kriging module¶
Available classes:
ArealKriging: Class performs kriging of areas with point support data within those polygons,
CentroidPoissonKriging: Class performs centroid-based Poisson Kriging of areas,
Kriging: Class for kriging interpolation of the unknown values at a given location (position).
ArealKriging
¶
Class initialization¶
pyinterpolate.kriging.ArealKriging(
semivariogram_model,
known_areas,
known_areas_points,
kriging_type='ata')
Class performs kriging of areas with point support data within those polygons.
INITIALIZATION PARAMS:
semivariogram_model: (TheoreticalSemivariogram) Theoretical Semivariogram used for data interpolation,
known_areas: (numpy array) areas in the form:
[area_id, polygon, centroid x, centroid y, value]
known_areas_points: (numpy array) points within areas in the form:
[
area_id,
[point_position_x, point_position_y, value]
]
kriging_type: (str) default
'ata'
;
'ata'
- area to area poisson kriging, 'atp'
- area to point poisson kriging.
Class public methods:¶
predict: predicts area value in an unknown location based on the area-to-area or area-to-point Poisson Kriging,
regularize_data: regularizes whole dataset and creates new values and error maps based on the kriging type.
ArealKriging.predict()
¶
ArealKriging.predict(self,
unknown_area_points,
number_of_neighbours,
max_search_radius)
Function predicts areal value in an unknown location based on the area-to-area or area-to-point Poisson Kriging.
INPUT:
unknown_area_points: (numpy array) points within an unknown area in the form:
[
area_id,
[point_position_x, point_position_y, value]
]
number_of_neighbours: (int) minimum number of neighbours to include in the algorithm,
max_search_radius: (float) maximum search radius (if number of neighbours within this search radius is smaller than
number_of_neighbours
parameter then additional neighbours are included up tonumber_of_neighbors
).
OUTPUT:
prediction, error, estimated mean, weights:
[
value_in_unknown_location,
error,
estimated_mean,
weights
]
ArealKriging.regularize_data()
¶
ArealKriging.regularize_data(self,
number_of_neighbours,
max_search_radius,
data_crs="EPSG:4326")
Function regularizes whole dataset and creates new values and error maps based on the kriging type.
If chosen type is area to area then function returns Geopandas GeoDataFrame
with area id, areal geometry, estimated value, estimated prediction error, RMSE of prediction.
If chosen type is area to point then function returns Geopandas GeoDataFrame
with area id, point coordinates, estimated value, estimated prediction error, RMSE of areal prediction.
Function do not predict unknown values, areas with NaN
are skipped.
INPUT:
number_of_neighbours: (int) minimum number of neighbours to include in the algorithm,
max_search_radius: (float) maximum search radius (if number of neighbours within this search radius is smaller than number_of_neighbours parameter then additional neighbours are included up to number_of_neighbours),
data_crs: (str) data crs, look into: https://geopandas.org/projections.html
OUTPUT:
regularized dataset (
GeoPandas GeoDataFrame
object).
CentroidPoissonKriging
¶
Class initialization¶
pyinterpolate.kriging.CentroidPoissonKriging(
semivariogram_model,
known_areas,
known_areas_points)
Class performs centroid-based Poisson Kriging of areas.
INITIALIZATION PARAMS:
semivariogram_model: (TheoreticalSemivariogram) Theoretical Semivariogram used for data interpolation,
known_areas: (numpy array) areas in the form:
[area_id, polygon, centroid x, centroid y, value]
known_areas_points: (numpy array) points within areas in the form:
[
area_id,
[point_position_x, point_position_y, value]
]
Class public methods:¶
predict: predicts areal value in a unknown location based on the centroid-based Poisson Kriging.
CentroidPoissonKriging.predict()
¶
CentroidPoissonKriging.predict(self,
unknown_area,
unknown_area_points,
number_of_neighbours,
max_search_radius,
weighted,
test_anomalies=True)
Function predicts areal value in a unknown location based on the centroid-based Poisson Kriging.
INPUT:
unknown_area: (numpy array) unknown area data in the form:
[area_id, polygon, centroid x, centroid y]
unknown_area_points: (numpy array) points within an unknown area in the form:
[
area_id,
[point_position_x, point_position_y, value]
]
number_of_neighbours: (int) minimum number of neighbours to include in the algorithm,
max_search_radius: (float) maximum search radius (if number of neighbours within this search radius is smaller than
number_of_neighbours
parameter then additional neighbours are included up to thenumber_of_neighbours
).weighted: (bool) distances weighted by population (
True
) or not (False
),test_anomalies: (bool) check if weights are negative.
OUTPUT:
prediction, error, estimated mean, weights:
[
value_in_unknown_location,
error,
estimated_mean,
weights
]
Krige
¶
Class initialization¶
pyinterpolate.kriging.Krige(semivariogram_model,
known_points)
Class for kriging interpolation of the unknown values at a given location (position). Class takes two arguments during the initialization: semivariogram model and known points list.
Class methods may raise ValueError
if estimated value is below 0. You may use try: ... except: ...
statement to overwrite those values with some constant or NaN
or you could use different semivariogram model. Sometimes this problem is related to the input data, especially to the clustered groups of points. In this case aggregate those clusters and then estimate semivariogram and perform kriging.
INITIALIZATION PARAMS:
semivariogram_model: (TheoreticalSemivariogram) Theoretical Semivariogram used for data interpolation,
known_points: (numpy array) dataset with known values and locations. Each column should represent different dimension and the last column represents values example:
[
[dim_x1, dim_y1, val_1],
[dim_x2, dim_y2, val_2]
]
Class public methods:¶
ordinary_kriging: predicts value at unknown location with Ordinary Kriging technique,
simple_kriging: predicts value at unknown location with Simple Kriging technique.
Krige.ordinary_kriging()
¶
Krige.ordinary_kriging(self,
unknown_location,
number_of_neighbours,
test_anomalies=True)
Function predicts value at unknown location with Ordinary Kriging technique.
INPUT:
unknown_location: (tuple) position of unknown location,
number_of_neighbours: (int) number of the closest locations to the unknown position which should be included in the modeling,
test_anomalies: (bool) check if weights are negative.
OUTPUT:
predicted, error, estimated mean, weights:
[
value_in_unknown_location,
error,
estimated_mean,
weights
]
Krige.simple_kriging()
¶
Krige.simple_kriging(self,
unknown_location,
number_of_neighbours,
global_mean,
test_anomalies=True)
Function predicts value at unknown location with Simple Kriging technique.
INPUT:
unknown_location: (tuple) position of unknown location,
number_of_neighbours: (int) number of the closest locations to the unknown position which should be included in the modeling,
global_mean: (float) global mean which should be known before processing,
test_anomalies: (bool) check if weights are negative.
OUTPUT:
predicted, error, estimated mean, weights:
[
value_in_unknown_location,
error,
mean,
weights
]


{% extends 'pages/base.html' %} {% block subtitle %} Contact {% endblock subtitle %} {% block header_js %} {{ super() }} {% endblock %} {% block navbar_breadcrumb %}
{% endblock navbar_breadcrumb %} {% block content %}Oppia is an open commons for education. And, true to this ethos, it is an open-source website created through the hard work and creativity of people like you! To get involved in the Oppia project and help us bring about our mission of free, high-quality universal education, reach out via our oppia-dev@ Google Group, or contact us at<EMAIL_ADDRESS>We're excited to hear from you!
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Use find -print0
or find -exec
to better handle non-alphanumeric filenames.
ls | xargs -n1 wc -w
find . -maxdepth 1 -print0 | xargs -0 -n1 wc -w
find . -maxdepth 1 -exec wc -w {} \;
Using -print0
separates each output with a NUL character, rather than a newline, which is safer to pipe into xargs
. Alternatively using -exec
avoids the problem of piping and parsing filenames in the first place.
See [[SC2012]] for more details on this issue.


pg_restore
Restore a PostgreSQL database from an archive file created by pg_dump.
More information: home page
- Restore an archive into an existing database:
pg_restore -d db_name archive_file.dump
- Same as above, customize username:
pg_restore -U username -d db_name archive_file.dump
- Same as above, customize host and port:
pg_restore -h host -p port -d db_name archive_file.dump
- List database objects included in the archive:
pg_restore --list archive_file.dump
- Clean database objects before creating them:
pg_restore --clean -d db_name archive_file.dump
- Use multiple jobs to do the restoring:
pg_restore -j 2 -d db_name archive_file.dump


CacheAttack
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No. We maintain that digital linguistics doesn't exist yet, and will not exist until we escape the print way of thinking about data. Moreover, the digital tools of linguistics are currently fractured. Each current piece of software is in itself helpful in various ways, but many of these programs are operating-system dependent, and generally only accomplish only a small portion of what the linguist needs to do. Our goal with Wugbot is not to fix this by making one application that can do everything, but rather to create the standards and the infrastructure so that the software programs that linguists build can speak to each other. In other words, we want to create an interface for documenting data, analyzing data, and archiving data. This interface will be able to pass data easily between different tools and, because we separate data from presentation, visualize that data in different ways.
Many linguists and computer scientists make a clear delineation between linguistics and computer science. We do not. Digital linguistics does not shy away from implementation issues, usability, or software development, because we recognize that these technologies are not different in principle from technologies like interlinear glossing or audio equipment, all of which form an integral part of the linguistic enterprise. These technology-related issues are not the focus of digital linguistics, but they must be dealt with adequately in order for the field of linguistics to progress successfully. Just as the documentary linguist is expected to understand some of the basic principles of audio formats and recording, we believe it is important that today's linguist have a basic understanding of data structure as it relates to the digital representation of data. We also feel that this is easy for any linguist to learn, and hope our tools can help teach linguists these skills.
Thus we incorporate software development into the core goalset of the digital linguistics subfield. We encourage linguists and language workers to choose the level of technical proficiency they desire to have. Whatever that level is, the digitally-informed linguist has an important role to play in the goal of bringing digital technologies to linguistics. We value complaints, feedback, and ideas for improvement, because this is an integral part of the software development process, and how new technologies are created. We believe that progress in the field of digital linguistics should be evaluated in terms of how populizable the outputs of the field are, i.e. the extent to which the tools created with Wugbot's' methodology gain traction in the field of linguistics more broadly.
Digital linguistics forces linguists - and especially those creating the tools of digital linguistics - to think deeply and carefully about the objects of linguistics and the nature of linguistic data. A famous scientist once remarked, 'You can understand when you are able to quantify it', but the much-admired phonetician Peter Ladefoged quipped instead, 'You can understand a thing when you can write a computer program to represent it.' In our experience, this has often been the case. Learning how to represent linguistic data has gotten us to think about the basic concepts of linguistics in often new ways that yield insights into linguistic science, or what it is we're really doing when we represent certain things in print.
Wugbot uses the web as its platform. Ironically, this does not mean that everything we do is online. Rather, web technologies have become so robust that their applications now extend far beyond exchanging data across the internet. It is now possible to write a program that works in web browsers like Internet Explorer, Firefox, Chrome, Safari, etc., that never has to go online at all. These offline applications are ideal because anybody with a web browser can use them. They don't depend on whether you are running Windows, Mac, Android or Linux, and they don't depend on an internet connection. In short, the browser becomes the program.
At the same time, digital linguistics technologies can take advantage of data exchange across the web, allowing linguists to store their data on a server (only if they want), and contribute to massive cross-linguistic databases for linguistic research.


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{% if project.photos|length %} {% for photo in project.photos[:5] %} {% endfor %} {% endif %}These are 5 of my featured projects. For a complete projects list, please visit my Portfolio at {{ root }}
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To make the task of setting up menus and other general-purpose features easier, XMF-Mosaic provides an init file facility. This file, init.o, is automatically loaded on start-up of the tool provided that it is placed in the directory referenced by the MOSAICINIT environment variable.
The following init file (init.xmf) is an example of a simple init file. To use it, compile the file in the MOSAICINIT directory. It contans a menu item which enables the init.xmf file to be conveniently edited via a File > Edit > Init menu.
// The init file is loaded on startup. It is used to automatically add user customised // menus and operation to XMF-Mosaic. These can be used for all sorts of // purposes, including setting up menus for specific projects, or running // any code you wish. To run the init file, it must be saved in the XMFINIT // directory (this must be available under your windows system environment // variables). Compile, save and load the init file before launching XMF-Mosaic // to run it. parserImport XOCL; import Clients; import XmfFileTree; import IO; // Add your customised menus and operations here. // Provides a drop down menu for editing the Init file. Note the sequence // elements denotes the menu hierarchy starting with the root File menu. // Use & to add shortcut key binding, e.g. Alt-F, Alt-E in this case to // get to the Edit menu. xmf.addDropDownMenuItem(Seq{"&File","Extras","&Edit","Main","Init"}, @Operation() XmfFile(null,xmf.initFile()).editText() end); // Provides a drop down menu for browsing the files in your project directory xmf.addDropDownMenuItem(Seq{"&File","Extras","&Browse","Main","Projects"}, @Operation() Directory(xmf.projDir(),Seq{".*.xmf"}).browse() end); // An example of a menu that is used to load one or more .xar files in your projects directory into // MyProjects xmf.addDropDownMenuItem(Seq{"&Demo","Main","Load Model"}, @Operation() @Busy("Loading Model") xmf.projectManager().getElement("MyProjects").loadMosaicProject(xmf.projDir() + "/Dir/File.xar",false) end end); // An example of a menu that is used to load one or more compiled .o files xmf.addDropDownMenuItem(Seq{"&Demo","Main","Load File"}, @Operation() @Busy("Loading File") (xmf.projDir() + "/Dir/Code.o").loadBin() end end); // An example of defining an operation that will be added automatically to Root context Root @Operation hello() format(stdout,"HelloWorld") end


--- layout: page title: "About me" description: "who is Martin Ingesen?" header-img: "img/home-bg.jpg" ---
I currently work as a senior associate at BDO CERT in Oslo, Norway.
I have a bachelor's degree in information technology with a specialization in programming from Westerdals Oslo School of Arts, Communication and Technology, and I am currently doing a part time master's degree in information security at NTNU, on the Digital Forensics track.
I have a strong passion for information security, and spends most of my time outside work keeping up to date with the security-world, and doing independent research around the subject. During my research I have found security vulnerabilities in several large websites and applications.
In 2017, I won (along with my friend Nikolai) the title "Master of Cyber Security 2017" at the HackCon conference in Oslo, Norway. The prize (title and 10 000NOK) went to the person with the most correct answers in a range of security questions, within a variety of categories.
I have had the honor to represent Norway in the European Cyber Security Challenge twice, in 2017 and 2018. In 2017 we placed 4th, and we placed 12th in 2018.
I am a holder of the SANS Lethal Forensicator coin.
I also enjoy participating in Capture-The-Flag contests and have won a few along with my team Edward Pwnden.
If you have any questions or would like to contact me, you can find me on Twitter, LinkedIn and {{site.email}}.
PST vil holde spionsiktet russer varetektsfengslet: Har fått besøk av ambassaden (Norwegian, 3.10.2018).
Om spionsiktet russer: – Lett å opprette «ondsinnet» nettverk (Norwegian, 27.09.2018).
Printernekt på Stortinget etter spionpågripelsen på fredag (Norwegian, 24.09.2018).
Tar kontroll over journalistens nettbank. – Lekende lett (Norwegian, 12.09.2018).
Ti av disse skal representere Norge i EM (Norwegian, 21.06.2018).
Martin (22) og Nikolai (23) til topps i kåringen av Norges beste på IT-sikkerhet (Norwegian, 16.02.2017).
Martin er Norges beste innen Cybersikkerhet (Video interview in Norwegian, 08.03.2017).
Behind paywall:
Best på IT-sikkerhet (Norwegian, 17.02.2017).
Nikolai (23) og Martin (22) er Norges beste sikkerhetseksperter (Norwegian, 17.02.2017).
Dette er Norges beste datasikkerhetsekspert (Norwegian, 17.02.2017).


Ideas: making it easier to extend Python’s syntax
You have an Excellent Idea ® to change the Python syntax and want to find a way to include your Excellent Idea ® in your Python programs. According to Python Developers Guide, this might be doable if you are willing to follow “a few steps” including:
Get a copy of the CPython’s code repository and all the required compilers for your platform.
Modify the grammar file to add rules for the new syntax.
Modify the AST generation code; this requires a knowledge of C
Compile the AST into bytecode
Recompile the modified Python interpreter
This … can be a rather daunting task. It might get a bit easier if you grab a copy of the currently unpublished book by Anthony Shaw, CPython Internals and read it from cover to cover, but it will still remain a major task. Furthermore, it would not be easy to share your work with others so that they can try it out.
However, there is a simpler way: it is possible to run code with a modified syntax using import hooks [or, in some cases as shown later, using a custom codec.]
What is an import hook
When you write something like:
import my_module
Python’s import machinery has to do the following:
Try to use various tools to find the module requested
Get the source code of that module
Execute that source code, subject to some information reported in step 1.
An import hook is an additional tool that you create to do these three steps.
Once written, you add it to sys.meta_path
so that Python’s import
machinery can make use of it.
Still, writing import hooks can be rather difficult.
[page 420] …it should be emphasized that Python’s module, package and import mechanism is one of the most complicated parts of the entire language – often poorly understood by even the most seasoned Python programmers unless they’ve devoted effort to peeling back the covers.… long discussion …[page 428] Assuming that your head hasn’t completely exploded at this point, … Last, but not least, spending some time sleeping with PEP 302 and the documentation for importlib under your pillow may be advisable.Python Cookbook, 3rd edition, by David Beazley and Brian K. Jones
ideas is designed to facilitate the creation of such import hooks, and be a repository for examples that can be used as starting points for new ideas.
Instead of figuring out how to write an import hook, using ideas you
can focus exclusively on what what might be needed to convert your proposed new
syntax into something that Python can understand – ideas will
take care of the rest, including inserting it in sys.meta_path
.
Warning
Doing something like what is described in this documentation is not recommended for production code.
But it can be fun! ;-)
Quick links to topics
To do
Todo
Try to create a different module object.
(The original entry is located in C:\Users\Andre\github\ideas\docs\source\constants.rst, line 123.)
Todo
Add these examples
I need to add some explanations and provide links to the following examples:
importing “best answer” from StackOverflow
installing module from pypi on request (David Beazley’s talk)
etc.
(The original entry is located in C:\Users\Andre\github\ideas\docs\source\excluded.rst, line 26.)


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Book Genre: {{ book.genre }}
Book Description: {{ book.description }}
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{{ title.mpaa_rating }}
Year: {{ title.year }}


Welcome to the Show-Me Coders project page!
This project was created as part of a three month long program
of instruction to demonstrate our grasp of the lessons learned during
the coursework and to gain the valuable skill of working as a team.


Dance Classes
We are able to teach an ever expanding array of disciplines within the school. We currently train pupils in ballet, tap, modern and jazz. All of our classes are taught by BBO & ISTD qualified staff with many years experience. We educate pre-school children from as young as 2 and half years and our senior pupils generally stay with us until they are 18. Our school thrives to cater to every individual's need. Whether you are interested in dance for a fun hobby, exercise or you are looking to have a career in professional theatre, we can help.
Pre-School
For the youngest children, pre-school classes start at the age of 2 and a half. We introduce basic ballet techniques alongside music mime and games aimed at building confidence and learning introductory skills. These include skipping and galloping within a relaxed class atmosphere.
Primary School / Beginners
From the age of 4, classes are more structured towards ballet, tap or modern dance technique. Class discipline is introduced here, but we still maintain a relaxed learning environment, whilst trying to instil a joy of dance. Beginners are welcome at any age and will be recommended a class of appropriate age and standard.
Advanced
More advanced students have a wide range of classes in all genres. We cater for all: from those who dance simply for pleasure and exercise, to those aiming for a career in dance. In an effort to keep abreast of what is happening in dance on the professional circuit, we have introduced regular guest teachers currently working in the West End.


Title 1
Title 2
- Part of the reason a well structured HTML document is so important
- is because HTML elements inherit stylistic properties
- You can see it's basic structure from the way I have formatted the tags with tabbing
Title 3
- but here is a more visual representation of the hierarchy of tags
- Tags that contain other tags are parents
- and the tags inside of them are their children in the following tree representation


Adobe Web Platform Team
background-blend-mode
property. It is not supported in this browser. Learn how to get a capable browser.
simple alpha compositing
blend-mode: overlay
background-blend-mode
property. It is not supported in this browser. Learn how to get a capable browser.
simple alpha compositing
blend-mode: overlay
source
backdrop
blended result
/* blend background images of the same element */
.element {
background: url(image1.jpg), url(image2.jpg);
background-color: rgba(215, 120, 25, 0.7);
background-blend-mode: overlay;
}
/* blend heading with container */
h2 {
mix-blend-mode: difference;
}
.container {
background-image: url(texture.png);
}
W3C Candidate Recommendation - Adobe, Canon
clip-path: circle(25% at 50% 50%);
-webkit-clip-path
property. It is not supported in this browser. Learn how to get a capable browser.
clip-path: ellipse(50% 40% at 50% 50%);
-webkit-clip-path
property. It is not supported in this browser. Learn how to get a capable browser.
clip-path: polygon(nonzero, 955px 405px, 483px 212px, 1px 400px, 0px 500px, 956px 500px);
-webkit-clip-path
property. It is not supported in this browser. Learn how to get a capable browser.
.element {
-webkit-clip-path: url(#clipping);
}
<svg>
<clipPath id="clipping">
<circle cx="50" cy="50" r="10"/>
</clipPath>
</svg>
.element {
/* use an image as a mask */
mask-image: url(mask.png);
}
.element {
/* use a <mask> element as mask */
mask-image: url(#mask);
}
<svg>
<mask id="mask">
<circle cx="50" cy="50" r="10"></circle>
</mask>
</svg>
W3C Working Draft - Adobe, Mozilla, Google
/* wrap the content inside a circle */
#content {
shape-inside: circle(10em at 50% 50%)
}
#coffee {
float: left;
shape-outside: circle(10em at 50% 50%);
}
.content{
/* shape defined by points of a polygon */
shape-inside: polygon(x1, y1 x2, y2 ... );
}
.content{
/* shape defined by the transparency of an image */
shape-inside: url(image.png);
shape-image-threshold: 0.5;
}
W3C Candidate Recommendation - Adobe, Microsoft
CSS Blending
CSS Masking
CSS Shapes


One of the buttons on a calculator that can be pressed in order to enter a number or perform an operation.
To key in a number is to type it into the calculator.


|
Hoard Overlay Unity
Boilerplate for account management
|
This object allows to store the encrypted passwords on local drive More...
Public Member Functions | |
| async Task | EncryptAndSaveCredentials (HoardID userID, IUserInputProvider credentials) |
| async Task< string > | DecryptPasswordFor (HoardID userID) |
| Decrypts the password More... | |
| bool | HasPasswordSaved (HoardID userID) |
| Has the user it's password saved? More... | |
| void | DeleteSavedPasswordFor (HoardID userID) |
| Clear the password from the local storage More... | |
Static Public Member Functions | |
| static CredentialsStorage | InitializeStorage (string storagePath=null) |
| Create and initialize singleton. More... | |
Protected Member Functions | |
| override byte[] | ProvideIV () |
| override byte[] | ProvideEncryptionPhrase () |
Properties | |
| string | StoragePath [get] |
| static CredentialsStorage | Instance [get] |
| Singleton instance of the storage More... | |
This object allows to store the encrypted passwords on local drive
Follows Singleton pattern. Accesible by @Instance static property
Please be reminded that storing the users password on the local storage can be dangerous.
| async Task<string> Hoard.ProfileUtilities.CredentialsStorage.DecryptPasswordFor | ( | HoardID | userID | ) |
Decrypts the password
Implements Hoard.ProfileUtilities.ICredentialStorage.
| void Hoard.ProfileUtilities.CredentialsStorage.DeleteSavedPasswordFor | ( | HoardID | userID | ) |
Clear the password from the local storage
Implements Hoard.ProfileUtilities.ICredentialStorage.
| async Task Hoard.ProfileUtilities.CredentialsStorage.EncryptAndSaveCredentials | ( | HoardID | userID, |
| IUserInputProvider | credentials | ||
| ) |
Implements Hoard.ProfileUtilities.ICredentialStorage.
| bool Hoard.ProfileUtilities.CredentialsStorage.HasPasswordSaved | ( | HoardID | userID | ) |
Has the user it's password saved?
|
static |
Create and initialize singleton.
| storagePath | Path to storage if null passed the standard windows path will be used |
|
protected |
|
protected |
|
staticget |
Singleton instance of the storage
|
get |


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|
Embedded Artistry Framework
Embedded Systems C++ Framework
|
#include "driver.hpp"
#include <cstdint>
#include <driver/communication_bus.hpp>
#include <etl/vector.h>
#include <inplace_function/inplace_function.hpp>
Classes | |
| struct | embvm::i2c::op_t |
| I2C operation definition. More... | |
| class | embvm::i2c::master |
| This class provides the I2C master interface definition. More... | |
Namespaces | |
| embvm | |
| Embedded framework core interfaces, classes, and definitions. | |
| embvm::i2c | |
| Definitions, functions, and classes related to I2C communication bus devices. | |
Typedefs | |
| using | embvm::i2c::addr_t = uint8_t |
| I2C address storage type. More... | |
| using | embvm::i2c::commBus = commBus< i2c::op_t, i2c::baud, i2c::I2C_MASTER_REQD_STATIC_FUNCTION_SIZE, i2c::status > |
| Convenience alias for the declaration of the I2C commBus. More... | |
Variables | |
| static constexpr size_t | embvm::i2c::I2C_MASTER_REQD_STATIC_FUNCTION_SIZE = 96 |
| Maximum size of the I2C master callback functor object. More... | |
| static constexpr uint8_t | embvm::i2c::ReadBit = 0x01 |
| Added to slave address to indicate a read command. More... | |
| struct embvm::i2c::op_t |
I2C operation definition.
The I2C bus operates in half-duplex mode, meaning that reads and writes do not occur simultaneously. The operation type represents a variety of operations which can be performed by an I2C device. The transmit options (tx_buffer and tx_size) are required by any operations using "write". The receive options (rx_buffer and rx_size) are required by any operations using "read".
| Class Members | ||
|---|---|---|
| uint8_t | address | Slave device address (7-bit). |
| operation | op | Operation to perform. |
| uint8_t * | rx_buffer |
Pointer to the receive buffer. Does not need to be specified for write-only commands. |
| size_t | rx_size |
Number of bytes to receive. Does not need to be specified for write-only commands. |
| const uint8_t * | tx_buffer |
Pointer to the transmit buffer. Does not need to be specified for read-only commands. |
| size_t | tx_size |
Number of bytes to transmit. Does not need to be specified for read-only commands. |


9 Logistic Regression - Ex: Maternal Risk Factor for Low Birth Weight Delivery
library(tidyverse)
library(haven) # read in SPSS dataset
library(furniture) # nice table1() descriptives
library(stargazer) # display nice tables: summary & regression
library(texreg) # Convert Regression Output to LaTeX or HTML Tables
library(texreghelpr) #
library(psych) # contains some useful functions, like headTail
library(car) # Companion to Applied Regression
library(sjPlot) # Quick plots and tables for models
library(pscl) # psudo R-squared function
library(glue) # Interpreted String Literals
library(interactions) # interaction plots
library(sjPlot) # various plots
library(performance) # r-squared values
9.1 Background
More complex example demonstrating modeling decisions
Another set of data from a study investigating predictors of low birth weight
id
infant’s unique identification number
Dependent variable (DV) or outcome
low
Low birth weight (outcome)- 0 = birth weight >2500 g (normal)
- 1 = birth weight < 2500 g (low))
bwt
actual infant birth weight in grams (ignore for now)
Independent variables (IV) or predictors
age
Age of mother, in yearslwt
Mother’s weight at last menstrual period, in poundsrace
Race: 1 = White, 2 = Black, 3 = Othersmoke
Smoking status during pregnancy:1 = Yes, 0 = Noptl
History of premature labor: 0 = None, 1 = One, 2 = two, 3 = threeht
History of hypertension: 1 = Yes, 0 = Noui
Uterine irritability: 1 = Yes, 0 = Noftv
Number of physician visits in 1st trimester: 0 = None, 1 = One, … 6 = six
9.1.1 Raw Dataset
The data is saved in a text file (.txt
) without any labels.
<- read.table("https://raw.githubusercontent.com/CEHS-research/data/master/Regression/lowbwt.txt",
lowbwt_raw header = TRUE,
sep = "",
na.strings = "NA",
dec = ".",
strip.white = TRUE)
::glimpse(lowbwt_raw) tibble
Rows: 189
Columns: 11
$ id <int> 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101~
$ low <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ age <int> 19, 33, 20, 21, 18, 21, 22, 17, 29, 26, 19, 19, 22, 30, 18, 18, ~
$ lwt <int> 182, 155, 105, 108, 107, 124, 118, 103, 123, 113, 95, 150, 95, 1~
$ race <int> 2, 3, 1, 1, 1, 3, 1, 3, 1, 1, 3, 3, 3, 3, 1, 1, 2, 1, 3, 1, 3, 1~
$ smoke <int> 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0~
$ ptl <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0~
$ ht <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0~
$ ui <int> 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1~
$ ftv <int> 0, 3, 1, 2, 0, 0, 1, 1, 1, 0, 0, 1, 0, 2, 0, 0, 0, 3, 0, 1, 2, 3~
$ bwt <int> 2523, 2551, 2557, 2594, 2600, 2622, 2637, 2637, 2663, 2665, 2722~
9.1.2 Declare Factors
<- lowbwt_raw %>%
lowbwt_clean ::mutate(id = factor(id)) %>%
dplyr::mutate(low = low %>%
dplyrfactor() %>%
::fct_recode("birth weight >2500 g (normal)" = "0",
forcats"birth weight < 2500 g (low)" = "1")) %>%
::mutate(race = race %>%
dplyrfactor() %>%
::fct_recode("White" = "1",
forcats"Black" = "2",
"Other" = "3")) %>%
::mutate(ptl_any = as.numeric(ptl > 0)) %>% # collapse into 0 = none vs. 1 = at least one
dplyr::mutate(ptl = factor(ptl)) %>% # declare the number of pre-term labors to be a factor: 0, 1, 2, 3
dplyr::mutate_at(vars(smoke, ht, ui, ptl_any), # declare all there variables to be factors with the same two levels
dplyr
factor,levels = 0:1,
labels = c("No", "Yes"))
Display the structure of the ‘clean’ version of the dataset
::glimpse(lowbwt_clean) tibble
Rows: 189
Columns: 12
$ id <fct> 85, 86, 87, 88, 89, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 1~
$ low <fct> birth weight >2500 g (normal), birth weight >2500 g (normal), ~
$ age <int> 19, 33, 20, 21, 18, 21, 22, 17, 29, 26, 19, 19, 22, 30, 18, 18~
$ lwt <int> 182, 155, 105, 108, 107, 124, 118, 103, 123, 113, 95, 150, 95,~
$ race <fct> Black, Other, White, White, White, Other, White, Other, White,~
$ smoke <fct> No, No, Yes, Yes, Yes, No, No, No, Yes, Yes, No, No, No, No, Y~
$ ptl <fct> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,~
$ ht <fct> No, No, No, No, No, No, No, No, No, No, No, No, Yes, No, No, N~
$ ui <fct> Yes, No, No, Yes, Yes, No, No, No, No, No, No, No, No, Yes, No~
$ ftv <int> 0, 3, 1, 2, 0, 0, 1, 1, 1, 0, 0, 1, 0, 2, 0, 0, 0, 3, 0, 1, 2,~
$ bwt <int> 2523, 2551, 2557, 2594, 2600, 2622, 2637, 2637, 2663, 2665, 27~
$ ptl_any <fct> No, No, No, No, No, No, No, No, No, No, No, No, No, Yes, No, N~
lowbwt_clean
# A tibble: 189 x 12
id low age lwt race smoke ptl ht ui ftv bwt ptl_any
<fct> <fct> <int> <int> <fct> <fct> <fct> <fct> <fct> <int> <int> <fct>
1 85 birth we~ 19 182 Black No 0 No Yes 0 2523 No
2 86 birth we~ 33 155 Other No 0 No No 3 2551 No
3 87 birth we~ 20 105 White Yes 0 No No 1 2557 No
4 88 birth we~ 21 108 White Yes 0 No Yes 2 2594 No
5 89 birth we~ 18 107 White Yes 0 No Yes 0 2600 No
6 91 birth we~ 21 124 Other No 0 No No 0 2622 No
7 92 birth we~ 22 118 White No 0 No No 1 2637 No
8 93 birth we~ 17 103 Other No 0 No No 1 2637 No
9 94 birth we~ 29 123 White Yes 0 No No 1 2663 No
10 95 birth we~ 26 113 White Yes 0 No No 0 2665 No
# ... with 179 more rows
9.2 Exploratory Data Analysis
%>%
lowbwt_clean ::table1("Age, years" = age,
furniture"Weight, pounds" = lwt,
"Race" = race,
"Smoking During pregnancy" = smoke,
"History of Premature Labor, any" = ptl_any,
"History of Premature Labor, number" = ptl,
"History of Hypertension" = ht,
"Uterince Irritability" = ui,
"1st Tri Dr Visits" = ftv,
splitby = ~ low,
test = TRUE,
output = "markdown")
| birth weight >2500 g (normal) | birth weight < 2500 g (low) | P-Value | |
|---|---|---|---|
| n = 130 | n = 59 | ||
| Age, years | 0.103 | ||
| 23.7 (5.6) | 22.3 (4.5) | ||
| Weight, pounds | 0.02 | ||
| 133.3 (31.7) | 122.1 (26.6) | ||
| Race | 0.082 | ||
| White | 73 (56.2%) | 23 (39%) | |
| Black | 15 (11.5%) | 11 (18.6%) | |
| Other | 42 (32.3%) | 25 (42.4%) | |
| Smoking During pregnancy | 0.04 | ||
| No | 86 (66.2%) | 29 (49.2%) | |
| Yes | 44 (33.8%) | 30 (50.8%) | |
| History of Premature Labor, any | <.001 | ||
| No | 118 (90.8%) | 41 (69.5%) | |
| Yes | 12 (9.2%) | 18 (30.5%) | |
| History of Premature Labor, number | <.001 | ||
| 0 | 118 (90.8%) | 41 (69.5%) | |
| 1 | 8 (6.2%) | 16 (27.1%) | |
| 2 | 3 (2.3%) | 2 (3.4%) | |
| 3 | 1 (0.8%) | 0 (0%) | |
| History of Hypertension | 0.076 | ||
| No | 125 (96.2%) | 52 (88.1%) | |
| Yes | 5 (3.8%) | 7 (11.9%) | |
| Uterince Irritability | 0.035 | ||
| No | 116 (89.2%) | 45 (76.3%) | |
| Yes | 14 (10.8%) | 14 (23.7%) | |
| 1st Tri Dr Visits | 0.389 | ||
| 0.8 (1.1) | 0.7 (1.0) |
9.3 Logistic Regression - Simple, unadjusted models
<- glm(low ~ age, family = binomial(link = "logit"), data = lowbwt_clean)
low1.age <- glm(low ~ lwt, family = binomial(link = "logit"), data = lowbwt_clean)
low1.lwt <- glm(low ~ race, family = binomial(link = "logit"), data = lowbwt_clean)
low1.race <- glm(low ~ smoke, family = binomial(link = "logit"), data = lowbwt_clean)
low1.smoke <- glm(low ~ ptl_any, family = binomial(link = "logit"), data = lowbwt_clean)
low1.ptl <- glm(low ~ ht, family = binomial(link = "logit"), data = lowbwt_clean)
low1.ht <- glm(low ~ ui, family = binomial(link = "logit"), data = lowbwt_clean)
low1.ui <- glm(low ~ ftv, family = binomial(link = "logit"), data = lowbwt_clean) low1.ftv
Note: the parameter estimates here are for the LOGIT scale, not the odds ration (OR) or even the probability.
::knitreg(list(low1.age, low1.lwt, low1.race, low1.smoke),
texregcustom.model.names = c("Age", "Weight", "Race", "Smoker"),
caption = "Simple, Unadjusted Logistic Regression: Models 1-4",
caption.above = TRUE,
digits = 3)
| Age | Weight | Race | Smoker | |
|---|---|---|---|---|
| (Intercept) | 0.385 | 0.998 | -1.155*** | -1.087*** |
| (0.732) | (0.785) | (0.239) | (0.215) | |
| age | -0.051 | |||
| (0.032) | ||||
| lwt | -0.014* | |||
| (0.006) | ||||
| raceBlack | 0.845 | |||
| (0.463) | ||||
| raceOther | 0.636 | |||
| (0.348) | ||||
| smokeYes | 0.704* | |||
| (0.320) | ||||
| AIC | 235.912 | 232.691 | 235.662 | 233.805 |
| BIC | 242.395 | 239.174 | 245.387 | 240.288 |
| Log Likelihood | -115.956 | -114.345 | -114.831 | -114.902 |
| Deviance | 231.912 | 228.691 | 229.662 | 229.805 |
| Num. obs. | 189 | 189 | 189 | 189 |
| p < 0.001; p < 0.01; p < 0.05 |
::knitreg(list(low1.ptl, low1.ht, low1.ui, low1.ftv),
texregcustom.model.names = c("Pre-Labor", "Hypertension", "Uterine", "Visits"),
caption = "Simple, Unadjusted Logistic Regression: Models 5-8",
caption.above = TRUE,
digits = 3)
| Pre-Labor | Hypertension | Uterine | Visits | |
|---|---|---|---|---|
| (Intercept) | -1.057*** | -0.877*** | -0.947*** | -0.687*** |
| (0.181) | (0.165) | (0.176) | (0.195) | |
| ptl_anyYes | 1.463*** | |||
| (0.414) | ||||
| htYes | 1.214* | |||
| (0.608) | ||||
| uiYes | 0.947* | |||
| (0.417) | ||||
| ftv | -0.135 | |||
| (0.157) | ||||
| AIC | 225.898 | 234.650 | 233.596 | 237.899 |
| BIC | 232.381 | 241.133 | 240.079 | 244.382 |
| Log Likelihood | -110.949 | -115.325 | -114.798 | -116.949 |
| Deviance | 221.898 | 230.650 | 229.596 | 233.899 |
| Num. obs. | 189 | 189 | 189 | 189 |
| p < 0.001; p < 0.01; p < 0.05 |
9.4 Logistic Regression - Multivariate, with Main Effects Only
Main-effects multiple logistic regression model
<- glm(low ~ age + lwt + race + smoke + ptl_any + ht + ui,
low1_1 family = binomial(link = "logit"),
data = lowbwt_clean)
summary(low1_1)
Call:
glm(formula = low ~ age + lwt + race + smoke + ptl_any + ht +
ui, family = binomial(link = "logit"), data = lowbwt_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6459 -0.7992 -0.5103 0.9388 2.2018
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.63691 1.23028 0.518 0.60467
age -0.03775 0.03781 -0.998 0.31808
lwt -0.01491 0.00704 -2.118 0.03419 *
raceBlack 1.21274 0.53248 2.278 0.02275 *
raceOther 0.80412 0.44843 1.793 0.07294 .
smokeYes 0.84640 0.40806 2.074 0.03806 *
ptl_anyYes 1.22175 0.46301 2.639 0.00832 **
htYes 1.83869 0.70324 2.615 0.00893 **
uiYes 0.71113 0.46311 1.536 0.12465
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 234.67 on 188 degrees of freedom
Residual deviance: 196.83 on 180 degrees of freedom
AIC: 214.83
Number of Fisher Scoring iterations: 4
9.5 Logistic Regression - Multivariate, with Interactions
Before removing non-significant main effects, test plausible interactions
Try interactions between age and lwt, age and smoke, lwt and smoke, 1 at a time
9.5.1 Age and Weight
<- glm(low ~ age + lwt + race + smoke + ptl_any + ht + ui + age:lwt,
low1_2 family = binomial(link = "logit"),
data = lowbwt_clean)
summary(low1_2)
Call:
glm(formula = low ~ age + lwt + race + smoke + ptl_any + ht +
ui + age:lwt, family = binomial(link = "logit"), data = lowbwt_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6426 -0.8004 -0.5163 0.9400 2.1989
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.1396293 4.0443041 0.282 0.77811
age -0.0597273 0.1724339 -0.346 0.72906
lwt -0.0188377 0.0309225 -0.609 0.54240
raceBlack 1.2071359 0.5341716 2.260 0.02383 *
raceOther 0.7977750 0.4505381 1.771 0.07661 .
smokeYes 0.8433655 0.4083953 2.065 0.03892 *
ptl_anyYes 1.2260463 0.4642795 2.641 0.00827 **
htYes 1.8389418 0.7034162 2.614 0.00894 **
uiYes 0.7142688 0.4642468 1.539 0.12391
age:lwt 0.0001718 0.0013146 0.131 0.89600
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 234.67 on 188 degrees of freedom
Residual deviance: 196.82 on 179 degrees of freedom
AIC: 216.82
Number of Fisher Scoring iterations: 5
<IP_ADDRESS> Compare Model Fits vs. Likelihood Ratio Test
anova(low1_1, low1_2, test = 'LRT')
# A tibble: 2 x 5
`Resid. Df` `Resid. Dev` Df Deviance `Pr(>Chi)`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 180 197. NA NA NA
2 179 197. 1 0.0170 0.896
<IP_ADDRESS> Type II Analysis of Deviance Table
Anova(low1_2, test = 'LR')
# A tibble: 8 x 3
`LR Chisq` Df `Pr(>Chisq)`
<dbl> <dbl> <dbl>
1 1.02 1 0.313
2 5.00 1 0.0254
3 6.27 2 0.0436
4 4.38 1 0.0364
5 7.13 1 0.00758
6 7.18 1 0.00738
7 2.33 1 0.127
8 0.0170 1 0.896
<IP_ADDRESS> Type III Analysis of Deviance Table
Anova(low1_2, test = 'LR', type = 'III')
# A tibble: 8 x 3
`LR Chisq` Df `Pr(>Chisq)`
<dbl> <dbl> <dbl>
1 0.119 1 0.730
2 0.372 1 0.542
3 6.27 2 0.0436
4 4.38 1 0.0364
5 7.13 1 0.00758
6 7.18 1 0.00738
7 2.33 1 0.127
8 0.0170 1 0.896
9.5.2 Age and Smoking
<- glm(low ~ age + lwt + race + smoke + ptl_any + ht + ui + age:smoke,
low1_3 family = binomial(link = "logit"),
data = lowbwt_clean)
summary(low1_3)
Call:
glm(formula = low ~ age + lwt + race + smoke + ptl_any + ht +
ui + age:smoke, family = binomial(link = "logit"), data = lowbwt_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7020 -0.8016 -0.4970 0.9055 2.2124
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.311029 1.483078 0.884 0.37670
age -0.068293 0.053865 -1.268 0.20485
lwt -0.014701 0.007001 -2.100 0.03575 *
raceBlack 1.126482 0.543684 2.072 0.03827 *
raceOther 0.768241 0.451199 1.703 0.08863 .
smokeYes -0.601286 1.782234 -0.337 0.73583
ptl_anyYes 1.197295 0.460992 2.597 0.00940 **
htYes 1.860851 0.705311 2.638 0.00833 **
uiYes 0.783999 0.472451 1.659 0.09703 .
age:smokeYes 0.063744 0.076634 0.832 0.40552
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 234.67 on 188 degrees of freedom
Residual deviance: 196.14 on 179 degrees of freedom
AIC: 216.14
Number of Fisher Scoring iterations: 5
<IP_ADDRESS> Compare Model Fits vs. Likelihood Ratio Test
anova(low1_1, low1_3, test = 'LRT')
# A tibble: 2 x 5
`Resid. Df` `Resid. Dev` Df Deviance `Pr(>Chi)`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 180 197. NA NA NA
2 179 196. 1 0.699 0.403
::compare_performance(low1_1, low1_3, rank = TRUE) performance
# A tibble: 2 x 12
Name Model AIC BIC R2_Tjur RMSE Sigma Log_loss Score_log Score_spherical
<chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 low1_1 glm 215. 244. 0.190 0.418 1.05 0.521 -24.6 0.0164
2 low1_3 glm 216. 249. 0.192 0.417 1.05 0.519 -24.5 0.0156
# ... with 2 more variables: PCP <dbl>, Performance_Score <dbl>
9.5.3 Weight and Smoking
<- glm(low ~ age + lwt + race + smoke + ptl_any + ht + ui + lwt:smoke,
low1_4 family = binomial(link = "logit"),
data = lowbwt_clean)
summary(low1_4)
Call:
glm(formula = low ~ age + lwt + race + smoke + ptl_any + ht +
ui + lwt:smoke, family = binomial(link = "logit"), data = lowbwt_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6816 -0.7874 -0.5251 0.8876 2.2365
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.74840 1.59439 1.097 0.27282
age -0.03768 0.03809 -0.989 0.32259
lwt -0.02362 0.01077 -2.193 0.02828 *
raceBlack 1.23110 0.53358 2.307 0.02104 *
raceOther 0.71646 0.45183 1.586 0.11281
smokeYes -1.13566 1.75557 -0.647 0.51770
ptl_anyYes 1.26472 0.46488 2.721 0.00652 **
htYes 1.74326 0.70738 2.464 0.01373 *
uiYes 0.80121 0.47044 1.703 0.08855 .
lwt:smokeYes 0.01555 0.01344 1.157 0.24740
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 234.67 on 188 degrees of freedom
Residual deviance: 195.44 on 179 degrees of freedom
AIC: 215.44
Number of Fisher Scoring iterations: 5
<IP_ADDRESS> Compare Model Fits vs. Likelihood Ratio Test
anova(low1_1, low1_4, test = 'LRT')
# A tibble: 2 x 5
`Resid. Df` `Resid. Dev` Df Deviance `Pr(>Chi)`
<dbl> <dbl> <dbl> <dbl> <dbl>
1 180 197. NA NA NA
2 179 195. 1 1.39 0.238
9.6 Logistic Regression - Multivariate, Simplify
No interactions are significant Remove non-significant main effects
9.6.1 Remove the least significant perdictor: ui
<- glm(low ~ age + lwt + race + smoke + ptl_any + ht,
low1_5 family = binomial(link = "logit"),
data = lowbwt_clean)
summary(low1_5)
Call:
glm(formula = low ~ age + lwt + race + smoke + ptl_any + ht,
family = binomial(link = "logit"), data = lowbwt_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6533 -0.8202 -0.5299 0.9709 2.1982
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 0.924910 1.202693 0.769 0.44187
age -0.042784 0.037567 -1.139 0.25475
lwt -0.015436 0.007044 -2.191 0.02843 *
raceBlack 1.168452 0.532577 2.194 0.02824 *
raceOther 0.814620 0.442740 1.840 0.06578 .
smokeYes 0.858332 0.404787 2.120 0.03397 *
ptl_anyYes 1.333970 0.457573 2.915 0.00355 **
htYes 1.740511 0.703104 2.475 0.01331 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 234.67 on 188 degrees of freedom
Residual deviance: 199.15 on 181 degrees of freedom
AIC: 215.15
Number of Fisher Scoring iterations: 4
9.7 Logistic Regression - Multivariate, Final Model
Since the mother’s age is theoretically a meaningful variable, it should probably be retained.
Revise so that age is interpreted in 5-year and lwt in 20 lb increments and the intercept has meaning.
<- glm(low ~ I((age - 20)/5) + I((lwt - 125)/20) + race + smoke + ptl_any + ht,
low1_6 family = binomial(link = "logit"),
data = lowbwt_clean)
summary(low1_6)
Call:
glm(formula = low ~ I((age - 20)/5) + I((lwt - 125)/20) + race +
smoke + ptl_any + ht, family = binomial(link = "logit"),
data = lowbwt_clean)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6533 -0.8202 -0.5299 0.9709 2.1982
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.8603 0.4092 -4.546 5.48e-06 ***
I((age - 20)/5) -0.2139 0.1878 -1.139 0.25475
I((lwt - 125)/20) -0.3087 0.1409 -2.191 0.02843 *
raceBlack 1.1685 0.5326 2.194 0.02824 *
raceOther 0.8146 0.4427 1.840 0.06578 .
smokeYes 0.8583 0.4048 2.120 0.03397 *
ptl_anyYes 1.3340 0.4576 2.915 0.00355 **
htYes 1.7405 0.7031 2.475 0.01331 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 234.67 on 188 degrees of freedom
Residual deviance: 199.15 on 181 degrees of freedom
AIC: 215.15
Number of Fisher Scoring iterations: 4
9.7.1 Several \(R^2\) measures with the pscl::pR2()
function
::pR2(low1_6) pscl
fitting null model for pseudo-r2
llh llhNull G2 McFadden r2ML r2CU
-99.5757045 -117.3359981 35.5205871 0.1513627 0.1713353 0.2409463
::r2(low1_6) performance
# R2 for Logistic Regression
Tjur's R2: 0.180
9.7.2 Parameter Estiamtes Table
<IP_ADDRESS> Using texreg::screenreg()
Default: parameters are in terms of the ‘logit’ or log odds ratio
::knitreg(low1_6,
texregsingle.row = TRUE,
digits = 3)
| Model 1 | |
|---|---|
| (Intercept) | -1.860 (0.409)*** |
| (age - 20)/5 | -0.214 (0.188) |
| (lwt - 125)/20 | -0.309 (0.141)* |
| raceBlack | 1.168 (0.533)* |
| raceOther | 0.815 (0.443) |
| smokeYes | 0.858 (0.405)* |
| ptl_anyYes | 1.334 (0.458)** |
| htYes | 1.741 (0.703)* |
| AIC | 215.151 |
| BIC | 241.085 |
| Log Likelihood | -99.576 |
| Deviance | 199.151 |
| Num. obs. | 189 |
| p < 0.001; p < 0.01; p < 0.05 |
The texreg
package uses an intermediate function called extract()
to extract information for the model and then put it in the right places in the table. I have writen a function called extract_glm_exp()
that is helpful.
::knitreg(extract_glm_exp(low1_6),
texregcustom.coef.names = c("BL: 125 lb, 20 yr old White Mother",
"Additional 5 years older",
"Additional 20 lbs pre-pregnancy",
"Race: Black vs. White",
"Race: Other vs. White",
"Smoking During pregnancy",
"History of Any Premature Labor",
"History of Hypertension"),
custom.model.names = "OR, Low Birth Weight",
single.row = TRUE,
ci.test = 1)
| OR, Low Birth Weight | |
|---|---|
| BL: 125 lb, 20 yr old White Mother | 0.16 [0.07; 0.33]* |
| Additional 5 years older | 0.81 [0.55; 1.16] |
| Additional 20 lbs pre-pregnancy | 0.73 [0.55; 0.95]* |
| Race: Black vs. White | 3.22 [1.13; 9.31]* |
| Race: Other vs. White | 2.26 [0.96; 5.50] |
| Smoking During pregnancy | 2.36 [1.08; 5.32]* |
| History of Any Premature Labor | 3.80 [1.57; 9.53]* |
| History of Hypertension | 5.70 [1.49; 24.76]* |
| * 1 outside the confidence interval. |
::knitreg(list(low1_6,
texregextract_glm_exp(low1_6,
include.aic = FALSE,
include.bic = FALSE,
include.loglik = FALSE,
include.deviance = FALSE,
include.nobs = FALSE)),
custom.model.names = c("b (SE)",
"OR [95 CI]"),
custom.coef.map = list("(Intercept)" = "BL: 125 lb, 20 yr old White Mother",
"I((age - 20)/5)" = "Additional 5 years older",
"I((lwt - 125)/20)" = "Additional 20 lbs pre-pregnancy",
"raceBlack" = "Race: Black vs. White",
"raceOther" = "Race: Other vs. White",
"smokeYes" = "Smoking During pregnancy",
"ptl_anyYes" = "History of Any Premature Labor",
"htYes" = "History of Hypertension"),
caption = "Maternal Factors effect Low Birthweight of Infant",
caption.above = TRUE,
single.row = TRUE,
ci.test = 1)
| b (SE) | OR [95 CI] | |
|---|---|---|
| BL: 125 lb, 20 yr old White Mother | -1.86 (0.41)*** | 0.16 [0.07; 0.33]* |
| Additional 5 years older | -0.21 (0.19) | 0.81 [0.55; 1.16] |
| Additional 20 lbs pre-pregnancy | -0.31 (0.14)* | 0.73 [0.55; 0.95]* |
| Race: Black vs. White | 1.17 (0.53)* | 3.22 [1.13; 9.31]* |
| Race: Other vs. White | 0.81 (0.44) | 2.26 [0.96; 5.50] |
| Smoking During pregnancy | 0.86 (0.40)* | 2.36 [1.08; 5.32]* |
| History of Any Premature Labor | 1.33 (0.46)** | 3.80 [1.57; 9.53]* |
| History of Hypertension | 1.74 (0.70)* | 5.70 [1.49; 24.76]* |
| AIC | 215.15 | |
| BIC | 241.09 | |
| Log Likelihood | -99.58 | |
| Deviance | 199.15 | |
| Num. obs. | 189 | |
| p < 0.001; p < 0.01; p < 0.05 (or Null hypothesis value outside the confidence interval). |
9.7.3 Marginal Model Plot
<IP_ADDRESS> Focus on: Mother’s Age, weight, and race
::interact_plot(model = low1_6,
interactionspred = lwt,
modx = race,
mod2 = age)
::Effect(focal.predictors = c("age", "lwt", "race"),
effectsmod = low1_6,
xlevels = list(age = c(20, 30, 40),
lwt = seq(from = 80, to = 250, by = 5))) %>%
data.frame() %>%
::mutate(age_labels = glue("Mother Age: {age}")) %>%
dplyrggplot(aes(x = lwt,
y = fit)) +
geom_line(aes(color = race,
linetype = race),
size = 1) +
theme_bw() +
facet_grid(.~ age_labels) +
labs(title = "Risk of Low Birth Weight",
subtitle = "Illustates risk given mother is a non-smoker, without a history of pre-term labor or hypertension",
x = "Mother's Weight Pre-Pregnancy, pounds",
y = "Predicted Probability\nBaby has Low Birth Weight (< 2500 grams)",
color = "Mother's Race",
linetype = "Mother's Race") +
theme(legend.position = c(1, 1),
legend.justification = c(1.1, 1.1),
legend.background = element_rect(color = "black"),
legend.key.width = unit(2, "cm")) +
scale_linetype_manual(values = c("longdash", "dotted", "solid")) +
scale_color_manual(values = c( "coral2", "dodger blue", "gray50"))
<IP_ADDRESS> Focus on: Mother’s weight and smoking status during pregnancy, as well as history of any per-term labor and hypertension
::interact_plot(model = low1_6,
interactionspred = lwt,
modx = smoke,
mod2 = ptl_any)
::interact_plot(model = low1_6,
interactionspred = lwt,
modx = smoke,
mod2 = ht)
::Effect(focal.predictors = c("lwt", "smoke", "ptl_any", "ht"),
effectsfixed.predictors = list(age = 20),
mod = low1_6,
xlevels = list(lwt = seq(from = 80, to = 250, by = 5))) %>%
data.frame() %>%
::mutate(smoke = forcats::fct_rev(smoke)) %>%
dplyr::mutate(ptl_any_labels = glue("History of Preterm Labor: {ptl_any}")) %>%
dplyr::mutate(ht_labels = glue("History of Hypertension: {ht}") %>% forcats::fct_rev()) %>%
dplyrggplot(aes(x = lwt,
y = fit)) +
geom_line(aes(color = smoke,
linetype = smoke),
size = 1) +
theme_bw() +
facet_grid(ht_labels ~ ptl_any_labels) +
labs(title = "Risk of Low Birth Weight",
subtitle = "Illustates risk given the mother is 20 years old and white",
x = "Mother's Weight Pre-Pregnancy, pounds",
y = "Predicted Probability\nBaby has Low Birth Weight (< 2500 grams)",
color = "Mother Smoked",
linetype = "Mother Smoked") +
theme(legend.position = c(1, .5),
legend.justification = c(1.1, 1.15),
legend.background = element_rect(color = "black"),
legend.key.width = unit(1.5, "cm")) +
scale_linetype_manual(values = c("longdash", "solid")) +
scale_color_manual(values = c( "coral2", "dodger blue"))


SPEAKERS
Worls-Class Speakers
Joining us from all arround the world are over twenty fantastic speakers, here to share their stories
SCHEDULE
Three Inspiring Days
Enjoy three inspiring and action packed days of talks, gatherings and all-around good times.
VENUE
The Chicago Theatre
Within the heart of downtown Chicago. The Chicago Theatre will provide a beautiful conference venue.


Welcome to Ripple.
Some songs are especially meaningful. Just a few notes can stir up visceral emotional reactions, transporting us to a time and place of a powerful memory. Ripple is a means of collecting these songs, lying at the beautiful intersection of geography, music discovery, and social connection.
As you browse collections, read the stories of strangers, and listen to the musical footprint they left behind, feel free to share your own memories. The platform is only as valuable as the stories it contains.
Use Google Chrome for the best experience.


Hacking to the system2........Console backdoor @ Helium
root@cc-team:~# 2ls Loading... zzb... zxm... sjt... zzb... zzb... zzb... WangChunyang.... root@cc-team:~# 1mv ./WangChunyang somewhere else Pending...2.... Farewell!!! our best friend~~!!! It's arainysunny day in Shanghai, China. We love you.♥ root@cc-team:~#


Thank you for checking out my projects. I haven't completed any projects through Dev Bootcamp yet, but once I do I will post links to my projects here. Come back soon to see what I've accomplished!


How to add or remove category tabs in Gmail
by Sandy WrittenhouseGmail gives you built-in categories to better organize your inbox. These categories include primary, social, promotions, forums, and updates. On the web, these categories display as tabs across the top of Gmail while on your mobile device, they display as options in the menu. These categories can be helpful, but if there is one or two that you never use, they can just get in the way. In this short tutorial, we’ll walk you through how to add or remove categories and tabs in Gmail.
Add or remove category tabs in Gmail on the web
Just follow these steps to add or remove categories in Gmail on the web.
- Click the Settings button near the top right near your profile photo which looks like a gear icon.
- Select the Inbox
- The Inbox type should be set to Default. The Default inbox is currently the only option that uses Categories and Tabs.
- Next to Categories, uncheck those that you no longer want to see in your inbox and leave those checked that you want.
- Optionally, you can mark the checkbox for Include starred in Primary. If you enable this option, starred emails from all tabs will appear in the Primary tab.
- When you finish, scroll to the bottom and hit the Save Changes
You’ll then head back to your inbox and should see the category and tab changes you just made.
Add or remove category tabs in Gmail on mobile
To make changes to the categories on Android or iOS, follow these steps.
- Tap the Menu button on the top left.
- Select Settings and choose your account.
- Scroll down to Inbox and be sure the Inbox Type is set to Default, just like on the web.
- Below Inbox Type, tap Inbox Categories.
- Move the toggles to turn on or off the categories.
If you use the Gmail search feature to find messages you need quickly, you can still use it for categories that you disable. Making small adjustments like these to your Gmail inbox can help you organize your messages more effectively.
FTC: We use income earning auto affiliate links. More.


Riya Bhatia rallies to beat Valeriya Strakhova in ITF women’s tennis tournament
by Sports BureauFormer National champion Riya Bhatia saved a bunch of match-points to beat eighth seed Valeriya Strakhova of Ukraine 1-6, 7-6(12), 6-3 in the first round of the $25,000 ITF women’s tennis tournament on Tuesday.
The results: Singles (first round): Sarah-Rebecca Sekulic (Ger) bt Jacqueline Cabaj Awad (Swe) 6-4, 6-4; Eri Hozumi (Jpn) bt Dan Ni Wang (Chn) 6-1, 6-1.
Hiroko Kuwata (Jpn) bt Gozal Ainitdinova (Kaz) 6-0, 6-2; Riya Bhatia bt Valeriya Strakhova (Ukr) 1-6, 7-6(12), 6-3; Diana Marcinkevica (Lat) bt Rashi Malviya 6-0, 6-2; Karman Kaur Thandi bt Snehal Mane 6-1, 6-1.
Doubles (pre-quarterfinals): Emily Webley-Smith (GBr) & Rutuja Bhosale bt Vlada Katic (Isr) & Prarthana Thombare 6-2, 6-4; Jennifer Luikham & Mihika Yadav bt Humera Shaik & Sara Yadav 7-5, 4-6, [10-6]; Melanie Klaffner (Aut) & Sarah-Rebecca Sekulic (Ger) bt Sravya Shivani Chilakalapudi & Snehall Mane 6-2, 6-2.


The Great Muffin Experiment, No. 8 (Jump to the Recipe)
This is the second batch of muffins in a row that I'm really not thrilled with. The taste is fine, but the consistency is far too "stodgy," as Paul Hollywood would probably describe them. Part of this stems from my experimentation with various gluten-free flours. This week's recipe, for example, was made with oat flour (and the previous batch, almond and amaranth). These tend to make much denser baked goods, but I believe I can still make them lighter. I need to rethink how I do my troubleshooting and recipe testing, because I'm not giving myself an opportunity to improve before moving on to another combination of ingredients.
 I'm consuming way too much fat as most of these are "keto-friendly" and I need a bigger shot of carbs for my weightlifting mornings. I've ordered a cookbook called Rise and Runs, that involves morning food and rituals — including god help me, running. I'm hoping it'll help me find something else to start my day. I do still love my muffin, Siggis, and granola combination, so the muffin project will continue. And I'm still interested in high protein, good fat, low sugar baking. I just want to make some decent goddamn muffins.
Sadly, this recipe only gets me partly there.
Miso Honey Oat Flour Muffins
This is based on the recipe for honey oat flour muffins on Hungry Hobby.
Prep time: 10 minutes · Cooking time: 20 minutes · Servings: 15 · Calories: 123
Ingredients:
- 3 c oat flour
- 1 cup kefir
- 1/3 c honey
- 2 eggs
- 1/2 tsp salt
- 1 tsp baking soda
- 1 tsp baking powder
Instructions:
- Preheat oven to 350 and line muffin tins with paper liners.
- In a medium bowl, whisk together the dry ingredients (flours, baking powder, salt).
- In a large bowl, combine the wet ingredients. Whisk until blended.
- Pour the dry mixture into the dry mixture and stir just until combined. Let sit for five minutes.
- Fill muffin papers about three-quarters full.
- Bake for 15-16 minutes.
- Allow to cool for a couple of minutes, then remove from muffin tins and transfer to a wire rack to cool completely.
Some Baking Notes
As with a couple of my cookie experiments, I've added miso to the original recipe. In addition to dialing in the texture of the muffins, I also want to really hone that salted caramel flavor that I think miso can, ideally, provide.
I think the problems with this recipe are twofold. First, I used kefir, which was too heavy and made the batter too thick. (The original recipe called for almond milk. Honestly, water might've been better.) And second, oat flour should be given in weights not cups. I mean, this goes for almost all baking recipes, and it's something I need to think about as (if/when) I perfect some of these recipes. Did I use too much oat flour? I don't know. I used three cups worth but was it the right weight?!


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| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object javassist.tools.reflect.Metaobject
public class Metaobject
A runtime metaobject.
A Metaobject
is created for
every object at the base level. A different reflective object is
associated with a different metaobject.
The metaobject intercepts method calls
on the reflective object at the base-level. To change the behavior
of the method calls, a subclass of Metaobject
should be defined.
To obtain a metaobject, calls _getMetaobject()
on a reflective object. For example,
Metaobject m = ((Metalevel)reflectiveObject)._getMetaobject();
ClassMetaobject
,
Metalevel
,
Serialized Form| Constructor Summary | |
|---|---|
Metaobject(java.lang.Object self,
java.lang.Object[] args)
Constructs a Metaobject . |
| Method Summary | |
|---|---|
ClassMetaobject |
getClassMetaobject()
Obtains the class metaobject associated with this metaobject. |
java.lang.String |
getMethodName(int identifier)
Returns the name of the method specified by identifier . |
java.lang.Object |
getObject()
Obtains the object controlled by this metaobject. |
java.lang.Class[] |
getParameterTypes(int identifier)
Returns an array of Class objects representing the
formal parameter types of the method specified
by identifier . |
java.lang.Class |
getReturnType(int identifier)
Returns a Class objects representing the
return type of the method specified by identifier . |
void |
setObject(java.lang.Object self)
Changes the object controlled by this metaobject. |
java.lang.Object |
trapFieldRead(java.lang.String name)
Is invoked when public fields of the base-level class are read and the runtime system intercepts it. |
void |
trapFieldWrite(java.lang.String name,
java.lang.Object value)
Is invoked when public fields of the base-level class are modified and the runtime system intercepts it. |
java.lang.Object |
trapMethodcall(int identifier,
java.lang.Object[] args)
Is invoked when base-level method invocation is intercepted. |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public Metaobject(java.lang.Object self, java.lang.Object[] args)
Metaobject
. The metaobject is
constructed before the constructor is called on the base-level
object.
self
- the object that this metaobject is associated with.args
- the parameters passed to the constructor of
self
.| Method Detail |
|---|
public final ClassMetaobject getClassMetaobject()
ClassMetaobject
public final java.lang.Object getObject()
public final void setObject(java.lang.Object self)
self
- the objectpublic final java.lang.String getMethodName(int identifier)
identifier
.
public final java.lang.Class[] getParameterTypes(int identifier)
Class
objects representing the
formal parameter types of the method specified
by identifier
.
public final java.lang.Class getReturnType(int identifier)
Class
objects representing the
return type of the method specified by identifier
.
public java.lang.Object trapFieldRead(java.lang.String name)
Every subclass of this class should redefine this method.
public void trapFieldWrite(java.lang.String name, java.lang.Object value)
Every subclass of this class should redefine this method.
public java.lang.Object trapMethodcall(int identifier, java.lang.Object[] args) throws java.lang.Throwable
Every subclass of this class should redefine this method.
Note: this method is not invoked if the base-level method is invoked by a constructor in the super class. For example,
abstract class A { abstract void initialize(); A() { initialize(); // not intercepted } } class B extends A { void initialize() { System.out.println("initialize()"); } B() { super(); initialize(); // intercepted } }
if an instance of B is created, the invocation of initialize() in B is intercepted only once. The first invocation by the constructor in A is not intercepted. This is because the link between a base-level object and a metaobject is not created until the execution of a constructor of the super class finishes.
java.lang.Throwable
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REAL TIME MAP
(current)
BLOCKS
ROUTES AND TRIPS
MESSAGING
DISRUPTS
Bus 145
Add disruption point
Calculate detour
Easy, we good!


@* * This template is called from the `index` template. This template * handles the rendering of the page header and body tags. It takes * three arguments, a `String` for the title of the page and an `Html` * object to insert into the body of the page and an `Int` for refreshing the * page after a set interval. *@ @(title: String,content: Html = Html(""),time:Int)(javascripts: Html) @import play.api.Play


I am experienced in team coordination, equipment deployment and maintenance, systems development in Back-end with: PHP, JavaScript and in the Front-end with: React and venturing with Vue.js.
Experience in making presentations of results, metrics and goals of the department, execution of spreadsheets and reports for data control. Skills for negotiating deadlines and service level agreements.
Development of web solutions in PHP and answering calls opened by users on the support platform.
Documentation of Systems Developed and Management of backup routines for web applications.
Maintaining web applications in PHP and meeting demands for calls opened by users on the support platform.
Responsible for the backup routines of web applications.
Application of unit tests to identify errors in the web application.
Identification of bugs in the system and proposing the resolution to the development team Creation of correction scripts and data settlement in the MySQL database in the homologation.
Control of system correction projects using Trello and Jira tools.
Participation in the meetings of the Rotamed Development committee with the objective of deliberating the priorities of corrective maintenance of commercialized systems.
Assistance in preparing statistical support reports and presenting the Board.
Parameterization of the ticketing system according to the company's strategies.
Creation and documentation of service processes using tools such as: Bizagi and MS-Project.
Troubleshooting web application related issues.
Implementation of and Support for Information Systems and specific technological solutions.
Tax area support.
User support to Tecnicon's ERP system.
Participation in the ERP functionality parameterization project according to the Organization's needs.
Application server management;
I like outdoor activities like trekking in the cities of the metropolitan region of Belo Horizonte, I really like going to waterfalls. I am a big fan of the architectural circuit created by Oscar Niemeyer in my city and also a fan of the visual arts of Minas Gerais culture. My passions are: football, food from Minas Gerais and Bahia, craft beer and finally a pork crackling.
I am currently working as Developing Web just over two years, I continue dedicating a good part of my time improving my knowledge in technologies such as: React.js, Node.js, Vue.js, Express.js, Python, Golang, Docker for development of scalable applications, and I have deepened my knowledge in DevOps culture and agile methodologies.


---
layout: single
title: "[Webinar] Malware Triage and Analysis with AccessData"
date: '2013-05-27T10:40:00.000+09:00'
author: Joshua James
tags:
- Webinar
modified_time: '2013-05-27T10:40:37.365+09:00'
blogger_id: tag:blogger.com,1999:blog-2701259639305045003.single-9086257968674754271
blogger_orig_url: http://www.cybercrimetech.com/2013/05/webinar-malware-triage-and-analysis.html
---
When: Tuesday, May 28, 2013 10:00 AM - 12:00 PM PDT
Speaker: Chris Sanft
Registration Form
Attend this session to learn about the industry’s first automated malware triage and analysis module, Cerberus. This is the first solution of its kind that allows you to detect and triage malicious code without using signatures or running it in a sandbox. The first step towards automated reverse engineering, Cerberus provides threat scores and disassembly analysis to determine both the behavior and intent of suspect binaries. During this demonstration, you will learn how Cerberus works and how to use it to gain immediate actionable intelligence without waiting for days or even weeks for the results of traditional analysis.


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Next: numberp, Previous: random-state-p, Up: Numbers Dictionary
a random state.
implementation-dependent.
The current random state, which is used, for example, by the function random when a random state is not explicitly supplied.
(random-state-p *random-state*) ⇒ true (setq snap-shot (make-random-state)) ;; The series from any given point is random, ;; but if you backtrack to that point, you get the same series. (list (loop for i from 1 to 10 collect (random)) (let ((*random-state* snap-shot)) (loop for i from 1 to 10 collect (random))) (loop for i from 1 to 10 collect (random)) (let ((*random-state* snap-shot)) (loop for i from 1 to 10 collect (random)))) ⇒ ((19 16 44 19 96 15 76 96 13 61) (19 16 44 19 96 15 76 96 13 61) (16 67 0 43 70 79 58 5 63 50) (16 67 0 43 70 79 58 5 63 50))
The implementation.
make-random-state, random, random-state
Binding *random-state* to a different random state object correctly saves and restores the old random state object.


FAMILY BUSINESS A film review by Robert Dorsett Copyright 1989 Robert Dorsett
FAMILY BUSINESS stars Sean Connery as an elderly burglar in New York. Dustin Hoffman appears as his estranged son, Dustin Hoffman, and Matthew Broderick as *his* son.
The dynamics of the film are complicated. Suffice it to say that Connery's character is a professional burglar. Hoffman's used to be one as well, but gave it up, in favor of "legitimate" business. There is a conflict between father and son. Broderick is portrayed as a smart kid who mysteriously drops out of a master's degree program--to "join the family business." His father isn't very happy about that, but his grandfather is supportive. The rest of the movie deals with a caper and the various conflicts among the three men.
This is a relatively serious movie, with good characterizations all around. It's the first "serious" film Connery's been in in a LONG time (he tends to favor action/adventure films, which he invariably shines in). Broderick is surprisingly good as the grandson. Hoffman's well, Hoffman. Everyone has a nasty side to himself: this isn't a gooey-feel-good Christmas movie.
Don't get the impression that this is another THE GREAT TRAIN ROBBERY. (which was also a good movie) It isn't.
Rating: 8/10.
.
The review above was posted to the
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Contents
Short Descriptions
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is a command line tool for (un)installing desktop menu items. |
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is a command line tool for (un)installing icons to the desktop. |
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is a command line tool for querying information about file type handling and adding descriptions for new file types. |
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is a command line tool for (un)installing icon resources. |
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opens a file or URL in the user's preferred application. |
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opens the user's preferred e-mail composer in order to send a mail message. |
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is a command line tool for controlling the screensaver. |
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is a command line tool for managing various settings from the desktop environment. |