Definition!
Programming languages are group of orders, instructions, and other syntax which are used in order to make a software
program. "High-level languages" are languages used by programmers to write a code where this code can be compiled into a
"low-level language" which can be recognized easily by the hardware of the computer. The main point of designing
high-level languages is to reduce the difficulty of reading and understanding it which makes the operation of writing
source code by the programmers using logical words and symbols is easy. For more clarification, words which are reserved
like (function, while, if, and else) are mainly used in the majority of programming languages, also symbols like (<,>,
==, and !=) are considered as common operators. High-level languages are similar to each other which is considered
as a good point for the programmers as they can easily understand source code written in multiple languages.
C++, Java, Perl, and PHP are examples of high-level programming languages. Compiled languages like C++ and Java are
known by this name because in order to run the source code, it must be compiled first. On the other hand,
interpreted
languages like Perl and PHP are known by this name since that the source code can be run through an interpreter
instead
of being compiled. Compiled languages are used in creating the software of the applications while interpreted
languages
are used in running scripts such as the scripts used to produce content used in dynamic websites. Assembly and
machine
languages are low-level languages. Assembly language consists of a list containing basic instructions and it is
harder
than a high-level language in reading. In seldom situations, the programmer may decide to use an assembly language
in
order to code a basic program to make sure that it operates efficiently. The assembler can also be used in
converting an
assembly code into machine code. The machine code or the machine language consists of binary codes series that a
computer's CPU understood them easily. This means that the machine language is not designed so that the humans can
read
it.
https://techterms.com/definition/programming_language
Types!
COBOL"1959": Dr. Grace Murray Hopper invented this computer programming language that could be used on all
types of
computers, COBOL is an abbreviation for "COmmon Business Oriented Language". It is used in ATMs, telephone systems,
hospital, computers of governments, cars systems and signals used in traffic. Parts of COBOL source code have been used
in the movie "The Terminator" vision display.
C"1972": It was developed at Bell Labs by Dennis Ritchie. C may be considered as the first high-level
language. The
high-level computer programming language is more similar to human language and different from the machine code. Many
programmers wanted an operating system known as UNIX to be used on different types of computers, that’s why C was
created. Many other languages such as Ruby, C#, Java, JavaScript, Perl, PHP, and Python was affected by this programming
language.
C++"1983": It was developed by Bjarne Stroustrup, it is considered as an extension of the C language. This
language is
used widely in the world. Engines of games and software with high-performance like Adobe Photoshop are the field of
usage of C++. C++ is still used in most of the packaged software.
Python"1991": It was created by Guido Van Rossum. It is easier in reading and it succeeded in reducing the number of
code lines less than many other computer programming languages. The British comedy group Monty Python was the reason for
naming this programming language by this name. Python is used in writing many frameworks for popular sites such as
Instagram.
Visual Basic"1991": It was developed by Microsoft. Visual Basic can use a graphical user interface (GUI) that
allows
programmers to choose and change chunks of code that have been selected before in a drag-and-drop fashion.
R"1993": It was developed at Auckland University by Ross Ihaka and Robert Gentleman in New Zealand. R is
named after the
initials of the developers. Statisticians and performers of different types of data analysis usually use R.
Java"1995": It was named at first as Oak where Sun Microsystems developed it. At first, it was created for boxes of
cables and hand-held devices, but later it was developed so it could be used in transferring information on the World
Wide Web. Ever device uses Java, from computers to smartphones to parking meters. It is being used by three billion
devices!
Ruby"1995": Yukihiro “Matz” Matsumoto made Ruby where he succeeded in combining parts of his favorite
programming
languages forming a new programming language of general-purpose that can do many programming objectives. It is
well-known in developing Web applications. The execution of Ruby code slow, but by using it the computer programmers can
quickly put together and activate a program.
JavaScript"1995": It was made by Brendan Eich in just 10 days. This language is usually used to improve many
of the
interactions of Web browser. Javascript is mainly used by every major website.
C#"2000": Microsoft developed it in order to merge the ability of computing of C++ with the simplicity of
Visual Basic.
C++ is the base for C#, but it is similar to Java in many ways. Most of Microsoft products use it where it mainly used
in the development of desktop applications.
Swift"2014": It was developed by Apple in order to replace C, C++, and Objective-C. Swift is characterized by
easy usage
and doesn't allow many mistakes. It is versatile and multilateral as it can be used in desktop, apps of mobile and cloud
services.
https://www.onlinecollegeplan.com/computer-programming-languages
Importance!
Computers have a great effect on the modern life of humans. These computers are run by using a computer program.
Computer programs are created by using programming languages, that’s why they are the foundation of all the luxury that
we feel. All programs that have been made are created by using these languages; rostering systems, virtual reality,
scheduling software, online games, anti-virus and so many other examples. Therefore mastering programming languages
leads to understanding the origin of all digital technology. Programming languages is the different types of expressions
and logical constructing rules whose aim is to generate repeated and organized tasks. They have great importance as they
allow creating different systems that serve tasks which is the same as the needs of users. There is a great diversity of
programming languages and these languages have different types of requirements; however, this variety consists of two
large groups, structured programming languages and object-oriented languages. In computer science, everything used is
largely due to programming languages.
Computers can make many calculation operations rapidly; however, if these benefits weren't for the programs that run on
it, it will be totally useless. These programs are divided into different types, in the presence of the operating system
as the most important thing which is the system on which the different applications are activated. Programming languages
are essential in the development of these programs where each type has its specific characteristics to be implemented.
In addition, in order to allow various responses to existing requirements, each programming language is created in turn
with a lower level. For more clarification, each program is made in a clear path; complex solutions follow the simple
solutions and so on, following the path that is created to the improved requirements. The science of programming
languages is like any other science that comes from a simple understanding. As we have said before, there are two types
of programming languages: structured programming and object oriented programming. The structured programming language
consists of a group of instructions and orders and also a group of application rules such as the propositional logic, it
plays an essential role in this case where all processes can be subject to logical relationships. On the other hand, in
the case of an object-oriented programming language, there is an intention to reflect in the language various
circumstances of actual existence; therefore, reference will be created to objects, classes, inheritances, attributes,
etc. Programming languages also consists of interpreted and compiled languages; the interpreted language must be
explained by another program, while the compiled language must be translated by using a compiler, during creating a file
in this process which is known as executable.
If you know how to use programming language, you will be able to develop capacities in terms of problem solving and
objective automation. Certainly this type of skills coupled with some basic expressions and concepts of algorithms
provides new job opportunities.
https://epodcastnetwork.com/the-importance-of-programming-languages


Name
HPL_pipid Simplify the pivot vector.
Synopsis
#include "hpl.h"
void
HPL_pipid(
HPL_T_panel *
PANEL
,
int *
K
,
int *
IPID
);
Description
HPL_pipid
computes an array IPID that contains the source and final
destination of matrix rows resulting from the application of N
interchanges as computed by the LU factorization with row partial
pivoting. The array IPID is such that the row of global index IPID(i)
should be mapped onto the row of global index IPID(i+1). Note that we
cannot really know the length of IPID a priori. However, we know that
this array is at least 2*N long, since there are N rows to swap and
broadcast. The length of this array must be smaller than or equal to
4*N, since every row is swapped with at most a single distinct remote
row. The algorithm constructing IPID goes as follows: Let IA be the
global index of the first row to be swapped.
For every row src IA + i with i in [0..N) to be swapped with row dst
such that dst is given by DPIV[i]:
Is row src the destination of a previous row of the current block,
that is, is there k odd such that IPID(k) is equal to src ?
Yes: update this destination with dst. For example, if the
pivot array is (0,2)(1,1)(2,5) ... , then when we swap rows 2 and 5,
we swap in fact row 0 and 5, i.e., row 0 goes to 5 and not 2 as it
was thought so far ...
No : add the pair (src,dst) at the end of IPID; row src has not
been moved yet.
Is row dst different from src the destination of a previous row of
the current block, i.e., is there k odd such that IPID(k) is equal to
dst ?
Yes: update IPID(k) with src. For example, if the pivot array
is (0,5)(1,1)(2,5) ... , then when we swap rows 2 and 5, we swap in
fact row 2 and 0, i.e., row 0 goes to 2 and not 5 as it was thought
so far ...
No : add the pair (dst,src) at the end of IPID; row dst has not
been moved yet.
Note that when src is equal to dst, the pair (dst,src) should not be
added to IPID in order to avoid duplicated entries in this array.
During the construction of the array IPID, we make sure that the
first N entries are such that IPID(k) with k odd is equal to IA+k/2.
For k in [0..K/2), the row of global index IPID(2*k) should be
mapped onto the row of global index IPID(2*k+1).
Arguments
PANEL (local input/output) HPL_T_panel *
On entry, PANEL points to the data structure containing the
panel information.
K (global output) int *
On exit, K specifies the number of entries in IPID. K is at
least 2*N, and at most 4*N.
IPID (global output) int *
On entry, IPID is an array of length 4*N. On exit, the first
K entries of that array contain the src and final destination
resulting from the application of the N interchanges as
specified by DPIV. The pairs (src,dst) are contiguously
stored and sorted so that IPID(2*i+1) is equal to IA+i with i
in [0..N)
See Also
HPL_pdlaswp00N,
HPL_pdlaswp00T,
HPL_pdlaswp01N,
HPL_pdlaswp01T.


404 Not Found
About
Mission
The Requested Resource Could Not Be Found (HTTP Code 404)
Ensure that the address is correct or use the navigation bar to return to a working site.


This weeks Newsflash is about the usefulness of the photoelectric effect. The photoelectric effect is sometimes called the photovoltaic effect and it is how we convert sun's light to electricity.
\Recent Progress in Solar Cell Technology\
Solar cell technology is a field of very active research nowadays. If we average over the full surface of the planet, we find that the sun gives us about 164 Watts of power per square meter.
There is a lot of energy to be gathered from the sun!
From reading the article, can you answer the following few questions?


Your tiddlywikis must be location in the subfolder appearing below
that must be located In the browsers download directory.
The browser option "Ask where to save each file" MUST be off.
In chrome this is in the advanced options in the 'Downloads' section.
Option for a once a day backup. Date is appended
to the name of the Tiddlywiki.
| backups: |
|


|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Packages that use rsg.mailchimp.api.campaigns | |
|---|---|
| rsg.mailchimp.api.campaigns | |
| rsg.mailchimp.api.lists |
| Classes in rsg.mailchimp.api.campaigns used by rsg.mailchimp.api.campaigns | |
|---|---|
| AbuseReport
Typed class for the Abuse Report that comes back from the API |
|
| BounceMessage
See http://www.mailchimp.com/api/rtfm/campaignbouncemessages.func.php Representation of the BounceMessage data |
|
| CampaignAdvice
|
|
| CampaignAnalytics
Typesafe structure representing the analytics data returned from the campaign analytics function |
|
| CampaignAnalytics.GoalConversions
Nested class that represents GoalConversions |
|
| CampaignContent
Simple class to hold Campaign content as a result of the campaignContent method See: http://www.mailchimp.com/api/rtfm/campaignCreate.func.php |
|
| CampaignDetails
See http://www.mailchimp.com/api/rtfm/campaigns.func.php Typesafe values for the campaign as returned from the campaigns API call |
|
| CampaignListFilters
Helper class to build the options up for the campaigns API call. |
|
| CampaignOptions
|
|
| CampaignOptions.TrackingOptions
|
|
| CampaignStats
Object representing the stats of a campaign as returned by the campaignStats function |
|
| ECommOrders
|
|
| ECommOrders.OrderDetails
|
|
| EepUrlStats
Object representing the EepUrlStats from the API |
|
| EepUrlStats.Status
|
|
| EmailAIMStats
Has the action, timestamp and url for the action taken by a recipient of a given Email address |
|
| EmailClickCounts
|
|
| EmailDomainPerformance
Object representation of the EmailDomainPerformance |
|
| EmailOpenCounts
|
|
| GeoOpens
Object representing the geo opens call result |
|
| SharedReportDetails
Holds detials of a shared report See: http://www.mailchimp.com/api/rtfm/campaignsharereport.func.php |
|
| TemplateDetails
Structure for the TemplateDetails as returned by the API http://www.mailchimp.com/api/rtfm/campaigntemplates.func.php |
|
| URLClickStats
Object representing the URL Click Stat from the API. |
| Classes in rsg.mailchimp.api.campaigns used by rsg.mailchimp.api.lists | |
|---|---|
| AbuseReport
Typed class for the Abuse Report that comes back from the API |
|
||||||||||
| PREV NEXT | FRAMES NO FRAMES |


Box Office: Anurag Basu’s untitled action comedy to release in March 2020
by Raj BaddhanAnurag Basu’s untitled next action-comedy has been confirmed to release in March 2020.
Directed by Anurag Basu, the film stars Abhishek Bachchan, Aditya Roy Kapur, Rajkumar Rao, Fatima Sana Shaikh, Sanya Malhotra, and Pankaj Tripathi. It is produced by Bhushan Kumar, Divya Khosla Kumar, Anurag Basu, Tani Somarita Basu, and Krishan Kumar.
The film releases on 13th March 2020.


Pepsi T pizzeria started the year 2021 by peter Anami.It was initialy called Tipsy tomato pizza named after a well known pizza shop.
Pepsi t pizzeria started as a small scale shop since I was targeting teenage people in Eldoret area.
It came as a suprise to notice that my pizza shop not only attracted litle children but also elderly people in the community.
Right now I am serving over a thousand people in the region.
Our services are for all, get your fevourite piza type at afordable price.currently we are every where countrywide. Place your order and it will be delivered to you.


Hey {{ user|capfirst }}
{% endblock %}Here you can view your bugs and features, update and delete them.
Your bugs:
{% if bugs %} {% for bug in bugs %}{{ bug.title|capfirst }}
{{ bug.description|capfirst }}
You currently have 0 bugs created, go create some here!
{% endif %}Your features:
{% if features %} {% for feature in features %}{{ feature.title|capfirst }}
{{ feature.description|capfirst }}
You currently have 0 features created, go create some here!
{% endif %}


3 Metaphors
This file asks a variety of exploratory questions related to the impact that metaphor framing has on donation.
First, we ask some immediately interesting alternative questions following our confirmatory analysis. After this, we test the relationship between metaphor framing and donation behavior on a more coarse scale. We then assess several other models, including tree based models.
3.1 Immediate Alternative Questions
Does donation behavior differ between participants in the literal condition and the journey condition?
journey_mask = trials$cond_metaphor != "battle"
model_journey_literal_base = lm(update(base.formula, donation ~ .), data = trials[journey_mask, ])
model_journey_literal_full = lm(update(base.formula, donation ~ . + cond_metaphor), data = trials[journey_mask, ])
compare_lm(model_journey_literal_base, model_journey_literal_full)
## Analysis of Variance Table
##
## Model 1: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + past_donations.z + age.z
## Model 2: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + past_donations.z + age.z + cond_metaphor
## Res.Df RSS Df Sum of Sq Pr(>Chi)
## 1 3046 780578
## 2 3045 780377 1 201.95 0.3747
## [1] Chisq Stat: 0.795
f.sq(
lm(update(base.formula, donation ~ 1), data = trials[journey_mask, ]),
model_journey_literal_base,
model_journey_literal_full
)
## [1] 0.0002587885
There is no significant difference in donation amounts between journey and literal conditions.
Does donation behavior differ between participants in the literal condition and the battle condition?
battle_mask = trials$cond_metaphor != "journey"
model_battle_literal_base = lm(update(base.formula, donation ~ .), data = trials[battle_mask, ])
model_battle_literal_full = lm(update(base.formula, donation ~ . + cond_metaphor), data = trials[battle_mask, ])
compare_lm(model_battle_literal_base, model_battle_literal_full)
## Analysis of Variance Table
##
## Model 1: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + past_donations.z + age.z
## Model 2: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + past_donations.z + age.z + cond_metaphor
## Res.Df RSS Df Sum of Sq Pr(>Chi)
## 1 3051 788677
## 2 3050 788247 1 430.24 0.197
## [1] Chisq Stat: 1.678
f.sq(
lm(update(base.formula, donation ~ 1), data = trials[battle_mask, ]),
model_battle_literal_base,
model_battle_literal_full
)
## [1] 0.0005458165
There is no significant difference in donation amounts between battle and literal conditions.
H: Donors contribute different amounts to a campaign which employs battle metaphors compared to a campaign which employs journey metaphors.
literal_mask = trials$cond_metaphor != "literal"
model_battle_journey_base = lm(update(base.formula, donation ~ .), data = trials[literal_mask, ])
model_battle_journey_full = lm(update(base.formula, donation ~ . + cond_metaphor), data = trials[literal_mask, ])
compare_lm(model_battle_journey_base, model_battle_journey_full)
## Analysis of Variance Table
##
## Model 1: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + past_donations.z + age.z
## Model 2: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + past_donations.z + age.z + cond_metaphor
## Res.Df RSS Df Sum of Sq Pr(>Chi)
## 1 2998 770373
## 2 2997 769240 1 1132.9 0.03565 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Chisq Stat: 4.449
## Warning in if (cls == c("lm") | cls == c("glm", "lm")) {: the condition has
## length > 1 and only the first element will be used
## MODEL INFO:
## Observations: 3023
## Dependent Variable: donation
## Type: OLS linear regression
##
## MODEL FIT:
## F(25,2997) = 3.038, p = 0.000
## R² = 0.025
## Adj. R² = 0.017
##
## Standard errors: OLS
## ------------------------------------------------------------------------
## Est. 2.5% 97.5% t val. p
## --------------------------- -------- --------- -------- -------- -------
## (Intercept) 25.346 17.568 33.125 6.389 0.000
## cond_sexfemale -0.597 -1.746 0.551 -1.020 0.308
## self_cancerY 5.880 3.174 8.587 4.260 0.000
## self_cancerOO 2.020 -4.066 8.107 0.651 0.515
## ff_cancerY 1.247 -0.003 2.498 1.955 0.051
## ff_cancerOO 3.263 -0.886 7.413 1.542 0.123
## genderF 0.625 -0.550 1.799 1.043 0.297
## genderNB 1.265 -6.929 9.459 0.303 0.762
## genderOO -4.771 -14.964 5.423 -0.918 0.359
## educationHS -4.424 -12.164 3.316 -1.121 0.263
## educationA -3.937 -11.787 3.912 -0.984 0.325
## educationB -2.767 -10.487 4.953 -0.703 0.482
## educationM -1.011 -8.857 6.834 -0.253 0.800
## educationP -2.769 -11.291 5.753 -0.637 0.524
## educationD -3.110 -11.825 5.605 -0.700 0.484
## educationOO -1.849 -13.362 9.663 -0.315 0.753
## socioeconomic10-25k -0.315 -2.480 1.849 -0.286 0.775
## socioeconomic25-50k 0.935 -1.050 2.919 0.924 0.356
## socioeconomic50-75k 1.250 -0.857 3.356 1.163 0.245
## socioeconomic75-100k 2.069 -0.347 4.485 1.679 0.093
## socioeconomic100-150k 1.205 -1.595 4.005 0.844 0.399
## socioeconomic>150k 3.103 -0.705 6.911 1.598 0.110
## socioeconomicOO -2.337 -6.136 1.462 -1.206 0.228
## past_donations.z 1.140 0.061 2.219 2.072 0.038
## age.z 0.994 -1.018 3.005 0.969 0.333
## cond_metaphorjourney -1.229 -2.376 -0.082 -2.101 0.036
## ------------------------------------------------------------------------
f <- f.sq(
lm(donation ~ 1, data = trials[literal_mask, ]),
model_battle_journey_base,
model_battle_journey_full
)
With the hold-out data of 1,012, we’d have 23% power to detect a significant main effect of metaphor given the two conditions levels.
There is a significant difference in donation amounts between participants exposed to battle metaphors and participants exposed to journey metaphors (Chisq = 4.0, p < 0.05).
f
## [1] 0.001472744
length(coef(model_battle_journey_base))-1
## [1] 24
power = pwr.f2.test(u = 1, v = NULL, f2 = f, power = 0.9)
power
##
## Multiple regression power calculation
##
## u = 1
## v = 7134.508
## f2 = 0.001472744
## sig.level = 0.05
## power = 0.9
trials %>%
filter(gender %in% c("M", "F")) %>%
ggplot(aes(cond_metaphor, donation, color = gender)) +
theme_minimal() +
# geom_jitter(alpha = 0.05, position = position_dodge(width = 0.3), ) +
geom_point(alpha = 0.1, position = position_jitterdodge(jitter.width = 0.2, jitter.height = 0.2, dodge.width = 0.4)) +
stat_summary(fun.data = mean_sdl, geom = "errorbar", width = 0.1, position = position_dodge(width = 0.4)) +
facet_grid(~cond_sex)
g = trials %>%
ggplot(aes(cond_metaphor, donation)) + ptheme +
stat_summary(fun.data = mean_se, geom = "errorbar", position = position_dodge(width = 0.35), width = 0.2) +
stat_summary(fun.data = mean_se, geom = "point", shape = 5, position = position_dodge(width = 0.35)) +
scale_x_discrete(labels = c("literal" = "Literal\n", "journey" = "Journey\n", "battle" = "Battle\n")) +
scale_y_continuous(labels = scales::dollar) +
coord_cartesian(ylim = c(22, 27)) +
labs(x = element_blank(), y = element_blank())
# geom_signif(comparisons = list(c("literal", "journey"), c("literal", "battle"), c("battle", "journey")),
# y_position = c(26.6, 26, 25.4),
# tip_length = 0.005,
# textsize = 2.75,
# annotations = c("list({\U03C7^2}(1) == 0.6, ~~ ns)",
# "list({\U03C7^2}(1) == 1.7, ~~ ns)",
# "list({\U03C7^2}(1) == 4.0, ~~ p < 0.05)"),
# parse = T)
g
# ggsave("metaphor_donation0.png", g, width = pwidth/1.8, height = pheight, bg = "transparent")
3.2 Coarse Donation Groups
3.2.1 Metaphor Condition and Presence on Likelihood of Wide Donation Group
Visualizations of donation amounts seems to convey that donations are generally trimodal, on $0, $25, and $50. What if participants donated according to a coarser scale than anticpated? They may have chosen an amount mentally represented as “small” or “medium” or “large”. In other words, we may see an effect if the donations are binned into three groups: lower, middle, and high; corresponding to $0-16.67, $16.67-33.33, and $33.33-50. Note the difference between these wide bins compared to the narrow bins previously explored (which are resurfaced soon).
trials$donation_wide_group = cut(trials$donation,
breaks = c(0, 50*1/3, 50*2/3, 50),
labels = c("small", "medium", "large"),
include.lowest = T)
Contingency table: Wide Donation Groups x Any Metaphor
## FALSE TRUE
##
## small 612 1126
## medium 475 994
## large 475 903
##
## FALSE TRUE
##
## small 0.133 0.246
## medium 0.104 0.217
## large 0.104 0.197
g = trials %>%
mutate(int = interaction(cond_metaphor, donation_wide_group)) %>%
group_by(int) %>%
mutate(y = n()) %>%
ggplot(aes(cond_metaphor, y, fill = int)) + ptheme +
geom_col(position = position_dodge(width = 0.8), width = 0.75) +
# dodgerblue1 chartreuse3
scale_fill_manual(values = c("grey90", "grey90", "grey90",
"grey80", "grey80", "grey80",
"grey70", "grey70", "grey70"),
breaks = c("battle.small",
"battle.medium",
"battle.large"),
labels = c("Small", "Medium", "Large"),
name = "Donation\nGroup") +
# scale_fill_manual(values = c("firebrick2", "grey94", "grey94",
# "firebrick4", "grey94", "grey94",
# "grey94", "grey94", "grey94"),
# breaks = c("literal.small",
# "literal.medium"),
# labels = c("Small", "Medium"),
# name = "Donation\nGroup") +
# scale_fill_manual(values = c("grey94", "dodgerblue1", "dodgerblue1",
# "grey94", "dodgerblue4", "dodgerblue4",
# "grey94", "grey94", "grey94"),
# breaks = c("battle.small",
# "battle.medium"),
# labels = c("Small", "Medium"),
# name = "Donation\nGroup") +
# scale_fill_manual(values = c("firebrick2", "dodgerblue1", "dodgerblue1",
# "firebrick4", "dodgerblue4", "dodgerblue4",
# "grey94", "grey94", "grey94"),
# breaks = c("battle.small",
# "literal.small",
# "battle.medium",
# "literal.medium"),
# labels = c("Small", "Small",
# "Medium", "Medium"),
# name = "Donation\nGroup") +
scale_x_discrete(labels = c("literal" = "Literal", "battle" = "Battle", "journey" = "Journey")) +
labs(title = "Count of Participants in Donation Groups by Metaphor Condition", x = "Donation Group", y = "Count of Participants") +
coord_cartesian(ylim = c(0, 680))
# # geom_signif(xmin = 1 - 0.13,
# # xmax = 2.5,
# # y_position = 650,
# # tip_length = 0.1,
# # textsize = 2.75,
# # annotations = "list({\U03C7^2}(1) == 4.2, ~~ p < 0.05)",
# # parse = T)
#
#
g
# ggsave("coarse_donations0.png", g, width = pwidth, height = pheight, bg = "transparent")
H: Donors are more likely to contribute to the large third when exposed to a campaign which employs battle or journey metaphors compared to a literal campaign
model_sm_l_base = glm(update(base.formula, I(donation_wide_group == "large") ~ .),
data = trials,
family = "binomial")
model_sm_l_full = glm(update(base.formula, I(donation_wide_group == "large") ~ . + cond_anymet),
data = trials,
family = "binomial")
compare_lm(model_sm_l_base, model_sm_l_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_anymet
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 4560 5527.5
## 2 4559 5527.4 1 0.1183 0.7309
## [1] Chisq Stat: 0.118
There is no evidence that participants who were exposed to metaphor were any more likely to be “large” donors.
H: Donors are more likely to contribute to the large third or the medium third when exposed to a campaign which employs battle or journey metaphors compared to a literal campaign
model_s_ml_base = glm(update(base.formula, I(donation_wide_group != "small") ~ .),
data = trials,
family = "binomial")
model_s_ml_full = glm(update(base.formula, I(donation_wide_group != "small") ~ . + cond_anymet),
data = trials,
family = "binomial")
compare_lm(model_s_ml_base, model_s_ml_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_wide_group != "small") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_wide_group != "small") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_anymet
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 4560 5998.8
## 2 4559 5996.8 1 1.9888 0.1585
## [1] Chisq Stat: 1.989
There is marginal evidence that participants who were exposed to metaphor were slightly more likely to be “large” or “medium” donors. We can break this down further to see differences in likelihood that metaphor exposed participants are more or less likely to be either “medium” or “large” donors.
mask = trials$donation_wide_group %in% c("small", "medium")
model_s_m_base = glm(update(base.formula, I(donation_wide_group == "medium") ~ .),
data = trials[mask, ],
family = "binomial")
model_s_m_full = glm(update(base.formula, I(donation_wide_group == "medium") ~ . + cond_anymet),
data = trials[mask, ],
family = "binomial")
compare_lm(model_s_m_base, model_s_m_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_wide_group == "medium") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_wide_group == "medium") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_anymet
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 3182 4381.6
## 2 3181 4378.0 1 3.5877 0.05821 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Chisq Stat: 3.588
## Warning in if (cls == c("lm") | cls == c("glm", "lm")) {: the condition has
## length > 1 and only the first element will be used
## MODEL INFO:
## Observations: 3207
## Dependent Variable: I(donation_wide_group == "medium")
## Type: Generalized linear model
## Family: binomial
## Link function: logit
##
## MODEL FIT:
## χ²(25) = 45.261, p = 0.008
## Pseudo-R² (Cragg-Uhler) = 0.019
## Pseudo-R² (McFadden) = 0.010
## AIC = 4429.995, BIC = 4587.895
##
## Standard errors: MLE
## ----------------------------------------------------------------------
## Est. 2.5% 97.5% z val. p
## --------------------------- -------- -------- ------- -------- -------
## (Intercept) -0.214 -1.163 0.735 -0.441 0.659
## cond_sexfemale -0.043 -0.183 0.097 -0.600 0.548
## self_cancerY 0.424 0.047 0.801 2.206 0.027
## self_cancerOO -0.051 -0.872 0.770 -0.121 0.904
## ff_cancerY 0.095 -0.055 0.246 1.240 0.215
## ff_cancerOO -0.104 -0.674 0.466 -0.359 0.720
## genderF 0.086 -0.058 0.231 1.175 0.240
## genderNB -0.065 -1.046 0.917 -0.129 0.897
## genderOO -0.699 -2.066 0.668 -1.002 0.316
## educationHS -0.337 -1.280 0.607 -0.700 0.484
## educationA -0.234 -1.190 0.721 -0.481 0.631
## educationB -0.073 -1.013 0.868 -0.151 0.880
## educationM 0.055 -0.902 1.011 0.112 0.911
## educationP 0.120 -0.911 1.151 0.228 0.819
## educationD -0.255 -1.327 0.817 -0.467 0.641
## educationOO -0.132 -1.619 1.355 -0.174 0.862
## socioeconomic10-25k 0.051 -0.209 0.311 0.383 0.701
## socioeconomic25-50k 0.040 -0.198 0.278 0.332 0.740
## socioeconomic50-75k -0.067 -0.322 0.188 -0.516 0.606
## socioeconomic75-100k 0.137 -0.158 0.432 0.910 0.363
## socioeconomic100-150k -0.014 -0.354 0.326 -0.081 0.935
## socioeconomic>150k -0.282 -0.784 0.221 -1.098 0.272
## socioeconomicOO -0.474 -0.962 0.014 -1.902 0.057
## past_donations.z 0.235 -0.132 0.601 1.255 0.210
## age.z 0.100 -0.145 0.345 0.797 0.425
## cond_anymetTRUE 0.143 -0.005 0.292 1.892 0.058
## ----------------------------------------------------------------------
f <- f.sq(
lm(donation ~ 1, data = trials[mask, ]),
model_s_m_base,
model_s_m_full
)
f
## [1] 0.0008194924
length(coef(model_s_m_base))-1
## [1] 24
power = pwr.f2.test(u = 1, v = NULL, f2 = f, power = 0.8)
power
##
## Multiple regression power calculation
##
## u = 1
## v = 9577.631
## f2 = 0.0008194924
## sig.level = 0.05
## power = 0.8
From G*Power, total sample size should equal 7969 to reach 80% power.
mask = trials$donation_wide_group %in% c("medium", "large") & trials$cond_metaphor %in% c("literal", "battle")
model_met_s_l_base = glm(update(base.formula, I(donation_wide_group == "large") ~ .),
data = trials[mask, ],
family = "binomial")
model_met_s_l_full = glm(update(base.formula, I(donation_wide_group == "large") ~ . + cond_metaphor),
data = trials[mask, ],
family = "binomial")
compare_lm(model_met_s_l_base, model_met_s_l_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 1895 2623.2
## 2 1894 2623.1 1 0.039688 0.8421
## [1] Chisq Stat: 0.04
mask = trials$donation_wide_group %in% c("small", "large")
model_s_l_base = glm(update(base.formula, I(donation_wide_group == "large") ~ .),
data = trials[mask, ],
family = "binomial")
model_s_l_full = glm(update(base.formula, I(donation_wide_group == "large") ~ . + cond_anymet),
data = trials[mask, ],
family = "binomial")
compare_lm(model_s_l_base, model_s_l_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_anymet
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 3091 4169.3
## 2 3090 4169.2 1 0.12196 0.7269
## [1] Chisq Stat: 0.122
There is evidence that participants exposed to metaphor are more likely to donate “medium” amounts than “small” amounts (Chisq = 4.2, p < 0.05). However, there is no evidence that participants who are exposed to metaphor are any more likely to donate “large” amounts than “small” amounts.
NH: Participants who were exposed to battle metaphor versus those exposed to journey metaphors were equally likely to choose to donate a “large” amount compared to a “small” amount.
mask = trials$donation_wide_group %in% c("small", "large") & trials$cond_metaphor %in% c("journey", "battle")
model_met_s_l_base = glm(update(base.formula, I(donation_wide_group == "large") ~ .),
data = trials[mask, ],
family = "binomial")
model_met_s_l_full = glm(update(base.formula, I(donation_wide_group == "large") ~ . + cond_metaphor),
data = trials[mask, ],
family = "binomial")
compare_lm(model_met_s_l_base, model_met_s_l_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 2004 2714.1
## 2 2003 2709.3 1 4.7393 0.02948 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Chisq Stat: 4.739
## Warning in if (cls == c("lm") | cls == c("glm", "lm")) {: the condition has
## length > 1 and only the first element will be used
## MODEL INFO:
## Observations: 2029
## Dependent Variable: I(donation_wide_group == "large")
## Type: Generalized linear model
## Family: binomial
## Link function: logit
##
## MODEL FIT:
## χ²(25) = 78.901, p = 0.000
## Pseudo-R² (Cragg-Uhler) = 0.051
## Pseudo-R² (McFadden) = 0.028
## AIC = 2761.331, BIC = 2907.329
##
## Standard errors: MLE
## -----------------------------------------------------------------------
## Est. 2.5% 97.5% z val. p
## --------------------------- -------- -------- -------- -------- -------
## (Intercept) 0.025 -1.147 1.197 0.042 0.966
## cond_sexfemale -0.092 -0.272 0.088 -1.005 0.315
## self_cancerY 0.886 0.443 1.330 3.916 0.000
## self_cancerOO 0.049 -0.856 0.954 0.107 0.915
## ff_cancerY 0.187 -0.010 0.384 1.857 0.063
## ff_cancerOO 0.359 -0.263 0.981 1.130 0.258
## genderF 0.036 -0.147 0.218 0.381 0.703
## genderNB -0.083 -1.402 1.235 -0.124 0.901
## genderOO -0.753 -2.414 0.909 -0.887 0.375
## educationHS -0.609 -1.779 0.561 -1.021 0.307
## educationA -0.516 -1.705 0.674 -0.850 0.395
## educationB -0.310 -1.477 0.857 -0.520 0.603
## educationM -0.056 -1.243 1.132 -0.092 0.927
## educationP -0.430 -1.734 0.873 -0.647 0.517
## educationD -0.380 -1.696 0.936 -0.566 0.572
## educationOO -0.184 -1.948 1.580 -0.204 0.838
## socioeconomic10-25k -0.080 -0.426 0.265 -0.456 0.649
## socioeconomic25-50k 0.141 -0.171 0.453 0.886 0.376
## socioeconomic50-75k 0.183 -0.147 0.512 1.086 0.277
## socioeconomic75-100k 0.204 -0.177 0.584 1.049 0.294
## socioeconomic100-150k 0.127 -0.305 0.560 0.577 0.564
## socioeconomic>150k 0.394 -0.184 0.973 1.336 0.182
## socioeconomicOO -0.265 -0.850 0.320 -0.889 0.374
## past_donations.z 1.503 0.537 2.470 3.049 0.002
## age.z -0.012 -0.331 0.307 -0.074 0.941
## cond_metaphorjourney -0.199 -0.379 -0.020 -2.175 0.030
## -----------------------------------------------------------------------
f <- f.sq(
lm(donation ~ 1, data = trials[mask, ]),
model_met_s_l_base,
model_met_s_l_full
)
f
## [1] 0.001749249
length(coef(model_met_s_l_base))-1
## [1] 24
power = pwr.f2.test(u = 1, v = NULL, f2 = f, power = 0.8)
power
##
## Multiple regression power calculation
##
## u = 1
## v = 4486.909
## f2 = 0.001749249
## sig.level = 0.05
## power = 0.8
From G*Power, a total sample size of 4,589 is required for 80% power.
Given the observed data for participants who saw either battle or journey metaphors, those who were exposed to battle metaphors were significantly more likely to donate a “large” amount compared to a “small” amount.
NH: Participants who were exposed to battle metaphor versus those exposed to journey metaphors were equally likely to choose to donate a “large” amount compared to a “medium” amount.
mask = trials$donation_wide_group %in% c("medium", "large") & trials$cond_metaphor %in% c("journey", "battle")
model_met_m_l_base = glm(update(base.formula, I(donation_wide_group == "large") ~ .),
data = trials[mask, ],
family = "binomial")
model_met_m_l_full = glm(update(base.formula, I(donation_wide_group == "large") ~ . + cond_metaphor),
data = trials[mask, ],
family = "binomial")
compare_lm(model_met_m_l_base, model_met_m_l_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_wide_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 1872 2592.4
## 2 1871 2590.3 1 2.1086 0.1465
## [1] Chisq Stat: 2.109
NS
H: Participants who were exposed to battle metaphor versus those exposed to journey metaphors were equally likely to choose to donate a “medium” amount compared to a “small” amount.
mask = trials$donation_wide_group %in% c("small", "medium") & trials$cond_metaphor %in% c("journey", "battle")
model_met_s_m_base = glm(update(base.formula, I(donation_wide_group == "medium") ~ .),
data = trials[mask, ],
family = "binomial")
model_met_s_m_full = glm(update(base.formula, I(donation_wide_group == "medium") ~ . + cond_metaphor),
data = trials[mask, ],
family = "binomial")
compare_lm(model_met_s_m_base, model_met_s_m_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_wide_group == "medium") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_wide_group == "medium") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 2095 2903.7
## 2 2094 2903.1 1 0.59357 0.441
## [1] Chisq Stat: 0.594
NS
3.2.2 Metaphor Condition and Presence on Likelihood of Narrow Donation Group
Because the campaign is fictional and participants are given an upper bound on their donations, there may not be the same motivations to donate specific amounts compared to a real world fundraiser. Participants may choose to donate under one of the three most salient responses: $0, $25, or $50, or something very close ($5 to $0 or $45 to $50).
Thus, we measure the effect of metaphor presence of participants’ decision to choose one of these three groups of donation amounts (small, medium, and large).
## FALSE TRUE
##
## 609 1143
## large 243 453
## medium 432 886
## small 278 541
##
## FALSE TRUE
##
## 0.133 0.249
## large 0.053 0.099
## medium 0.094 0.193
## small 0.061 0.118
H: Donors are more likely to contribute a “large” donation ($45 or $50) when exposed to a campaign which employs battle or journey metaphors compared to a literal campaign
mask = trials$donation_narrow_group != ""
model_sm_l_base = glm(update(base.formula, I(donation_narrow_group == "large") ~ .),
data = trials[mask, ],
family = "binomial")
model_sm_l_full = glm(update(base.formula, I(donation_narrow_group == "large") ~ . + cond_anymet),
data = trials[mask, ],
family = "binomial")
compare_lm(model_sm_l_base, model_sm_l_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_narrow_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_narrow_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_anymet
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 2808 3101.5
## 2 2807 3101.0 1 0.58633 0.4438
## [1] Chisq Stat: 0.586
There is no evidence that there is an effect of metaphor presence on participants’ decision to make a small or medium donation compared to a large donation.
H: Donors are more likely to contribute a “large” or “medium” donation ($20, $25, $30) when exposed to a campaign which employs battle or journey metaphors compared to a literal campaign
mask = trials$donation_narrow_group != ""
model_s_ml_base = glm(update(base.formula, I(donation_narrow_group != "small") ~ .),
data = trials[mask, ],
family = "binomial")
model_s_ml_full = glm(update(base.formula, I(donation_narrow_group != "small") ~ . + cond_anymet),
data = trials[mask, ],
family = "binomial")
compare_lm(model_s_ml_base, model_s_ml_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_narrow_group != "small") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_narrow_group != "small") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_anymet
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 2808 3354.7
## 2 2807 3354.4 1 0.28235 0.5952
## [1] Chisq Stat: 0.282
There is no evidence that there is an effect of metaphor presence on participants’ decision to make a small donation compared to a medium or large donation.
We can attempt to break down this last result into a small-medium contrast and small-large contrast:
mask = trials$donation_narrow_group %in% c("small", "medium")
model_s_m_base = glm(update(base.formula, I(donation_narrow_group == "medium") ~ .),
data = trials[mask, ],
family = "binomial")
model_s_m_full = glm(update(base.formula, I(donation_narrow_group == "medium") ~ . + cond_anymet),
data = trials[mask, ],
family = "binomial")
compare_lm(model_s_m_base, model_s_m_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_narrow_group == "medium") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_narrow_group == "medium") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_anymet
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 2112 2806.6
## 2 2111 2805.9 1 0.73555 0.3911
## [1] Chisq Stat: 0.736
There is no evidence that there is an effect of metaphor presence on participants’ decision to make a small donation compared to a medium donation.
mask = trials$donation_narrow_group %in% c("small", "large")
model_s_l_base = glm(update(base.formula, I(donation_narrow_group == "large") ~ .),
data = trials[mask, ],
family = "binomial")
model_s_l_full = glm(update(base.formula, I(donation_narrow_group == "large") ~ . + cond_anymet),
data = trials[mask, ],
family = "binomial")
compare_lm(model_s_l_base, model_s_l_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_narrow_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_narrow_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_anymet
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 1490 2022.7
## 2 1489 2022.6 1 0.16003 0.6891
## [1] Chisq Stat: 0.16
There is no evidence that there is an effect of metaphor presence on participants’ decision to make a small donation compared to a large donation.
NH: Participants who were exposed to battle metaphor versus those exposed to journey metaphors were equally likely to choose to donate a “large” amount compared to a “small” amount.
mask = trials$donation_narrow_group %in% c("small", "large") & trials$cond_metaphor %in% c("journey", "battle")
model_met_s_l_base = glm(update(base.formula, I(donation_narrow_group == "large") ~ .),
data = trials[mask, ],
family = "binomial")
model_met_s_l_full = glm(update(base.formula, I(donation_narrow_group == "large") ~ . + cond_metaphor),
data = trials[mask, ],
family = "binomial")
compare_lm(model_met_s_l_base, model_met_s_l_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_narrow_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_narrow_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 969 1317.7
## 2 968 1316.4 1 1.2636 0.261
## [1] Chisq Stat: 1.264
Given the observed data for participants who saw either battle or journey metaphors, those who were exposed to battle metaphors were significantly more likely to donate a “large” amount compared to a “small” amount.
NH: Participants who were exposed to battle metaphor versus those exposed to journey metaphors were equally likely to choose to donate a “large” amount compared to a “medium” amount.
mask = trials$donation_narrow_group %in% c("medium", "large") & trials$cond_metaphor %in% c("journey", "battle")
model_met_m_l_base = glm(update(base.formula, I(donation_narrow_group == "large") ~ .),
data = trials[mask, ],
family = "binomial")
model_met_m_l_full = glm(update(base.formula, I(donation_narrow_group == "large") ~ . + cond_metaphor),
data = trials[mask, ],
family = "binomial")
compare_lm(model_met_m_l_base, model_met_m_l_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_narrow_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_narrow_group == "large") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 1314 1668.7
## 2 1313 1667.8 1 0.97847 0.3226
## [1] Chisq Stat: 0.978
H: Participants who were exposed to battle metaphor versus those exposed to journey metaphors were equally likely to choose to donate a “medium” amount compared to a “small” amount.
mask = trials$donation_narrow_group %in% c("small", "medium") & trials$cond_metaphor %in% c("journey", "battle")
model_met_s_m_base = glm(update(base.formula, I(donation_narrow_group == "medium") ~ .),
data = trials[mask, ],
family = "binomial")
model_met_s_m_full = glm(update(base.formula, I(donation_narrow_group == "medium") ~ . + cond_metaphor),
data = trials[mask, ],
family = "binomial")
compare_lm(model_met_s_m_base, model_met_s_m_full)
## Analysis of Deviance Table
##
## Model 1: I(donation_narrow_group == "medium") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z
## Model 2: I(donation_narrow_group == "medium") ~ cond_sex + self_cancer +
## ff_cancer + gender + education + socioeconomic + past_donations.z +
## age.z + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 1402 1866.2
## 2 1401 1866.1 1 0.11042 0.7397
## [1] Chisq Stat: 0.11
3.3 Broader Alternative Questions
3.3.1 Random Forest Regression
Use Random Forest regression models to quantify the importance of metaphor on donation behavior.
library(randomForest)
## randomForest 4.6-14
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
## The following object is masked from 'package:gridExtra':
##
## combine
## The following object is masked from 'package:dplyr':
##
## combine
## The following object is masked from 'package:ggplot2':
##
## margin
<IP_ADDRESS> Any Metaphor compared to Literal
# estimate <- function(data, ix) {
# rf_null = randomForest(update(base.formula, donation ~ . ),
# data = data[ix, ],
# mtry = 2,
# nPerm = 3,
# ntree = 100)
#
# rf_anymet = randomForest(update(base.formula, donation ~ . + cond_anymet),
# data = data[ix, ],
# mtry = 2,
# nPerm = 3,
# ntree = 100)
#
# return(mean(rf_null$mse) - mean(rf_anymet$mse))
# }
#
# tic()
# bs <- boot(trials, estimate, R = 16)
# toc()
#
# tic()
# ci <- boot.ci(bs, type = "perc")
# toc()
# ci
# cor(model.matrix(~ 0 + past_donations + urgent + sympathy + self_cancer + ff_cancer + age, data = trials))
Here are the relative variable importance measures for each variable over 1,000 trees, including cond_anymet
.
# imp <- cbind(importance(rf_anymet), se = rf_anymet$importanceSD) %>%
# as.data.frame()
# imp <- cbind(imp, var = rownames(imp))
# rownames(imp) <- NULL
# colnames(imp)[1] <- "PercentMSEInc"
# imp %>%
# arrange(PercentMSEInc)
Here are the same numbers but plotted with two standard errors (derived via permutation).
# imp %>%
# arrange(PercentMSEInc) %>%
# mutate(var = factor(var, var)) %>%
# ggplot(aes(var, PercentMSEInc)) + itheme +
# labs(x = "Predictor", y = "Percent MSE Increase") +
# geom_errorbar(aes(ymin = PercentMSEInc - 2*se, ymax = PercentMSEInc + 2*se), width = 0.15) +
# geom_point(shape = 16) +
# geom_text(aes(label = round(PercentMSEInc, 2)), nudge_x = 0.3, size = 3) +
# coord_flip()
<IP_ADDRESS> Battle compared to Journey
# rf_battle_journey = randomForest(update(base.formula, donation ~ . + cond_metaphor),
# data = trials %>% filter(cond_metaphor %in% c("battle", "journey")),
# mtry = 2,
# nPerm = 3,
# importance = T,
# ntree = 1000,
# do.trace = 250)
# rf_battle_journey
Here are the relative variable importance measures for each variable, including cond_anymet
.
# imp <- cbind(importance(rf_battle_journey), se = rf_battle_journey$importanceSD) %>%
# as.data.frame()
# imp <- cbind(imp, var = rownames(imp))
# rownames(imp) <- NULL
# colnames(imp)[1] <- "PercentMSEInc"
# imp %>%
# arrange(PercentMSEInc)
Here are the same numbers but plotted with two standard errors (derived via permutation).
# imp %>%
# arrange(PercentMSEInc) %>%
# mutate(var = factor(var, var)) %>%
# ggplot(aes(var, PercentMSEInc)) + itheme +
# labs(x = "Predictor", y = "Percent MSE Increase") +
# geom_errorbar(aes(ymin = PercentMSEInc - 2*se, ymax = PercentMSEInc + 2*se), width = 0.15) +
# geom_point(shape = 16) +
# geom_text(aes(label = round(PercentMSEInc, 2)), nudge_x = 0.3, size = 3) +
# coord_flip()
3.3.2 Gradient Boosted Trees
Use Extreme Gradient Boosted Trees to measure the importance of metaphor on donation behavior.
# library(xgboost)
# train <- model.matrix(update(base.formula, ~ 0 + . + cond_anymet), trials)
# data <- xgb.DMatrix(data = train, label = trials$donation)
#
# xgboost_anymet <- xgboost(data = data,
# nrounds = 10,
# max_depth = 3,
# subsample = 0.5)
# imp <- xgb.importance(model = xgboost_anymet)
# imp
3.3.3 Generalized Additive Models
There might be some extra information encoded in people’s past donation behavior and age which isn’t properly captured by linear coefficients. Here we’ll model the immediate alternative questions as above but using additive smoothing splines on these two covariates.
First, load up a common GAM library.
library(mgcv)
## Loading required package: nlme
##
## Attaching package: 'nlme'
## The following object is masked from 'package:glue':
##
## collapse
## The following object is masked from 'package:dplyr':
##
## collapse
## This is mgcv 1.8-31. For overview type 'help("mgcv-package")'.
<IP_ADDRESS> Any Metaphor compared to Literal
gam_anymet_base <- gam(donation ~ cond_sex + self_cancer + ff_cancer + gender + education + socioeconomic +
s(past_donations.z) + s(age.z),
data = trials)
gam_anymet_full <- gam(donation ~ cond_sex + self_cancer + ff_cancer + gender + education + socioeconomic +
s(past_donations.z) + s(age.z) + cond_anymet,
data = trials)
compare_lm(gam_anymet_base, gam_anymet_full)
## Analysis of Deviance Table
##
## Model 1: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + s(past_donations.z) + s(age.z)
## Model 2: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + s(past_donations.z) + s(age.z) + cond_anymet
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 4546.7 1153194
## 2 4545.7 1153147 1.0108 46.672 0.6723
## [1] Chisq Stat: 0.186
<IP_ADDRESS> Battle compared to Literal
mask = trials$cond_metaphor %in% c("battle", "literal")
gam_battle_literal_base <- gam(donation ~ cond_sex + self_cancer + ff_cancer + gender + education + socioeconomic +
s(past_donations.z) + s(age.z),
data = trials[mask, ])
gam_battle_literal_full <- gam(donation ~ cond_sex + self_cancer + ff_cancer + gender + education + socioeconomic +
s(past_donations.z) + s(age.z) + cond_metaphor,
data = trials[mask, ])
compare_lm(gam_battle_literal_base, gam_battle_literal_full)
## Analysis of Deviance Table
##
## Model 1: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + s(past_donations.z) + s(age.z)
## Model 2: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + s(past_donations.z) + s(age.z) + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 3040.4 778016
## 2 3039.3 777577 1.0508 439.05 0.2016
## [1] Chisq Stat: 1.736
<IP_ADDRESS> Journey compared to Literal
mask = trials$cond_metaphor %in% c("journey", "literal")
gam_journey_literal_base <- gam(donation ~ cond_sex + self_cancer + ff_cancer + gender + education + socioeconomic +
s(past_donations.z) + s(age.z),
data = trials[mask, ])
gam_journey_literal_full <- gam(donation ~ cond_sex + self_cancer + ff_cancer + gender + education + socioeconomic +
s(past_donations.z) + s(age.z) + cond_metaphor,
data = trials[mask, ])
compare_lm(gam_journey_literal_base, gam_journey_literal_full)
## Analysis of Deviance Table
##
## Model 1: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + s(past_donations.z) + s(age.z)
## Model 2: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + s(past_donations.z) + s(age.z) + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 3034.1 765650
## 2 3033.1 765552 0.97175 98.605 0.5201
## [1] Chisq Stat: 0.396
<IP_ADDRESS> Battle compared to Journey
mask = trials$cond_metaphor %in% c("battle", "journey")
gam_battle_journey_base <- gam(donation ~ cond_sex + self_cancer + ff_cancer + gender + education + socioeconomic +
s(past_donations.z) + s(age.z),
data = trials[mask, ])
gam_battle_journey_full <- gam(donation ~ cond_sex + self_cancer + ff_cancer + gender + education + socioeconomic +
s(past_donations.z) + s(age.z) + cond_metaphor,
data = trials[mask, ])
compare_lm(gam_battle_journey_base, gam_battle_journey_full)
## Analysis of Deviance Table
##
## Model 1: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + s(past_donations.z) + s(age.z)
## Model 2: donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + s(past_donations.z) + s(age.z) + cond_metaphor
## Resid. Df Resid. Dev Df Deviance Pr(>Chi)
## 1 2985.6 753514
## 2 2984.6 752587 1.0016 926.9 0.05526 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] Chisq Stat: 3.721
## Warning in cls == c("glm", "lm"): longer object length is not a multiple of
## shorter object length
## Warning in if (cls == c("lm") | cls == c("glm", "lm")) {: the condition has
## length > 1 and only the first element will be used
##
## Family: gaussian
## Link function: identity
##
## Formula:
## donation ~ cond_sex + self_cancer + ff_cancer + gender + education +
## socioeconomic + s(past_donations.z) + s(age.z) + cond_metaphor
##
## Parametric coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 25.72714 3.94114 6.528 7.81e-11 ***
## cond_sexfemale -0.59682 0.58174 -1.026 0.305009
## self_cancerY 5.16780 1.37542 3.757 0.000175 ***
## self_cancerOO 2.30784 3.07855 0.750 0.453524
## ff_cancerY 1.07844 0.63591 1.696 0.090011 .
## ff_cancerOO 3.10886 2.10004 1.480 0.138877
## genderF 0.42876 0.59490 0.721 0.471134
## genderNB 0.93629 4.14687 0.226 0.821386
## genderOO -4.34142 5.15509 -0.842 0.399765
## educationHS -4.86384 3.91385 -1.243 0.214067
## educationA -4.29629 3.97047 -1.082 0.279313
## educationB -3.25634 3.90647 -0.834 0.404586
## educationM -1.57931 3.96945 -0.398 0.690757
## educationP -3.35747 4.31452 -0.778 0.436525
## educationD -3.87207 4.41185 -0.878 0.380203
## educationOO -1.86710 5.82328 -0.321 0.748516
## socioeconomic10-25k 0.06097 1.10567 0.055 0.956028
## socioeconomic25-50k 1.37241 1.02651 1.337 0.181333
## socioeconomic50-75k 1.60311 1.08456 1.478 0.139483
## socioeconomic75-100k 2.25035 1.24039 1.814 0.069743 .
## socioeconomic100-150k 1.38293 1.43421 0.964 0.335002
## socioeconomic>150k 2.76567 1.93816 1.427 0.153696
## socioeconomicOO -2.57157 1.92177 -1.338 0.180959
## cond_metaphorjourney -1.11083 0.58084 -1.912 0.055915 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Approximate significance of smooth terms:
## edf Ref.df F p-value
## s(past_donations.z) 6.941 7.697 5.509 1.31e-06 ***
## s(age.z) 5.587 6.708 3.568 0.00113 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## R-sq.(adj) = 0.0345 Deviance explained = 4.58%
## GCV = 255.08 Scale est. = 252 n = 3023
3.4 Summary
Between the battle and journey condition, there is significant difference in the amount individuals decide to donate, with those in the battle condition donating about $1.15 more than those in the journey condition.
The donation distribution appears trimodal, which might indicate that participants had a coarser mental model for how much to donate than anticipated. For example, donations may be broken in small, medium, or large amounts. According to this assumption, we find that participants in either metaphor condition are 1.17 times more likely than those in the literal condition to donate a medium amount over a small amount. We also find that participants in the battle condition are 1.22 times more likely than those in the journey condition to donate a large amount over a small amount.
Perhaps participants are sticking to donations by $5 increments, which means we should expect the coarse donations to fall as $0 or $5; $20, $25, or $30, or $45 or $50 (small, medium, and large groups). We see no significant difference in how likely individuals are to donate to one of these groups depending on which condition they’re in.
We next attempted other methods for modeling the importance of metaphor on donation behavior. First, Random Forest regression models. Second, gradient boosted regression models.
Perhaps we can tease out a little more variance in donation behavior as complex functions of past donation behavior and age. We attempt this using Generalized Additive Models. We find that splines on number of past donations and age are significant predictors of donation amount. The metaphor condition no longer has the same significant effect on model fit (p < 0.1).


{{template config_path="design/email/header_template"}}
Hello admin,
You received a request from:
Please go to admin page -> CUSTOM ESTIMATE to see more detail.
Thank you, Trade Banner!


Blog Archive 2012 Big ride around the Peak District May 13 2012 2011 Alport Castles from Fairholmes Feb 13 2011 2010 Yesterday’s Matlock biking epic Oct 31 2010 Oct 29 2010 Crackpot done Oct 25 2010 Yorkshire bound for Booze and Crackpot Oct 22 2010 How to organize my Flickr photos Apr 25 2010 Snowdon training prep - done!!! Apr 13 2010 Easter Buttermere visit Apr 05 2010 Shock! Horror! A Peak District walk without a hill! Mar 28 2010 What do I do to the weather… Mar 27 2010 1/2 Edale Skyline attempt Mar 21 2010 A few piccies from drive to work Mar 11 2010 Great day over Edale Mar 08 2010 A facebook / wordpress ‘mashup’ test…. Mar 08 2010 A wintery Whernside Feb 18 2010 Much more photography stuff on the go. Jan 09 2010 2009 New SLR for Chrimbo delivered…. Dec 27 2009 Similarities between ‘going lightweight’ and single speed mountain biking Nov 28 2009 Update on knee Nov 15 2009 And the news is…. Nov 08 2009 It’s time to try the knee Nov 07 2009 IT band syndrome Oct 18 2009 Quick update on the knee Oct 09 2009 The good, the bad and the ugly Jul 06 2009 Brief respite from the Rum Chronicles… Jul 01 2009 Reflections on our trip to the Isle of Rum - part 3 - getting to the top of Rum Jun 26 2009 Reflections on our trip to the Isle of Rum - part 2 - journey to Mallaig… Jun 21 2009 Reflections on our trip to the Isle of Rum - part 1… Jun 21 2009 Lakes catchup Apr 30 2009 2009 Sant weekender! Apr 30 2009 Wildcamp on high street Feb 21 2009 Testing Jan 20 2009 Snow, hail and wind in Keswick Jan 18 2009 Forthcoming ‘year of camps’ starts this weekend Jan 13 2009 2008 Walking in Yorkshire. Dec 15 2008 Forthcoming sleeping toys Dec 15 2008 Derwent Edge with Phil ‘The Rant’ Sant Nov 09 2008 A walk around the chase Nov 02 2008 Ambleside to Keswick 2 dayer Oct 05 2008 (Back again) again Sep 24 2008 It’s been a while! May 09 2008 A sunny walk from Grasmere Mar 28 2008 All packed up for a Lakeland 2 dayer Mar 25 2008 Finally!! A proper walk Mar 24 2008 Managed to get out on the bike first first ride again Mar 14 2008 Once again, I am reading Trail with enthusiasm Feb 02 2008 Another arthroscopy needed Jan 16 2008 2007 More news on knee Dec 06 2007 Borrowdale Bash Nov 25 2007 Looking forward to a Keswick weekend Nov 20 2007 Dogging in the Chase Nov 07 2007 New day, new bike Nov 04 2007 Suggestions for a photographic spot around Keswick Oct 18 2007 Mam Tor / Ladybower bike ride Oct 02 2007 Another knee update - it works !! Sep 26 2007 Knee problems again Sep 22 2007 Back on the mountain bike Sep 05 2007 Post arthroscopy report 1 (2 weeks after operation) Aug 25 2007 Post arthroscopy report 1 (24 hours later) Aug 12 2007 Finally away from Orange Broadband Aug 09 2007 Torn knee cartilage Aug 04 2007 Knee problems AGAIN over the peak district Jul 07 2007 A while since my last walk. Jun 30 2007 Ruddy ankles May 27 2007 Scafell Pike - success at last May 21 2007 Tent pegs? Sorted!! May 16 2007 Worries about Terra Nova Laser Comp May 15 2007 Project wildcamp is a go….. May 14 2007 Final preperations for Project Peaks Scafell attempt 2. May 14 2007 Project Peaks update May 10 2007 Project Peaks 1 - The Pike Apr 22 2007 Blisters in Edale Apr 18 2007 2nd Project Peaks walk Apr 10 2007 First Project Peaks training walk Apr 07 2007 Weekend without walking :( Mar 31 2007 Another walk over Church Stretton Mar 07 2007 2nd attempt at Arenig Fawr Mar 07 2007 Another attempt at Edale Skyline Feb 18 2007 Snow on the Chase Feb 13 2007 Bog snorkeling on Brown Knoll Feb 05 2007 First full circuit of Church Stretton hills Jan 29 2007 The birth of Project Peaks Jan 08 2007 Raining in the Chase Jan 07 2007 2006 Wet and windy over Caer Caradoc Dec 31 2006 Orange Broadband is great Dec 29 2006 First proper walk Dec 29 2006 Got the slow shutter stylee running Dec 27 2006 I’m back on the hills. Dec 23 2006 Got my chrimbo presents Dec 03 2006 The Malverns with the Sants Dec 02 2006 Longnor to Chrome Hill Dec 02 2006 Cannock Chase Nov 19 2006 Lose Hill / Win Hill Nov 14 2006 Photography at Cannock Chase. Oct 29 2006 My First Court Case Oct 27 2006 Further update to antisocial behaviour issues. Oct 26 2006 Antisocial behaviour progress? Oct 20 2006 blogging cuz I can’t walk Oct 17 2006


-webkit-box-lines
Specifies whether a flexible box should contain multiple lines of content.
This property applies only to flexible box layouts. For more information about flexible boxes, see http://www.w3.org/TR/css3-layout/.
-webkit-box-lines: <behavior>;
If multiple
, the flexible box can contain multiple lines of content; if single
, only one line is allowed.
| multiple | The box can contain multiple lines of content. |
| single | The box can contain only one line of content. |


Home
About
Trending Now....
Messages
Contact Us
Log In
If already a member, Log in.
Email:
Password:
If you're not a member, fill out the form below.
First Name:
Last Name:
Email:
Password:
What is your gender?
Male
Female


Artur is DevOps specialist and Java developer focused on providing Continuous Integration/Continuous Delivery (CI/CD) solutions to a variety of Polish and international companies.
Find Out MoreContinuous Delivery is the extension of Continuous Integration techniques that are focused on the delivery of well tested, stable software that is easy to deploy on a large scale to the production environment.
Get Started!Artur is providing the consultancy on deploying CI/CD philosophy to many renowned customers that operate in Poland and internationally. You can become one of them...
Artur has worked for over 10 years for top Polish corporations.
He is providing not only the knowledge and expertise but also set of tools.
The sky is the limit! Scale your deployment process to the whole globe.
Could it work if it's not made with pure passion and love?
Everything Artur is doing he do with true love. His biggest passion is to share with others that is why he has chosen Open Source software as his favorite.
Trough so many years at the top of the Polish IT scene he has gained a unique insight into a variety of technologies and methodologies. Now he is utilizing his knowledge and skills to provide your organization with the state of art in the delivery process of software development.
Yes, he indeed has his habits and favorites but on the other hand, he has proved that he learns new things quickly and can adapt to the new situations. He will choose a technological stack and will deploy the CI/CD process to your organization in the best possible manner...
Ready to start your next project with me? Give me a call or send me an email and I will get back to you as soon as possible!


litelist v0.0.1 API Reference
Modules
Litelist keeps the contexts that define your domain and business logic
Application entrypoint
The Auth context
Handles authentication errors
Uses Guardian to inspect user token
Neighbor model
Guardian pipeline to test if a user is signed in. Uses the Guardian package
The Comments context
The Comment schema
The Discussions context
Discussion schema
The home for all factories
Arc image uploader
Image schema
The Moderation context
Flag schema
Plug adds @current_neighbor to conn. @current_neighbor is nil if no one is signed in
Plug adds @current_neighbor to conn. @current_neighbor is nil if no one is signed in
The Posts context
The Posts schema
Implementation of the full-text user search for posts
Defines a quantum Scheduler
Scheduler that can remove expired posts at a regular interval
Settings using Mnesia and Amnesia wrapper
The entrypoint for defining your web interface, such as controllers, views, channels and so on
Conveniences for translating and building error messages
Form Helpers are how LiteList can use a single form for all of its resources
A module providing Internationalization with a gettext-based API
Module with named helpers generated from LitelistWeb.Router
Utility functions for exporting data
ForSale Utility functions
Utility functions for views


Affine schemes are the building blocks of schemes, since any scheme admits an open cover by affine schemes.
Intuitively, an affine scheme is a subset of \(\A^n\) defined by the vanishing of a set of polynomials. A little bit more formally, the vanishing set turns out to only carry partial information. What "really" carries all the required information is the "ring of regular functions". For the vanishing sets \(X\) in \(\A^n\) of polynomials, this is, tautologically, the quotient of \(k[x_1,...,x_n]\) modulo the ideal of the polynomials defining \(X\). Here, there is a fundamental subtlety: the subset point of view "forgets" some of the information carried by the ideal! This also highlights the main difference between varieties and schemes. Varieties are determined by their sets of points, at least if the base field is algebraically closed. However, schemes package more information and remember the vanishing ideal (or, more intrinsically, the quotient ring).
This difference is already evident for "zero-dimensional schemes" in \(\A^1\). The vanishing set of \(x\) is the single point \(x=0\). The same is true for any positive power of \(x\): for any integer \(n \geq 1\), the vanishing set of \(x^n\) is the single point \(x=0\). Yet, the ideals \((x^n)\) and \((x^m)\) are only equal if \(n=m\). This means that the schemes \(k[x]/(x^n)\) and \(k[x]/(x^m)\) are only isomorphic if \(n=m\), even though the vanishing sets of the two ideals in \(\A^1\) coincide.
\(\frac{1}{2} \in \Q\)
A polynomial with coefficients in \(\C\) and positive degree admits a root.
A polynomial with coefficients in \(\C\) and of degree n admits n roots counted with multiplicities.
Schemes are perfectly geared for keeping track of the intuitive notion of multiplicity. In the varieties statement of the fundamental theorem of algebra, the degree of a polynomial is only used to rule out constant polynomials. In the schemes statement, the theorem asserts that the degree of the polynomial is encoded in the scheme defined by the vanishing of the polynomial itself. In a physical analogy, you can think of the degree of a polynomial as a measure of the mass of the scheme. A system of particles with positive total mass must have at least one particle. The scheme knows the total mass, the variety only knows whether the mass is positive or not.


He for She, Him for Her
Ladies, do you recognize the women from the picture above ? They are none other than our beautiful disney princesses, with a hint of 21st century and a dash of awesomeness :*
What is it like, being a woman today. Is it following the #metoo trend, or is it or about the #heforShe campaign ?  Or is being married by 26, having a family by 30 too much of a cliche now.
Who are you? Let me help.
Brave. Beautiful. Bold. Tough. Compassionate. Intelligent. Achiever. Sassy.
But not just that. Women are also,
Ditzy. Clumsy. Impatient. Egoistic. Judgemental. Lame. Shrill.
And you know what all of this makes them ? It makes them Real. It makes them exist. I could have just chosen the good qualities today, and masked the bitter ones. But then I thought no more idolizing. No more worshipping. It’s time to get real, it’s time to face the truth!
Us chickas, we love to live our life on the edge. We like to experiment. We like to indulge. We like to think with a clear head, make a decision, and then act on it in no time! Because who here has any time to waste anyway. Our edge can be going on a road trip to Ladakh, or sitting on our desk and creating something awesome out of nothing. It can also be about joining a cult, being a hippy and living life with people who are as high on joys as we are.  That’s right, I’m talking about those labradoodles.
We can be anything. And everything. And not because the world told us to be, because we chose to be.
Be awesome. Be you!
#beAwesomeBeYou #womensday #disney #heForShe #himForHer #girliyapa
Stay tuned for more!
Cheers,
Niyanta
Stay in touch
Howdy partner. Let's touch base on niyanta150792@gmail.com


Oil falls as big US crude build offsets hopes for US-China trade talks
Oil prices dropped on Wednesday after industry data showed a larger-than-expected build-up in U.S. crude stockpiles, but expectations for an easing of trade tensions between the United State and China capped losses.
SEOUL: Oil prices fell on Wednesday, pulled down by a larger-than-expected build-up in U.S. crude stocks, after gaining for three straight sessions on expectations of an easing of in U.S.-China trade tensions.
Brent crude futures were at US$62.60 a barrel by 0330 GMT, down 36 cents, or 0.6per cent. Brent settled up 1.3per cent on Tuesday.
U.S. West Texas Intermediate (WTI) crude futures fell 29 cents, or 0.5per cent, to US$56.94 per barrel, having closed up 1.2per cent in the previous session.
U.S. crude inventories rose by 4.3 million barrels in the week ended Nov. 1 to 440.5 million barrels, according to data from the American Petroleum Institute (API) released on Tuesday. That was nearly triple analysts' forecast for an increase of 1.5 million barrels.
Official data from the Energy Information Administration (EIA) is due later on Wednesday.
"This morning's price action suggests that Asia is being more circumspect about oil and is concerned that bearish news was ignored entirely overnight," Jeffrey Halley, senior market analyst at OANDA.
However, hopes remain for a breakthrough on trade in talks between the United States and China, the world's two biggest oil consumers, keeping price falls in check.
China is pushing U.S. President Donald Trump to drop more tariffs imposed on Beijing as part of a 'Phase One' U.S.-China trade deal, according to people familiar with the negotiations.
"Investors will continue to take cues from U.S.-China trade talks," ANZ Research said in a note.
Looking ahead, next year's oil market outlook may have upside potential, Mohammad Barkindo, Secretary-General of the Organization of the Petroleum Exporting Countries (OPEC) said on Tuesday.
But in the next five years, OPEC would supply a diminishing amount of oil, squeezed by rising U.S. shale output and other rival sources, according to the oil producer group's 2019 World Oil Outlook, released on Tuesday.
OPEC and its partners, including Russia, previously agreed to cut oil production by 1.2 million barrels per day until March 2020. They will meet in early December to review output policy.
(Reporting by Jane Chung; Editing by Kenneth Maxwell and Richard Pullin)


The Tribute adapter allows potential and existing DAO members to contribute any amount of ERC-20 tokens to the DAO in exchange for any amount of DAO internal tokens (e.g., UNITS or LOOT tokens already registered with the DAO Bank). If the proposal passes, the requested internal tokens are minted to the applicant (which effectively makes the applicant a member of the DAO if not already one) and the tokens provided as tribute are transferred to the Bank extension.
The Tribute adapter is similar to the Onboarding adapter in that both allow for joining the DAO (or increasing a stake in the DAO) through the exchange of contributed assets for DAO internal tokens. However, there are key differences:
The Onboarding adapter allows both Ether and ERC-20 tokens to be contributed. The Tribute adapter accepts only ERC-20 tokens.
The Onboarding adapter mints a fixed amount of internal tokens to the applicant based on the amount of assets contributed. In other words, an onboarding proposal does not specify the amount of internal tokens requested. That is calculated from the DAO's configurations and the amount of assets contributed. The Tribute adapter has more open-ended proposal parameters. The proposer can request any amount of internal tokens to be minted in exchange for any amount of any ERC-20 tokens contributed. The worthiness of that transfer proposal for the DAO is left to the vote of its members.
A tribute is made by a member first submitting a proposal specifying (1) the applicant who wishes to join the DAO (or increase his stake in the DAO), (2) the amount and type of internal tokens the applicant desires (e.g., member UNITS), and (3) the amount, type, and owner of ERC-20 tokens that will transfer to the DAO in exchange for those internal tokens. The applicant and actual owner of the ERC-20 tokens can be separate accounts (e.g., the token owner is providing tribute on behalf of the applicant). The internal token type requested must be already registered with the DAO Bank and will usually be pre-defined UNITS or LOOT tokens in the DAO. The proposal submission does not actually transfer the ERC-20 tokens from its owner. That occurs only after the proposal passes and is processed.
The proposal is also sponsored in the same transaction when it is submitted. When a DAO member sponsors the proposal, the voting period begins allowing members to vote for or against the proposal. Only a member can sponsor the proposal.
After the voting period is done along with its subsequent grace period, the proposal can be processed. Any account can process the proposal. However, prior to processing a passed proposal, the owner of the ERC-20 tribute tokens must first separately approve
the Tribute adapter as spender of the tokens provided as tribute. Upon processing, if the vote has passed, the requested internal tokens are minted to the applicant (which effectively makes the applicant a member of the DAO if not already one). The tribute token is registered with the DAO Bank (if not already registered), and the amount of tribute tokens are added to the Guild balance and transferred from the token owner to the Bank extension.
Upon processing, if the vote has failed (i.e., more NO votes then YES votes or a tie), no further action is taken (the owner of the ERC-20 tribute tokens still retains ownership of the tokens).
A DAO Bank extension must exist and be configured with proper access for this adapter.
The DAO internal tokens to be minted to the applicant must be registered with the DAO Bank.
DAORegistry Access Flags: SUBMIT_PROPOSAL
, NEW_MEMBER
.
Bank Extension Access Flags: ADD_TO_BALANCE
, REGISTER_NEW_TOKEN
.
proposals
: All tribute proposals handled by each DAO.ProposalDetails
:
id
: The proposal id.applicant
: The applicant address (who will receive the DAO internal tokens and become a member; this address may be different than the actual owner of the ERC-20 tokens being provided as tribute).tokenToMint
: The address of the DAO internal token to be minted to the applicant.requestAmount
: The amount requested of DAO internal tokens.token
: The address of the ERC-20 tokens that will be transferred to the DAO in exchange for DAO internal tokens.tributeAmount
: The amount of tribute tokens.tributeTokenOwner
: The owner of the ERC-20 tokens being provided as tribute.BankExtension
DaoRegistry
SPONSORED
, PROCESSED
.Voting
/**
* @notice default fallback function to prevent from sending ether to the contract.
*/
receive() external payable
/**
* @notice Configures the adapter for a particular DAO.
* @notice Registers the DAO internal token with the DAO Bank.
* @dev Only adapters registered to the DAO can execute the function call (or if the DAO is in creation mode).
* @dev A DAO Bank extension must exist and be configured with proper access for this adapter.
* @param dao The DAO address.
* @param tokenAddrToMint The internal token address to be registered with the DAO Bank.
*/
function configureDao(DaoRegistry dao, address tokenAddrToMint)
external
onlyAdapter(dao)
/**
* @notice Creates and sponsors a tribute proposal to start the voting process.
* @dev Applicant address must not be reserved.
* @dev Only members of the DAO can sponsor a tribute proposal.
* @param dao The DAO address.
* @param proposalId The proposal id (managed by the client).
* @param applicant The applicant address (who will receive the DAO internal tokens and become a member).
* @param tokenToMint The address of the DAO internal token to be minted to the applicant.
* @param requestAmount The amount requested of DAO internal tokens.
* @param tokenAddr The address of the ERC-20 tokens that will be transferred to the DAO in exchange for DAO internal tokens.
* @param tributeAmount The amount of tribute tokens.
* @param tributeTokenOwner The owner of the ERC-20 tokens being provided as tribute.
* @param data Additional information related to the tribute proposal.
*/
function submitProposal(
DaoRegistry dao,
bytes32 proposalId,
address applicant,
address tokenToMint,
uint256 requestAmount,
address tokenAddr,
uint256 tributeAmount,
address tributeTokenOwner,
bytes memory data
) external reentrancyGuard(dao)
/**
* @notice Processes a tribute proposal to handle minting and exchange of DAO internal tokens for tribute tokens (passed vote).
* @dev Proposal id must exist.
* @dev Only proposals that have not already been processed are accepted.
* @dev Only sponsored proposals with completed voting are accepted.
* @dev The owner of the ERC-20 tokens provided as tribute must first separately `approve` the adapter as spender of those tokens (so the tokens can be transferred for a passed vote).
* @dev ERC-20 tribute tokens must be registered with the DAO Bank (a passed proposal will check and register the token if needed).
* @param dao The DAO address.
* @param proposalId The proposal id.
*/
function processProposal(DaoRegistry dao, bytes32 proposalId)
external
reentrancyGuard(dao)
No events are emitted from this adapter.


Tints & Shades
Select a colour using the colour wheels.
Press and hold the black and white semi circles.
Play


Gateway
BOT
26-Oct-18 04:54 PM
[dscrdvrsc] <Gameoverzoin> @Hollowman9000 trying to shield on GUI lol and there is error
[dscrdvrsc] <Hollowman9000> What is the error?
[dscrdvrsc] <Hollowman9000> @Gameoverzoin
[dscrdvrsc] <Gameoverzoin> code error -8
[dscrdvrsc] <Hollowman9000> Sending to a z address?
[dscrdvrsc] <Gameoverzoin> yeap
[dscrdvrsc] <Hollowman9000> Try “shield all coinbase transactions”?
[dscrdvrsc] <Gameoverzoin> yeap
[dscrdvrsc] <Gameoverzoin> i see coins on public address
[dscrdvrsc] <Gameoverzoin> B address
[dscrdvrsc] <Hollowman9000> @miketout
[dscrdvrsc] <Hollowman9000> Not sure what the error code means
[dscrdvrsc] <Gameoverzoin> @team
[dscrdvrsc] <Gameoverzoin> thanks in advance
(edited)
[dscrdvrsc] <Hollowman9000> How old are the coins?
[dscrdvrsc] <Gameoverzoin> 3 month i think
[dscrdvrsc] <Hollowman9000> Mined transaction or unlocked early reward
[dscrdvrsc] <Hollowman9000> Ah
[dscrdvrsc] <Gameoverzoin> unlocked
[dscrdvrsc] <Hollowman9000> Not trying to send to a sapling address are you?
[dscrdvrsc] <Hollowman9000> Begins with zs
[dscrdvrsc] <Gameoverzoin> not to sapling usual private
[dscrdvrsc] <Gameoverzoin> zc
[dscrdvrsc] <Gameoverzoin> it was mined before but then freezing
[dscrdvrsc] <Hollowman9000> Try generating a new z address?
[dscrdvrsc] <2Faze> Where can i get information about circulating and total supply?
[dscrdvrsc] <Gameoverzoin> @Hollowman9000 same result
Checkout the community contributed #VerusCoin staking and supply spreadsheet (thank you Discord::Cragorn#4547). Easily see max, available and circulating supply, and calculate your staking rewards from any block height: https://t.co/AIUjroDlVD
#staking #proofofstake
[dscrdvrsc] <Hollowman9000> Try that @2Faze
[dscrdvrsc] <Hollowman9000> System clock set right? @Gameoverzoin
[dscrdvrsc] <Hollowman9000> Chain synced 100%?
[dscrdvrsc] <Hollowman9000> Running native?
[dscrdvrsc] <Gameoverzoin> Yeap
[dscrdvrsc] <Hollowman9000> Damnit man
[dscrdvrsc] <Gameoverzoin> 100% synchro
[dscrdvrsc] <Gameoverzoin> 221375
[dscrdvrsc] <Hollowman9000> “Try turning your computer (agama) off and restarting” :smiley_abused:
[dscrdvrsc] <Hollowman9000> Running out of ideas over here
[dscrdvrsc] <Gameoverzoin> lol
[dscrdvrsc] <Gameoverzoin> thanks anyway
[dscrdvrsc] <Hollowman9000> @Gameoverzoin I had a cli hiccup the other day, restarted with a -rescan and that fixed it
[dscrdvrsc] <Hollowman9000> Maybe shut down agama, and upon startup select the -rescan from the menu below the native button
[dscrdvrsc] <Hollowman9000> Worth a shot since I have no idea what error code 8 is
[dscrdvrsc] <Hollowman9000> Rescan May take some time but it might fix it
[dscrdvrsc] <Gameoverzoin> last time i loose my coins when i restart wallet lol)
[dscrdvrsc] <Hollowman9000> Yikes
[dscrdvrsc] <Gameoverzoin> it was before freezing
[dscrdvrsc] <Hollowman9000> Do you have your private keys backed up?
[dscrdvrsc] <Gameoverzoin> i loosing 3000
[dscrdvrsc] <Hollowman9000> Do you remember your address that had the 3000?
[dscrdvrsc] <Gameoverzoin> Coins was mined
[dscrdvrsc] <Gameoverzoin> on stack
[dscrdvrsc] <Gameoverzoin> and not shielded
[dscrdvrsc] <Hollowman9000> Same wallet.dat?
[dscrdvrsc] <Gameoverzoin> yeap
[dscrdvrsc] <Gameoverzoin> same
[dscrdvrsc] <Hollowman9000> -rescan May find them again
[dscrdvrsc] <Gameoverzoin> lol
[dscrdvrsc] <Gameoverzoin> already doing that
[dscrdvrsc] <Gameoverzoin> admins saying me that coins was lost becoze i'm not shielded it, Before freezing you must be shielded it and coins not saving as i understand
[dscrdvrsc] <Gameoverzoin> 1000 was locked
[dscrdvrsc] <Gameoverzoin> and 2600 was lost
[dscrdvrsc] <Hollowman9000> Doesn’t make much sense but I’m not a tech expert
[dscrdvrsc] <Gameoverzoin> waiting for the mike answer
[dscrdvrsc] <Hollowman9000> Let the rescan finish and @miketout or @michaeltoutjr could chime in maybe
[dscrdvrsc] <2Faze> So max circulating supply one time will be about ~50 million is that right?
[dscrdvrsc] <Hollowman9000> Circulating is somewhere between 9-10 million right now
[dscrdvrsc] <Hollowman9000> Max supply is 80 million
[dscrdvrsc] <Hollowman9000> Current total supply is just short of 29 million
[dscrdvrsc] <Hollowman9000> According to my bootleg spread sheet
[dscrdvrsc] <Hollowman9000> 192 x 10080
384 x 43200
192 x 43200
96 x 43200
48 x 43200
24 x 38511
[dscrdvrsc] <Hollowman9000> Locked + unlocked currently
[dscrdvrsc] <Hollowman9000> Almost 34 million locked + unlocked sorry
[dscrdvrsc] <Hollowman9000> Spreadsheet busted
[dscrdvrsc] <2Faze> Got it thx


 I think it's best to think of these conceptually because, in a sense, they are pragmatic creations that exist on top of the fundamental layers of ruby.
Enumerable is a mixin module. What is a mixin module? Objects in Ruby can't inherit from multiple classes, which can be a problem because say you want to use the same method on a hash that you used on an array. You think, yes I should be able to do this because the iterable structure of this object would allow for such a method, but the inheritance rules of Ruby don't allow for methods to fall their way down into different classes. So how do you get around that? You create a module, which is essentially a databank of constants and methods and then you include it in whatever classes you want. Enumerable is such a module and, since it's included in a number of classes (Range, Hash, Array), it is mixed in. It is mixed in precisely because of the reason you would want to share these methods. The classes are similar enough--specifically, you can iterate through them-- that they can be manipulated in the same manner. It makes sense.
Enumerator, by contrast, is an object. But it is an object that is returned after an enumerable mixin method is called on another object, so let's first get to our method, cycle.
Cycle is a method included in the Enumerable mixin. It has a very intuitive name. It does exactly what it says it does; it cycles through something. If you don't pass an argument, it cycles on infinitely. If you pass an Integer as an argument, it cycles through the object for however many times specified. If you pass a block it executes the code on each iterable element in the object over however many cycles you specified. If you pass no block it returns an enumerator object.
Cycle provides a useful service for when you want to iterate over an object multiple times. The only other way to do this would be with some incrementer method with the object that is manipulated placed somewhere within it. It turns out looking like an ugly bit of code that could be improved upon, especially if you're pursuing the Ruby way!
Some questions you might ask are, "When is this method useful? How often would I use this anyway?" Well those are good questions because they lead to more involved use of Ruby and its methods. Let's move back to the Enumerator object. Let's use cycle to create one.
So as you can see, you've got an object linked to the method, cycle. This is essentially what the Enumerator object is. Like I said earlier, it's almost a conceptual object. It is an object that is enumerable in the way cycle is defined. Think of it this way, this cycle enumerator object can be cycled through infinitely. Once you get to the end of an iteration, you restart at the beginning the next time. Any enumerator object that isn't attached to cycle ends once the object iterates through one full time. There is no return to the first element. The iteration for that object is defined by the method that is called upon it.
So if you're following along, you might be getting a clearer picture of why this is helpful. You have an object, say an array. You want to have more control over that object; you want to make iterations restart, allow them to cycle. What do you do? You create a copy as a cycle enumerator object! Just like that, you have more power. You use that power to compliment your Ruby skills and incorporate other features to produce what you want.
Success! You have more control, and you can move on to bigger projects.


@import views.html.util._
@import views.html.triage.outcomePage
@this(outcomePage: outcomePage)
@(sessionId: String, resumeCode: String)
@outcomePage("Item not found", sessionId, resumeCode, false) {
You have not found your item in the UK Military List. This does not mean that your item is not controlled.
Warning:
Always double-check if your items are controlled. If you export controlled items without the correct export licence
you could:
- have your items seized
- face a fine
- serve up to 10 years in prison
What you should do next
-
Get someone who knows the technical details of your details of your item to help you check the choices you've
made against the technical specifications of your item.
-
Go back to verify the type of item and which category it falls under within the UK Military List.
-
Consider whether your item is specially designed for military use or not. If your item is not military than you
should consider the dual use list.
Export control licenses
You cannot export items without a licence if:
- you know or suspect they'll be used in a weapons of mass destruction programme or delivery system (also known
as 'end-use controls')
- you're exporting dual-use items that do not usually need an export licence but you know or suspect they might
be incorporated into military equipment for use in embargoed countries
- there are embargoes, sanctions or other restrictions which cover your export destination
Find out more about
WMD (opens in a new tab)
and
military (opens in a new tab)
end use controls and any
sanctions and embargoes (opens in a new tab)
.
}


Laura Koemmpel
Projects
Music
Teaching
Blog
I am currently hosting a private personal blog on Substack. Contact me if you're interested in the address!


Phys. alloys Rev. binding {border-style random nearest px;} #ffffff;} ./PhysRevB.. calculations atoms Chroneos SQS defect defects Figure dependence atom composition diffusion ./. E-centres Lett. energy neighbour respect using alloy DFT Jiang charge energetics structures −xGe (x = ., Appl. alloys. cells energies germanium present px; study {font-family Chem. Grimes configurations font-size form local pairs processes properties quasirandom self-diffusion substitutional ./PhysRevLett.. Bracht E-centre Fig. Schwingenschlögl Sci. density double; experimental performed previous silicon (i.e. (refer .). Bader basis border-color border-width calculated compositions concentration considered consistent different environment impact initio model negative phosphorous relaxed solid; special structure temperature work {font-style {font-weight Commons Creative Saltas alloy. approach atomic bold bound bowing compared complexes conjunction courier; electronic em; function functional gradient group host however, non-linear oxides pair plane pt;} seven shown supercell supercells thermodynamic used vacancy. wave


{% extends "layout/main.html" %} {% load material_forms %} {% block content %}
Register to the AVA project to download plugin and contribute to the ecosystem !