hours before. It was as if Nature had repented of her momentary passion and was endeavouring to make amends to an injured world by its warmth and its sunshine. A chorus of birds in the garden below filled the whole air with their wonder and congratulations.
Down in the hall I found a number of the shipwrecked sailors, looking all the better for their night’s repose, who set up a buzz of pleasure and gratitude upon seeing me.
Arrangements had been made to drive them to Wigtown, whence they were to proceed to Glasgow by the evening train, and my father had given orders that each should be served with a packet of sandwiches and hard-boiled eggs to sustain him on the way.
Captain Meadows thanked us warmly in the name of his employers for the manner in which we had treated them, and he called for three cheers from his crew, which were very heartily given. He and the mate walked down with us after we had broken our fast to have a last look at the scene of the disaster.
The great bosom of the bay was still heaving convulsively, and its waves were breaking into sobs against the rocks, but there was none of that wild turmoil which we had seen in the early morning. The long, emerald ridges, with their little, white crests of foam, rolled slowly and majestically in, to break with a regular rhythm—the panting of a tired monster.
A cable length from the shore we could see the mainmast of the barque floating upon the waves, disappearing at times in the trough of the sea, and then shooting up towards Heaven like a giant javelin, shining and dripping as the rollers tossed it about. Other smaller pieces of wreckage dotted the waters, while innumerable spars and packages were littered over the sands. These were being drawn up and collected in a place of safety by gangs of peasants. I noticed that a couple of broad-winged gulls were hovering and skimming over the scene of the shipwreck, as though many strange things were visible to them beneath the waves. At times we could hear their raucous voices as they cried to one another of what they saw.
“She was a leaky old craft,” said the captain, looking sadly out to sea, “but there’s always a feeling of sorrow when we see the last of a ship we have sailed in. Well, well, she would have been broken up in any case, and sold for firewood.”
“It looks a peaceful scene,” I remarked. “Who would imagine that three men lost their lives last night in those very waters?”
“Poor fellows,” said the captain, with feeling, “Should they be cast up after our departure, I am sure, Mr. West, that you will have them decently interred.”
I was about to make some reply when the mate burst into a loud guffaw, slapping his thigh and choking with merriment.
“If you want to bury them,” he said, “you had best look sharp, or they may clear out of the country. You remember what I said last night? Just look at the top of that ‘ere hillock, and tell me whether I was in the right or not?”
There was a high sand dune some little distance along the coast, and upon the summit of this the figure was standing which had attracted the mate’s attention. The captain threw up his hands in astonishment as his eyes rested upon it.
“By the eternal,” he shouted, “it’s Ram Singh himself! Let us overhaul him!”
Taking to his heels in his excitement he raced along the beach, followed by the mate and myself, as well as by one or two of the fishermen who had observed the presence of the stranger.
The latter, perceiving our approach, came down from his post of observation and walked quietly in our direction, with his head sunk upon his breast, like one who is absorbed in thought.
I could not help contrasting our hurried and tumultuous advance with the gravity and dignity of this lonely Oriental, nor was the matter mended when he raised a pair of steady, thoughtful dark eyes and inclined his head in a graceful, sweeping salutation. It seemed to me that we were like a pack of schoolboys in the presence of a master.
The stranger’s broad, unruffled brow, his clear, searching gaze, firm-set yet sensitive mouth, and clean-cut, resolute expression, all combined to form the most imposing and noble presence which I had ever known. I could not have imagined that such imperturbable calm and at the same time such a consciousness of latent strength could have been expressed by any human face.
He was dressed in a brown velveteen coat, loose, dark trousers, with a shirt that was cut low in the collar, so as to show the muscular, brown neck, and he still wore the red fez which I had noticed the night before.
I observed with a feeling of surprise, as we approached him, that none of these garments showed the slightest indication of the rough treatment and wetting which they must have received during their wearer’s submersion and struggle to the shore.
“So you are none the worse for your ducking,” he said in a pleasant, musical voice, looking from the captain to the mate. “I hope that your poor sailors have found pleasant quarters.”
“We are all safe,” the captain answered. “But we had given you up for lost—you and your two friends. Indeed, I was just making arrangements for your burial with Mr. West here.”
The stranger looked at me and smiled.
“We won’t give Mr. West that trouble for a little time yet,” he remarked; “my friends and I came ashore all safe, and we have found shelter in a hut a mile or so along the coast. It is lonely down there, but we have everything which we can desire.”
“We start for Glasgow this afternoon,” said the captain; “I shall be very glad if you will come with us. If you have not been in England before you may find it awkward traveling alone.”
“We are very much indebted to you for your thoughtfulness,” Ram Singh answered; “but we will not take advantage of your kind offer. Since Nature has driven us here we intend to have a look about us before we leave.”
portrait of Sir Arthur Conan Doyle
“As you like,” the captain said, shrugging his shoulders. “I don’t think you are likely to find very much to interest you in this hole of a place.”
“Very possibly not,” Ram Singh answered with an amused smile. “You remember Milton’s lines:
‘The mind is its own place, and in itself Can make a hell of Heaven, a heaven of Hell.’
I dare say we can spend a few days here comfortably enough. Indeed, I think you must be wrong in considering this to be a barbarous locality. I am much mistaken if this young gentleman’s father is not Mr. James Hunter West, whose name is known and honoured by the pundits of India.”
“My father is, indeed, a well-known Sanscrit scholar,” I answered in astonishment.
“The presence of such a man,” observed the stranger slowly, “changes a wilderness into a city. One great mind is surely a higher indication of civilization than are incalculable leagues of bricks and mortar.
“Your father is hardly so profound as Sir William Jones, or so universal as the Baron Von Hammer-Purgstall, but he combines many of the virtues of each. You may tell him, however, from me that he is mistaken in the analogy which he has traced between the Samoyede and Tamulic word roots.”
“If you have determined to honour our neighbourhood by a short stay,” said I, “you will offend my father very much if you do not put up with him. He represents the laird here, and it is the laird’s privilege, according to our Scottish custom, to entertain all strangers of repute who visit this parish.”
My sense of hospitality prompted me to deliver this invitation, though I could feel the mate twitching at my sleeves as if to warn me that the offer was, for some reason, an objectionable one. His fears were, however, unnecessary, for the stranger signified by a shake of the head that it was impossible for him to accept it.
“My friends and I are very much obliged to you,” he said, “but we have our own reasons for remaining where we are. The hut which we occupy is deserted and partly ruined, but we Easterns have trained ourselves to do without most of those things which are looked upon as necessaries in Europe, believing firmly in that wise axiom that a man is rich, not in proportion to what he has, but in proportion to what he can dispense with. A good fisherman supplies us with bread and with herbs, we have clean, dry straw for our couches; what could man wish for more?”
“But you must feel the cold at night, coming straight from the tropics,” remarked the captain. “Perhaps our bodies are cold sometimes. We have not noticed it. We have all three spent many years in the Upper Himalayas on the border of the region of eternal snow, so we are not very sensitive to inconveniences of the sort.”
“At least,” said I, “you must allow me to send you over some fish and some meat from our larder.”
“We are not Christians,” he answered, “but Buddhists of the higher school. We do not recognize that man has a moral right to slay an ox or a fish for the gross use of his body. He has not put life into them, and has assuredly no mandate from the Almighty to take life from them save under most pressing need.  “And now, Captain Meadows, I must bid you adieu, thanking you for your kindness during the voyage, and you, too, good-bye—you will command a ship of your own before the year is out. I trust, Mr. West, that I may see you again before I leave this part of the country. Farewell!”
He raised his red fez, inclined his noble head with the stately grace which characterized all his actions, and strode away in the direction from which he had come.
“Let me congratulate you, Mr. Hawkins,” said the captain to the mate as we walked homewards. “You are to command your own ship within the year.”
“No such luck!” the mate answered, with a pleased smile upon his mahogany face, “still, there’s no saying how things may come out. What d’ye think of him, Mr. West?”
“Why,” said I, “I am very much interested in him. What a magnificent head and bearing he has for a young man. I suppose he cannot be more than thirty.”
“Forty,” said the mate.
“Sixty, if he is a day,” remarked Captain Meadows. “Why, I have heard him talk quite familiarly of the first Afghan war. He was a man then, and that is close on forty years ago.”
“Wonderful!” I ejaculated. “His skin is as smooth and his eyes are as clear as mine are. He is the superior priest of the three, no doubt.”
“The inferior,” said the captain confidently. “That is why he does all the talking for them. Their minds are too elevated to descend to mere worldly chatter.”
“They are the strangest pieces of flotsam and jetsam that were ever thrown upon this coast,” I remarked. “My father will be mightily interested in them.”
“Indeed, I think the less you have to do with them the better for you,” said the mate.  But here we are all aboard and the anchor tripped, so we must bid you good-bye.”
The wagonette had just finished loading up when we arrived, and the chief places, on either side of the driver, had been reserved for my two companions, who speedily sprang into them. With a chorus of cheers the good fellows whirled away down the road, while my father, Esther, and I stood upon the lawn and waved our hands to them until they disappeared behind the Cloomber woods, en route for the Wigtown railway station. Barque and crew had both vanished now from our little world, the only relic of either being the heaps of debris upon the beach, which were to lie there until the arrival of an agent from Lloyd’s.


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--- title: "COVID-19 and R Part III" author: "Drew Tyre" date: "2020-03-15" output: word_document: default html_document: code_folding: hide draft: no slug: COVID-19_and_R_III tags_include: - R - OPD - COVID19 categories: Research ---
In today’s post I want to summarize some information about what to do (and what not to do) in the face of the COVID-19 pandemic, and examine how my predictions are holding up. I feel a little bit guilty because the topic is serious, and this data reflects real suffering, but I am learning a lot with this real-time forecasting. In my normal work I only get one data point per year. Which means building up skill in forecasting takes a loooong time.
I’ve also realized I have a few different possible audiences with different needs. There are intelligent and interested people like my mother who want the bottom line explained but don’t care how I get there. There are students who want to learn more about how to use R. And then there are some serious data nerds who want to know about my every choice in the analysis and ask why I’m not doing y or z. So I’m going to put the bottom line at the top, so that my mother doesn’t have to scroll too far on her tablet (seriously, this was a conversation we just had). So if you want the gory details scroll down.
EDIT 2020-03-16: DISCLAIMER: These unofficial results are not peer reviewed, and should be treated as such. My goal is to learn about forecasting in real time, how simple models compare with more complex models, and even how to compare different models.
Saturday’s count of new cases in the USA was within my prediction interval.1 The prediction interval is the wider ribbon of grey around the dashed line. For any one day, if you imagine 100 different copies of reality, 95 of the 100 would have new cases that fall inside the grey ribbon. So this prediction is still looking good for my model. However, I would also expect that the points would fluctuate above and below the dashed line. I’m assuming that the rate of new cases per case is not changing. If the “social distancing” is working to reduce the spread, then the number of new cases should start to drop away from my prediction. This is sort of what is shown in the figure, but not severe enough yet to make me throw out my simple model. Yet.
My friend Brenda Pracheil (also a data nerd) offered up an interesting hypothesis for the lower number of cases reported yesterday: the weekend hypothesis. Fewer doctors, clinics etc. open on the weekend, so fewer people getting tested and reported. Too early to judge if that’s happening or not, but I’ll keep it in mind! The point that is farthest off my fitted line is a Monday.
Wash your hands especially after going out and touching things other people have touched. Seriously. Soap more effective than hand sanitizer.
 Apparently wearing a mask makes you more likely to touch your face, and that’s bad.
If you can, work from home and regardless avoid businesses and activities where you’ll end up closer than 1 m (3 feet) from others. This “social distancing” reduces the number of people you come in contact with per day, which reduces the rate at which the virus spreads. This is not an all or nothing thing. Tomas Pueyo, clearly a fellow (but wealthier and more successful!) data nerd, wrote a great article that shows the effects of social distancing (among other interesting things). This chart in particular shows the benefit of social distancing: Reducing the number of people you come in contact with by 25% would postpone the epidemic peak by weeks, and reduce the size of the epidemic. A little social distancing goes a long way.
Don’t expect this to be over in 2 weeks. If you’re exposed, and you isolate yourself, after 2 weeks you can be sure you didn’t get infected by that exposure. But the epidemic is going to go on for quite a while! Months. Which leads me to …
Don’t buy enough toilet paper for a year. “Stocking up” reduces the number of times you go to the store for supplies, and isn’t meant to enable you to weather the apocalypse. If you’re in a grocery store, and there is some toilet paper, buy a package even if you don’t need it. But don’t buy 20, that’s just being silly. And rushing around to every store in town looking for toilet paper just increases your contact rate!
Don’t panic. Yes, lots of people are going to get sick, but if you’re healthy and under 60 you are very unlikely to die or even need serious medical help. You should practice social distancing, even so, because that helps the broader community including people that are more likely to suffer severe disease from the virus.
Do offer to help elderly or unhealthy neighbors and relatives. Offer to bring them supplies so they don’t have to risk going out. If you do that, when you deliver, keep your distance! You don’t want to be the vector for someone getting ill.
Stay well everyone, and wash your hands!


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Apache host the web server using mod_proxy, which proxy to the default webrick server for rails.
The server is RHEL 7.1, with Apache 2.4.
Rails Production Settings
1. Production key
Generate your secret key.
Either export it to your environment variable SECRET_KEY_BASE, or put it into config/secret.yml.
2. Do not uglify JS
Dependency injection in angularJS might become problematic in production settings if we enable uglify JS. The real way to fix this is keep using following programming style while coding in angular.
Here we fix it by disabling the uglify JS in production settings in rails.
Edit the config/environments/productions.rb.
3. Asset settings
Edit the config/environments/productions.rb.
In application.rb.
Apache settings
RHEL 7 Settings
At last, you have two choice, disable the selinux or simply allow the port.


--- layout: post title: Join thegoodvibes to Win Exciting Goodies date: 2014-06-12 17:28:00.000000000 +05:30 type: post published: true status: publish categories: - Amazing - Marketing - Students Ambasador tags: [] meta: blogger_blog: www.technoruler.com blogger_author: VISWAPRASATH KS blogger_cb097c283b9bbfde97b4822e7250157d_permalink: '5676847126503845734' author: login: viswaprasath email<EMAIL_ADDRESS>display_name: Vitchu first_name: '' last_name: '' ---
There are lot of opportunities knocking each and every day in our life.
What if you get free talktime for making your friend join, paid trips and cool gadgets and so on. Yes it is possible and it is easy to do.Win goodies, merchandise, some of the best gadgets and smart phones, all expense paid trip to Leh-Ladakh-Pondicherry, and the MegaPrize - a place of the Viber #goodvibes World Tour.
You lead the #goodvibes campaign for Viber anytime, anywhere at your own convenience. Your voice is the voice of the brand. You would take the brand’s philosophy of connecting freely and help us make #goodvibes and Viber the next big thing.
Share any content - status updates, tweets, pictures, music links, selfies, outings, celebrations, etc and just hashtag #goodvibes on any social media platforms you use. Encourage more people around you to share #goodvibes, while you’re at it. There will be a some additional simple tasks to take up on our #goodvibes messenger Viber -one of the fastest growing mobile applications. And, no this won’t be too time consuming, at all.
It will come naturally to you if you are someone who believes in connecting freely and spreading good vibes through your social charm.
Share and Win exciting goodies
Other than the free talk-time you win when your friends join, and the #goodvibes you will get back, we want to make sure there is more for you ;)
Your good vibes circle back to you, and so we are giving away a whole lot of freebies, gift vouchers, some of the best smart-phones and gadgets, all-paid trips to Leh-Ladakh, Pondicherry & Goa, and to the one who shares the most #goodvibes - a WORLD TOUR!
Students who successfully participate in the campaign will also get a certificate of participation by Viber.
Join Viber in building an awesome and dedicated community of Viber #goodvibes ambassadors from different colleges, all over India.
The program will officially be initiated in different phases. Sign-up by 24th June to make it to the first batch,that starts shortly after. Viber ’ll send you the #goodvibes missions and introduce you to the community platform where you can manage your work and collect your performance based rewards. : )


The plot
method is a generic method for plotting objects
of class, "cvCovEst"
. The method is designed as a tool for diagnostic
and exploratory analysis purposes when selecting a covariance matrix
estimator using cvCovEst
.
# S3 method for cvCovEst plot( x, dat_orig, estimator = NULL, plot_type = c("summary"), stat = c("min"), k = NULL, leading = TRUE, abs_v = TRUE, switch_vars = FALSE, min_max = FALSE, ... )
| x | An object of class, |
|---|---|
| dat_orig | The |
| estimator | A |
| plot_type | A |
| stat | A |
| k | A |
| leading | A |
| abs_v | A |
| switch_vars | A |
| min_max | A |
| ... | Additional arguments passed to the plot method. These are not explicitly used and should be ignored by the user. |
A plot object
This plot method is designed to aide users in understanding the
estimation procedure carried out in cvCovEst()
. There are
currently four different values for plot_type
that can be called:
"eigen"
- Plots the eigenvalues associated with the
specified estimator
and stat
arguments in decreasing
order.
"risk"
- Plots the cross-validated risk of the specified
estimator
as a function of the hyperparameter values passed to
cvCovEst()
. This type of plot is only compatible with
estimators which take hyperparameters as arguments.
"heatmap"
- Plots a covariance heat map associated with the
specified estimator
and stat
arguments. Multiple
estimators and performance stats may be specified to produce grids of
heat maps.
"summary"
- Specifying this plot type will run all of the
above plots for the best performing estimator selected by
cvCovEst()
. These plots are then combined into a single
panel along with a table containing the best performing estimator
within each class. If the optimal estimator selected by
cvCovEst()
does not have hyperparameters, then the risk
plot is replaced with a table displaying the minimum, first quartile,
median, third quartile, and maximum of the cross-validated risk
associated with each class of estimator.
The stat
argument accepts five values. They each correspond to a
summary statistic of the cross-validated risk distribution within a class
of estimator. Possible values are:
"min"
- minimum
"Q1"
- first quartile
"median"
- median
"Q3"
- third quartile
"max"
- maximum


- The Info. -
Smoking wormwood in doses equal to 8 - 12 bowls produced a slight perceptual change I often experiance with marijuana I call the dollhouse effect. Objects appear more seperate and seem as though they look and are positioned exactly as they are supposed to, much like items in a very carefully crafted dollhouse. I also felt a slight positive mood lift and a little sillier than I typically do. I did not experiance a high, though I believe a higher dose could be capable of producing a high.
The smoke was cool and easy on the throat, Soft light and easy to hold in the lungs, but slightly unpleasant tasting, though the flavor is easy to ignore. The dried plant material burns easily and thuroughly. I did not feel any unpleasant physical effects whatsoever.
- The story -
Not too long ago I visited a local herbalist, crossing my fingers and hoping that Salvia Divinorum would possibly grace one of the shelves. No salvia. I did find a few other herbs with names I recognized, mostly Atropine and Scolopamine containing plants and Wormwood. I picked a fatty sack of dried wormwood for a little more than a buck an ounce. I splurged and bought 2 full ounces. I couldn't recall what the effects were or how it is ingested, but I did remember that it was an important faction in absinthe, that strange drink that made Ewen McGregor trip balls in 'Moulin Rouge', and that everyone wants to try, but never thinks to buy over the internet from another country (Honestly guys, they would sell it to children over the net, just look for it, it's easier than making your own).
Well, I checked out info and found that some people love it and some people hate it. There was very little info on smoked wormwood, but it looks safe enough even though Thujone is poisonous, so I decided I would look into it a bit. On six occasions I have smoked Raw dried wormwood. In chronological order, my experiances have been with one bowl, then two, then three, then one fat bowled bong full (approx. Equal to four bowls), then two bong fulls, then wih three bong fulls.
I experianced no 'high' with any of my doses. In the two later sessions though I did notice a visual/perceptual effect I experiance after smoking Marijuana that I call the dollhouse effect. Objects become more pronounced and seperate and everything looks as though it is a more intricate and more perfectly crafted version of what it normally is. This is not as much a visual effect as it is a perceptul effect. This causes a feeling that I am in a giant dollhouse-world, and while I'm on pot this makes me uneasy about touching and moving things, possibly because my grandma used to dissapprove of my sisters and me touching her carefully constructed dollhouse. when I experianced this effect via wormwood, I didn't feel at all uneasy about touching things. Maybe a higher dose of wormwood would make me feel more uncomfortable, who knows. only time.
Also with the higher doses, I felt my spirit lifted to some small degree and felt a little more silly/giggly than I normally do, especially during the final, highest dosed experiance. I have decided not to increase my dose any higher, since it takes so damn long to smoke that much herb, and leave my results on smoking raw wormwood as 'inconclusive'. I feel like it has potential to get me high or buzzed at very least in a higher dose. I would like to work with wormwood again, though in a different method of ingestion. I'm planning makeing LiHouLong's 'thuge' Thujone Hashish and testing that, and I may purchase some absinthe online as I'm really quite curious now after reading all those reports.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


This is the simulation script. Simulation, cell and traffic profile parameters can be set here.
| Variable | bw |
List containing each CC's bandwidth for the simulation. |
| Variable | fr |
String with frequency range (FR) to use. 'FR1' for FR1, or 'FR2' for FR2. |
| Variable | band |
String with used band for simulation. In TDD mode it is important to set correctly a band from the next list: n257, n258, n260, n261. |
| Variable | tdd |
Boolean indicating if the cell operates in TDD mode. |
| Variable | buf |
Integer with the maximum Bytes the UE Bearer buffer can tolerate before dropping packets. |
| Variable | schedulerInter |
String indicating the Inter Slice Scheduler to use. For only one Slice simulations use ''. If the simulation includes more than one slice, set '' for Round Robin, 'RRp' for Round Robin Plus, or 'PFXY' for Proportional Fair with X=numExp and Y=denExp. |
| Variable | t_sim |
Simulation duration in milliseconds. |
| Variable | debMode |
Boolean indicating if debugging mode is active. In that case, an html log file will be generated with schedulers operation. Note that in simulations with a high number of UEs this file can turn quite heavy. |
| Variable | measInterv |
Time interval (in milliseconds) between meassures for statistics reports. |
| Variable | interSliceSchGr |
Inter slice scheduler time granularity in milliseconds. |
| Variable | env |
Environment instance needed by simpy for runing PEM methods |
| Variable | cell1 |
Cell instance for running the simulation |
| Variable | UEgroup2 |
Undocumented |
| Variable | UEgroup1 |
Group of users with defined traffic profile, capabilities and service requirements for which the sumulation will run. |
| Variable | UEgroups |
UE group list for the configured simulation |
| Variable | procCell |
Undocumented |
| Variable | procInter |
Undocumented |
String with used band for simulation. In TDD mode it is important to set correctly a band from the next list: n257, n258, n260, n261.
str
)
Integer with the maximum Bytes the UE Bearer buffer can tolerate before dropping packets.
int
)
String indicating the Inter Slice Scheduler to use. For only one Slice simulations use ''. If the simulation includes more than one slice, set '' for Round Robin, 'RRp' for Round Robin Plus, or 'PFXY' for Proportional Fair with X=numExp and Y=denExp.
str
)
Boolean indicating if debugging mode is active. In that case, an html log file will be generated with schedulers operation. Note that in simulations with a high number of UEs this file can turn quite heavy.
bool
)
Time interval (in milliseconds) between meassures for statistics reports.
float
)
Group of users with defined traffic profile, capabilities and service requirements for which the sumulation will run.
More than one can be instantiated in one simulation. For each one of them, the UEgroup instance must be added in the UEgroups list.
UEgroupN = UEgroup(nuDL,nuUL,pszDL,pszUL,parrDL,parrUL,label,dly,avlty,schedulerType,mimo_mode,layers,cell,hdr,t_sim,measInterv,env,sinr):
label: must contain substring according to the type of service: eMBB, mMTC, URLLC
schedulerType: RR: Rounf Robin, PF: Proportional Fair (10, 11)
mimo_mode: SU, MU
layers: in SU-MIMO is the number of layers/UE, in MU-MIMO is the number of simultaneous UE to serve with the same resources
sinr: is a string starting starting with S if all ues have the same sinr or D if not. The value next will be the initial sinr of each ue or the maximum.


CRITICAL CARE By Harvey Karten, Ph.D. Live Entertainment Director: Sidney Lumet Writer: Steven Schwartz Cast: James Spader, Helen Mirren, Kyra Sedgwick, Anne Bancroft, Albert Brooks
"Critical Care," a spoof of doctors and hospitals more concerned with a vital bottom line than in restoring their patients' health, is only mildly satiric, but that's OK. A book or movie or theater piece which seeks to ridicule an institution of society does not have to hit it over the head with a sledgehammer. Many mild-manned lampoons are effective indeed. You need look only to such masterworks as the music of Gilbert and Sullivan or the comedies of Oscar Wilde to witness the power of restrained ridicule. Greedy lawyers, self- seeking doctors, and pseudo-religious hypocrites have been taunted for a while now. Didn't Shakespeare's King Henry VI say "let's kill all the lawyers," Moliere's Tartuffe explain "To sin in private is not a sin," and Paddy Chayefsky's George C. Scott complain "We cure nothing"? What we need, then, is a new way of looking at the evils that professionals and institutions practice. Despite Steven Schwartz's clever writing and Sidney Lumet's dutiful direction, "Critical Care" is not that novelty.
The setting of "Critical Care," which is filmed almost completely in a studio, has the airless look of an ultra-modern hospital, one which is surprisingly quiet for an urban medical center, making Dr. Werner Ernst's (James Spader) complaints about overwork seem strange. The story focuses on professionals' reactions to two patients near-death; one is comatose and kept alive only technically, the other quite conscious but virtually unable to move and eager for death. By the time the film ends, Dr. Ernst has gone through a metamorphosis, changing from a self-serving, skirt chasing physician eager to make up in money and partying for all the suffering he endured through ten years of college, to a true professional who feels entitled for the very first time to call himself a doctor.
The drama takes off when Felicia (Kyra Sedgwick), a bimbo model whose father is in a persistent vegetative state, asks that the old man be removed from life support. The conflict arises when Connie (Margo Martindale), her apparently devout, bible-thumping sister, refuses to authorize the pulling of the plug, insisting that dad hears everything she says to him and communicates by squeezing her hand. In short order the hypocrisy of both women is exposed, but not before Ernst is seduced by Felicia, leading him into a serious case of blackmail.
Ernst discusses the case repeatedly with his supervising physician, Dr. Butz (Albert Brooks)--who is unrecognizable in the disguise of an alcoholic geezer with his mind as much on money as Ernst's is on models. Butz comes on as the classic case of the medical profession's obsession with insurance. Reminding Ernst to treat even hopeless cases if they have comprehensive, catastrophic coverage and reject those who have none, Butz is the comic center of the film whose other characters seem to speak primarily in the hushed tones of a public library.  Lumet proves his point by displaying some of the suffering which terminal patients go through while being kept only technically alive.
Lumet, who has done far more vigorous work in the past including "Serpico," "Dog Day Afternoon" and "Network," is just connecting the dots this time around, slapping doctors, lawyers and religious hypocrites on the wrist ever so gently, allowing Wallace Shawn some embarrassingly banal dialogue as the Devil's advocate. Rated R. Running Time: 109 minutes. (C) 1997 Harvey Karten
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Netting of positions
Netting is the process of consolidation of positions that occurs when two positions in the same instrument and on same account are in opposite direction and thus fully or partially reduces the total market exposure in the instrument. It is the process in which a buy position and sell position are matched to remove both or yield a remainder, if one is bigger than the other.
Since an account may have many positions in the same instrument opened at various times, the client may choose to close them explicitly or implicitly.
https://www.developer.saxo/openapi/learn/position-netting
Response:
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JS code:
Click button to show code.


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@ [exclusive execution ID] ({(sequence)}|LABEL)
(execute) Execute the sequence on a new fiber.
Execute the sequence on a new fiber．'Fiber' is a kind of thread in some othor languages, the sequence is executed simultaniously.
For example, "&{[5f4w3]}a0.1" accelarates after firing 5 bullets<EMAIL_ADDRESS>accelarates with firing 5 bullets.
And you can omit "@0". If you write "{...}" or labels without f*, n*, & or @* command, "@0" is executed.
When [exclusive execution ID] >= 1 is specifyed, stop the other fiber with same ID and execute new fiber.
Writing with a format of "{sequence}", All fiber parameters are copyed to the called sequence. On the other hand writing with a label, all fiber parameters are initialized.
For example, "f4w10@{f}" fires 2 bullets with speed of 4, "#A{f} f4w10@A" fires 2 bullets with speed of 4 and 1 respectively.
@o [object ID] (, [object ID] , ... ) ({(sequence)}|LABEL)
(execute Object) Execute a sequence with a new fiber on another objects created by n or f command with specifying [Object ID].
When you create a new object by the 'n', 'nc', 'f' or 'fc' command with [Object ID], the '@o' command executes the sequence on another objects that have specifyed [Object ID].
If you specify plural [Object ID] in the arguments, the sequence is executed on grand-child object. For example, "@o1,2{...}" is same as "<EMAIL_ADDRESS>In the '@o' command, all fiber parameters are always initialized and you cannot specify the [exclusive execution ID].
@ko [end status] ({(sequence)}|LABEL)
(execute when Kill Object) Execute sequence when the object is destroyed with specifyed [end status].
Assign a sequence that execute when the object is destroyed. The sequence can be assigned each [end status].
The [end status] is specifyed by ko command or CMLObject.destroy() in your program.
The sequence is executed only 1 frame and waiting command (like "w*" and "~") stops the execution.
^@ ({(sequence)}|LABEL)
(fake execute) Do nothing, but change the sequence refered by<EMAIL_ADDRESS>command.
Do nothing, but change the sequence refered by<EMAIL_ADDRESS>command.


In the first season, James Gordon, a new recruit in the Gotham City Police Department is paired with veteran detective Harvey Bullock to solve the murders of Thomas and Martha Wayne. Gordon meets the Waynes' son Bruce who is now in the care of butler Alfred Pennyworth. In addition, he also encounters gang member Oswald Cobblepot, forensics worker Edward Nygma, street orphans Selina Kyle and Pamela "Ivy" Pepper, assistant district attorney Harvey Dent, and medical doctor Leslie Thompkins. Gordon becomes involved with Gotham's crime families including gangster Fish Mooney and crime lords Carmine Falcone and Salvatore Maroni. In the second season, Gordon deals with a series of events that are being orchestrated by Theo Galavan and his sister Tabitha as part of a plan to take over Gotham as the new Mayor of Gotham City and exacting revenge against the Wayne Family. After Galavan is murdered in his run for mayor, the Gotham City Police Department deals with the actions of Victor Fries. The enigmatic Hugo Strange and his assistant Ethel Peabody conduct a series of bizarre experiments underneath Arkham Asylum in the underground Indian Hill facility that is secretly owned by Wayne Enterprises and overseen by the Court of Owls.
In the third season taking place six months later, Gordon has become a bounty hunter as he works to track down Indian Hill experiments that escaped as well as a revived Fish Mooney. While planning to get Leslie Thompkins back, he also encounters Carmine Falcone's son Mario Calvi as well as hypnotist Jervis Tetch who arrives to look for his sister Alice where she possesses poisonous blood that drives many crazy in various ways. Meanwhile, Ivy is aged up to an adult woman following an encounter with one of Fish Mooney's Indian Hill followers and Jerome Valeska is revived to resume his previous vendetta against the city. Gordon has to deal with the Court of Owls working on behalf of Ra's al Ghul and the League of Shadows, Penguin and Ivy teaming up to form an army, and Edward Nygma embraces his new identity as "The Riddler".
In the fourth season, Bruce Wayne is a masked vigilante, patrolling Gotham's streets at night, wearing dark clothes and a hooded black balaclava. Cobblepot begins a campaign to dominate the city's underworld by profiting off licensed crime, Nygma finds his mental faculties diminished as a side-effect of having been flash-frozen, Jonathan Crane is seen embracing his role as the Scarecrow, the League of Shadows begin their next plot involving an ancient embalming knife, and Butch Gilzean seemingly returns from the dead as Solomon Grundy.


Welcome to the AppSheet App Jam! During this lab we will explore how to create our first AppSheet app and the different features the platform offers. Keep in mind that the intention of this lab is to give a high overview of the solution and help you initiate into the citizen developer's world! Let's start!
Pre-requisites:
- Login into http://appsheet.com
- Click on Login and Select Google as your best way to sign in.
- Select the Google Account you would like to use for this Lab
- Allow permissions to access Google resources on its behalf. After this lab you can revoke these permissions if needed. Click allow.
- In this screen you will see different settings of your account. From allowed sources you have added to integration with third party auth, data stores or Channels, to billing information.
- Do click under My Apps. An overlay frame will allow you to choose between "Start with your data", "Start with an idea" or "Start with a sample app". During this short lab, we will explore a sample app. Click "Start with a sample app".
- Select a name for your app. In this case we will use AppSheetLab1. Under categories, select Logistics. Select Shipment Tracking as the sample app to deploy.
- Wait until the new app is set up.
- Close the pop-up informational help tool.
AppSheet divides its interface in 4 main locations:
- Top bar menu: Here we can go back to the main AppSheet webpage, review our team's work, explore more sample apps, connect with support and go into settings for our own account.
- Left navigation bar: We will explore this bar during the lab. It has information about the data we are connecting with, the interfaces we will use in our app, workflows and behaviors, security settings or special features like intelligence. We can also manage access to our apps and further settings of our current application.
- Right live preview app: Every change we make in the app will be reflected in this fully functional app in the right. If we make a change in the interface, the change will be automatically reflected in the preview so we can continuously acknowledge all the changes we might want to do.
- Main panel: Here is where we will perform most of the changes once we select the different options in the left navigation bar.
Tables and Columns
Tables are comprised of columns, which identify the different components of each record. A record is a row of data in your spreadsheet. For instance, a table of addresses would have a column each for street number, street name, city, state, and zip code. Rows contain the data for each individual piece. For the address example, each address would have its own row.
- In the Left navigation bar, click on Data.
We now see four different tables: Users, Vendors, Building_Sites and Delivery_Packages. Click on Users.
- Under the different tabs, we can see Storage, Security, Scale, Localization and Documentation.
- The first one, Storage, gives us information about the source of the data, in this case, Google Sheets.
- The second one, Security, gives us Security measures to prevent information to be shown in the application or restrict visibility of the table depending on the role of the user accessing the app.
- The third one, Scale, gives us the possibility of sourcing the data under different worksheets (with Business Plan).
- Localization, the forth one, identifies the locale or language the worksheet is. This will help auto detecting fields and columns.
- Documentation, the last one, let's you comment this table for other collaborators to better align with the content of the same.
- Click in View Source. This will open a new tab in your browser with the worksheet you are using as the main data source of your application. Explore the different tabs and fields to see the different intents of the application.
- Go back to AppSheet and, just besides View Source, click on View Columns. This will swap the main panel with information about the data columns and how AppSheet interpreted the fields in the worksheet. If we click on one of the edit icons of each field, we open a contextual menu with more information to change about these fields. In this example click on the edit button of User_Name. 1.We want to change the display name of this field. For that, we will scroll to the DISPLAY NAME column and click to open the menu. Let's change the display name of this field to Name. Click on the text box and the Expression Assistant will open. Get familiarized with this Expression Assistant since it is a powerful tool to create conditions, expressions, deep links and auto-generate values in many of the contexts of AppSheet. Write Name in the text box. Click Save and then Done.
- To see the changes reflected on our preview application we need to save the application. Click on the Save button, top right corner of the Top bar menu.
- Now, in the application, click in the menu to display the different data sources added to the app. The click on Users.
Select one of the users by clicking on it. As you can see, the Name field has a display customization.
- One last thing to notice in this brief lab, is the reference columns. In this case, the last three entries of this table: Related Building_Sites, Related Delivery_Packages and Related Building_Sites By Site_Contact. These three fields are used to establish a relationship between tables. If we click on the FORMULA text field, the Expression Assistant will show up and we can see how to reference columns to other rows in different tables. In this case, the vale of Site_Admin must be equal to UserId, which is a Key field in this table.
Take a quick look at the table Delivery_Packages as we did for Users. Review its columns as well. Notice the different FORMULA and SHOW? fields where we decide if we show the field based on the content of the same.
When you show the contents of a table in your app, you do not need to show every row, every column, and every action. Instead, you can "slice" the data by choosing a subset of the columns, actions, and rows. A slice is a subset of the rows, columns, and actions of a table.
- Go to the Slices menu on top of the main panel and select Delivered. Notice that this slice has a condition: [Package_Status]=Delivered. This condition provides a row filter (show/not show). If the Package_Status has the value Delivered, then the row will be shown in the slice. If not, the row will be hidden.
- Under Slice Columns, we can select all the columns or just select those columns that we want to show in this slice. You can remove Package_Driver by clicking the trash icon at the right of this column.
- Within the app preview, click on Overview in the navigation bar. Scroll down until Delivered and select one of the delivered packages.
You should be able to see that all the Package_Driver is not present any more. Test a few fields and, at any time, if you want to recover the fields into the slice, click on the 'Add' button at the bottom of the list.
Let's focus now on views. Views allow you to control how, when, and where data is presented to the app user, and how the user interacts with the data. For that, click on UX under the navigation bar. Under the Views tab, we have 3 different options, Create views, Primary views and Menu views. We can also search for specific views (this is really handy when we have rich and powerful apps).
- Let's click on Drivers in the Primary Views category.
- Now, in the app preview section, click the Drivers icon in the navigation bar to see real-time changes in the app.
- We are going to change the view type from Table to Gallery.
- In this view, we are showing pictures and names of the Delivery_Packages. This is not useful when searching by Drivers so, under For this data, change it Users. The changes should be reflected immediately.
- Finally, one last change before we move on. Let's sort the list by User_Name. To do this, under View Options, click Add and select User_Name as the sorting option.
Actions
By default, standard behaviors are automatically applied to every app based on the structure of the data (tables and slices). As the app creator, you can change and augment these standard behaviors. The Behavior tab of the editor is the place to make these changes.
The primary behavioral concept is an action. An action is an operation that changes the state of the app. Some of the actions are automatically defined by the system (system-defined) and can be modified by you, the app creator. You can also explicitly create new actions.
For this lab, we will explore one of the current actions.
- On the Left Navigation bar, click on Behavior. This will show the current actions assigned in the sample app.
- For the sake of simplicity, we will review Delivery_Time. Click on that behavior to see the implementation.
The important piece to understand is the flow of a behavior: "For a record of a table, do this by setting these columns". In this case:
- For Delivery_Packages table, set the values of some columns in this row, these columns being Delivery_Time with the value NOW(). In other words, whenever we drop off a package, we set the delivery time to the current time.
Try yourself to drop off a package and see how the field changes.
Feel free to explore more complex actions in this example or even create/update actions to test the flow in the app preview.
Workflows
It is important to trigger certain actions whenever something happens that might need to go outside of the application. For example, send an email to the customer once the package has been delivered or send a webhook to a certain external app that would trigger a complex business workflow in another system. You can do this easily with workflows.
Let's explore what happens here when a package is assigned to a driver.
- Click on the section When this happens...: Based on a target data (Delivery_Packages), on an add only action.
- Click on the section If this is true: Only if the added data fits this condition:
AND([_thisrow_before].[Package_Driver] <> [_thisrow_after].[Package_Driver],ISNOTBLANK([Package_Driver]),[Package_Status]="Assigned")
Basically, if we assigned a package to a driver (not blank) and it's different from a previously assigned driver.
- Click on the section Do this: We send an email to the package driver.
Now it's time to see your app in action! For that, on the left navigation bar, click on Not Deployed. You will see a bunch of warnings that should be addressed before deploying the app. For simplicity, we will go ahead and deploy by clicking on Move app to deployed state despite errors.
Congratulations! Your app has been deployed! To see a link to access your app, click on Users > Links. Copy the Browser link and access your app!
You have successfully created your first AppSheet app. Feel free to explore other examples or create your own based on your data. Here is a page to AppSheet documentation to see all the features and capabilities of the platform.
Become a Citizen developer!


Bayesian Imputation of Missing Covariates
2019-08-21
Chapter 1 General Introduction
It can be assumed that missing values exist since we started to collect data. The need to find ways to deal with them emerged as the amount of data that was collected grew, but the willingness to provide information to the parties collecting it (often the government for the purpose of the census) abated. The inefficiency due to the loss of information resulting from only using the complete cases for analysis was no longer considered acceptable (Scheuren 2005).
The methods that were used to handle missing values in the 50s and 60s replaced the missing values by a single value, such as ``hot-deck’’ imputation, where a missing value is replaced by an observed value of a case that is similar to the case with the missing value with regards to other, observed characteristics (Andridge and Little 2010; Behrmann 1954; Nordbotten 1963; Gorinson 1969). The idea of replacing a missing datum with multiple values (and arguably the beginning of the development of more sophisticated missing data methods) traces back to Donald B. Rubin, who first described his idea in 1977 (Rubin 2004). He states that
“[…] of course (1) imputing one value for missing datum can’t be correct in general, and (2) in order to insert sensible values for a missing datum we must rely more or less on some model relating unobserved values to observed values.”
The Bayesian framework naturally lends itself to missing data settings, treating missing values as unobserved random variables that have a distribution which depends on the observed data (Rubin 1978a, 1978b). Nevertheless, initially it was not used in these settings since calculations can quickly become very involved when parts of the data are unobserved, and the computational procedures nowadays used to overcome these difficulties were not yet available. In this thesis, we focus on inference on missing data under the Bayesian paradigm but compare it to other, commonly used approaches.
1.1 Mechanisms and Patterns of Missing Data
1.1.1 Missing Data Mechanism
In the context of missing data, strictly speaking, the term “data” does not only refer to the values of those variables that were intended to be measured, but also includes the missing data indicator, a binary variable, that describes if a value was observed or not.
Using this missingness indicator, a model for the missing data mechanism can be postulated. This model describes how the probability of a value being missing is related to other characteristics of the same unit. It can be written as \[p(\mathbf R\mid \mathbf X_{obs}, \mathbf X_{mis}, \boldsymbol\psi),\] where \(\mathbf R\) is the missing data indicator matrix and \(\mathbf X_{obs}\) and \(\mathbf X_{mis}\) denote the part of the data that is completely observed and the part of the data that has missing values for some subjects, respectively.
In general, the model for the missing data mechanism must be taken into account when analysing incomplete data, however, under certain conditions, it may be ignored. Conditions for ignorability were introduced by Rubin (1976) and are closely connected to the well-established classification of missing data mechanisms described below (Little and Rubin 2002).
Missing Completely at Random
The data is said to be missing completely at random (MCAR) when the probability of a value being missing does not depend on any data, that is, when \(p(\mathbf R\mid \mathbf X_{obs}, \mathbf X_{mis}, \boldsymbol\psi) = p(\mathbf R\mid \boldsymbol\psi)\). This type of missingness may occur when samples are lost, participants miss a visit or do not fill in a question in a questionnaire, because of reasons that are unrelated with the study for which the data was supposed to be collected.
Missing at Random
In settings in which the probability of a value being missing does depend on factors associated with the study, but those factors have been observed, i.e., are part of \(\mathbf X_{obs}\), the missing data mechanism is called missing at random (MAR) and can be formally described as \(p(\mathbf R\mid \mathbf X_{obs}, \mathbf X_{mis},\boldsymbol\psi) = p(\mathbf R\mid \mathbf X_{obs},\boldsymbol\psi)\). Missing values of this type may occur for instance when in a survey about the consumption of sweets overweight participants are less likely to respond to the question how much chocolate they eat, and the weight of all participants is known. Another example is a longitudinal study where subjects are excluded from future measurements once a critical value is exceeded. MAR includes MCAR as a special case.
Missing Not at Random
When the assumption of MAR does not hold, and the probability of a value being missing does depend on unobserved data, which may either be the missing value itself, unobserved values of other variables, or variables that have not been recorded at all, the missing data mechanism is called missing not at random (MNAR) and can be written as \(p(\mathbf R\mid \mathbf X_{obs}, \mathbf X_{mis},\boldsymbol\psi) \neq p(\mathbf R\mid \mathbf X_{obs},\boldsymbol\psi).\) Examples for data missing not at random would be if, in the above survey, participants who are overweight are more likely not to report their weight, or, if in the longitudinal study the value that exceeded the threshold would not be recorded. Since \(\mathbf X_{mis}\) is not observed, it is not possible to test whether the assumption of MAR holds. Good knowledge of how the data was obtained and why values are missing is necessary to make appropriate assumptions about the missing data mechanism.
Ignorability
In settings where the missing data mechanism is MAR and the parameters of the missingness model, \(\boldsymbol\psi\), are a priori independent / distinct of the parameters of the data model, \(\boldsymbol\theta\), i.e., \(p(\boldsymbol\psi, \boldsymbol\theta) = p(\boldsymbol\psi) \; p(\boldsymbol\theta)\), the missingness process does not need to be explicitly modelled when Bayesian or likelihood inference for \(\boldsymbol\theta\) is performed. Then, the missingness is called ignorable. Throughout this thesis, we assume that this is the case.
1.1.2 Missing Data Pattern
When values are missing in multiple variables, different patterns of missingness can arise. If variables can be ordered such that if a value is missing, the values of all variables following in the sequence are also always missing, the missing data pattern is called monotone (left panel of Figure 1.1). If such an order does not exist, the missing data pattern is non-monotone (right panel of Figure 1.1).
Monotone missing data patterns typically arise in longitudinal studies with drop-out, where once a patient has left the study, no further measurements are available. In studies where multiple variables measuring different aspects are obtained, either at the same time point or over time, non-monotone missing data patterns occur more frequently, since study participants do not return a particular questionnaire or miss a single visit to the research centre. In this thesis, we consider general, non-monotone missing data patterns.
1.2 Multiple Imputed Datasets
When the concept of multiple imputation was developed in the 1970s, a requirement for a practical way to deal with missing data was that it allowed many researchers to analyse the incomplete data. Moreover, it was essential that these analyses could be done using only standard techniques and software tools, which required complete, balanced data, and without the need of in-depth knowledge of missing data methods (Scheuren 2005). Especially the second part of this requirement is still relevant today since analyses are often performed by researchers without expert knowledge in statistics, who are usually only familiar with standard complete data methods and are not versed in Bayesian methodology.
Rubin’s solution to this practicality issue was to perform imputation once and to distribute the imputed data to various researchers. In this imputation procedure, multiply imputed, i.e., completed, versions of the original data were produced, which differed only in the values that had been filled in for the missing observations. This allowed retaining some information on the uncertainty about the missing values, while each of the datasets could be analysed using standard methods. Since the imputed datasets are not identical, the estimates obtained from the analysis of each dataset will vary. This variation in the obtained estimates allows assessing the additional uncertainty in the effect estimates that is caused by the missing values (Rubin 2004).
In the following, we first give an overview of some popular (Bayesian and non-Bayesian) methods that have been proposed for creating imputed values and then describe the most commonly used procedure to pool the results from the analyses of multiple imputed datasets.
1.2.1 Methods for Imputation
The task to create the imputed values requires to sample from the (posterior) predictive distribution of the unobserved data, given the observed data. Especially in larger datasets, with missing values in multiple variables, possibly of different measurement levels, and a non-monotone missing data pattern, this distribution is multivariate and not of any standard form.
Joint Model Imputation
Rubin’s suggestion in his initial paper on multiple imputation from 1977 (Rubin 2004) was to approximate the posterior predictive distribution with a multivariate normal distribution if variables are continuous, or with a multinomial distribution if data is categorical. Since sampling from either of these distributions is fast and readily available in software, this approach, especially using the multivariate normal distribution, is still used today (Carpenter and Kenward 2013). To allow for covariates of mixed type, the assumption is made that categorical variables have an underlying normal distribution and that different categories are observed depending on the value of that latent distribution. Due to its ability to impute incomplete baseline as well as time-varying covariates in multi-levels settings, we apply and investigate this approach in Chapter 5 of this thesis.
Expectation Maximization
A general approach that allows performing likelihood inference when parts of the data are unobserved, is the Expectation Maximization (EM) algorithm, introduced by Dempster, Laird, and Rubin (1977). The algorithm alternates between the expectation (E) step, in which the expected value of missing data, conditional on observed data and current estimates of the parameters, is obtained, and the maximisation (M) step, in which parameter values are estimated by maximizing the likelihood of the parameters given the current values of the missing data. Even though the approach was not specifically developed to create multiple imputations, it could be applied in this way, if, once the algorithm has converged, not the expectation of missing values is determined, but instead multiple values are drawn from the estimated distribution. In settings where incomplete variables have non-linear associations with other variables, however, the distribution in the M-step may not have a closed form and updating the parameters becomes difficult.
Data Augmentation
Data augmentation, an approach similar to the EM algorithm, was proposed by Tanner and Wong (1987). It can be thought of as a Bayesian version of the EM algorithm since it has the same two-step structure, but the E step is replaced by imputation of missing values and the M step by estimation of the posterior distribution instead of maximization of the likelihood. The motivation behind augmenting the data is that sampling from \(p(\boldsymbol\theta \mid \mathbf X)\) is often easier than sampling from \(p(\boldsymbol\theta \mid \mathbf X_{obs})\) and works well if sampling of \(p(\mathbf X_{mis}\mid \mathbf X_{obs}, \boldsymbol\theta)\) is possible. Nonetheless, even when Monte Carlo integration is used, sampling from distributions that are not members of the exponential family may be difficult, diminishing the attractiveness of the approach. While Tanner and Wong (1987) aimed to estimate the posterior distribution, Li (1988) focused on imputing values but used essentially the same algorithm.
The approach for analysis of incomplete data and imputation of missing values followed throughout this thesis uses data augmentation in conjunction with the Gibbs sampler. The joint distribution of all data, observed and unobserved, as well as the parameters, is specified in a sequence of univariate distributions. Using Gibbs sampling, missing values are imputed by draws from the full conditional distributions arising from this joint distribution. Once the observed data is augmented by the imputed values, posterior inference for the parameters of interest can be obtained as if the data had been complete. The approach is described in detail in Chapter 2.
Multiple Imputation using Chained Equations
The nowadays most popular approach for creating multiple imputations, introduced by Van Buuren, Boshuizen, and Knook (1999), also uses the idea of the Gibbs sampler but is a (mostly) frequentist approach. In multiple imputation using chained equations (MICE), also called multiple imputation using a fully conditional specification (FCS), a full conditional distribution is specified for each incomplete variable and imputed values are sampled from these distributions. If a multivariate distribution exists that has the specified distributions as its full conditionals, the algorithm is a Gibbs sampler.
Specifying the full conditional distributions directly has the advantage that it allows for a flexible algorithm, in which distributions can be tailored to the measurement level of each variable, and sampling is performed on a variable by variable basis using samplers that are easy to implement. The MICE algorithm (Van Buuren 2012) starts by randomly drawing starting values from the set of observed values. Then, in each iteration \(t = 1, \ldots, T\), it cycles once through all incomplete variables \(\mathbf x_j,\; j = 1,\ldots,p\). For each incomplete variable \(j\), the currently completed data except \(x_j\) is defined as \(\mathbf{\dot X}_{-j}^t = \left(\mathbf{\dot x}_1^t, \ldots, \mathbf{\dot x}_{j-1}^t, \mathbf{\dot x}_{j+1}^{t-1}, \ldots, \mathbf{\dot x}_p^{t-1}\right).\) The parameters of the model for \(\mathbf x_j\), \(\boldsymbol{\dot\theta}_j^t\), are sampled from their distribution conditional on the observed part of \(\mathbf x_j\) and the currently completed data of the other variables from subjects that have \(\mathbf x_j\) observed: \[p(\boldsymbol\theta_j^t \mid \mathbf{x}_j^{obs}, \mathbf{\dot X}_{-j}, \mathbf R).\] Imputed values \(\mathbf{\dot x}_j^t\) are drawn from the predictive distribution of the missing values \(\mathbf{x}_{j}^{mis}\) given the other variables and parameters \(\boldsymbol{\dot\theta}_j^t\), \[p(\mathbf x_j^{mis}\mid \mathbf X_{-j}^t, \mathbf R, \boldsymbol \theta_j^t).\] By filling in the imputed values of the last iteration, i.e., \(\left(\mathbf{\dot x}_1^\top, \ldots, \mathbf{\dot x}_p^\top\right)\) into the original, incomplete, data, one imputed dataset is created. The algorithm is run multiple times with different starting values to create a set of multiply imputed datasets.
A drawback of the MICE algorithm is that there is no guarantee that a joint distribution exists that has the specified conditional distributions as its full conditionals. If no such distribution exists, the algorithm may not converge to the correct distribution. Despite this theoretical limitation, it has been shown to work well in practice as long as the conditional distributions fit the data well enough (Zhu and Raghunathan 2015). In settings where incomplete covariates are involved in non-linear functional forms or interactions, or with complex outcomes, such as survival or longitudinal outcomes, specification of correct imputation models is often not feasible (Bartlett et al. 2015; Carpenter and Kenward 2013). Even specification of models that adequately include all information necessary to obtain valid imputations is not straightforward, and, in practice, often not even attempted when imputation is performed by researchers who are unaware that naive use of imputation software will lead to violation of important assumptions and thereby faulty imputations and biased inference. The performance of MICE when used naively for imputation of covariates in longitudinal data is the topic of Chapter 2 of this thesis.
1.2.2 Pooling Results from Multiple Imputed Datasets
Irrespective of the method used for producing imputations, the results from the analyses of the multiple imputed datasets need to be combined in a manner that takes into account the added uncertainty due to the missing values. The formulas for pooling of such results proposed by Rubin and Schenker (1986) (see also Rubin (1987)), usually referred to as Rubin’s Rules, have gained wide acceptance and are outlined in the following.
For a parameter vector \(\mathbf Q\) the overall estimate, pooled over the analyses of \(m\) imputed datasets, can be calculated as the mean over the estimates from these analyses, \[\mathbf{\overline Q} = \frac{1}{m} \sum_{\ell = 1}^m\mathbf{\widehat Q}_\ell,\] where \(\mathbf{\widehat Q}_\ell\) denotes the estimate obtained from the \(\ell\)-th imputed set. The overall variance of \(\mathbf Q\) consists of the within imputation variances \(\mathbf{\overline W}\), which can be calculated by averaging over the estimated variances of the \(\mathbf Q_\ell\) from each imputed dataset, \(\mathbf{\widehat W}_\ell\), \[\mathbf{\overline W} = \frac{1}{m} \sum_{\ell = 1}^m \mathbf{\widehat W}_\ell,\] and the between imputation variance, \(\mathbf B\), calculated as \[\mathbf B = \frac{1}{m-1}\sum_{\ell = 1}^m \left(\mathbf{\widehat Q}_\ell - \mathbf{\overline Q}\right)\left(\mathbf{\widehat Q}_\ell - \mathbf{\overline Q}\right)^\top.\] Following Rubin and Schenker (1986), the total variance, \(\mathbf T\) is the sum of within and between imputation variance, plus an additional term \(\mathbf B/m\), correcting for the finite number of imputations, i.e., \(\mathbf T = \mathbf{\overline W} + \mathbf B + \mathbf B/m\). The relative increase in variance that is due to the missing values can be estimated as \(r_m = \left(\mathbf B + \mathbf B/m \right) / \mathbf{\overline W}\).
The \((1 - \alpha)\) 100% confidence interval for scalar \(Q\) can be calculated as \(\overline Q \pm t_\nu(\alpha/2)\sqrt{T},\) where \(t_{\nu}\) is the \(\alpha/2\) quantile of the \(t\)-distribution with \(\nu = \left(m - 1\right)\left(1 + r_m^{-1}\right)^2\) degrees of freedom.
The corresponding p-value is the probability \(Pr\{F_{1,\nu} > \left(Q_0-\overline Q\right)^2/T\},\) where \(F_{1,\nu}\) is a random variable that has an F distribution with \(1\) and \(\nu\) degrees of freedom, and \(Q_0\) is the null hypothesis value (typically zero).
In the above calculation, the degrees of freedom \(\nu\) are derived under the assumption that there are infinite degrees of freedom in the complete data, denoted \(\nu_{com}\) (Barnard and Rubin 1999). Since this is not a reasonable assumption for small datasets, Barnard and Rubin (1999) proposed a different calculation of the degrees of freedom for the \(t\)-distribution \[\tilde \nu = \left(\frac{1}{\nu} + \frac{1}{\hat\nu_{obs}}\right)^{-1} = \nu_m\left(1 + \frac{\nu}{\hat\nu_{obs}}\right)^{-1} = \nu_{com}\left[\left\{\lambda(\nu_{com})(1-\hat\gamma_m)\right\}^{-1} + \frac{\nu_{com}}{\nu} \right]\] where the observed-data degrees of freedom, \(\nu_{obs}\), are estimated as \(\hat\nu_{obs} = \lambda(\nu_{com})\nu_{com}(1-\hat\gamma_m)\), \(\hat\gamma_m = r_m/(1+r_m)\) and \(\lambda(\nu) = (\nu + 1)(\nu + 3).\) This small-sample version of Rubin’s Rules is implemented in the R package mice and used in this thesis.
1.3 The Bayesian Framework
Since the focus of this thesis is on inference for missing data under the Bayesian paradigm, in this section we will briefly introduce the Bayesian framework and some relevant concepts.
The idea behind the Bayesian paradigm is that inference about an unknown parameter can be obtained by updating an initial guess or prior belief about that parameter with data (Bayes, Price, and Canton 1763; Laplace 1774).
1.3.1 Bayes Theorem
The posterior distribution, i.e., the distribution of a parameter \(\boldsymbol\theta\) conditional on the data \(\mathbf X\), can be expressed as \[p(\boldsymbol\theta\mid X) = \frac{p(\mathbf X\mid \boldsymbol\theta)\; p(\boldsymbol\theta)}{\int_{-\infty}^\infty p(\mathbf X\mid\boldsymbol\theta)\; p(\boldsymbol\theta)\; d\boldsymbol\theta}.\] In the above equations, \(p(\boldsymbol\theta)\) denotes the prior distribution of \(\boldsymbol\theta\), i.e., the distribution of \(\boldsymbol\theta\) that is assumed without taking into account the collected data, \(p(\mathbf X\mid\boldsymbol\theta)\) is the likelihood of the data given the parameter, and the denominator constitutes the marginal distribution of the data, i.e., the distribution of data under all possible values of \(\boldsymbol\theta\), and is often called the normalizing constant. Since this normalizing constant does not depend on \(\boldsymbol \theta\), Bayes theorem is often simplified to \[p(\boldsymbol \theta\mid \mathbf X) \propto p(\mathbf X\mid \boldsymbol\theta)\; p(\boldsymbol\theta),\] i.e., the posterior distribution is proportional to the product of the likelihood and the prior distribution.
In the Bayesian framework, the distribution relating unobserved values, \(\mathbf X_{mis}\), to observed values, \(\mathbf X_{obs}\), referred to by Rubin in his statement given above, is called the posterior predictive distribution of the missing data given the observed data, \[p(\mathbf X_{mis}\mid \mathbf X_{obs}) = \int p(\mathbf X_{mis}\mid \mathbf X_{obs}, \boldsymbol\theta)\; p(\boldsymbol\theta\mid \mathbf X_{obs}) \; d\boldsymbol\theta.\]
In practice, an analytic calculation of the posterior distribution is often not feasible. In that case, it has to be approximated or determined numerically. A numeric method that is frequently used and works in complex settings is the Monte Carlo method.
1.3.2 Monte Carlo Methods
Instead of determining the posterior distribution analytically, the Monte Carlo method (Metropolis and Ulam 1949) draws random samples from it and uses these samples to calculate summary measures of the distribution, to approximate the corresponding measures of the posterior distribution.
Using the central limit theorem, the precision of this approximation to the sample mean, \(\bar{\theta}\), can be determined as \[\bar{\theta'} \pm 1.96\; \text{sd}(\theta')/\sqrt{K},\] where \(K\) is the number of independently sampled values \(\theta'\), and \(\text{sd}(\theta')/\sqrt{K}\) is called the Monte Carlo error.
In high-dimensional settings, however, even “just” sampling from the posterior distribution may not be feasible, since no sampler is available for direct sampling from a multivariate distribution of unknown form, and factorization of the distribution would require solving multiple integrals (Lesaffre and Lawson 2012). The development of Markov Chain Monte Carlo methods (Metropolis et al. 1953; Hastings 1970) was crucial in overcoming this difficulty.
1.3.3 Markov Chain Monte Carlo
The idea behind Markov Chain Monte Carlo (MCMC) sampling is to construct a Markov chain that has the distribution to be sampled from as its stationary distribution. It is an iterative procedure in which a sequence of random variables is created by repeatedly drawing values from a distribution that depends on the previously drawn value. MCMC methods, hence, perform dependent sampling. By creating a Markov chain that has the posterior distribution as its stationary distribution, samples from that stationary distribution can, thus, be regarded as a sample from the posterior distribution.
1.3.4 Gibbs Sampler
The Gibbs sampler, introduced by Geman and Geman (1984), facilitates sampling from high-dimensional distributions by splitting a multivariate problem into a set of univariate problems. It uses the property that a joint distribution is fully specified by its corresponding set of full conditional distributions. Iteratively sampling from these, often univariate, distributions is usually relatively easy. Using Gibbs sampling to obtain draws in an MCMC chain, hence, allows sampling from high-dimensional posterior distributions.
1.3.5 Convergence, Mixing and Thinning
As previously mentioned, samples from an MCMC chain are only samples from the posterior distribution once the chain has converged, i.e., when the distribution of the values remains stable throughout further iterations. In order to obtain valid inferences, convergence of the chains must be checked, and, if necessary, samples from before the chain has converged need to be discarded. The iterations before the chain has converged are called burn-in period.
Convergence may be checked visually, by plotting the drawn values against the iteration number in a so-called traceplot, which should show a horizontal band with no apparent trends or patterns. In this thesis, we additionally evaluated convergence using a statistical criterion developed by Gelman and Rubin (1992). It uses multiple, independent chains for the same parameter, and compares within and between chain variances. When this criterion, which we refer to as the Gelman-Rubin criterion in the rest of this thesis, is close enough to one, say, no more than 1.2, the MCMC chains can be assumed to have converged.
Another potential issue when working with MCMC methods is that they perform dependent sampling. When this dependence is strong it can take many iterations before the MCMC chain converges and, moreover, many iterations until the chain has sufficiently explored the whole range of the posterior distribution. In order to provide enough information about the posterior distribution, a chain with high auto-correlation may have to be continued for more iterations than can practically be handled. To reduce the number of samples that have to be stored, a chain may be thinned out so that only a reduced number of samples is saved.
1.4 Motivating Datasets
The research presented in this thesis is motivated by several large cohort studies. Such studies are especially prone to missing values since typically a large number of variables are measured, and many variables are self-reported (i.e., by questionnaire) or participants have to visit a research centre for measurements to be taken. Moreover, participants are from the general population and may not always see a direct personal benefit in complying with the study protocol.
Two datasets that were used in this thesis for demonstration of the statistical methods under investigation, as well as the real world application of the proposed approach, are briefly introduced in the following sections.
The Generation R Study
The Generation R Study (Kooijman et al. 2016) is an ongoing longitudinal population-based prospective cohort study from fetal life until young adulthood, investigating growth development and health of children, conducted in Rotterdam, the Netherlands. Approximately 10000 pregnant women from the Rotterdam area with an expected delivery date between 2002 and 2006 were included and are followed up, together with their children, until the offspring is 18 years of age. Data is collected at scheduled visits to the research centre as well as by questionnaire, phone interviews or home visits, and augmented by registry information. Among other things, information on maternal diet and lifestyle, health and complications during pregnancy, child growth and health outcomes (e.g., asthma or infectious diseases), behaviour and cognition, body composition and obesity, and eye and tooth development, is collected at different time points.
The National Health and Nutrition Examination Survey
The National Health and Nutrition Examination Survey (NHANES), conducted by the National Center for Health Statistics in the United States, is a large cohort of children and adults and investigates a broad range of health and nutrition related issues (National Center for Health Statistics (NCHS) 2011). Since 1999, a new cohort of approximately 5000 participants, chosen representatively of the US population, is included every year. Data on demographic, socioeconomic, dietary and health-related topics is obtained by interviews and physiological, dental and medical examinations, and laboratory tests are performed. The study is designed to, among other things, investigate risk factors for and prevalence of diseases, get insights into how nutrition (advice) may be used for disease prevention and to inform public health policies.
1.5 Outline of this Thesis
This section provides a brief overview of the content of the subsequent chapters.
In Chapter 2 a fully Bayesian approach to analysis and imputation of data with incomplete covariate information is described in detail for the setting with a continuous longitudinal outcome and incomplete baseline covariates.
Moreover, different approaches to the handling of a longitudinal outcome in multiple imputation using MICE are investigated, both the naive but commonly used, and other, more sophisticated techniques. MICE and the fully Bayesian approach are compared using a simulation study and a real data example from the Generation R Study, in which the association between maternal diet during pregnancy and gestational weight throughout pregnancy is analysed. This application is motivated by the study conducted in Chapter 3.
Chapters 3 and 4 contain applications of the presented Bayesian approach using data from the Generation R Study. In Chapter 3 the association between guideline-based (a priori) and data-driven (a posteriori) dietary patterns with gestational weight gain and trajectories of gestational weight are investigated. Using the approach presented in Chapter 2, simultaneous analysis of the trajectories of gestational weight and imputation of incomplete baseline covariates is performed. The imputed data is then used in the secondary analysis to investigate the association of diet with weight gain.
Chapter 4 examines the effect of sugar containing beverage consumption by pregnant women on body composition of their offspring. Measures of body composition are the body mass index (BMI), which was measured repeatedly, and the fat mass index and fat-free mass index, both measured when children where approximately six years of age. All three outcomes are modelled jointly, and imputation of incomplete baseline covariates is performed using the Bayesian approach presented in Chapter 2.
In Chapter 5, the approach of Chapter 2 is extended to time-varying covariates. Additional issues that arise with such covariates, specifically the potential endogeneity and non-linear shape of the association with the outcome are considered. Advantages and disadvantages as well as the performance of the proposed approach are compared to multiple imputation using a multivariate normal model in a simulation study and two research questions from the Generation R Study: the association between blood pressure and weight of mothers during pregnancy, and the association of maternal gestational weight and child BMI from birth until five years of age.
The implementation of our fully Bayesian approach to jointly analyse and impute incomplete data into the R package JointAI is described in Chapter 6. Functionality of the package to analyse incomplete data using generalized linear mixed models or generalized linear regression models, which may include non-linear forms or interaction terms, is demonstrated in detail. Data from the NHANES study as well as data simulated to mimic data from a longitudinal cohort study, such as the Generation R Study, is used to illustrate this functionality.
The thesis concludes in Chapter 7 with a general discussion of limitations of the proposed approach which arise from the model assumptions, and propositions as to how the approach and its implementation in software should be further improved and extended to facilitate valid inference with incomplete data in a wider range of applications and settings.
References
Andridge, Rebecca R., and Roderick J. A. Little. 2010. “A Review of Hot Deck Imputation for Survey Non-Response.” International Statistical Review 78 (1): 40–64. https://doi.org/10.1111/j.1751-5823.2010.00103.x.
Barnard, John, and Donald B. Rubin. 1999. “Miscellanea. Small-Sample Degrees of Freedom with Multiple Imputation.” Biometrika 86 (4): 948–55. https://doi.org/10.1093/biomet/86.4.948.
Bartlett, Jonathan W., Shaun R. Seaman, Ian R. White, and James R. Carpenter. 2015. “Multiple Imputation of Covariates by Fully Conditional Specification: Accommodating the Substantive Model.” Statistical Methods in Medical Research 24 (4): 462–87. https://doi.org/10.1177/0962280214521348.
Bayes, Thomas, Richard Price, and John Canton. 1763. “An Essay towards solving a Problem in the Doctrine of Chances.” Philosophical Transactions 53: 370–418. https://doi.org/10.1098/rstl.1763.0053.
Behrmann, Herbert I. 1954. “Sampling Technique in an Economic Survey of Sugar Cane Production.” South African Journal of Economics 22 (3): 326–36. https://doi.org/10.1111/j.1813-6982.1954.tb01646.x.
Carpenter, James R., and Michael G. Kenward. 2013. Multiple Imputation and Its Application. John Wiley & Sons, Ltd. https://doi.org/10.1002/9781119942283.
Dempster, Arthur P., Nan M. Laird, and Donald B. Rubin. 1977. “Maximum likelihood from incomplete data via the EM algorithm.” Journal of the Royal Statistical Society. Series B (Methodological), no. 1: 1–38. http://www.jstor.org/stable/2984875.
Gelman, Andrew, and Donald B. Rubin. 1992. “Inference from Iterative Simulation Using Multiple Sequences.” Statistical Science 7 (4): 457–72. https://doi.org/10.1214/ss/1177011136.
Geman, Stuart, and Donald Geman. 1984. “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images.” IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 6: 721–41. https://doi.org/10.1109/TPAMI.1984.4767596.
Gorinson, Morris. 1969. “How the Census Data Will Be Processed.” Monthly Labor Review (Pre-1986); Washington 92 (12): 42. http://www.jstor.org/stable/41837533.
Hastings, Wilfred K. 1970. “Monte Carlo Sampling Methods Using Markov Chains and Their Applications.” Biometrika 57 (1): 97–109. https://doi.org/doi:10.2307/2334940.
Kooijman, Marjolein N., Claudia J. Kruithof, Cornelia M. van Duijn, Liesbeth Duijts, Oscar H. Franco, Marinus H. van IJzendoorn, Johan C. de Jongste, et al. 2016. “The Generation R Study: Design and Cohort Update 2017.” European Journal of Epidemiology 31 (12): 1243–64. https://doi.org/10.1007/s10654-016-0224-9.
Laplace, Pierre-Simon. 1774. “Mémoire Sur La Probabilité Des Causes Par Les évènemens.” Mémoires de Mathématque et Physique, Presentés à L’Académie Royale Des Sciences, Par Divers Savans & Lûs Dans Ses Assemblées, Tome Sixiéme 66: 621–56.
Lesaffre, Emmanuel M. E. H., and Andrew B. Lawson. 2012. Bayesian Biostatistics. John Wiley & Sons. https://doi.org/10.1002/9781119942412.
Li, Kim-Hung. 1988. “Imputation using Markov chains.” Journal of Statistical Computation and Simulation 30 (1): 57–79.
Little, Roderick J. A., and Donald B. Rubin. 2002. Statistical Analysis with Missing Data. Hoboken, New Jersey: John Wiley & Sons, Inc. https://doi.org/10.1002/9781119013563.
Metropolis, Nicholas, Arianna W. Rosenbluth, Marshall N. Rosenbluth, Augusta H. Teller, and Edward Teller. 1953. “Equation of State Calculations by Fast Computing Machines.” The Journal of Chemical Physics 21 (6): 1087–92. https://doi.org/10.1063/1.1699114.
Metropolis, Nicholas, and Stanislaw Ulam. 1949. “The Monte Carlo Method.” Journal of the American Statistical Association 44 (247): 335–41. https://doi.org/10.2307/2280232.
National Center for Health Statistics (NCHS). 2011. “National Health and Nutrition Examination Survey Data.” U.S. Department of Health and Human Services, Centers for Disease Control and Prevention. https://www.cdc.gov/nchs/nhanes/.
Nordbotten, Svein. 1963. “Automatic Editing of Individual Statistical Observations.” In. Statistical Standards and Studies 3. United Nations Statistical Commission; Economic Commission for Europe. Conference of European Statisticians. http://hdl.handle.net/11250/178265.
Rubin, Donald B. 1976. “Inference and Missing Data.” Biometrika 63 (3): 581–92. https://doi.org/10.2307/2335739.
Rubin, Donald B. 1978a. “Bayesian Inference for Causal Effects: The Role of Randomization.” The Annals of Statistics 6 (1): 34–58. https://doi.org/10.1214/aos/1176344064.
Rubin, Donald B. 1978b. “Multiple Imputations in Sample Surveys - A Phenomenological Bayesian Approach to Nonresponse.” In Proceedings of the Survey Research Methods Section of the American Statistical Association, 1:20–34. American Statistical Association.
Rubin, Donald B. 1987. Multiple Imputation for Nonresponse in Surveys. Wiley Series in Probability and Statistics. Wiley. https://doi.org/10.1002/9780470316696.
Rubin, Donald B. 2004. “The Design of a General and Flexible System for Handling Nonresponse in Sample Surveys.” The American Statistician 58 (4): 298–302. https://doi.org/10.1198/000313004X6355.
Rubin, Donald B., and Nathaniel Schenker. 1986. “Multiple Imputation for Interval Estimation from Simple Random Samples with Ignorable Nonresponse.” Journal of the American Statistical Association 81 (394): 366–74. https://doi.org/10.2307/2289225.
Scheuren, Fritz. 2005. “Multiple Imputation: How It Began and Continues.” The American Statistician 59 (4): 315–19. https://doi.org/10.1198/000313005X74016.
Tanner, Martin A., and Wing Hung Wong. 1987. “The Calculation of Posterior Distributions by Data Augmentation.” Journal of the American Statistical Association 82 (398): 528–40. https://doi.org/10.2307/2289457.
Van Buuren, Stef. 2012. Flexible Imputation of Missing Data. Chapman & Hall/CRC Interdisciplinary Statistics. Taylor & Francis.
Van Buuren, Stef, Hendriek C. Boshuizen, and Dick L. Knook. 1999. “Multiple Imputation of Missing Blood Pressure Covariates in Survival Analysis.” Statistics in Medicine 18 (6): 681–94. https://doi.org/10.1002/(SICI)1097-0258(19990330)18:6<681::AID-SIM71>3.0.CO;2-R.
Zhu, Jian, and Trivellore E. Raghunathan. 2015. “Convergence Properties of a Sequential Regression Multiple Imputation Algorithm.” Journal of the American Statistical Association 110 (511): 1112–24. https://doi.org/10.1080/01621459.2014.948117.


(PHP 4 >= 4.0.2, PHP 5, PHP 7)
pspell_new — Load a new dictionary
$language
[, string $spelling
[, string $jargon
[, string $encoding
[, int $mode
= 0
]]]] ) : intpspell_new() opens up a new dictionary and returns the dictionary link identifier for use in other pspell functions.
For more information and examples, check out inline manual pspell website:» http://aspell.net/.
language
The language parameter is the language code which consists of the two letter ISO 639 language code and an optional two letter ISO 3166 country code after a dash or underscore.
spelling
The spelling parameter is the requested spelling for languages with more than one spelling such as English. Known values are 'american', 'british', and 'canadian'.
jargon
The jargon parameter contains extra information to distinguish two different words lists that have the same language and spelling parameters.
encoding
The encoding parameter is the encoding that words are expected to be in. Valid values are 'utf-8', 'iso8859-*', 'koi8-r', 'viscii', 'cp1252', 'machine unsigned 16', 'machine unsigned 32'. This parameter is largely untested, so be careful when using.
mode
The mode parameter is the mode in which spellchecker will work. There are several modes available:
PSPELL_FAST
- Fast mode (least number of
suggestions)
PSPELL_NORMAL
- Normal mode (more suggestions)
PSPELL_BAD_SPELLERS
- Slow mode (a lot of
suggestions)
PSPELL_RUN_TOGETHER
- Consider run-together words
as legal compounds. That is, "thecat" will be a legal compound,
although there should be a space between the two words. Changing this
setting only affects the results returned by
pspell_check(); pspell_suggest()
will still return suggestions.
PSPELL_FAST
,
PSPELL_NORMAL
and
PSPELL_BAD_SPELLERS
are mutually exclusive, so you
should select only one of them.
Returns the dictionary link identifier on success 或者在失败时返回 FALSE
.
Example #1 pspell_new()
<?php
$pspell_link = pspell_new("en", "", "", "",
(PSPELL_FAST|PSPELL_RUN_TOGETHER));
?>


Accessible, screen-reader friendly, flexible, CSS-only toggle switches with full keyboard access and mobile support, using standard form controls.
These examples use Foundation, you can also use the toggles alone, or with Bootstrap.


11.8. All-Pairs Shortest Paths (optional) (WORK IN PROGRESS)¶
We next consider the problem of finding the shortest distance between all pairs of vertices in the graph, called the all-pairs shortest paths problem. To be precise, for every \(u, v \in \mathbf{V}\), calculate \(d(u, v)\).
One solution is to run Dijkstra’s algorithm
for finding the shortest path
\(|\mathbf{V}|\) times, each
time computing the shortest path from a different start vertex.
If \(\mathbf{G}\) is sparse
(that is, \(|\mathbf{E}| = \Theta(|\mathbf{V}|)\))
then this is a good solution, because the total cost will be
\(\Theta(|\mathbf{V}|^2 + |\mathbf{V}||\mathbf{E}| \log
|\mathbf{V}|) = \Theta(|\mathbf{V}|^2 \log |\mathbf{V}|)\)
for the version of Dijkstra’s algorithm based on priority queues.
For a dense graph, the priority queue version of Dijkstra’s algorithm
yields a cost of \(\Theta(|\mathbf{V}|^3 \log |\mathbf{V}|)\),
but the version using MinVertex
yields a cost
of \(\Theta(|\mathbf{V}|^3)\).
Another solution that limits processing time to \(\Theta(|\mathbf{V}|^3)\) regardless of the number of edges is known as Floyd’s algorithm. It is an example of dynamic programming. The chief problem with solving this problem is organizing the search process so that we do not repeatedly solve the same subproblems. We will do this organization through the use of the \(k\)-path. Define a k-path from vertex \(v\) to vertex \(u\) to be any path whose intermediate vertices (aside from \(v\) and \(u\)) all have indices less than \(k\). A 0-path is defined to be a direct edge from \(v\) to \(u\). Figure illustrates the concept of \(k\)-paths.
Define \({\rm D}_k(v, u)\) to be the length of the shortest
\(k\)-path from vertex \(v\) to vertex \(u\).
Assume that we already know the shortest \(k\)-path from \(v\)
to \(u\).
The shortest \((k+1)\)-path either goes through vertex \(k\)
or it does not.
If it does go through \(k\), then the best path is
the best \(k\)-path from \(v\) to \(k\) followed by the
best \(k\)-path from \(k\) to \(u\).
Otherwise, we should keep the best \(k\)-path seen before.
Floyd’s algorithm simply checks all of the possibilities in a triple
loop.
Here is the implementation for Floyd’s algorithm.
At the end of the algorithm, array D
stores the all-pairs shortest
distances.
/** Compute all-pairs shortest paths */
static void Floyd(Graph G, int[][] D) {
for (int i=0; i<G.n(); i++) { // Initialize D with weights
for (int j=0; j<G.n(); j++) {
if (G.weight(i, j) != 0) { D[i][j] = G.weight(i, j); }
}
}
for (int k=0; k<G.n(); k++) { // Compute all k paths
for (int i=0; i<G.n(); i++) {
for (int j=0; j<G.n(); j++) {
if ((D[i][k] != Integer.MAX_VALUE) &&
(D[k][j] != Integer.MAX_VALUE) &&
(D[i][j] > (D[i][k] + D[k][j])))
{
D[i][j] = D[i][k] + D[k][j];
}
}
}
}
}
# Compute all-pairs shortest paths
def Floyd(G, D):
for i in range(G.n()): # Initialize D with weights
for j in range(G.n()):
if G.weight(i, j) != 0:
D[i][j] = G.weight(i, j)
for k in range(G.n()): # Compute all k paths
for i in range(G.n()):
for j in range(G.n()):
if (D[i][k] != math.inf and
D[k][j] != math.inf and
D[i][j] > D[i][k] + D[k][j]
):
D[i][j] = D[i][k] + D[k][j]
Clearly this algorithm requires \(\Theta(|\mathbf{V}|^3)\) running time, and it is the best choice for dense graphs because it is (relatively) fast and easy to implement.


What you need
This section is: Trust or Company Service Provider
For this section you may need to know:
- if you only sell off-the-shelf companies
- if you create complex corporate structures for clients, or plan to do so in the future
- the Money Laundering Regulation reference number of the Trust or Company Service Provider you use
You'll also need to know:
- what kind of Trust or Company Service Provider you are
- which services your business provides
- whether or not you use the services of another Trust or Company Service Provider


mgnx had problem yes... we notice fork yesterday. Alrights onto it.
1
1
CHMEX10-Jan-19 05:00 AM
@bishop explorer is again up to the longest height
jasonabis11-Jan-19 01:15 PM
Does this mean I need to do anything for my wallet? Just load and rescan?
ComputerGenie 👴👴👴11-Jan-19 01:24 PM
Via command line:
komodo-cli -ac_name=MGNX getblockhash 178455
via Agama "cli" in options section:
getblockhash 178455
0d8bd0e85cba465a29ee835fddec4d25ae70f7cef7e0b090876aa7c58e7b2ac4
should be the return or you're on the wrong fork
(edited)
jasonabis12-Jan-19 04:36 PM
Oy. I got:
Error code: -8
Block height out of range
CHMEX12-Jan-19 04:37 PM
You might have a forked chain
Resync or bootstrap
jasonabis12-Jan-19 04:38 PM
Don't know how to bootstrap. When I loaded the wallet, I did the resync with disk option.


This interesting port lies 50nm SW of Genoa.
Position 44° 7'N, 8° 15'E.
The port here has now been developed as a marina and provides good shelter. It is about a 5 minute walk from the centre of the town.
Last updated November 2011.