Cacti - T. pachanoi & Cannabis
Context:
It is the final night of the Rainbow Gathering in New Zealand, the moon was full earlier on in the morning. Tonight is the fullmoon celebration. I have done acid and mushrooms before, but have only actually 'tripped' once. About 4 days earlier, I had a dream about a san pedro cactus.
I have been asked to help prepare this san pedro cactus, donated by some random person. It is about 4 foot long, and .5 foot wide. It is suggested that we share this cactus with as many people as possible, as it is not wanted to be a cliquey thing. However as we are preparing it, we learn that there is also some more cactus about. We keep our preperation quiet, as we do not want to get everybodys hopes up that they will be trippin of their nut. At the other cactus prep station, people have started eatin the cactus raw.
Preperation:
To prepare it, we start by removing the spikes. Then we pull of the skin, and then chop the dark green, and some of the lighter green into a pot. We then chop the green into smaller pieces, on avage about 1cm. We add water and boil it for hours. During this time, some of the cactus got spilt. We then try strain the cactus through a cloth, but it doesn't really work. But the brew looks liquid enough to swallow.
Dosing:
We now have about 7 cups of liquid. There are four of us, R, V, Y and myself. We each do a cup each. Y takes ages to get his down, probably 15-30 minutes. I take my cup down in less than 2 minutes. I find it easiest just to take massive gulps and get it down. After taking it, we all agree that it was actually quite easy, though at the time our throats were gagging. There is still some cactus left, and it never gets used, it actually gets thrown out in the morning! (*gasp*)
It is starting to get dark, and everybody is around the bonfire. V has made some space (cannabis) cake. I eat about 2.5 doses of it. It is quite potent. Earlier on I had one dose, and it made me walk around in circles a bit.
It is about 1/2 hr after dosing, V decides that giving out the space cake is getting a bit intense for him, and gives it to me to give out. I hand more out, and go smoke a joint in a tent.
I then go back, and give the rest of the cake to someone else and go dancing.
Setting:
There is a big bonfire, and people playing drums. There is about 50 people, most of them have eaten the strong space cake, and a few have taken the other brew of cactus, I have no idea how strong it was.
The dancing feels really nice. At the time I thought it was the cactus working, but in retrospect it was probably just the cake. I am noticing the clouds look amazing. I keep seeing these ghostly boy figures in them, the most interesting one was one of a ghostboy reading from the palm of a hand.
I do not know where V and R have gone. But Y is still around playing drums (he is an amazing musician, I already know this). So at this point, I am feeling really good, with increased appreciation for music, and closed eye visuals that are associated with cannabis. It is just like being really stoned.
Then people are getting vocally into the drumming, adding their own sounds to this drum beat.  I add three 'dooh dooh dooh' kinda sounds, and thats when the trip hits me.
The Trip:
As soon as it happens, I have a feeling that I have just completed a micheal jackson trip trigger (thats the only way to say it). I am convinced that the drum beat and everything was a micheal jackson song, and the words I just said were the lyrics he would have said, even though I do not know much MJ. Everybody is looking at me and laughing, it is all a big joke. I'm laughing too and go spinning around.
 It feels like I am drawing energy in through one end of the spiral, and passing it out the other end, where it goes onto everyone else.
I then make a few 'zippy zip zowww' sounds, which Y finds really amzing, and asks me for more. So I do, and I agree it is amazing. When I make these sounds its like my counciousness goes flying in the in the air in spiral shapes. For examples if I make 'zzoooow, zooow zoow zow zow w, www, www, www' sound the sprial starts close to the ground and is really wide, and gets further away from the ground and closer together. Everybody really enjoys theses sounds. It feels like often that I am an alien conciousness, comunicating information through these sounds.
At some point I sit on the ground and close my eyes, I have a visualisation of a girl coming up to me, and offering me candy, which I accept. I interpret this as me meeting mescalato, as described in Carlos Castinidas 'teachings of Don Juan'. A dude offers to do some therapy on me, straighting my back. He lifts me on to his back, it hurts, and I feel like jesus being crucified.
For the rest of the night I keep making amazing sounds, dancing, drumming, then runnning off in spirals, collapsing, and feeling like I'm being crucified, in order to get more energy for everybody to keep partying. When I am dancing, it is like I am bouncing the vibrations of the drums in certain ways as to control them. It feels like I am controlling the drum beat through my dancing, rather than the other way around.
At some point in the night I talk to a guy who isn't having such a good time. I tell him a few realisations I'm having and tell him that often frustation occurs when we cling on to our thoughts, as it creates a 'drag' in our mind. He says 'like how, oh... I see, like that'.
Also, throught out the night I keep going for fire wood misions. Sometimes it is really easy and I find the firewood easily, but other times I just get tangled up in big branches and get confused. At one time I felt like the earth mother was telling me to give the supplies (firewood) to the troops. I felt like a soldier on a battle ground.
I drum, and I feel like I am drumming up a storm. I am convinced that a storm is coming. I have a hallucination of a lighting bolt hitting the ground right near the fire, and I hear someone say something about a lightning bolt too.
As the night goes on, and the sun starts rising, people start losing energy, but are still tripping. I am still full of energy, and keep dancing etc. Through My dancing I am able to get people to start druming etc. Every now and then M starts drumming this same beat again, which gets everybody going again.
I dance in particlar ways to send energy around to make them laugh. It is not that my dancing is funny, indeed, they do not need to be looking at me, but I am manupulating energy to make them laugh. I manupulate some energy to make a song, I hear someone say 'this is going to be a beatiful song.' A few mintues later T starts playing her gutair and singing, I lie down and I feel my heart being taken away.
It is morning now. V comes up, he is naked. I have not seen him since dosing. He tells me how he saw the winking cyote. I am not able to sleep for the next few hours, I end up sleeping some time in the afternoon.
Retrospective:
This trip taught me that all trippers are connected by the spiral loop. The answer to the enternal question is in the spiral. I understand what the beatles mean by yellow submarine (I was wearing my Yellow Submarine t-shirt). The yellow submarine is the atom explorer of conciousness, it is our mind following the spiral around, exploring. Through making sounds I am able to send the Yellow submarine where I want. I learnt the importance of music in understanding reality. Reality is simply vibration, and music, esp simple drum beats is a good way to measure it.
Writing this report has taught me that tripping is pretty indescribable, I haven't nearly illustrated well enough what was happening.
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.


In early spring the leaves have a bold green appearance and can have small white berries. The plant emits a faint smell reminiscent of solvent. In wooded areas (pictured) the leaves will be big and leafy.
In early summer it tends to turn a dull greenish red. It can be easy to spot in contrast next to blackberries and other plant types. The leaves become waxy with the poison oil urushiol. In an open field environment (pictured) the plant tends to have smaller leaves.
In the fall the leaves turn from dull green to red, as the leaves fall off and plant becomes dormant. It may lose it's berries at this stage. Although the plant is in the process of becoming dormant, the oils that cause the rash stick around all year long.
In the winter time it can become difficult to identify. The leaves drop off and only the vines remain. The best way to identify it at this stage is to look for sticks growing straight up out of the ground with cinnamon colored bark. Although it has become dormant the plant is still infested with Urushiol and just as volatile.
It can grow low to the ground on the edge of paths and clearings.
It can grow in to bush like bunches that look very similar to other plants.
It can head up to the canopy, creating thick vines that look similar to english ivy. If you ever find firewood with vines on it be careful handling it.
Here it is shown mimicing low growing plants called service berry on the left, blackberry bushes in the middle and salal on the right. It can morph shape to blend in with other plants.


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My Project
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#include <Map.hpp>
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| virtual bool | load_map (std::string pathname) |
| virtual int | serialize (char *buffer) |
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| _object * | getObjects () |
| ~Map () | |
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| int8_t | type |
| char | name [50] |
| _object * | objects |
| int16_t | length |
| int16_t | width |
| int16_t | max_objects |
| int16_t | n_object |
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| Map::~Map | ( | ) |
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{% extends '_layouts/layout_2-3.html' %} {% block page_title %}My NHS Account{{serviceName}}{% endblock %} {% block content %}
Your NHS account is now secured with your phone 07779 000333.


class: center, middle, inverse, title-slide # ETC3250 Project Presentation ### Zhuangyi Zhu, Stephany Sar, Cindy Yang, Linguo Yang --- .deep-purple[ # Methodology ] .font1[ - We chose to use .green[XGboost] because it utilises gradient boosting which combines the least impactful variables together in order to make them more relevant to the model. - Compared to the benchmark random forest model, XGboost will recursively produce trees that correct errors from past trees. Something the random forest method does not do. - This means a lower Mean Squared Error and higher accuracy for our model, given we take out noise to prevent overfitting. ] .img-fill[![](model accuracy after each iteration.png)] --- class: split-two with-border border-black hide-row3-col1 hide-row4-col1 .column.bg-main1[ .split-four[ .row.bg-main1[.font_large[.content[ *Step 1* : Reading the training data and it's outcomes. ]]] .row.bg-main2[.font_large[.content[ *Step 2* : Analysing the variable importance, transforming variables, and removing noise variables. ]]] .row.bg-main3[.font_large[.content[ *Step 3* : Building a gradient boosting model using the XGboost package ]]] .row.bg-main4[.font_large[.content[ *Step 4* : Tuning parameters through cross-validation in order to prevent overfitting variables ]]] ]] .column.bg-main1[ .split-two[ .row.bg-main1[.content[
 .font1[ .green[Confusion Matrix]] ]] .row.bg-main2[.content[
 .font1[ .green[Variable Importance after removing noises and cross validation]] ]]]] --- .deep-purple[ # Results ] .font2[ - The final model produced a prediction accuracy of .deep-orange[0.92691] on 50% of the testing data, and .deep-orange[0.92499] on the final 50% of the testing data. - There isn't a huge disparity between both accuracy rates indicating we have a stable model - This is a improvement of .green[over 3%] compared to the benchmark model of 89%. ] --- .deep-purple[ # Improvements & Conclusion ] .font2[ - Drastically improved precision of predicting each outcome, finally achieving .deep-orange[99.7%] precision for predicting winning shots, .deep-orange[91%] for forced errors, and .deep-orange[92.4%] for unforced errors - With enough testing, further research into the individual variables, or using other methods such as neural networks, the accuracy could definitely reach above 95%, and automation of coding points would be achievable. ]


Survey results released
Between September 2020 and January 2021, our open data practices survey targeting researchers in eight African countries was live via the JISC Online Survey Tool. The survey was available in three languages including English, French and Portuguese. During this time we received 83 responses including:
- English: 68
- French: 12
- Portuguese: 3
Data
Discrepancies between some of the survey questions across the three languages (such as the question ordering, type and options) as well as the necessary omission of some responses due to ineligibility of some of the respondents (e.g. respondent did not answer the question regarding country), led to a smaller number of usable responses. The final data set consisted of 64 responses, including:
- English: 58
- French: 4
- Portuguese: 2
The distribution of respondents’ countries and the discipline of their highest degree is shown in the figure below:
Information on the data cleaning process can be found in the README.md document on the SODP Data Analysis GitHub site. This GitHub site also contains the data files (raw and cleaned) as well as the R code used for the data visualisations and Shiny App.
Interactive web app for data visualisation
Due to the large number of questions in the dataset and the requirement for cross referencing questions relating to the IAD framework with demographics, we developed an interactive web app where users can explore the data visually. The app is developed in Shiny, “an R package that makes it easy to build interactive web apps straight from R”. The data visualisations can all be found on the SODP Shiny App.
Data visualisation
In this blog post, we share some of the insights gained from the survey results. These are just a few examples of the interactive plots available from the SODP data visualisation Shiny App. The results from all of the survey questions can be explored and cross tabulated with any of the respondents’ demographics. The full suite of the interactive plots are available on the SODP data visualisation Shiny App. We caution readers not to draw conclusions based on these plots due to the small, non-representative dataset.


This model represents an ecosystem of a population of "prey", like rabbits, and "predators", like coyotes. As the number of prey increases then the number of predators will increas because there's more food for them. As the number of predators increase then the number of prey being eaten also increases, which decreases their total population. This puts pressure on the predators because now there's less food for them to eat so their numbers start decreasing, which allows the number of prey to increase. And so the cycle continues.
There needs to be a balance between the predators and prey for the ecosystem to be in balance. If the two species are not in harmony, like too many predators being born, then both species can go extinct.
The sliders represent the relative likelyhoods of births or deaths of the population.
If you cause an extinction hit the "page reload" button to start again.
This is a phase diagram of the Lotka-Volterra predator-prey model. The math behind this is explained in Chapter 2 of this book: N. Boccara, Modeling Complex Systems: Second Edition, Graduate Texts in Physics, 25 DOI 10.1007/978-1-4419-6562-2 2, Springer Science+Business Media, LLC 2010 . A preview of Chapter 2 is available from the publisher. Perhaps v2 I'll add a time series plot which helps demonstrate how the phase-shift in the predator-prey populations.


EPURWP.COM
EPURWP is a tool which allows updates and modifications of the
white pages database.
Two optional parameters may follow.
The first will specify how many days will go by before a data is valid.
If the temporary part of the record has not changed during this time,
it will be considered as stable and transferred to the primary (used) part.
The second parametre states how many days will go by before the record is
deleted from the database. This is how many days go by with no updates
or refreshes of this user's data.
This page was last updated


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Data Scientist I - Atlanta, GA 30309
Other Locations Available
Dublin, Ga
Arlington, VA
The Data Scientist I position supports projects and workstreams that help organizations manage data and develop standard reports that enable evaluation and optimization of business performance and operations. Critical components of this position include supporting the development and delivery of strategies, project approaches, deliverables, and recommendations and performing research and analysis. The Data Scientist I will also support the development of databases and rigorous methods of analysis that meet the needs of diverse clientele.
Duties and Responsibilities:
- Lead key activities or deliverables pertaining to a project workstream, manage team members, plan and prioritize work assignments and propose next steps.
- Prepare for and support facilitation of client meetings, internal meetings and working sessions.
- Execute day to day correspondence with client, as directed, and drive client satisfaction through the delivery of quality service/work products and results.
- Support internal initiatives and non-billable projects while meeting utilization target.
- Communicate effectively and concisely.
- Execute against and assist in formulating technical approaches, frameworks and strategies based on guidance received from leadership.
- Mature and apply critical thinking to problem solving using analysis.
- Understand project scope and client environment; begin to assess downstream impact of change and apply to future projects.
- Support the collection, preparation, and management of client data and publicly available datasets such as the Federal Employee Viewpoint Survey (FEVS), the American Community Survey (ACS), and the Current Population Survey (CPS).
- Contribute to the design of rigorous study protocols for clients and deliverables. Support the analysis of population survey and human resources data, including descriptive and inferential statistical tests (T-tests, ANOVA, Chi-Square, Pearson Correlation, Linear and Logistic Regression, etc.).
Required Education and Experience:
- Bachelor’s Degree and 2+ years of experience
Communication Skills
- Excellent oral and written communications required
- Must be able to communicate the results of complex research to clients
Technical Skills
- Microsoft Office Suite, particularly Excel, proficiency is required
Preferred Experience:
- Access and other database software experience is preferred
- Tableau or other data visualization software experience is preferred
- Competence in SPSS, R, or Python is a plus
Critical Thinking and Problem-Solving Skills:
- Strong critical thinking skills and the ability to solve complex problems using
analysis and creativity to successfully provide a solution
- Ability to multi-task, without losing focus of priorities
- Ability to design and carry out complex quantitative research


UKCloud Limited (“UKC”) and Virtual Infrastructure Group Limited (“VIG”) (together “the Companies”) – in Compulsory Liquidation
On 25 October 2022, the Companies were placed into Liquidation with the Official Receiver appointed as Liquidator and J Robinson and A M Hudson simultaneously appointed as Special Managers to manage the liquidation process on behalf of the Official Receiver.
Further information regarding the Liquidations can be found here: https://www.gov.uk/government/news/virtual-infrastructure-group-limited-and-ukcloud-limited-information-for-creditors-and-interested-parties
Contact details:
For any general queries relating to the Liquidations please email<EMAIL_ADDRESS>For customer related queries please email<EMAIL_ADDRESS>For supplier related queries please email<EMAIL_ADDRESS>Considerations when scaling an OpenShift cluster
Infrastructure applications
With your UKCloud for Red Hat OpenShift cluster we deploy a number of infrastructure applications by default. These are quality of service features that are intended to help you with administration, management and application development within your cluster. Examples include the EFK stack, Hawkular Metrics, the integrated registry, the service catalog and, with v3.11 deployments onwards, Prometheus and Grafana.
These applications run their components on the infrastructure nodes (these are the nodes with the naming convention worker-infra). Your cluster comes out of the box with three of these nodes and each has 16GB of memory and two vCPUs. This provides an adequate amount of resource and good level of resiliency for the infrastructure components to support a production deployment. If you're looking to scale out the cluster to a larger size and start running more intensive applications or builds, these nodes may not be enough. For example, the more applications and nodes you have in your environment, the more logs will be gathered off the nodes by fluentd and sent off to Elasticsearch for indexing. This will result in significantly higher resource usage. In the same vein, the more pods and nodes that are running in your environment, the more stats Hawkular Metrics will be gathering and storing in the Cassandra database.
Recommendations
Scaling of the infrastructure applications to make them more resilient is possible but may require additional infrastructure nodes to be deployed. For example to scale Elasticsearch to three nodes/containers we recommend having a minimum of four infrastructure nodes. To have these applications scaled up or down raise a Service Request via the My Calls section of the UKCloud Portal.
Finally, you'll want to consider the storage usage of the infrastructure applications. As previously mentioned, the more applications and nodes you have in your environment the more logs will be gathered off the nodes by fluentd and sent off to Elasticsearch for indexing. This means the persistent volume used by Elasticsearch will have more data to store. By default, we provide 14 days retention and a 40GB persistent volume with Elasticsearch. If this volume fills to 100% then the EFK stack will not work until indices are deleted or more space is provided. If you're relying on the EFK stack for your logs then you'll want to determine an average of how much storage your cluster is consuming on logs per day and request an increase of the persistent volume to fit your desired retention.
Planning your scale out
You'll want to estimate how many pods you are expecting to fit on a node. Things to take into account are the CPU, memory and storage (if your pod is going to be writing to the local file system rather than persistent storage) requirements of your applications. This will give you an idea of the size and amount of nodes you'll need. For more information regarding planning for application requirements, see the following OpenShift documentation page: https://docs.openshift.com/container-platform/3.11/scaling_performance/cluster_limits.html#scaling-performance-planning-your-environment-according-to-application-requirements
Scaling
If you'd like to scale up your cluster, raise a Service Request via the My Calls section of the UKCloud Portal and provide us with the size, network connectivity and quantity of nodes you'd like to add. The same process should be followed to scale a cluster down, this time providing the details of the node(s) you want to remove.
Note
The network connectivity you request for the scaled up nodes must be present in the cluster.
Note
OpenShift v4 clusters support self-service scaling. You can find out more information in How to scale an OpenShift v4 cluster.
Further information
Below are some useful OpenShift documentation pages regarding scaling of a cluster:
https://docs.openshift.com/container-platform/3.11/scaling_performance/scaling_cluster_metrics.html https://docs.openshift.com/container-platform/3.11/scaling_performance/scaling_cluster_monitoring.html
Feedback
If you find a problem with this article, click Improve this Doc to make the change yourself or raise an issue in GitHub. If you have an idea for how we could improve any of our services, send an email to feedback@ukcloud.com.


Preparation time
less than 30 mins
Cooking time
no cooking required
Serves
Serves 12
Dietary
The ready-made supermarket versions of egg nog have given it a bad press, but this deliciously creamy recipe is a little taste of Christmas history. George Washington, the first American president, even made his own egg nog, and is very clear about ageing it for a few days to let the flavour mellow.
Whisk the egg yolks in a bowl with 100g/3½ oz of the caster sugar until the mixture is pale yellow and thick (this is best done with an electric handheld mixer).
Stir in the milk, double cream, and the rum and/or bourbon. You can add a little grated nutmeg as well, if you like your nog this way.
Pour the mixture into two 750ml/1½ pint bottles with stoppers and store in the fridge for up to two weeks. (The alcohol will prevent any spoilage of the eggs or cream.)
Place the egg whites in a box and freeze until ready to serve the egg nog.
When ready to serve, defrost the egg whites into a clean metal or glass bowl. Using a handheld electric mixer, whisk the egg whites until foamy and opaque. Add the remaining 50g of sugar and whisk the egg whites until soft peaks form when the whisk is removed.
Pour the egg nog from the bottles into a large bowl and fold in the egg whites until well combined. Ladle the egg nog into glass tumblers and serve with a little freshly grated nutmeg over the top of each glass.


@message
Endpoints:
#Get all restaurants
GET /restaurants
#Get restaurant by id
GET /restaurants/:id
#Save a restaurant
POST /restaurants
#Get all cities
GET /cities
#Create a reservation
POST /reservations
}
How this sample app works
This app was deployed to Heroku, either using Git or by using Heroku Button on the repository.
When Heroku received the source code, it fetched all the dependencies in the build.sbt, creating a deployable slug.
The platform then spins up a dyno, a lightweight container that provides an isolated environment in which the slug can be mounted and executed.
You can scale your app, manage it, and deploy over 150 add-on services, from the Dashboard or CLI.


fun debug(enableDebug: Boolean = false): PendingTasks
Turn on and off debug log messages from Wendy.
Designed like a builder pattern so you can: PendingTasks(this, Factory()).enableDebug()


seed
Paste newline separated bingo goals
Goal Distribution Weight requires goal grouping to have an effect
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--- layout: home title: Rockdust Blends ---
A unique, natural volcanic, metamorphic blend created to enrich soil and increase fertility and growth. It is carefully selected, tested, extracted and crushed from rare deposits in the Canadian Shield. It has over 70 essential minerals and trace elements required for healthy, strong crops. Greenstone is highly paramagnetic and has qualities that attract and repel soil particles which increase flocculation and respiration. This improves tilth and porosity of your soil and stimulates growth. Using Greenstone is a tried and tested way of creating ideal diverse soil conditions for quality yields. Apply anytime, 200-1000 lb per acre, or 40 lb per 1,000 sq. ft. For orchards 10-20 lb per tree, as an additive to potting soil 1-10 ratio, $19 (fine grind) or $17.50 (chips) for 40 lb pail
An introductory Starter Mix for Vegetables, Fruit, Greenhouses, Orchards, Lawns and Golf Courses. A unique blend of volcanic and sedimentary paramagnetic complexes that offer true value with over 70 trace elements and minerals. Also has natural occurring amino acids and B vitamins. Apply Spring or Fall, 200-1000 lb per acre, or 40 lb per 1,000 sq. ft. For orchards 10-20 lb per tree, as an additive to potting soil 1-10 ratio, $21 for 40 lb pail
A Calcium Sulphate Blend for Composting, Gardens, Flowers, Ginseng, and Trees of all species. Remedy Salt Damaged Walkways, Boulevards, Lawns and Contaminated Soil. Contains 76% pure calcium carbonate (with 20% available calcium), 15% sulphur, 6% silica, trace manganese, zinc and an assortment of trace elements. It works by enhancing the natural breakdown process which stabilizes the usable nutrients and elements. Adding Gypsum+ to manure reduces odors by locking up ammonia, turning carbonate of ammonia into odorless insoluble ammonium sulphate, which prevents leaching. Composting manure with Gypsum+ increases organic matter of static piles and fertility of lagoons. It also detoxifies chemical residuals in contaminated soil. Gypsum+ is an essential component for methionine production and other sulphur based amino acids such as taurine and cysteine. These aminos chelate lead and aluminum. Gypsum+ regulates potassium, magnesium and iron ratios. Control phosphorous and transform it into insoluble calcium hydroxysulphate with Gypsum+. Manage your soil pH with Gypsum+ and promote your nutrient availability the natural way. Apply to land early spring and fall 200-2000 lb per acre, or 200 lb (1:10 ratio) per ton of stockpile manure or to control odors in barns sprinkle at a 1:4 ratio, avoid gumming in compost piles 1:6 ratio. For lawns or gardens and golf courses, 35 lb per 1,000 sq. ft. in spring and fall $12.50 for 35 lb pail.
A Superb Calcium Soil Conditioner for Bulbs, start up Lawns, Gardens, Trees, Resodding and Golf Courses. Contains 4 calciums and paramagnetic limestone with a diverse mineral spectrum. Apply spring or fall 200-500 lb per acre, for lawns and gardens 40 lb per 1,200 sq. ft, 10 lb per tree, or as a feed supplement, at a ratio of 1:20, $22.50 for 40 lb pail.
a Deluxe Full Spectrum Mineral Blend. For lawns, Gardens, Flowers, Ginseng or Trees of all species. Contains 4 calciums, sea vegetables, peat moss, silica and an impressive line-up of macro and micronutrients. Apply 200-1000 lb per acre in spring or autumn, after the leaves fall. Suggest 5-10 lb per tree, 1/2-1 lb per berry bush, for lawns or gardens and golf courses, 40 lb per 1500 sq. ft garden, $29 for 40 lb pail.
Works as Seed Inoculate and also Stimulates Exotic Mushrooms. Tara’s Tarn ingredients are extracted from one of the Earth’s primordial sea bottoms. When planting legumes or forages, dust seeds prior to drilling or broadcasting. For mushrooms, dust bed or logs. (mix 1 oz per lb of seed). $6 for 1oz or $50 for 10 lb pail.
a Deluxe Greenhouse Houseplant Blend, ideal for flowers, herbs, sprouts, bonsai, tropicals and to enhance compost piles. $14 for 500 grams Nutrient Tea a Natural Solution that Enhances Growth for Lawns, Golf Courses, Trees, Shrubs, Hedges, Flowers, Fruit and all Garden Vegetables. Contains fish emulsion, lignin, orange oil, humates, sea nectar, vitamin C, copper sulphate and sugars with a pH level of 6. To treat lawns 3 oz per gallon of water covers 1000 sq. ft. Apply extra after mowing or to green up trouble spots from damage or drought. To avoid excessive winter burn, spray 2 weeks prior to freeze-up then add organic matter. Other uses include foliar misting, top dressing soil, presoak seeds or dip cuttings. Apply 1 gal per acre seasonally in quart applications every 3 weeks. $12.50 per litre, $36 per gal
a Soil Binder, Dust Suppressant and Erosion Deterrent, for Gardens, Fields, Golf Courses and Lawns. Natural Nectar is ideal for knitting sandy soil, composting, separating clay and dust control. Composed of humates, lignin, carbohydrates, vitamin C and orange oil, H302. Compliments the Krebs (series of biochemical reactions) and Ergs (energy released grams per second) cycles and provides natural water soluble cellulose. Spray 1-3 gal per acre. $7.50 litre, $25 per gal
a Natural Cleanser that prohibits pathogens. Ideal for Gardens, Fields, Lawns and Golf Courses, Vegetables, Fruits, Flowers, Forages and Berries. Orange Blossom is a citrus surfactant that has insecticidal properties which ward off certain caterpillars and soft shelled insects. It is a natural cleaner & carbon source disinfectant for soil. Spray on soil in spring and fall. 1 gal per acre @ $15 per gal
a Blend of Sphagnum Peat and Premium Compost. Absorbs ammonia and deodorizes the environment naturally. Ideal for Gardens, Resodding Lawns & Golf Courses, Trees, Shrubs, Hedges, Vegetables & Fruit. Improves compacted or sandy soil & has a low salt index. It is high in nutrients and humic acids and contains no pathogens. Compliments the ion exchange capacity. $5 for 35 lb
a Natural Cold Processed Fructose that’s enzyme active. Ideal for Gardens, Fields, Lawns and Golf Courses. It promotes bacteria proliferation in the organic breakdown of residuals. Assists the carbon cycle. As a natural sugar it can also be used for diabetic jams, jellies and baking. Spray 6-8 oz per acre on fields or lawns $69 for 50 lb, $5 for 2 lb


{% extends "index.html" %} {% block welcome %}
Register to accesss our application here. Your email will be your username. You can create a nickname, and this is the name that will be visible to you (and possibly other users also using the app).
A randomized password will automatically be sent to your email account.
If you don't select any application (i.e. deselect all), all application will be automatically selected for your account.


Sign up form
Username:
Must Enter a Username!
Must be a minimum of 2 Characters!
First Name:
Must Enter a first Name!
Must be a minimum of 2 Characters!
Last Name:
Must Enter a Last Name!
Must be a minimum of 2 Characters!
Password:
Must Enter a password!
Must be a minimum of 6-8 Characters!
Email:
Enter email!
Please enter a valid One
location:
Please Enter a Valid Address


Seems that in IE9 / IE10 the inlined SVG is not shown if is not escaped, URLEncoded, or encoded in Base64. I've choose to escape the SVG because is slightly smaller and is still posible to adjust some parameters by hand as escaped code is pretty readeable (opposite to Base64).
Anothe fun bug is that Firefox does not renders correctly inlined code that include the hash symbol so it has to be replaced by '%23'
Below plain text version of the inlined SVG, Remember this ones only work in modern browsers.


{{ stage }}
IHO {{ doctype }}
{%- if unpublished -%} {{- stage -}} {%- else -%} {%- if publisheddate and publisheddate != "XXXX" -%} Published {{ publisheddate -}} {%- endif -%} {%- endif -%}


Sylvanian Families Catalogues, DVDs, Books, & Magazines
Sylvanian Families DVD: Stories told by Bernard Cribbins
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An exception thrown to indicate that a requirement is not satisfied.
This class stores and reports statistics on TestResult objects.
This class stores and reports statistics on TestResult objects.
Created by leslie.lam on 12/14/17 Based on java class created by michael.ottati on 9/3/15.
This abstract class describes the methods available on a TestResult.
This abstract class describes the methods available on a TestResult. The values of a TestResult are filled in by the infrastructure. The lone exception to this is ResponseTime. A test may declare its own notion of ResponseTime and inject it into the TestResult with the setter for that attribute.
This capability is allowed to accommodate the possibility that there is a more accurate representation of test response time than wall clock time.
Created by leslie.lam on 12/13/17. Based on a java interface created by michael.ottati.
Thread-local reference to the result of measuring a test.
Thread-local reference to the result of measuring a test.
Created by leslie.lam on 12/14/17. Based on java class created by michael.ottati on 9/17/15.
An exception thrown to indicate that a requirement is not satisfied.
Created by leslie.lam on 12/14/17. Based on java class created by michael.ottati on 9/18/15.


This will not run correctly! There will be bugs! You have been warned!
Steps to start Raggle:
Raggle
from the Raggle Start MenuThere are all sorts of bugs at the moment. The UI is counter-intuitive, a bunch of features aren't accessible from the web interface, and there is a complete lack of documentation. But hey, it's a start. ;-)


RT @farbandish: TIL the maiden flight of the British TSR‐2 bomber was abruptly constrained mid-air when the pilot found out the plane desig…
8/2/2019, 9:58:35 AM
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RT @farbandish: TIL the maiden flight of the British TSR‐2 bomber was abruptly constrained mid-air when the pilot found out the plane desig…
8/2/2019, 9:58:35 AM
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Profile
Resume
Blog
Contact
Check my social media!
Marcus Vinicius Costa Ribeiro
Professional FrontEnd
Check my resume
Reach me
Marcusvcrd@gmail.com


The a href attribute
An absolute URL:
The a tag
dylanking
Your browser does not support the audio tag.


Dwyane Wade and wife Gabrielle Union went berserk courtside after big plays from son Zaire Wade and Bronny James in their first high school game, Business Insider - Business Insider Singapore
by Meredith Cash, Business Insider US- Dwyane Wade and wife Gabrielle Union got out of their seats and enthusiastically cheered for Bronny James and Zaire Wade during their first high school basketball game at Sierra Canyon.
- The Los Angeles Lakers had a game that day, so LeBron James streamed the game to watch his eldest son play.
- Wade, who is very close friends with James, reassured his buddy that he and Union had him covered in cheering for Bronny.
- Visit Business Insider’s homepage for more stories.
There’s no question that Dwyane Wade was an absolute legend throughout his 17-year career on the hardwood. But now that he’s retired, he’s taken on a new courtside role as cheerleader.
Wade’s son, Zaire, and LeBron James’ son, LeBron “Bronny” James Jr., both began their first high school seasons at Sierra Canyon this year. Their first game was broadcast on ESPN, and Wade and his wife, Gabrielle Union, were featured prominently on the sidelines.
The duo got out of their seats and cheered whenever Zaire and Bronny made good plays.
LeBron James couldn’t be at his eldest son’s first high school game because his Los Angeles Lakers had a game that evening, but he was able to stream the game from the road.
Wade, who is very close friends with James, reassured his buddy that he and Union had him covered on the cheering front.
Fifteen of Sierra Canyon’s games will be broadcast on ESPN this season, and both Zaire and Bronny should figure prominently in the lineup.
- Read more:
- Magic Johnson lauds LeBron James, says he’s ‘playing as well as I’ve ever seen him play’
- Russell Wilson and the Seahawks bamboozled the Eagles with a trick play that ended in a glorious 33-yard touchdown
- Here are the new, sleek NBA ‘City’ uniforms from Nike that all 30 teams will be wearing
- Rob Gronkowski and Venus Williams danced with the Laker Girls in a hilarious bit with James Corden


Class EventType
Summary description for MutationEventType.
Assembly: SharpVectors.Dom.dll
Syntax
public sealed class EventType
Fields
Abort
Declaration
public static readonly EventType Abort
Field Value
Change
Declaration
public static readonly EventType Change
Field Value
Click
Declaration
public static readonly EventType Click
Field Value
DomActivate
Declaration
public static readonly EventType DomActivate
Field Value
DomAttributeNameChanged
Declaration
public static readonly EventType DomAttributeNameChanged
Field Value
DomAttrModified
Declaration
public static readonly EventType DomAttrModified
Field Value
DomCharacterDataModified
Declaration
public static readonly EventType DomCharacterDataModified
Field Value
DomElementNameChanged
Declaration
public static readonly EventType DomElementNameChanged
Field Value
DomFocusIn
Declaration
public static readonly EventType DomFocusIn
Field Value
DomFocusOut
Declaration
public static readonly EventType DomFocusOut
Field Value
DomNodeInserted
Declaration
public static readonly EventType DomNodeInserted
Field Value
DomNodeInsertedIntoDocument
Declaration
public static readonly EventType DomNodeInsertedIntoDocument
Field Value
DomNodeRemoved
Declaration
public static readonly EventType DomNodeRemoved
Field Value
DomNodeRemovedFromDocument
Declaration
public static readonly EventType DomNodeRemovedFromDocument
Field Value
DomSubtreeModified
Declaration
public static readonly EventType DomSubtreeModified
Field Value
Error
Declaration
public static readonly EventType Error
Field Value
KeyDown
Declaration
public static readonly EventType KeyDown
Field Value
KeyUp
Declaration
public static readonly EventType KeyUp
Field Value
Load
Declaration
public static readonly EventType Load
Field Value
MouseDown
Declaration
public static readonly EventType MouseDown
Field Value
MouseMove
Declaration
public static readonly EventType MouseMove
Field Value
MouseOut
Declaration
public static readonly EventType MouseOut
Field Value
MouseOver
Declaration
public static readonly EventType MouseOver
Field Value
MouseUp
Declaration
public static readonly EventType MouseUp
Field Value
Reset
Declaration
public static readonly EventType Reset
Field Value
Resize
Declaration
public static readonly EventType Resize
Field Value
Declaration
public static readonly EventType Scroll
Field Value
Select
Declaration
public static readonly EventType Select
Field Value
Submit
Declaration
public static readonly EventType Submit
Field Value
TextInput
Declaration
public static readonly EventType TextInput
Field Value
Unload
Declaration
public static readonly EventType Unload
Field Value
Properties
Name
Declaration
public string Name { get; }
Property Value
NamespaceUri
Declaration
public string NamespaceUri { get; }
Property Value


Class ConnectorAdminFileAdapter
- java.lang.Object
-
- de.uplanet.lucy.server.file.admin.AbstractUPAdminFileAdapter
-
- de.uplanet.lucy.server.businesslogic.rtdata.file.admin.DefaultAdminFileAdapter
-
- de.uplanet.lucy.server.odata.connector.files.admin.ConnectorAdminFileAdapter
-
- All Implemented Interfaces:
de.uplanet.lucy.server.file.admin.IAdminFileAdapter
,de.uplanet.lucy.server.file.admin.IUPAdminFileAdapter
public final class ConnectorAdminFileAdapter extends de.uplanet.lucy.server.businesslogic.rtdata.file.admin.DefaultAdminFileAdapter
-
-
Constructor Summary
Constructors Constructor Description ConnectorAdminFileAdapter(de.uplanet.lucy.file.IFileStorageAlias p_fileStorageAlias, java.lang.String p_strAdapterGuid, java.lang.String p_strAppGuid, de.uplanet.lucy.server.property.IPropertyCollection p_properties)
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description void
adapterRemoved(de.uplanet.jdbc.JdbcConnection p_conn, de.uplanet.jdbc.JdbcConnection p_ddlConn)
Is called when the file field / adapter is removed from the application.void
applicationPublished(java.lang.String p_strAppGuid, de.uplanet.jdbc.JdbcConnection p_conn)
Is called after the application was published to the database (After the database operations)void
applicationRemoved(java.lang.String p_strAppGuid, de.uplanet.jdbc.JdbcConnection p_conn, de.uplanet.jdbc.JdbcConnection p_ddlConn)
Is called when the application was deleted.void
prepareAdapterRemove(de.uplanet.jdbc.JdbcConnection p_conn)
Called before DDL and other database operations on publish.-
Methods inherited from class de.uplanet.lucy.server.businesslogic.rtdata.file.admin.DefaultAdminFileAdapter
canDelete, createStorageProcessorInstance, getAdapterGuid, getScaledImagePath, getStoragePath, isFileStoragePath, shouldFilesDeleteOnApplicationRemove
-
-
-
-
Method Detail
-
prepareAdapterRemove
public void prepareAdapterRemove(de.uplanet.jdbc.JdbcConnection p_conn)
Description copied from interface:de.uplanet.lucy.server.file.admin.IAdminFileAdapter
Called before DDL and other database operations on publish.- Specified by:
prepareAdapterRemove
in interfacede.uplanet.lucy.server.file.admin.IAdminFileAdapter
- Overrides:
prepareAdapterRemove
in classde.uplanet.lucy.server.businesslogic.rtdata.file.admin.DefaultAdminFileAdapter
- Parameters:
p_conn
- The database connection.
-
adapterRemoved
public void adapterRemoved(de.uplanet.jdbc.JdbcConnection p_conn, de.uplanet.jdbc.JdbcConnection p_ddlConn)
Description copied from interface:de.uplanet.lucy.server.file.admin.IAdminFileAdapter
Is called when the file field / adapter is removed from the application.- Specified by:
adapterRemoved
in interfacede.uplanet.lucy.server.file.admin.IAdminFileAdapter
- Overrides:
adapterRemoved
in classde.uplanet.lucy.server.businesslogic.rtdata.file.admin.DefaultAdminFileAdapter
- Parameters:
p_conn
- The "normal" database connection, that connection should be used for non DDL operations.p_ddlConn
- The "DDL" database connection, that connection should be used for any DDL operations.
-
applicationRemoved
public void applicationRemoved(java.lang.String p_strAppGuid, de.uplanet.jdbc.JdbcConnection p_conn, de.uplanet.jdbc.JdbcConnection p_ddlConn)
Description copied from interface:de.uplanet.lucy.server.file.admin.IAdminFileAdapter
Is called when the application was deleted. To do additional clean ups (the adapterRemoved method should called before).- Specified by:
applicationRemoved
in interfacede.uplanet.lucy.server.file.admin.IAdminFileAdapter
- Overrides:
applicationRemoved
in classde.uplanet.lucy.server.businesslogic.rtdata.file.admin.DefaultAdminFileAdapter
- Parameters:
p_strAppGuid
- The application GUID.p_conn
- The "normal" database connection, that connection should be used for non DDL operations.p_ddlConn
- The "DDL" database connection, that connection should be used for any DDL operations.
-
applicationPublished
public void applicationPublished(java.lang.String p_strAppGuid, de.uplanet.jdbc.JdbcConnection p_conn)
Description copied from interface:de.uplanet.lucy.server.file.admin.IAdminFileAdapter
Is called after the application was published to the database (After the database operations)- Specified by:
applicationPublished
in interfacede.uplanet.lucy.server.file.admin.IAdminFileAdapter
- Overrides:
applicationPublished
in classde.uplanet.lucy.server.businesslogic.rtdata.file.admin.DefaultAdminFileAdapter
- Parameters:
p_strAppGuid
- The application GUID.p_conn
- The "normal" database connection, that connection should be used for non DDL operations.
-
-


Big O is a way to measure an algorithm's space and time complexity.
If you already know about Big O and want to go into the specific sections then click one of the sections below.
1. Time Complexity
2. Space Complexity
Suppose you want to find the fastest way to get to a store. Which is the best way?
Q: How would you choose the best way?
A: The one that is the fastest.
Q: How to measure the efficiency of these algorithms?
A: Measure the space complexity and the time complexity of the algorithm. These two complexities will be explained later.
Q: What is time complexity?
A: The execution time of the algorithm that is denoted by the letter O (Big O).
Q: What is space complexity?
A: The amount of space of the algorithm during execution that is denoted by the letter O (Big O).
This is a chart showing all of the Big O time complexities. The complexities that are horrible are more dominant, but slower than the other time complexities.
Simplfying Big O Rules
1. We will get rid of constants such as numbers like 1, 2, or etc, so 3n would turn to n.
2. We will get rid of less dominant terms such as, if there was n^2 + n, we would get rid of n since it is less dominant. Also in cases like n + 3 it will be O(n) since 3 is just O(1) and it is less dominant than O(n).
In this example, we got a runtime like 2n+1 and now we want to simplify it into Big O. First, we will get rid of the less dominant terms.
O(2n + 1 )
1 gets dropped since non dominant terms get dropped
O(2n)
2 gets dropped since constants get dropped
Answer: O(n)
In this example we got a runtime like n^2+n+1 and now we want to simplfy it into Big O. First we will get rid of the less
dominant terms.
O(n^2 + n + 1 )
1 gets dropped since non dominant terms get dropped
O(n^2 + n)
n gets dropped since n is a less dominant term
Answer: O(n^2)
Example of an algorithm that has O(1) runtime (Pesudocode)
READ number
DISPLAY number * 3
Despite the value of the number, it will always print that number multiplied by three meaning the time is constant which is why the runtime is O(1).
Example of an algorithm that has O(1) runtime (Java)
The O(n) runtime describes an algortihm that is porportional to its linear runtime.
Example of an algorithm that has O(n) runtime (Pesudocode)
FOR each element in that array
DISPLAY that element
ENDFOR
Since it will go through every element, so it depends upon how long the array is. If the length of an array is one, it will take quickly to go through the array. However, if the length is 1000, it will take a lot longer. In addition, it increases linearly as the length of an array increases by 1 its time increases by a bit too.
Example of an algorithm that has O(n) runtime (Java)
The O(n^2) runtime describes an algorithm that is porportional to its quadratic input of its size. For instance, an algorithm that has an input of 10 runs 100 times.
Example of an algorithm that has O(n^2) runtimne (Pesudocode)
For each row
For each element in that row
DISPLAY that element
ENDFOR
ENDFOR
Note: Array in the pseudocode input is n by n
This is an example of a nested for loop. Since it is going through every element for each row, its run time is O (n^2).
If the array length is three rows with three elements in each row then it will run nine times because 3^2 is 9.
Example of an algorithm that has O(n^2) runtimne (Java)
The O(log n) runtime describes an algorithm that is proportional to its logarithm of that input. Just remember that in computer science when we talk about log n the base is always 2. If an input is 16 then it will run 4 times.
Example of an algorithm that has O(log n) runtimne (Pesudocode)
READ x
WHILE x is greater than 0
SET x as x divided by 2
ENDWHILE
This is a binary tree. Starting at the top which is 27 and as we choose to go left or right, we are eliminating the other half. For instance, if we choose to pick 40 instead of 14 then we will omit 14, 7, and 20. Then we go to 30 we omit 50. Now we have reached to the end of the tree and we hit 3 values. The floor of log base 2 of 7 is 3 hence this is why the Big O is O(log n).
The O(nlog n) runtime describes a an algorithm is proportional to the nlogn of the input.
Example of an algorithm that has O(nlogn) runtime (Java). Also the image on the left shows how the Big O is O(nlogn). Basically, divide is log n and merge is n which is why the Big O turns out to be O(nlogn).
The O(2^n) runtime describes an algorithm that is proportional of the 2^n of the input.
Example of an algorithm that has O(2^n) runtimne (Java). Basically, the output doubles each time. It doubles from 2 to 4 to 8. This is why the runtime is O(2^n).
The O(n!) runtime describes an algorithm that is proportional of the n! of the input.
Example of an algorithm that has O(n!) runtime in Java. On the image on the right, if the input is 3 then for each element in the first loop of length 3 we traverse each element in length 2 and the traverse the final loop of length 1. So 3*2*1 = 6 which matches 3!.
On another note, I want to talk about that an O(n) algorithm is proportional to its input. Meaning, lets say if an input array is a 2D Array and it runs O(n^2) but since they are proportional to each other, the Big O is O(n).
1. Sometimes there can be more than one input that affects the runtime, so the runtime can be like O(a + log b). In this case, a is the length of one array while b is the length of the other array. Both for loops do not affect each other so the runtime is O(a+b). Another example is finding the sum of a 2D rectangular array where one input is the number of rows and the other input is the length of each row (assuming that a and b does not have the same value). In this case, the Big O is O(ab).
2. The Big O runtime O(n^2) can also have different exponents, such as O(n^3) which is slower than O(n^2) runtime. The higher the exponent the slower the runtime. An example is a triple nested for loop.
3. The Big O runtime O(2^n) can also have a different base, such as 3^n which is slower than 2^n. The higher the base the slower the runtime.
Space complexity is the measure of how much memory a program is required for execution.
Typical memory used by primitive types
| Type | Bytes |
|---|---|
| Booelan | 1 |
| Byte | 1 |
| Char | 2 |
| Int | 4 |
| Float | 4 |
| Long | 8 |
| Double | 8 |
Examples of Space Complexity (Assume that we are using the chart above)
Example 1: int x= 7;
The space complexity is O(1) since an int is 4 bytes, so it will be O(4) which is O(1).
Example 2: char a= 'x';
The space complexity is O(1) since a char is 2 bytes. so it will be O(2) which is O(1).
Example 3: An Array of Integers
The space complexity is O(n) because we are talking about an integer array that holds many integers. Each integer is 4 bytes, so the space complexity of an array is O(4n) which is O(n).
Example 4: Linked Lists
The space complexity is O(n) because we are talking about a list that holds many nodes. n represents a node which means it is O(n).
The space complexity of the method below is O(n) because we are talking about an integer array that holds many integers and two integer variables. Each variable space complexity is O(4). The array space complexity is 4n since it is holding a number of integer values. With the space complexity of the method as O(4n + 4 + 4) the 4s get dropped and the constant, 4, also gets dropped which makes the Big O: O(n).


Feedback: Recreating Good Days
Culture Blog
August 27th, 2015
There are good days and there are bad days. And more often than not, it is not completely within our control to decide which it might be. However, if you can isolate a few commonalities between the good and the bad, you will be able to prepare and set yourself up for the best chance on a good day.
When it comes to pairing, setting yourself up to be the best partner is crucial. For the most part, all of my pairing sessions have been successful, but there are definitely sessions I wish I could have been better. I really like working on a team, I have always been that way. However, I do find it tough sometimes to work on a team if we are all working on one thing together. I often like it better if we are tasked to complete separate tasks that fit in the same larger picture. So pairing on coding challenges throws me a bit off of my element. However, I find it very rewarding to solve a problem with someone else, and I find it extremely beneficial to see how someone else approaches a problem. I sometimes have a hard time approaching a problem in a different way once I have found one way to solve it.
Pairing can definitely be frustrating for me. The most frustrating is if I am not prepared. I for the most part, need time alone to think about an issue and the best way to approach it. If I do allot myself that time or tell my partner to hang on for a couple minutes, I end up coding from behind and playing catch up. This is frustrating because then I am not quite able to grasp the challenge with my partner. However, with practice and proper preparation like reading through the challenge before and taking a stab once on a piece of pen and paper has solved this issue.
The feedback I have received have been predictable. For one of the pairing sessions I wasn’t able to contribute my best to, I received nice words, but weaker scores and a nod to the fact that I should have prepared better. Feedback for the most part is only helpful when you do something wrong. When you are having a good day, you are most likely a good pair. Its when you pair poorly and receive not as good feedback, you can really look back and see how you can improve. Writing feedback has been fine. I have had good pairs thus far. There isn’t really a hard part for me other than really trying to find something to point out that can help them in the future.


Bummed to miss #railsconf this year, but birthday lunch for this little guy makes it all worth it.
4/22/2015, 2:42:30 PM
Favs: 2
Retweets: 0
link--- layout: tweet ---
Bummed to miss #railsconf this year, but birthday lunch for this little guy makes it all worth it.
4/22/2015, 2:42:30 PM
Favs: 2
Retweets: 0
link


I'm an H1 Element: gjpqy
I'm an H2 Element: gjpqy
I'm an H3 Element: gjpqy
I'm an H4 Element: gjpqy
I'm an H5 Element: gjpqy
I'm an H6 Element: gjpqy
I'm a paragraph Element!
As am I'm a paragraph Element too.
I'm a paragraph Element!
As am I'm a paragraph Element too.


---
layout: page
title: Images
permalink: /gallery/
#social_image: /assets/share.jpg
type: page
rel: gallery
css: gallery/gallery.css
#add amps components required for this page if they are not global ones in config.yml
#amp-form: yes
#amp-lightbox: yes
#amp-bind: yes
---
{%- include components/amp-img/amp-img.html class="gallery-image" src="Market-Square.jpg" alt="Dustin Childs " width="1065" height="699" layout="responsive" -%}
{%- include components/amp-img/amp-img.html class="gallery-image" src="Darcys-Westshore-2.jpg" alt="Dustin Childs " width="500" height="889" layout="responsive" -%}
{%- include components/amp-img/amp-img.html class="gallery-image" src="Craig-Smart.jpg" alt="Dustin Childs " width="500" height="889" layout="responsive" -%}
{%- include components/amp-img/amp-img.html class="gallery-image" src="Dustin-Childs-1.jpg" alt="Dustin Childs " width="1065" height="700" layout="responsive" -%}
{%- include components/amp-img/amp-img.html class="gallery-image" src="instagram.jpg" alt="Dustin Childs " width="500" height="755" layout="responsive" -%}
{%- include components/amp-img/amp-img.html class="gallery-image" src="Dustin-Childs-2.jpg" alt="Dustin Childs " width="500" height="755" layout="responsive" -%}
{%- include components/amp-img/amp-img.html class="gallery-image" src="dustin-wendy.jpg" alt="Dustin Childs " width="280" height="280" layout="responsive" -%}
{%- include components/amp-img/amp-img.html class="gallery-image" src="dustin-light.jpg" alt="Dustin Childs " width="420" height="280" layout="responsive" -%}
{% comment %}
{%- include components/amp-img/amp-img.html class="gallery-image" src="dustin-dark.jpg" alt="Dustin Childs " width="420" height="280" layout="responsive" -%}
{% endcomment %}


In the previous example, the call to the external query uses the current binding.
More in line with the SPARQL-Generate philosophy, a sub-Template query can also be embedded inside the TEMPLATE clause.
The example below generates a modified JSON document from the reports.


{%extends "registration/base.html"%} {% load bootstrap3 %} {% block content %}
Password or Username is incorrect
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Network Time Protocol Year 2000 Conformance Statement
The Mad Hatter and the March Hare are discussing whether the Teapot
serial number should have two or four digits.
The universal timescale can be implemented using a binary counter of indefinite width and with the unit seconds bit placed somewhere in the middle. The counter is synchronized to UTC such that it runs at the same rate and the units increment coincides with the UTC seconds tick. The NTP timescale is constructed from 64 bits of this counter, of which 32 bits number the seconds and 32 bits represent the fraction. With this design, the counter runs in 136-year cycles, called eras, the latest of which began with a counter value of zero at 0h 1 January 1900. The design assumption is that further low order bits, if required, are provided by local interpolation, while further high order bits, when required, are provided by external means. The important point to be made here is that the high order bits must ultimately be provided by astronomers and disseminated to the population by international means. Ultimately, should a need exist to align a particular NTP era to the current calendar, the operating system in which NTP is embedded must provide the necessary high order bits, most conveniently from the file system or flash memory.
The NTP timescale is almost never used directly by system or application programs. The generic Unix kernel keeps time in seconds and microseconds (or nanoseconds) to provide both time of day and interval timer functions. In order to synchronize the Unix clock, NTP must convert to and from its representation and Unix representation. Unix kernels implement the time of day function using two 32-bit counters, one representing the seconds since Unix life began and the other the microseconds or nanoseconds of the second. In principle, the seconds counter will wrap around in 136-year eras, the next of which will begin in 2106. How the particular Unix semantics interprets the counter values is of concern, but is beyond the scope of discussion here.
While incorrect time values due to the NTP timescale and protocol design or reference implementation upon the epoch of the next century are highly unlikely, hazards remain due to incorrect software external to NTP. These hazards include the Unix kernel and library routines which convert Unix time to and from conventional civil time in seconds, minutes, hours, days and years. Although NTP uses these routines to format monitoring data displays, they are not used to read or set the NTP clock. They may in fact cause problems with certain application programs, but this is not an issue which concerns NTP correctness.
While it is extremely unlikely that NTP will produce incorrect time values upon the epoch, it is possible that some external source to which NTP synchronizes may produce a discontinuity which could then induce a NTP discontinuity. The NTP primary (stratum 1) time servers, which are the ultimate time references for the entire NTP population, obtain time from various sources, including radio and satellite receivers and telephone modems. Not all sources provide year information and not all of these provide time in four-digit form. In point of fact, the NTP reference implementation does not use the year information, even if available. Instead, the year information is provided from the file system, which itself depends on the Unix clock.
The NTP protocol specification requires the apparent NTP time derived from external servers to be compared to the file system time before the clock is set. If the discrepancy is over 1000 seconds, an error alarm is raised requiring manual intervention. This makes it very unlikely that even a clique of seriously corrupted NTP servers will result in incorrect time values. In the case of embedded computers with no file system, the design assumption is that the current era be established from flash memory or a clock chip previously set by manual means.
It is essential that any clock synchronization protocol, including NTP, include provisions for multiple-server redundancy and multiple- route diversity. Past experience has demonstrated the wisdom of this approach, which protects clients against hardware and software faults, as well as incorrectly operating reference sources and sometimes even buggy software. For the most reliable service, we recommend multiple reference sources for primary servers, including a backup radio or satellite receiver or telephone modem. We also recommend that primary servers run NTP with other primary servers to provide additional redundancy and mutual backup should the reference sources themselves fail or operate incorrectly.


8 Extensions of Logistic Regression - Ex: Canadian Women’s Labour-Force Participation
library(tidyverse)
library(haven) # read in SPSS dataset
library(furniture) # nice table1() descriptives
library(stargazer) # display nice tables: summary & regression
library(texreg) # Convert Regression Output to LaTeX or HTML Tables
library(psych) # contains some useful functions, like headTail
library(car) # Companion to Applied Regression
library(sjPlot) # Quick plots and tables for models
library(car) # Companion to Applied Regression (a text book - includes datasets)
library(MASS) # Support Functions and Datasets
library(nnet) # Multinomial Log-Linear Models
8.1 Background
The Womenlf
data frame has 263 rows and 4 columns. The data are from a 1977 survey of the Canadian population.
Dependent variable (DV) or outcome
partic
Labour-Force Participation, a factor with levels:fulltime
Working full-time
not.work
Not working outside the home
parttime
Working part-time
Indepdentend variables (IV) or predictors
hincome
Husband’s income, in $1000’schildren
Presence of children in the household, a factor with levels:absent
no children in the home
present
at least one child at home
region
A factor with levels:Atlantic
Atlantic CanadaBC
British ColumbiaOntario
Prairie
Prairie provincesQuebec
8.1.1 Raw Dataset
The data is included in the carData
package which installs and loads with the car
package.
data(Womenlf, package = "carData") # load the internal data
tibble::glimpse(Womenlf) # glimpse a bit of the data
Observations: 263
Variables: 4
$ partic <fct> not.work, not.work, not.work, not.work, not.work, not...
$ hincome <int> 15, 13, 45, 23, 19, 7, 15, 7, 15, 23, 23, 13, 9, 9, 4...
$ children <fct> present, present, present, present, present, present,...
$ region <fct> Ontario, Ontario, Ontario, Ontario, Ontario, Ontario,...
Womenlf %>%
dplyr:: filter(row_number() %in% sample(1:nrow(.), size = 10)) # select a random sample of 10 rows
# A tibble: 10 x 4
partic hincome children region
<fct> <int> <fct> <fct>
1 not.work 13 present Ontario
2 fulltime 15 absent Prairie
3 not.work 9 absent Ontario
4 not.work 23 present Atlantic
5 fulltime 17 present Ontario
6 fulltime 27 absent BC
7 fulltime 1 absent Prairie
8 not.work 13 present Quebec
9 not.work 13 present Quebec
10 fulltime 6 present Quebec
Notice the order of the factor levels, especially for the partic
factor
'data.frame': 263 obs. of 4 variables:
$ partic : Factor w/ 3 levels "fulltime","not.work",..: 2 2 2 2 2 2 2 1 2 2 ...
$ hincome : int 15 13 45 23 19 7 15 7 15 23 ...
$ children: Factor w/ 2 levels "absent","present": 2 2 2 2 2 2 2 2 2 2 ...
$ region : Factor w/ 5 levels "Atlantic","BC",..: 3 3 3 3 3 3 3 3 3 3 ...
We can view the order of the factors levels
[1] "fulltime" "not.work" "parttime"
8.1.2 Declare Factors
Womenlf_clean <- Womenlf %>%
dplyr::mutate(working_ord = dplyr::case_when(partic == "fulltime" ~ 2, # manually re-code a factor
partic == "not.work" ~ 0, # into a numeric variable
partic == "parttime" ~ 1) %>%
factor(levels = c(0, 1, 2), # maually declare the level order
labels = c("Not at All", # and labels of the levels)
"Part Time",
"Full Time"))) %>%
dplyr::mutate(working_any = dplyr::case_when(partic == "fulltime" ~ 1, # manually re-code a factor
partic == "not.work" ~ 0, # into a numeric variable
partic == "parttime" ~ 1) %>%
factor(levels = c(0, 1), # maually declare the level order
labels = c("Not at All", # and labels of the levels)
"At Least Part Time"))) %>%
dplyr::mutate(working_full = dplyr::case_when(partic == "fulltime" ~ 1, # manually re-code a factor
partic == "not.work" ~ 0, # into a numeric variable
partic == "parttime" ~ 0)%>%
factor(levels = c(0, 1), # maually declare the level order
labels = c("Less Than Full Time", # and labels of the levels)
"Full Time"))) %>%
dplyr::mutate(working_type = dplyr::case_when(partic == "fulltime" ~ 1, # manually re-code a factor
partic == "parttime" ~ 0)%>% # into a numeric variable
factor(levels = c(0, 1), # maually declare the level order
labels = c("Part Time", # and labels of the levels)
"Full Time")))
Display the structure of the ‘clean’ version of the dataset
'data.frame': 263 obs. of 8 variables:
$ partic : Factor w/ 3 levels "fulltime","not.work",..: 2 2 2 2 2 2 2 1 2 2 ...
$ hincome : int 15 13 45 23 19 7 15 7 15 23 ...
$ children : Factor w/ 2 levels "absent","present": 2 2 2 2 2 2 2 2 2 2 ...
$ region : Factor w/ 5 levels "Atlantic","BC",..: 3 3 3 3 3 3 3 3 3 3 ...
$ working_ord : Factor w/ 3 levels "Not at All","Part Time",..: 1 1 1 1 1 1 1 3 1 1 ...
$ working_any : Factor w/ 2 levels "Not at All","At Least Part Time": 1 1 1 1 1 1 1 2 1 1 ...
$ working_full: Factor w/ 2 levels "Less Than Full Time",..: 1 1 1 1 1 1 1 2 1 1 ...
$ working_type: Factor w/ 2 levels "Part Time","Full Time": NA NA NA NA NA NA NA 2 NA NA ...
8.2 Exploratory Data Analysis
Three versions of the outcome
# Knit to Website: output = "html"
# Knit to PDF: output = "latex2"
# View on Screen: output = ""text", or "markdown", "html"
Womenlf_clean %>%
furniture::table1(working_ord, working_any,
working_full, working_type,
na.rm = FALSE, # do NOT restrict to complete cases!!!
output = "html")
| Mean/Count (SD/%) | |
|---|---|
| n = 263 | |
| working_ord | |
| Not at All | 155 (58.9%) |
| Part Time | 42 (16%) |
| Full Time | 66 (25.1%) |
| NA | 0 (0%) |
| working_any | |
| Not at All | 155 (58.9%) |
| At Least Part Time | 108 (41.1%) |
| NA | 0 (0%) |
| working_full | |
| Less Than Full Time | 197 (74.9%) |
| Full Time | 66 (25.1%) |
| NA | 0 (0%) |
| working_type | |
| Part Time | 42 (16%) |
| Full Time | 66 (25.1%) |
| NA | 155 (58.9%) |
Other Predisctors, univariate
# Knit to Website: output = "html"
# Knit to PDF: output = "latex2"
# View on Screen: output = ""text", or "markdown", "html"
Womenlf_clean %>%
furniture::table1("Husband's Income, $1000's" = hincome,
"Children In the Home" = children,
"Region of Canada" = region,
output = "html")
| Mean/Count (SD/%) | |
|---|---|
| n = 263 | |
| Husband’s Income, $1000’s | |
| 14.8 (7.2) | |
| Children In the Home | |
| absent | 79 (30%) |
| present | 184 (70%) |
| Region of Canada | |
| Atlantic | 30 (11.4%) |
| BC | 29 (11%) |
| Ontario | 108 (41.1%) |
| Prairie | 31 (11.8%) |
| Quebec | 65 (24.7%) |
# Knit to Website: output = "html"
# Knit to PDF: output = "latex2"
# View on Screen: output = ""text", or "markdown", "html"
Womenlf_clean %>%
furniture::table1("Husband's Income, $1000's" = hincome,
"Children In the Home" = children,
"Region of Canada" = region,
splitby = ~ working_ord,
row_wise = TRUE, # show row %s rather than default column %s
test = TRUE,
output = "html")
| Not at All | Part Time | Full Time | P-Value | |
|---|---|---|---|---|
| n = 155 | n = 42 | n = 66 | ||
| Husband’s Income, $1000’s | 0.002 | |||
| 15.6 (7.2) | 16.0 (8.1) | 12.1 (6.1) | ||
| Children In the Home | <.001 | |||
| absent | 26 (32.9%) | 7 (8.9%) | 46 (58.2%) | |
| present | 129 (70.1%) | 35 (19%) | 20 (10.9%) | |
| Region of Canada | 0.71 | |||
| Atlantic | 20 (66.7%) | 4 (13.3%) | 6 (20%) | |
| BC | 14 (48.3%) | 8 (27.6%) | 7 (24.1%) | |
| Ontario | 64 (59.3%) | 17 (15.7%) | 27 (25%) | |
| Prairie | 17 (54.8%) | 6 (19.4%) | 8 (25.8%) | |
| Quebec | 40 (61.5%) | 7 (10.8%) | 18 (27.7%) |
8.2.1 Husband’s Income
Womenlf_clean %>%
ggplot(aes(x = working_ord,
y = hincome)) +
geom_jitter(position=position_jitter(0.2)) +
stat_summary(fun.y = mean,
geom = "errorbar",
aes(ymax = ..y..,
ymin = ..y..),
width = .75,
color = "red",
size = 1)
8.2.2 Children in Home
8.2.3 Region of Canada
8.3 Hierarchical (nested) Logistic Regression
For an \(m-\)category polytomy dependent variable is respecified as a series of \(m – 1\) nested dichotomies. A single or combined levels of outcome compared to another single or combination of levels. Then they are analyzed using a series of binary logistic regressions, such that:
- Dichotomies selected based on theory
- Avoid redundancy
- Similar to contrast coding, but for outcome
For this dataset example, the outcome (partic
) has \(3-\)categories, so we will investigate TWO nested dichotomies
- outcome =
working_any
- outcome =
working_type
8.3.1 Role of Predictors on ANY working
Fit a regular logistic model with all three predictors regressed on the binary indicator for any working. Use the glm()
function in the base \(R\) stats
package.
fit_glm_1 <- glm(working_any ~ hincome + children + region,
data = Womenlf_clean,
family = binomial(link = "logit"))
summary(fit_glm_1)
Call:
glm(formula = working_any ~ hincome + children + region, family = binomial(link = "logit"),
data = Womenlf_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7927 -0.8828 -0.7283 0.9562 2.0074
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.26771 0.55296 2.293 0.0219 *
hincome -0.04534 0.02057 -2.204 0.0275 *
childrenpresent -1.60434 0.30187 -5.315 1.07e-07 ***
regionBC 0.34196 0.58504 0.585 0.5589
regionOntario 0.18778 0.46762 0.402 0.6880
regionPrairie 0.47186 0.55680 0.847 0.3967
regionQuebec -0.17310 0.49957 -0.347 0.7290
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 356.15 on 262 degrees of freedom
Residual deviance: 317.30 on 256 degrees of freedom
AIC: 331.3
Number of Fisher Scoring iterations: 4
Check if region
is statistically significant with the drop1()
function from the base \(R\) stats
package. This may be done with a Likelihood Ratio Test (test = "LRT"
, which is the same as test = "Chisq"
for glm
models).
# A tibble: 4 x 5
Df Deviance AIC LRT `Pr(>Chi)`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 NA 317. 331. NA NA
2 1 322. 334. 5.13 0.0236
3 1 348. 360. 30.5 0.0000000326
4 4 320. 326. 2.43 0.657
Since the region doesn’t have exhibit any effect on odds a women is in the labor force, remove that predictor in the model to simplify to a ‘best’ final model. Also, center husband’s income at a value near the mean so the intercept has meaning.
fit_glm_2 <- glm(working_any ~ I(hincome - 14) + children,
data = Womenlf_clean,
family = binomial(link = "logit"))
summary(fit_glm_2)
Call:
glm(formula = working_any ~ I(hincome - 14) + children, family = binomial(link = "logit"),
data = Womenlf_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6767 -0.8652 -0.7768 0.9292 1.9970
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.74351 0.24312 3.058 0.00223 **
I(hincome - 14) -0.04231 0.01978 -2.139 0.03244 *
childrenpresent -1.57565 0.29226 -5.391 7e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 356.15 on 262 degrees of freedom
Residual deviance: 319.73 on 260 degrees of freedom
AIC: 325.73
Number of Fisher Scoring iterations: 4
The texreg
package uses an intermediate function called extract()
to extract information for the model and then put it in the right places in the table. We can invervene by writing our own extract_exp()
function to use instead.
extract_exp <- function(fit_glm){
beta = coef(fit_glm)
betaci = confint(fit_glm)
fit_glm_exp = texreg::extract(fit_glm)
fit_glm_exp@coef = exp(beta)
<EMAIL_ADDRESS>= exp(betaci[, 1])
<EMAIL_ADDRESS>= exp(betaci[, 2])
return(fit_glm_exp)
}
# Knit to Website: texreg::htmlreg()
# Knit to PDF: texreg::texreg()
# View on Screen: texreg::screenreg()
texreg::htmlreg(extract_exp(fit_glm_2),
custom.coef.names = c("BL: No children, Husband Earns $14,000/yr",
"Husband's Income, $1000's",
"Children in the Home"),
custom.model.names = "OR, Women is in the Workforce at All",
single.row = TRUE,
custom.note = "* The value of '1' is outside the confidence interval for the OR")
| OR, Women is in the Workforce at All | ||
|---|---|---|
| BL: No children, Husband Earns $14,000/yr | 2.10 [1.32; 3.44]* | |
| Husband’s Income, $1000’s | 0.96 [0.92; 1.00]* | |
| Children in the Home | 0.21 [0.12; 0.36]* | |
| AIC | 325.73 | |
| BIC | 336.45 | |
| Log Likelihood | -159.87 | |
| Deviance | 319.73 | |
| Num. obs. | 263 | |
| * The value of ‘1’ is outside the confidence interval for the OR |
Interpretation:
Among women without children in the home and a husband making $14,000 annually, there is about a 2:1 odds she is in the workforce.
For each additional thousand dollars the husband makes, the odds ratio decreases by about 4 percent.
If there are children in the home, the odds of being in the workforce is nearly a fifth as large.
model: working_any ~ I(hincome - 14) + children
hincome effect
hincome
1 10 20 30 40
0.5476466 0.4527392 0.3514450 0.2619655 0.1886414
children effect
children
absent present
0.6707323 0.2964731
effects::Effect(focal.predictors = c("hincome", "children"),
xlevels = list(hincome = seq(from = 1, to = 45, by = .1)),
mod = fit_glm_2) %>%
data.frame() %>%
ggplot(aes(x = hincome,
y = fit,
color = children,
linetype = children)) +
geom_vline(xintercept = 14, color = "gray25") + # reference line for intercept
geom_line(size = 1) +
theme_bw() +
labs(x = "Husband's Income, in $1000's",
y = "Predicted Probability of\nWomen Being in the Workforce",
color = "Children in\nthe Home:",
linetype = "Children in\nthe Home:") +
theme(legend.position = c(1, 1),
legend.justification = c(1.1, 1.1),
legend.background = element_rect(color = "black")) +
coord_cartesian(ylim = c(0, 1))
8.3.2 Role of Predictors on TYPE of work
Fit a regular logistic model with all three predictors regressed on the binary indicator for level/type of working.
fit_glm_3 <- glm(working_type ~ hincome + children + region,
data = Womenlf_clean,
family = binomial(link = "logit"))
summary(fit_glm_3)
Call:
glm(formula = working_type ~ hincome + children + region, family = binomial(link = "logit"),
data = Womenlf_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5202 -0.8048 0.3583 0.7201 1.9957
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.76164 1.05718 3.558 0.000373 ***
hincome -0.10475 0.04032 -2.598 0.009383 **
childrenpresent -2.74781 0.56893 -4.830 1.37e-06 ***
regionBC -1.18248 1.02764 -1.151 0.249865
regionOntario -0.14876 0.84703 -0.176 0.860589
regionPrairie -0.39173 0.96310 -0.407 0.684200
regionQuebec 0.14842 0.93300 0.159 0.873612
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 144.34 on 107 degrees of freedom
Residual deviance: 101.84 on 101 degrees of freedom
(155 observations deleted due to missingness)
AIC: 115.84
Number of Fisher Scoring iterations: 5
Check if region
is statistically significant with the drop1()
function from the base \(R\) stats
package. This may be done with a Likelihood Ratio Test (test = "LRT"
, which is the same as test = "Chisq"
for glm
models).
# A tibble: 4 x 5
Df Deviance AIC LRT `Pr(>Chi)`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 NA 102. 116. NA NA
2 1 110. 122. 7.84 0.00512
3 1 134. 146. 31.9 0.0000000162
4 4 104. 110. 2.65 0.618
Since the region doesn’t have exhibit any effect on odds a working women is in the labor force full time, remove that predictor in the model to simplify to a ‘best’ final model. Also, center husband’s income at a value near the mean so the intercept has meaning.
fit_glm_4 <- glm(working_type ~ I(hincome - 14) + children,
data = Womenlf_clean,
family = binomial(link = "logit"))
summary(fit_glm_4)
Call:
glm(formula = working_type ~ I(hincome - 14) + children, family = binomial(link = "logit"),
data = Womenlf_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4047 -0.8678 0.3949 0.6213 1.7641
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.97602 0.43024 4.593 4.37e-06 ***
I(hincome - 14) -0.10727 0.03915 -2.740 0.00615 **
childrenpresent -2.65146 0.54108 -4.900 9.57e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 144.34 on 107 degrees of freedom
Residual deviance: 104.49 on 105 degrees of freedom
(155 observations deleted due to missingness)
AIC: 110.49
Number of Fisher Scoring iterations: 5
The texreg
package uses an intermediate function called extract()
to extract information for the model and then put it in the right places in the table. We can invervene by writing our own extract_exp()
function to use instead.
# Knit to Website: texreg::htmlreg()
# Knit to PDF: texreg::texreg()
# View on Screen: texreg::screenreg()
texreg::htmlreg(list(extract_exp(fit_glm_2), extract_exp(fit_glm_4)),
custom.coef.names = c("BL: No children, Husband Earns $14,000/yr",
"Husband's Income, $1000's",
"Children in the Home"),
custom.model.names = c("Working at All",
"Full vs. Part-Time"),
single.row = TRUE,
custom.note = "* The value of '1' is outside the confidence interval for the OR")
| Working at All | Full vs. Part-Time | ||
|---|---|---|---|
| BL: No children, Husband Earns $14,000/yr | 2.10 [1.32; 3.44]* | 7.21 [3.34; 18.45]* | |
| Husband’s Income, $1000’s | 0.96 [0.92; 1.00]* | 0.90 [0.83; 0.97]* | |
| Children in the Home | 0.21 [0.12; 0.36]* | 0.07 [0.02; 0.19]* | |
| AIC | 325.73 | 110.49 | |
| BIC | 336.45 | 118.54 | |
| Log Likelihood | -159.87 | -52.25 | |
| Deviance | 319.73 | 104.49 | |
| Num. obs. | 263 | 108 | |
| * The value of ‘1’ is outside the confidence interval for the OR |
Interpretation:
Among working women without children in the home and a husband making $14,000 annually, there is more than 7:1 odds she is working full time verses part time.
For each additional thousand dollars the husband makes, the odds ratio decreases by about 10 percent.
If there are children in the home, the odds of being in the workforce is drastically reduced.
model: working_type ~ I(hincome - 14) + children
hincome effect
hincome
1 10 20 30 40
0.8829045 0.7416987 0.4955346 0.2515165 0.1031024
children effect
children
absent present
0.8830579 0.3475686
effects::Effect(focal.predictors = c("hincome", "children"),
xlevels = list(hincome = seq(from = 1, to = 45, by = .1)),
mod = fit_glm_4) %>%
data.frame() %>%
ggplot(aes(x = hincome,
y = fit,
color = children,
linetype = children)) +
geom_vline(xintercept = 14, color = "gray25") + # reference line for intercept
geom_line(size = 1) +
theme_bw() +
labs(x = "Husband's Income, in $1000's",
y = "Predicted Probability of\nEmployed Women Work Full Time vs. Part Time",
color = "Children in\nthe Home:",
linetype = "Children in\nthe Home:") +
theme(legend.position = c(1, 1),
legend.justification = c(1.1, 1.1),
legend.background = element_rect(color = "black")) +
coord_cartesian(ylim = c(0, 1))
8.4 Multinomial (nominal) Logistic Regression
Multinomial Logistic Regression fits a single model by specifing a reference level of the outcome and comparing each additional level to it. In our case we will choose not working
as the reference category adn get a set of parameter estimates (betas) for each of the two options part time
and full time
.
Use multinom()
function in the base \(R\) nnet
package. You will also need the MASS
and \(R\) package (only to compute MLEs). Make sure to remove cases with missing data on predictors before modeling or use the na.action = na.omit
optin in the multinom()
model command.
fit_multnom_1 <- nnet::multinom(working_ord ~ I(hincome - 14) + children + region,
data = Womenlf_clean)
# weights: 24 (14 variable)
initial value 288.935032
iter 10 value 208.470682
iter 20 value 207.732796
iter 20 value 207.732796
iter 20 value 207.732796
final value 207.732796
converged
Call:
nnet::multinom(formula = working_ord ~ I(hincome - 14) + children +
region, data = Womenlf_clean)
Coefficients:
(Intercept) I(hincome - 14) childrenpresent regionBC
Part Time -1.7521373 0.005261435 0.1462009 1.0863549
Full Time 0.7240428 -0.100034170 -2.6977927 -0.4599247
regionOntario regionPrairie regionQuebec
Part Time 0.2856917 0.5747258 -0.1105184
Full Time 0.1135573 0.4681016 -0.3116829
Std. Errors:
(Intercept) I(hincome - 14) childrenpresent regionBC
Part Time 0.7204798 0.02468887 0.4901621 0.7193077
Full Time 0.6102008 0.02901623 0.3876731 0.7837044
regionOntario regionPrairie regionQuebec
Part Time 0.6175050 0.7259118 0.6873048
Full Time 0.6175128 0.7332449 0.6515172
Residual Deviance: 415.4656
AIC: 443.4656
Reduce the model by removing the region
variable.
# weights: 12 (6 variable)
initial value 288.935032
iter 10 value 211.456740
final value 211.440963
converged
Call:
nnet::multinom(formula = working_ord ~ I(hincome - 14) + children,
data = Womenlf_clean)
Coefficients:
(Intercept) I(hincome - 14) childrenpresent
Part Time -1.3357927 0.00688932 0.02149927
Full Time 0.6215469 -0.09723492 -2.55867912
Std. Errors:
(Intercept) I(hincome - 14) childrenpresent
Part Time 0.4340062 0.02345463 0.4690285
Full Time 0.2585136 0.02809670 0.3622077
Residual Deviance: 422.8819
AIC: 434.8819
Check if we need to keep the region
variable in our model.
# A tibble: 2 x 7
Model `Resid. df` `Resid. Dev` Test ` Df` `LR stat.` `Pr(Chi)`
<fct> <dbl> <dbl> <fct> <dbl> <dbl> <dbl>
1 I(hincome - ~ 520 423. "" NA NA NA
2 I(hincome - ~ 512 415. 1 vs~ 8 7.42 0.492
Here is one way to extract the parameter estimates, but recall they are in terms of the logit or log-odds, not probability.
# A tibble: 6 x 6
y.level term estimate std.error statistic p.value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 Part Time (Intercept) 0.263 0.434 -3.08 0.0021
2 Part Time I(hincome - 14) 1.01 0.0235 0.294 0.769
3 Part Time childrenpresent 1.02 0.469 0.0458 0.963
4 Full Time (Intercept) 1.86 0.259 2.40 0.0162
5 Full Time I(hincome - 14) 0.907 0.0281 -3.46 0.0005
6 Full Time childrenpresent 0.0774 0.362 -7.06 0
The effects::allEffects()
function provides probability estimates for each outcome level for different levels of the predictors.
model: working_ord ~ I(hincome - 14) + children
hincome effect (probability) for Not at All
hincome
1 10 20 30 40
0.4265713 0.5819837 0.6888795 0.7333353 0.7439897
hincome effect (probability) for Part Time
hincome
1 10 20 30 40
0.1041120 0.1511290 0.1916462 0.2185644 0.2375547
hincome effect (probability) for Full Time
hincome
1 10 20 30 40
0.46931674 0.26688731 0.11947430 0.04810031 0.01845552
children effect (probability) for Not at All
children
absent present
0.3339937 0.7122698
children effect (probability) for Part Time
children
absent present
0.08828253 0.19236144
children effect (probability) for Full Time
children
absent present
0.57772376 0.09536878
The texreg
package know how to handle this type of model and displays the parameteri estimates in two seperate columns. It is unable to use the extract_exp()
helper function to exponentiate the betas.
# Knit to Website: texreg::htmlreg()
# Knit to PDF: texreg::texreg()
# View on Screen: texreg::screenreg()
texreg::htmlreg(fit_multnom_2,
custom.coef.names = c("BL: No children, Husband Earns $14,000/yr",
"Husband's Income, $1000's",
"Children in the Home"),
single.row = TRUE)
| Part Time | Full Time | ||
|---|---|---|---|
| BL: No children, Husband Earns $14,000/yr | -1.34 (0.43)** | 0.62 (0.26)* | |
| Husband’s Income, $1000’s | 0.01 (0.02) | -0.10 (0.03)*** | |
| Children in the Home | 0.02 (0.47) | -2.56 (0.36)*** | |
| AIC | 434.88 | 434.88 | |
| BIC | 456.31 | 456.31 | |
| Log Likelihood | -211.44 | -211.44 | |
| Deviance | 422.88 | 422.88 | |
| Num. obs. | 263 | 263 | |
| p < 0.001, p < 0.01, p < 0.05 |
(Intercept) I(hincome - 14) childrenpresent
Part Time 0.2629496 1.0069131 1.02173205
Full Time 1.8618058 0.9073428 0.07740692
Interpretation:
Among women without children in the home and a husband making $14,000 annually, there is about 1:4 odds she is working part time verses not at all. and a 1.8:1 odds she is working full time.
For each additional thousand dollars the husband makes, the odds ratio decreases by about 10 percent that she is working full time, yet stay the same that she works part time.
If there are children in the home, the odds of working part time increase by 2 percent and there is a very unlikely change she works full time.
effects::Effect(focal.predictors = c("hincome", "children"),
xlevels = list(hincome = seq(from = 1, to = 45, by = .1)),
mod = fit_multnom_2) %>%
data.frame() %>%
dplyr::select(hincome, children, starts_with("prob")) %>%
dplyr::rename(none = prob.Not.at.All,
part = prob.Part.Time,
full = prob.Full.Time) %>%
tidyr::gather(key = work_level,
value = fit,
none, part, full) %>%
dplyr::mutate(work_level = factor(work_level,
levels = c("none", "part", "full"),
labels = c("Not at All",
"Part Time",
"Full Time"))) %>%
ggplot(aes(x = hincome,
y = fit,
color = work_level %>% fct_rev,
linetype = work_level %>% fct_rev)) +
geom_vline(xintercept = 14, color = "gray25") + # reference line for intercept
geom_line(size = 1) +
facet_grid(. ~ children, labeller = label_both) +
theme_bw() +
labs(x = "Husband's Income, in $1000's",
y = "Predicted Probability",
color = "Woman Works:",
linetype = "Woman Works:") +
theme(legend.key.width = unit(1, "cm")) +
coord_cartesian(ylim = c(0, 1)) +
scale_linetype_manual(values = c("solid", "longdash", "dotted"))
effects::Effect(focal.predictors = c("hincome", "children"),
xlevels = list(hincome = seq(from = 1, to = 45, by = .1)),
mod = fit_multnom_2) %>%
data.frame() %>%
dplyr::select(hincome, children, starts_with("prob")) %>%
dplyr::rename(none = prob.Not.at.All,
part = prob.Part.Time,
full = prob.Full.Time) %>%
tidyr::gather(key = work_level,
value = fit,
none, part, full) %>%
dplyr::mutate(work_level = factor(work_level,
levels = c("none", "part", "full"),
labels = c("Not at All",
"Part Time",
"Full Time"))) %>%
ggplot(aes(x = hincome,
y = fit,
color = children %>% fct_rev,
linetype = children %>% fct_rev)) +
geom_vline(xintercept = 14, color = "gray25") + # reference line for intercept
geom_line(size = 1) +
facet_grid(. ~ work_level) +
theme_bw() +
labs(x = "Husband's Income, in $1000's",
y = "Predicted Probability",
color = "Children in\nthe Home:",
linetype = "Children in\nthe Home:") +
theme(legend.position = c(1, 1),
legend.justification = c(1.1, 1.1),
legend.background = element_rect(color = "black"),
legend.key.width = unit(1, "cm")) +
coord_cartesian(ylim = c(0, 1)) +
scale_linetype_manual(values = c("solid", "longdash", "dotted"))
8.5 Proportional-odds (ordinal) Logistic Regression
This type of logisit regression model forces the predictors to have similar relationship with the outcome (slopes), but different means (intercepts). This is called the proportional odds assumption.
Use polr()
function in the base \(R\) MASS
package. While outcome variable may be a regualr factor
, it is prefereable to specify it as an ordered
factor.
fit_polr_1 <- MASS::polr(working_ord ~ hincome + children,
data = Womenlf_clean)
summary(fit_polr_1)
Call:
MASS::polr(formula = working_ord ~ hincome + children, data = Womenlf_clean)
Coefficients:
Value Std. Error t value
hincome -0.0539 0.01949 -2.766
childrenpresent -1.9720 0.28695 -6.872
Intercepts:
Value Std. Error t value
Not at All|Part Time -1.8520 0.3863 -4.7943
Part Time|Full Time -0.9409 0.3699 -2.5435
Residual Deviance: 441.663
AIC: 449.663
Not at All|Part Time Part Time|Full Time
0.1569171 0.3902663
hincome childrenpresent
0.9475262 0.1391841
2.5 % 97.5 %
hincome 0.9109815 0.9834852
childrenpresent 0.0784888 0.2422588
model: working_ord ~ hincome + children
hincome effect (probability) for Not at All
hincome
1 10 20 30 40
0.3968718 0.5166413 0.6469356 0.7585238 0.8433820
hincome effect (probability) for Part Time
hincome
1 10 20 30 40
0.22384583 0.21001062 0.17311759 0.12800001 0.08713914
hincome effect (probability) for Full Time
hincome
1 10 20 30 40
0.37928237 0.27334810 0.17994676 0.11347618 0.06947888
children effect (probability) for Not at All
children
absent present
0.2579513 0.7140863
children effect (probability) for Part Time
children
absent present
0.2057297 0.1472491
children effect (probability) for Full Time
children
absent present
0.5363190 0.1386646
# Knit to Website: texreg::htmlreg()
# Knit to PDF: texreg::texreg()
# View on Screen: texreg::screenreg()
texreg::htmlreg(fit_polr_1,
custom.coef.names = c("Husband's Income, $1000's",
"Children in the Home"),
single.row = TRUE)
| Model 1 | ||
|---|---|---|
| Husband’s Income, $1000’s | -0.05 (0.02)** | |
| Children in the Home | -1.97 (0.29)*** | |
| AIC | 449.66 | |
| BIC | 463.95 | |
| Log Likelihood | -220.83 | |
| Deviance | 441.66 | |
| Num. obs. | 263 | |
| p < 0.001, p < 0.01, p < 0.05 |
Interpretation:
Among women without children in the home and a husband making $14,000 annually, there is a 26% chance she is not working, 21% change she is working part time and just over a 53% change she is working full time.
For each additional thousand dollars the husband makes, the odds ratio decreases by about 5 percent that she is working part time vs not at all and 5% that she is working full time vs part time.
If there are children in the home, the odds ratio of working part time vs not at all decreases by 86% and similartly the odds ratio fo working full time vs part time also decreases by 86%.
effects::Effect(focal.predictors = c("hincome", "children"),
xlevels = list(hincome = seq(from = 1, to = 45, by = .1)),
mod = fit_polr_1) %>%
data.frame() %>%
dplyr::select(hincome, children, starts_with("prob")) %>%
dplyr::rename(none = prob.Not.at.All,
part = prob.Part.Time,
full = prob.Full.Time) %>%
tidyr::gather(key = work_level,
value = fit,
none, part, full) %>%
dplyr::mutate(work_level = factor(work_level,
levels = c("none", "part", "full"),
labels = c("Not at All",
"Part Time",
"Full Time"))) %>%
ggplot(aes(x = hincome,
y = fit,
color = work_level %>% fct_rev,
linetype = work_level %>% fct_rev)) +
geom_vline(xintercept = 14, color = "gray25") + # reference line for intercept
geom_line(size = 1) +
facet_grid(. ~ children, labeller = label_both) +
theme_bw() +
labs(x = "Husband's Income, in $1000's",
y = "Predicted Probability",
color = "Woman Works:",
linetype = "Woman Works:") +
theme(legend.key.width = unit(1, "cm")) +
coord_cartesian(ylim = c(0, 1)) +
scale_linetype_manual(values = c("solid", "longdash", "dotted"))
effects::Effect(focal.predictors = c("hincome", "children"),
xlevels = list(hincome = seq(from = 1, to = 45, by = .1)),
mod = fit_polr_1) %>%
data.frame() %>%
dplyr::select(hincome, children, starts_with("prob")) %>%
dplyr::rename(none = prob.Not.at.All,
part = prob.Part.Time,
full = prob.Full.Time) %>%
tidyr::gather(key = work_level,
value = fit,
none, part, full) %>%
dplyr::mutate(work_level = factor(work_level,
levels = c("none", "part", "full"),
labels = c("Not at All",
"Part Time",
"Full Time"))) %>%
ggplot(aes(x = hincome,
y = fit,
color = children %>% fct_rev,
linetype = children %>% fct_rev)) +
geom_vline(xintercept = 14, color = "gray25") + # reference line for intercept
geom_line(size = 1) +
facet_grid(. ~ work_level) +
theme_bw() +
labs(x = "Husband's Income, in $1000's",
y = "Predicted Probability",
color = "Children in\nthe Home:",
linetype = "Children in\nthe Home:") +
theme(legend.position = c(1, 1),
legend.justification = c(1.1, 1.1),
legend.background = element_rect(color = "black"),
legend.key.width = unit(1, "cm")) +
coord_cartesian(ylim = c(0, 1)) +
scale_linetype_manual(values = c("solid", "longdash", "dotted"))


- Date of test25/02/2014
Certificate issued (Pass)
- Odometer reading186,828 Miles
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- Test expiry date24/02/2015
Full Test History
- Date of test25/02/2014
Certificate issued (Pass)
- Odometer reading186,828 Miles
- Test number149946854486
- Test expiry date24/02/2015
- Date of test09/01/2013
Certificate issued (Pass)
- Odometer reading186,318 Miles
- Test number468429703070
- Test expiry date08/01/2014
- Date of test14/12/2012
Certificate issue refused (Fail)
- Odometer reading186,307 Miles
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- Date of test05/12/2011
Certificate issued (Pass)
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- Test expiry date04/12/2012
- Date of test02/12/2010
Certificate issued (Pass)
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- Date of test04/12/2009
Certificate issued (Pass)
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- Date of test03/12/2008
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- Date of test03/12/2008
Certificate issue refused (Fail)
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Certificate issued (Pass)
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Certificate issued (Pass)
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- Date of test02/11/2005
Certificate issued (Pass)
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- Test number414246005332
- Test expiry date01/11/2006


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We could make filters here, or something fancy
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