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rebound | Java library that models spring dynamics | Animation library

 by   facebookarchive Java Version: v0.3.8 License: Non-SPDX

 by   facebookarchive Java Version: v0.3.8 License: Non-SPDX

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kandi X-RAY | rebound Summary

rebound is a Java library typically used in User Interface, Animation applications. rebound has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However rebound has a Non-SPDX License. You can download it from GitHub.
Rebound is a java library that models spring dynamics. Rebound spring models can be used to create animations that feel natural by introducing real world physics to your application. Rebound is not a general purpose physics library; however, spring dynamics can be used to drive a wide variety of animations. The simplicity of Rebound makes it easy to integrate and use as a building block for creating more complex components like pagers, toggles, and scrollers.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • rebound has a medium active ecosystem.
  • It has 5470 star(s) with 865 fork(s). There are 398 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 23 open issues and 27 have been closed. On average issues are closed in 23 days. There are 8 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of rebound is v0.3.8
rebound Support
Best in #Animation
Average in #Animation
rebound Support
Best in #Animation
Average in #Animation

quality kandi Quality

  • rebound has 0 bugs and 0 code smells.
rebound Quality
Best in #Animation
Average in #Animation
rebound Quality
Best in #Animation
Average in #Animation

securitySecurity

  • rebound has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • rebound code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
rebound Security
Best in #Animation
Average in #Animation
rebound Security
Best in #Animation
Average in #Animation

license License

  • rebound has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
rebound License
Best in #Animation
Average in #Animation
rebound License
Best in #Animation
Average in #Animation

buildReuse

  • rebound releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • rebound saves you 2058 person hours of effort in developing the same functionality from scratch.
  • It has 4519 lines of code, 381 functions and 56 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
rebound Reuse
Best in #Animation
Average in #Animation
rebound Reuse
Best in #Animation
Average in #Animation
Top functions reviewed by kandi - BETA

kandi has reviewed rebound and discovered the below as its top functions. This is intended to give you an instant insight into rebound implemented functionality, and help decide if they suit your requirements.

  • Advances to the current velocity .
  • Handle a touch event .
  • Generate the hierarchy .
  • Called when an item is clicked .
  • Handle a row touch event .
  • Renders the views .
  • Run the animation on a frame time .
  • Check the constraints .
  • Computes the layout of the image .
  • SpringSpringBinding .

rebound Key Features

A Java library that models spring dynamics and adds real world physics to your app.

Getting Durbin-Watson figure from statsmodels.api

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from statsmodels.formula.api import ols
from statsmodels.stats.stattools import durbin_watson
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.standard_normal((500,4)))
df.columns = ["rating", "points", "assists", "rebounds"]


#fit multiple linear regression model
model = ols('rating ~ points + assists + rebounds', data=df)
res = model.fit()

dw = durbin_watson(res.resid)
print(f"Durbin-Watson: {dw}")
Durbin-Watson: 1.9818102986170278
from statsmodels.stats.diagnostic import acorr_ljungbox
lb = acorr_ljungbox(res.resid)
print(lb)
     lb_stat  lb_pvalue
1   0.003400   0.953500
2   0.774305   0.678988
3   1.412020   0.702720
4   1.890551   0.755881
5   2.176684   0.824197
6   2.397583   0.879749
7   3.186928   0.867188
8   3.639602   0.888089
9   3.793818   0.924451
10  5.639786   0.844565
-----------------------
from statsmodels.formula.api import ols
from statsmodels.stats.stattools import durbin_watson
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.standard_normal((500,4)))
df.columns = ["rating", "points", "assists", "rebounds"]


#fit multiple linear regression model
model = ols('rating ~ points + assists + rebounds', data=df)
res = model.fit()

dw = durbin_watson(res.resid)
print(f"Durbin-Watson: {dw}")
Durbin-Watson: 1.9818102986170278
from statsmodels.stats.diagnostic import acorr_ljungbox
lb = acorr_ljungbox(res.resid)
print(lb)
     lb_stat  lb_pvalue
1   0.003400   0.953500
2   0.774305   0.678988
3   1.412020   0.702720
4   1.890551   0.755881
5   2.176684   0.824197
6   2.397583   0.879749
7   3.186928   0.867188
8   3.639602   0.888089
9   3.793818   0.924451
10  5.639786   0.844565
-----------------------
from statsmodels.formula.api import ols
from statsmodels.stats.stattools import durbin_watson
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.standard_normal((500,4)))
df.columns = ["rating", "points", "assists", "rebounds"]


#fit multiple linear regression model
model = ols('rating ~ points + assists + rebounds', data=df)
res = model.fit()

dw = durbin_watson(res.resid)
print(f"Durbin-Watson: {dw}")
Durbin-Watson: 1.9818102986170278
from statsmodels.stats.diagnostic import acorr_ljungbox
lb = acorr_ljungbox(res.resid)
print(lb)
     lb_stat  lb_pvalue
1   0.003400   0.953500
2   0.774305   0.678988
3   1.412020   0.702720
4   1.890551   0.755881
5   2.176684   0.824197
6   2.397583   0.879749
7   3.186928   0.867188
8   3.639602   0.888089
9   3.793818   0.924451
10  5.639786   0.844565
-----------------------
from statsmodels.formula.api import ols
from statsmodels.stats.stattools import durbin_watson
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.standard_normal((500,4)))
df.columns = ["rating", "points", "assists", "rebounds"]


#fit multiple linear regression model
model = ols('rating ~ points + assists + rebounds', data=df)
res = model.fit()

dw = durbin_watson(res.resid)
print(f"Durbin-Watson: {dw}")
Durbin-Watson: 1.9818102986170278
from statsmodels.stats.diagnostic import acorr_ljungbox
lb = acorr_ljungbox(res.resid)
print(lb)
     lb_stat  lb_pvalue
1   0.003400   0.953500
2   0.774305   0.678988
3   1.412020   0.702720
4   1.890551   0.755881
5   2.176684   0.824197
6   2.397583   0.879749
7   3.186928   0.867188
8   3.639602   0.888089
9   3.793818   0.924451
10  5.639786   0.844565

Display dynamic field inputs in JSON - Jquery

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function convertFormToJSON(form) {
  const vals = $(form).serializeArray(); // Encodes the set of form elements as an 
  return vals.reduce((acc,cur) => {
    if (cur.name.startsWith('playerName')) {
      acc.push({[cur.name]:cur.value})
    }
    else acc[acc.length-1][cur.name] = cur.value
    return acc;
  },[])
}
$(document).ready(function() {
  var maxField = 10; //Input fields increment limitation
  var addButton = $('.add_button'); //Add button selector
  var wrapper = $('.field_wrapper'); //Input field wrapper
  var fieldHTML = '<div id="legs[]""><input type="text" id="name" name="playerName[]" placeholder="Enter player name" required=""/><select class="statType" id="statType" name="statType[]"><option id="1">Points</option><option id="2">Rebounds</option><option id="3">Assists</option></select><select class="overUnder" id="overUnder" name ="overUnder[]"><option id="over">Over</option><option id="under">Under</option></select><input type="number" id="amount" class="amount" name="statAmount[]" required=""/><a href="javascript:void(0);" class="remove_button" title="Remove field">Remove Leg</a></div>'
  var x = 1; //Initial field counter is 1

  //Once add button is clicked
  $(addButton).click(function() {
    //Check maximum number of input fields
    if (x < maxField) {
      x++; //Increment field counter
      $(wrapper).append(fieldHTML); //Add field html
    }
  });

  //Once remove button is clicked
  $(wrapper).on('click', '.remove_button', function(e) {
    e.preventDefault();
    $(this).parent('div').remove(); //Remove field html
    x--; //Decrement field counter
  });
});

function convertFormToJSON(form) {
  const vals = $(form).serializeArray(); // Encodes the set of form elements as an 
  return vals.reduce((acc,cur) => {
    if (cur.name.startsWith('playerName')) {
      acc.push({[cur.name]:cur.value})
    }
    else acc[acc.length-1][cur.name] = cur.value
    return acc;
  },[])
}

$("#bet-slip").on("submit", function(e) {
  e.preventDefault();
  const form = $(e.target);
//  console.log(form);
  const json = convertFormToJSON(form);
  console.log(json);
});
<form id="bet-slip">
  <div class="field_wrapper">
    <div id="legs[]">
      <input type="text" id="name" name="playerName[]" placeholder="Enter player name" required="" />
      <select class="statType" id="statType" name="statType[]">
        <option value="1">Points</option>
        <option value="2">Rebounds</option>
        <option value="3">Assists</option>
      </select>
      <select class="overUnder" id="overUnder" name="overUnder[]">
        <option id="over">Over</option>
        <option id="under">Under</option>
      </select>
      <input type="number" id="amount" class="amount" name="statAmount[]" required="" />
      <a href="javascript:void(0);" class="add_button" title="Add field">Add Leg</a>
    </div>
  </div>
  <button type="submit">Submit Legs</button>
</form>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
-----------------------
function convertFormToJSON(form) {
  const vals = $(form).serializeArray(); // Encodes the set of form elements as an 
  return vals.reduce((acc,cur) => {
    if (cur.name.startsWith('playerName')) {
      acc.push({[cur.name]:cur.value})
    }
    else acc[acc.length-1][cur.name] = cur.value
    return acc;
  },[])
}
$(document).ready(function() {
  var maxField = 10; //Input fields increment limitation
  var addButton = $('.add_button'); //Add button selector
  var wrapper = $('.field_wrapper'); //Input field wrapper
  var fieldHTML = '<div id="legs[]""><input type="text" id="name" name="playerName[]" placeholder="Enter player name" required=""/><select class="statType" id="statType" name="statType[]"><option id="1">Points</option><option id="2">Rebounds</option><option id="3">Assists</option></select><select class="overUnder" id="overUnder" name ="overUnder[]"><option id="over">Over</option><option id="under">Under</option></select><input type="number" id="amount" class="amount" name="statAmount[]" required=""/><a href="javascript:void(0);" class="remove_button" title="Remove field">Remove Leg</a></div>'
  var x = 1; //Initial field counter is 1

  //Once add button is clicked
  $(addButton).click(function() {
    //Check maximum number of input fields
    if (x < maxField) {
      x++; //Increment field counter
      $(wrapper).append(fieldHTML); //Add field html
    }
  });

  //Once remove button is clicked
  $(wrapper).on('click', '.remove_button', function(e) {
    e.preventDefault();
    $(this).parent('div').remove(); //Remove field html
    x--; //Decrement field counter
  });
});

function convertFormToJSON(form) {
  const vals = $(form).serializeArray(); // Encodes the set of form elements as an 
  return vals.reduce((acc,cur) => {
    if (cur.name.startsWith('playerName')) {
      acc.push({[cur.name]:cur.value})
    }
    else acc[acc.length-1][cur.name] = cur.value
    return acc;
  },[])
}

$("#bet-slip").on("submit", function(e) {
  e.preventDefault();
  const form = $(e.target);
//  console.log(form);
  const json = convertFormToJSON(form);
  console.log(json);
});
<form id="bet-slip">
  <div class="field_wrapper">
    <div id="legs[]">
      <input type="text" id="name" name="playerName[]" placeholder="Enter player name" required="" />
      <select class="statType" id="statType" name="statType[]">
        <option value="1">Points</option>
        <option value="2">Rebounds</option>
        <option value="3">Assists</option>
      </select>
      <select class="overUnder" id="overUnder" name="overUnder[]">
        <option id="over">Over</option>
        <option id="under">Under</option>
      </select>
      <input type="number" id="amount" class="amount" name="statAmount[]" required="" />
      <a href="javascript:void(0);" class="add_button" title="Add field">Add Leg</a>
    </div>
  </div>
  <button type="submit">Submit Legs</button>
</form>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
-----------------------
function convertFormToJSON(form) {
  const vals = $(form).serializeArray(); // Encodes the set of form elements as an 
  return vals.reduce((acc,cur) => {
    if (cur.name.startsWith('playerName')) {
      acc.push({[cur.name]:cur.value})
    }
    else acc[acc.length-1][cur.name] = cur.value
    return acc;
  },[])
}
$(document).ready(function() {
  var maxField = 10; //Input fields increment limitation
  var addButton = $('.add_button'); //Add button selector
  var wrapper = $('.field_wrapper'); //Input field wrapper
  var fieldHTML = '<div id="legs[]""><input type="text" id="name" name="playerName[]" placeholder="Enter player name" required=""/><select class="statType" id="statType" name="statType[]"><option id="1">Points</option><option id="2">Rebounds</option><option id="3">Assists</option></select><select class="overUnder" id="overUnder" name ="overUnder[]"><option id="over">Over</option><option id="under">Under</option></select><input type="number" id="amount" class="amount" name="statAmount[]" required=""/><a href="javascript:void(0);" class="remove_button" title="Remove field">Remove Leg</a></div>'
  var x = 1; //Initial field counter is 1

  //Once add button is clicked
  $(addButton).click(function() {
    //Check maximum number of input fields
    if (x < maxField) {
      x++; //Increment field counter
      $(wrapper).append(fieldHTML); //Add field html
    }
  });

  //Once remove button is clicked
  $(wrapper).on('click', '.remove_button', function(e) {
    e.preventDefault();
    $(this).parent('div').remove(); //Remove field html
    x--; //Decrement field counter
  });
});

function convertFormToJSON(form) {
  const vals = $(form).serializeArray(); // Encodes the set of form elements as an 
  return vals.reduce((acc,cur) => {
    if (cur.name.startsWith('playerName')) {
      acc.push({[cur.name]:cur.value})
    }
    else acc[acc.length-1][cur.name] = cur.value
    return acc;
  },[])
}

$("#bet-slip").on("submit", function(e) {
  e.preventDefault();
  const form = $(e.target);
//  console.log(form);
  const json = convertFormToJSON(form);
  console.log(json);
});
<form id="bet-slip">
  <div class="field_wrapper">
    <div id="legs[]">
      <input type="text" id="name" name="playerName[]" placeholder="Enter player name" required="" />
      <select class="statType" id="statType" name="statType[]">
        <option value="1">Points</option>
        <option value="2">Rebounds</option>
        <option value="3">Assists</option>
      </select>
      <select class="overUnder" id="overUnder" name="overUnder[]">
        <option id="over">Over</option>
        <option id="under">Under</option>
      </select>
      <input type="number" id="amount" class="amount" name="statAmount[]" required="" />
      <a href="javascript:void(0);" class="add_button" title="Add field">Add Leg</a>
    </div>
  </div>
  <button type="submit">Submit Legs</button>
</form>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>

Keras TextVectorization adapt throws AttributeError

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import tensorflow as tf


d ={"Title": ["Malaysia testing 3 people for bird flu; says outbreak isolate",
           "Kroger's Profit Climbs, Misses Forecast (Reuters)",
           "Blasts Shake Najaf as U.S. Planes Attack Rebels"], 
 "Description": [
                 "Kerry Camp Makes Video to Defuse Attacks (AP)", 
                 "Malaysian officials on Saturday were testing three people who fell ill in a village hit by the deadly H5N1 bird flu strain, after international health officials warned that the virus appeared to be entrenched in parts of ", 
                 " NAJAF, Iraq (Reuters) - Strong blasts were heard in the  besieged city of Najaf early Sunday as U.S. military planes  unleashed cannon and howitzer fire and a heavy firefight  erupted."
 ]}

train_text = tf.data.Dataset.from_tensor_slices(d).batch(2)

max_features = 5000
sequence_length = 250

vectorize_layer = tf.keras.layers.TextVectorization(
    max_tokens=max_features,
    output_mode='int',
    output_sequence_length=sequence_length)

#This example assumes that you have already excluded the labels.
#train_text = raw_train_ds.map(lambda x, y: x)

train_text = train_text.map(lambda x: tf.concat([x['Title'], x['Description']], axis=0))
vectorize_layer.adapt(train_text)

Computing relative frequencies based on dictionary

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library("quanteda")
## Package version: 3.2
## Unicode version: 13.0
## ICU version: 69.1
## Parallel computing: 12 of 12 threads used.
## See https://quanteda.io for tutorials and examples.

tok_before_failure <- tokens(tail(data_corpus_inaugural, 5))
dict <- data_dictionary_LSD2015[1:2]

tokens_lookup(tok_before_failure, data_dictionary_LSD2015[1:2], nomatch = "other") %>%
  dfm() %>%
  dfm_weight(scheme = "prop")
## Document-feature matrix of: 5 documents, 3 features (0.00% sparse) and 4 docvars.
##             features
## docs           negative   positive     other
##   2005-Bush  0.03719723 0.09169550 0.8711073
##   2009-Obama 0.04428731 0.07182732 0.8838854
##   2013-Obama 0.03366422 0.07337074 0.8929650
##   2017-Trump 0.02831325 0.07409639 0.8975904
##   2021-Biden 0.04049168 0.06182213 0.8976862

Django URL dispatcher and lists?

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# no need to convert to list type 
items = list(Nbav8.objects.using('totals').all())

Pandas append row without specifying columns

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>>> df
  points rebounds assists
3     10        7      11
1     12        7       8
2     12        8      10
>>> df.loc[max(df.index) + 1] = 'my', 'new', 'row'
>>> df
  points rebounds assists
3     10        7      11
1     12        7       8
2     12        8      10
4     my      new     row
-----------------------
>>> df
  points rebounds assists
3     10        7      11
1     12        7       8
2     12        8      10
>>> df.loc[max(df.index) + 1] = 'my', 'new', 'row'
>>> df
  points rebounds assists
3     10        7      11
1     12        7       8
2     12        8      10
4     my      new     row

Django filter column with OR statement

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home_team_list2 = PreviousLossesNbav1WithDateAgg.objects.filter(
Q(away_team_field="Chicago") | Q(home_team_field="Chicago")
).values_list('actual_over_under_result_field', flat=True)
SELECT `previous_losses_nbav1_with_date_agg`.`ACTUAL OVER UNDER RESULT:` 
FROM `previous_losses_nbav1_with_date_agg` WHERE 
(`previous_losses_nbav1_with_date_agg`.`AWAY TEAM:` = Chicago OR 
`previous_losses_nbav1_with_date_agg`.`HOME TEAM:` = Chicago)
-----------------------
home_team_list2 = PreviousLossesNbav1WithDateAgg.objects.filter(
Q(away_team_field="Chicago") | Q(home_team_field="Chicago")
).values_list('actual_over_under_result_field', flat=True)
SELECT `previous_losses_nbav1_with_date_agg`.`ACTUAL OVER UNDER RESULT:` 
FROM `previous_losses_nbav1_with_date_agg` WHERE 
(`previous_losses_nbav1_with_date_agg`.`AWAY TEAM:` = Chicago OR 
`previous_losses_nbav1_with_date_agg`.`HOME TEAM:` = Chicago)

Creating a scoring system in Pygame

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if tracker >= a and tracker < b:
  points += x
elif tracker >= b and tracker < c:
  points += x
elif tracker >= c and tracker <= d:
  points += x

C++ : rebind variable of generic type

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#include<iostream>
#include<utility>

struct C
{
    int n;
    static C make_new(int n) {return C{n};}
    ~C() = default;
    C(int n): n(n) {}
    C(const C &) = delete;
    C(C&&) = delete;
};

template <typename T>
void inline rebind(T& re_bin_ref, T&& val) {
    re_bin_ref.~T();
    new (&re_bin_ref) T {val};
}

template <typename T, typename... TArgs>
void inline emplace_rebind(T& re_bin_ref, TArgs&&... args) {
    re_bin_ref.~T();
    new (&re_bin_ref) T {std::forward<decltype(args)>(args)...};
}

template <typename T>
void inline procedural_rebind(T& re_bin_ref, auto f) {
    re_bin_ref.~T();
    new (&re_bin_ref) T { f() };
}

int main()
{
    
    std::cout << "\nREBINDING:\n";
    C re_bin = C::make_new(0);
    std::cout << "init: " << re_bin.n << std::endl;
    
    //...
    
    re_bin.~C();
    new (&re_bin) C {C{1}};
    std::cout << "pr-value: " << re_bin.n << std::endl;
    
    //...
    
    //re_bin.~C();
    //new (&re_bin) C {std::move<C>(C::make_new(2))}; //requires C&&
    //std::cout << "x-value: " << re_bin.n << std::endl;
    
    //...
    
    //rebind(re_bin, C::make_new(3)); //requires C&&
    //std::cout << "explicit rebind: " << re_bin.n << std::endl;

    //...
    
    emplace_rebind(re_bin, 4);
    std::cout << "explicit emplace rebind: " << re_bin.n << std::endl;

    //...

    procedural_rebind(re_bin, [](){return C{5};});
    std::cout << "explicit pr-value procedural rebind: " << re_bin.n << std::endl;
    
    return 0;
}
-----------------------
template <typename T> //unconstrained type
void func()
{
    //in some complex procedural logic
    std::optional<T> rebindable;
    rebindable.emplace(/*r-value*/);

    //...
    //some procedure dependent case:
    rebindable.reset();
    rebindable.emplace(/*different r-value*/);
}

How to change the range of theta for each plotly scatterpolar category

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import plotly.graph_objects as go

first = 'John'
last = 'Doe'

range_pts = 50
range_ast = 15
range_rbs = 20
range_stl = 5
range_blk = 5

ranges = [range_pts, range_ast, range_rbs, range_stl, range_blk]

categories = [f'Points ({range_pts})', f'Assists ({range_ast})', f'Rebounds ({range_rbs})', f'Steals ({range_stl})', f'Blocks ({range_blk})']
all_averages = [26, 7, 11, 2, 1]

for idx, value in enumerate(ranges):
    all_averages[idx] = all_averages[idx]/ranges[idx]


trace = go.Scatterpolar(r = all_averages, theta = categories, fill = 'toself', name = f'{first} {last}')
data = [trace]
figure = go.Figure(data = data, layout = None)
figure.update_polars(radialaxis=dict(visible=False,range=[0, 1]))
figure.show()

Community Discussions

Trending Discussions on rebound
  • Getting Durbin-Watson figure from statsmodels.api
  • Android build failed. showing &quot;Resource compilation failed. Check logs for details.&quot;
  • Display dynamic field inputs in JSON - Jquery
  • Keras TextVectorization adapt throws AttributeError
  • Computing relative frequencies based on dictionary
  • Django URL dispatcher and lists?
  • Pandas append row without specifying columns
  • Django filter column with OR statement
  • Cannot COPY into nonexistent table when table exists
  • Creating a scoring system in Pygame
Trending Discussions on rebound

QUESTION

Getting Durbin-Watson figure from statsmodels.api

Asked 2022-Mar-10 at 09:32

I can't extract the durbin-watson as a value on it's own from the statsmodel.api, or find anywhere any documentation to help (i found alot of documentation on it's parent library, but i couldn't decode any of it).

The value is being calculated and can be seen by doing the following model summary (i've been following the guidance here: https://www.statology.org/durbin-watson-test-python/)

from statsmodels.formula.api import ols

#fit multiple linear regression model
model = ols('rating ~ points + assists + rebounds', data=df).fit()

#view model summary
print(model.summary())

however i just can't pull out that one figure. Any ideas?

Also - it looks like DW works on a confidence interval. Can you 'set' the model to work on say 95% confidence? I essentially want to perform the test multiple times and if the DW figure is in the 95% CI, return a yes or no to continue the program.

Thanks

ANSWER

Answered 2022-Mar-10 at 09:32

You need to use the durbin_watson function directly.

from statsmodels.formula.api import ols
from statsmodels.stats.stattools import durbin_watson
import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.standard_normal((500,4)))
df.columns = ["rating", "points", "assists", "rebounds"]


#fit multiple linear regression model
model = ols('rating ~ points + assists + rebounds', data=df)
res = model.fit()

dw = durbin_watson(res.resid)
print(f"Durbin-Watson: {dw}")

which produces

Durbin-Watson: 1.9818102986170278

Critical values for DW statistics are not available in statsmodels. The Ljung-Box test for serial correlation is a more general approach that has critical values available.

from statsmodels.stats.diagnostic import acorr_ljungbox
lb = acorr_ljungbox(res.resid)
print(lb)

which gives

     lb_stat  lb_pvalue
1   0.003400   0.953500
2   0.774305   0.678988
3   1.412020   0.702720
4   1.890551   0.755881
5   2.176684   0.824197
6   2.397583   0.879749
7   3.186928   0.867188
8   3.639602   0.888089
9   3.793818   0.924451
10  5.639786   0.844565

The left column is the test statistic for no serial correlation and the right is the p-value.

Source https://stackoverflow.com/questions/71408558

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