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fonnesbeck/Technology Paper.ipynb

Created August 25, 2015 01:19
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Technology Paper.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Technology Effects Analysis"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Import modules and set options\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"pd.set_option('display.notebook_repr_html', False)\n",
"pd.set_option('display.max_columns', 20)\n",
"pd.set_option('display.max_rows', 100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/site-packages/pandas/io/parsers.py:1170: DtypeWarning: Columns (31,33,41,74) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" data = self._reader.read(nrows)\n"
]
}
],
"source": [
"lsl_dr = pd.read_csv('lsl_dr.csv', index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"0 initial_assessment_arm_1 2005-2006 0 1 0 0 3 \n",
"1 initial_assessment_arm_1 2005-2006 0 1 0 0 3 \n",
"2 initial_assessment_arm_1 2005-2006 0 1 0 0 3 \n",
"3 initial_assessment_arm_1 2005-2006 0 1 0 0 3 \n",
"4 initial_assessment_arm_1 2010-2011 0 0 7 0 2 \n",
"\n",
" _mother_ed father_ed premature_age ... known_synd \\\n",
"0 2 2 8 ... 0 \n",
"1 2 2 8 ... 0 \n",
"2 2 2 8 ... 0 \n",
"3 2 2 8 ... 0 \n",
"4 6 6 7 ... 0 \n",
"\n",
" synd_or_disab race age_test domain school score \\\n",
"0 NaN 0 48 Articulation 521 41 \n",
"1 NaN 0 48 Expressive Vocabulary 521 71 \n",
"2 NaN 0 48 Language 521 56 \n",
"3 NaN 0 48 Language 521 55 \n",
"4 0 NaN NaN NaN NaN NaN \n",
"\n",
" test_name test_type academic_year_start \n",
"0 NaN Goldman 2005 \n",
"1 NaN EVT 2005 \n",
"2 PLS receptive 2005 \n",
"3 PLS expressive 2005 \n",
"4 NaN NaN 2010 \n",
"\n",
"[5 rows x 74 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Indicator for non-profound hearing loss"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['deg_hl_below6'] = lsl_dr.degree_hl<6\n",
"lsl_dr.loc[lsl_dr.degree_hl.isnull(), 'deg_hl_below6'] = np.nan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Indicator for first intervention outside OPTION"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['int_outside_option'] = lsl_dr.age > lsl_dr.age_int\n",
"lsl_dr.loc[lsl_dr.age < lsl_dr.age_int, 'int_outside_option'] = np.nan"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Indicator for high school graduation of mother"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['mother_hs'] = lsl_dr.mother_ed > 1\n",
"lsl_dr.loc[lsl_dr.mother_ed.isnull(), 'mother_hs'] = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Joint commission benchmark"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 22. , 18. , 17. , 3. , 5. , nan, 2. , 1. ,\n",
" 13. , 10. , 36. , 28. , 26. , 21. , 41. , 0. ,\n",
" 6. , 25. , 7. , 35. , 34. , 27. , 15. , 24. ,\n",
" 4. , 14. , 40. , 42. , 12. , 9. , 50. , 33. ,\n",
" 8. , 23. , 32. , 60. , 31. , 11. , 30. , 20. ,\n",
" 67. , 49. , 48. , 1.5, 19. , 2.5, 39. , 52. ,\n",
" 16. , 38. , 29. , 51. , 46. , 45. , 54. , 44. ,\n",
" 88. , 65. , 81. , 116. , 72. , 57. , 62. , 43. ,\n",
" 78. , 83. , 61. , 107. , 64. , 74. , 37. , 77. ,\n",
" 96. , 97. , 79. , 47. , 53. , 59. , 84. , 95. ,\n",
" 80. , 0.5, 58. , 56. , 86. , 98. , 85. , 75. ,\n",
" 119. , 66. , 70. , 63. , 140. , 126. , 133. , 103. ,\n",
" 87. , 76. , 55. , 68. , 92. , 71. , 154. , 89. ,\n",
" 152. ])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.onset_1.unique()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['ident_by_3'] = ident_by_3 = lsl_dr.onset_1 <= 3\n",
"lsl_dr['program_by_6'] = program_by_6 = lsl_dr.age <= 6\n",
"lsl_dr['jcih'] = ident_by_3 & program_by_6\n",
"lsl_dr.loc[lsl_dr.onset_1.isnull() | lsl_dr.age.isnull(), 'jcih'] = None"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create age in years variable"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['age_years'] = lsl_dr.age/12."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retain only 4-year-olds"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr = lsl_dr[(lsl_dr.age_test>=48) & (lsl_dr.age_test<60)]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(6873, 81)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.shape"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1613,)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.study_id.unique().shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary Statistics for Paper"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_students = lsl_dr.drop_duplicates(subset='study_id')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1613, 81)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.shape"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1593.000000\n",
"mean 29.700565\n",
"std 15.676539\n",
"min 1.000000\n",
"25% 18.000000\n",
"50% 32.000000\n",
"75% 41.000000\n",
"max 68.000000\n",
"Name: age, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.5236612702366127"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.male.mean()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1 841\n",
"0 765\n",
"dtype: int64"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.male.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1501.000000\n",
"mean 1.161226\n",
"std 0.928434\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 1.000000\n",
"75% 2.000000\n",
"max 3.000000\n",
"Name: sib, dtype: float64"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.sib.describe()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1613\n",
"mean 0.4575325\n",
"std 0.4983478\n",
"min False\n",
"25% 0\n",
"50% 0\n",
"75% 1\n",
"max True\n",
"Name: degree_hl, dtype: object"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(unique_students.degree_hl == 6).describe()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"220"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.deg_hl_below6.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0 738\n",
"1 655\n",
"dtype: int64"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.deg_hl_below6.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1613\n",
"mean 0.3787973\n",
"std 0.485238\n",
"min False\n",
"25% 0\n",
"50% 0\n",
"75% 1\n",
"max True\n",
"Name: degree_hl, dtype: object"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"((unique_students.degree_hl < 6) & (unique_students.degree_hl > 1)).describe()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1006.000000\n",
"mean 0.580517\n",
"std 0.493720\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 1.000000\n",
"75% 1.000000\n",
"max 1.000000\n",
"Name: mother_hs, dtype: float64"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.mother_hs.describe()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1413.000000\n",
"mean 0.213022\n",
"std 0.409588\n",
"min 0.000000\n",
"25% 0.000000\n",
"50% 0.000000\n",
"75% 0.000000\n",
"max 1.000000\n",
"Name: synd_or_disab, dtype: float64"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.synd_or_disab.describe()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"male \n",
"0 False 670\n",
" True 95\n",
"1 False 750\n",
" True 91\n",
"dtype: int64"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.groupby('male').program_by_6.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"sib \n",
"0 False 346\n",
" True 38\n",
"1 False 562\n",
" True 90\n",
"2 False 267\n",
" True 37\n",
"3 False 145\n",
" True 16\n",
"dtype: int64"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.groupby('sib').program_by_6.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"deg_hl_below6 \n",
"0 False 638\n",
" True 100\n",
"1 False 574\n",
" True 81\n",
"dtype: int64"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.groupby('deg_hl_below6').program_by_6.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"mother_hs \n",
"0 False 367\n",
" True 55\n",
"1 False 489\n",
" True 95\n",
"dtype: int64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.groupby('mother_hs').program_by_6.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"synd_or_disab \n",
"0 False 976\n",
" True 136\n",
"1 False 273\n",
" True 28\n",
"dtype: int64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.groupby('synd_or_disab').program_by_6.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"ident_by_3 \n",
"False False 1013\n",
" True 34\n",
"True False 414\n",
" True 152\n",
"dtype: int64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.groupby('ident_by_3').program_by_6.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"jcih \n",
"0 False 928\n",
" True 17\n",
"1 True 152\n",
"dtype: int64"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.groupby('jcih').program_by_6.value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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YCJKkgoEgSQJqXEJTgytz0ZCebTtL6VdSczIQ9lKZi4Yc947DS+lXUnNyykiSBDhCkMaV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\nIEiSCgaCJAmA/w+nsoeAk7rRaQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10a5c3e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"grouped_age = unique_students.replace({'jcih':{1:'JCIH', 0:'non-JCIH'}}).groupby('jcih')['age_years']\n",
"fig, ax = plt.subplots()\n",
"\n",
"for k, v in grouped_age:\n",
" v.hist(label=k, alpha=.75, ax=ax, grid=False)\n",
"\n",
"ax.legend()\n",
"plt.xlabel('Age (years)'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"students_data = lsl_dr.copy()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"students_data['school_id'] = students_data.school.replace({int(s):i for i,s in enumerate(students_data.school.unique())})"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"students_data = students_data[students_data.domain=='Language']"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(2594, 82)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"students_data.shape"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"option_scores = students_data.drop_duplicates('study_id', take_last=True)[['score', 'school_id', \n",
" 'deg_hl_below6', 'mother_hs']].dropna()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"option_scores = option_scores.astype(int)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"option_scores.to_csv('option_scores.csv', index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create technology indicators"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unimodal = ~lsl_dr[[u'bilateral_ci',\n",
" u'bilateral_ha', \n",
" u'bimodal']].sum(1).astype(bool)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr['unimodal_ci'] = unimodal & lsl_dr.cochlear\n",
"lsl_dr['unimodal_ha'] = unimodal & lsl_dr.hearing_aid"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"bilateral_ci 2136\n",
"bilateral_ha 2261\n",
"bimodal 791\n",
"unimodal_ci 311\n",
"unimodal_ha 320\n",
"dtype: int64"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr[[u'bilateral_ci',\n",
" u'bilateral_ha', \n",
" u'bimodal', 'unimodal_ci', 'unimodal_ha']].sum(0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since bilateral hearing aid is the most prevalent category, it will be used as the baseline category."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"tech_index = lsl_dr[[u'bilateral_ci',\n",
" u'bilateral_ha', \n",
" u'bimodal', 'unimodal_ci', 'unimodal_ha']].sum(1).astype(bool)\n",
"\n",
"technology_subset = lsl_dr[tech_index]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Receptive Language Test Score Model"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"2 initial_assessment_arm_1 2005-2006 0 1 0 0 3 \n",
"187 initial_assessment_arm_1 2014-2015 0 0 2 0 2 \n",
"210 initial_assessment_arm_1 2014-2015 0 0 0 0 1 \n",
"220 initial_assessment_arm_1 2012-2013 0 1 0 0 2 \n",
"253 initial_assessment_arm_1 2011-2012 0 1 0 0 2 \n",
"\n",
" _mother_ed father_ed premature_age ... academic_year_start \\\n",
"2 2 2 8 ... 2005 \n",
"187 6 6 8 ... 2014 \n",
"210 5 6 7 ... 2014 \n",
"220 4 6 7 ... 2012 \n",
"253 6 6 8 ... 2011 \n",
"\n",
" deg_hl_below6 int_outside_option mother_hs ident_by_3 program_by_6 \\\n",
"2 0 1 0 False False \n",
"187 1 1 NaN False False \n",
"210 1 1 1 False False \n",
"220 1 1 1 False False \n",
"253 0 1 NaN False False \n",
"\n",
" jcih age_years unimodal_ci unimodal_ha \n",
"2 0 4.500000 False False \n",
"187 0 3.583333 False False \n",
"210 0 4.083333 False False \n",
"220 0 4.666667 True False \n",
"253 0 4.416667 False False \n",
"\n",
"[5 rows x 83 columns]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filter domain and English-only, and appropriate bilateral subset\n",
"receptive_language_dataset = technology_subset[(technology_subset.domain=='Language') \n",
" & (technology_subset.test_type=='receptive') \n",
" & (technology_subset.non_english==False)]\n",
"\n",
"receptive_language_dataset.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the size of the resulting dataset to be used in this analysis"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(941, 83)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mean_score = receptive_language_dataset.groupby('study_id')['score'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset = receptive_language_dataset.drop_duplicates('study_id')"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset = receptive_language_dataset.set_index('study_id')"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset['mean_score'] = mean_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Specify criterion to use: `jcih`, `ident_by_3`, `program_by_6`"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"criterion_labels = {'jcih': 'JCIH',\n",
" 'ident_by_3': 'Diagnosed by 3 months',\n",
" 'program_by_6': 'Intervention by 6 months'}\n",
"\n",
"criterion = 'jcih'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Proportion of missing values for each variable"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 0.00\n",
"school 0.00\n",
"score 0.00\n",
"male 0.00\n",
"time 0.04\n",
"family_inv 0.16\n",
"sib 0.06\n",
"deg_hl_below6 0.01\n",
"mother_hs 0.33\n",
"synd_or_disab 0.14\n",
"jcih 0.20\n",
"dtype: float64"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"covs = ['age_years', 'school', 'score', 'male', 'time',\n",
" 'family_inv', 'sib', 'deg_hl_below6', 'mother_hs', 'synd_or_disab'] + [criterion]\n",
"(receptive_language_dataset[covs].isnull().sum() / receptive_language_dataset.shape[0]).round(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Drop individuals with missing `age`, since there is <1% of them"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset = receptive_language_dataset[receptive_language_dataset.age.notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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EsjXsXz6cy6/bt+5+rr50SSCDU9B9B49kx+47193PFdsvmEE1wHps1CXLZyf5XJILk9zR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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10d50c438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"receptive_language_dataset.score.hist(grid=False, bins=25, figsize=(10, 6))\n",
"plt.xlabel('Standard Scores'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10f2cfe80>"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXsAAAEBCAYAAACZhwWsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngAZQ9gDQAMoeABrw/9C31YaZ6otzAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10ac8de80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"receptive_language_dataset.mother_ed.hist()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Final analysis dataset size"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(871, 83)"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"mother_hs 33.3\n",
"jcih 19.3\n",
"family_inv 15.8\n",
"synd_or_disab 14.0\n",
"sib 5.5\n",
"time 3.6\n",
"deg_hl_below6 1.3\n",
"male 0.0\n",
"score 0.0\n",
"school 0.0\n",
"age_years 0.0\n",
"dtype: float64"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"missing_pct = (receptive_language_dataset[covs].isnull().mean()*100).round(1)\n",
"missing_pct.sort(ascending=False)\n",
"missing_pct"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset['age_amp_missing'] = receptive_language_dataset.age_amp.isnull()\n",
"by_age_amp_missing = receptive_language_dataset.groupby('age_amp_missing')"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" male synd_or_disab deg_hl_below6 mother_hs\n",
"age_amp_missing \n",
"False 0.510885 0.221106 0.469298 0.616034\n",
"True 0.478022 0.250000 0.386364 0.654206"
]
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_age_amp_missing['male', 'synd_or_disab', 'deg_hl_below6', 'mother_hs',\n",
" 'non_english'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset['jcih_missing'] = receptive_language_dataset.jcih.isnull()\n",
"by_jcih_missing = receptive_language_dataset.groupby('jcih_missing')"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" male synd_or_disab deg_hl_below6 mother_hs\n",
"jcih_missing \n",
"False 0.513514 0.219110 0.467811 0.626033\n",
"True 0.464286 0.260563 0.385093 0.608247"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_jcih_missing['male', 'synd_or_disab', 'deg_hl_below6', 'mother_hs',\n",
" 'non_english'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.2484691924990403"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_dataset.age_years.mean()"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from pymc import Bernoulli, Normal, Uniform, invlogit, logit, deterministic, MCMC, Matplot, MAP\n",
"from pymc import Lambda, Dirichlet, Categorical, Beta, NegativeBinomial, Exponential, T\n",
"from numpy.ma import masked_values\n",
"\n",
"n_covs = 12\n",
"\n",
"def generate_model(dataset):\n",
" \n",
" # Extract data to local variables\n",
" age_years = dataset.age_years.values\n",
" school = dataset.school.values\n",
" score = dataset.score.values\n",
" non_english = dataset.non_english.astype(int).values\n",
" male = dataset.male.values\n",
" sib = dataset.sib.values\n",
" synd_or_disab = dataset.synd_or_disab.values\n",
" int_outside_option = dataset.int_outside_option.values\n",
" synd_or_disab = dataset.synd_or_disab.values\n",
" study_id = dataset.index.values\n",
" ident_by_3 = dataset.ident_by_3\n",
" program_by_6 = dataset.program_by_6\n",
" bilateral_ci = dataset.bilateral_ci\n",
" bimodal = dataset.bimodal\n",
" unimodal_ci = dataset.unimodal_ci\n",
" unimodal_ha = dataset.unimodal_ha\n",
" \n",
" # Transform some data\n",
" age_std = age_years - age_years.mean()\n",
" \n",
" # Imputation of family involvement\n",
" n_family_inv_cats = len(dataset.family_inv.unique())\n",
" p_family_inv = Dirichlet(\"p_family_inv\", np.ones(n_family_inv_cats), \n",
" value=[1./n_family_inv_cats]*(n_family_inv_cats-1))\n",
" family_inv_masked = masked_values(dataset.family_inv.fillna(0.5), value=0.5)\n",
" x_family_inv = Categorical('x_family_inv', p_family_inv, value=family_inv_masked, \n",
" observed=True)\n",
" \n",
" # Imputation of hearing loss\n",
" p_hl = Beta(\"p_hl\", 1, 1, value=0.5)\n",
" hl_masked = masked_values(dataset.deg_hl_below6.values, value=np.nan)\n",
" x_hl = Bernoulli('x_hl', p_hl, value=hl_masked, observed=True)\n",
" \n",
" # Imputation of maternal education\n",
" p_mother_hs = Beta(\"p_mother_hs\", 1, 1, value=0.5)\n",
" mother_hs_masked = masked_values(dataset.mother_hs.values, value=np.nan)\n",
" x_mother_hs = Bernoulli('x_mother_hs', p_mother_hs, value=mother_hs_masked, observed=True)\n",
" \n",
" # Imputation of previous disability\n",
" p_synd_or_disab = Beta(\"p_synd_or_disab\", 1, 1, value=0.5)\n",
" synd_or_disab_masked = masked_values(dataset.synd_or_disab.values, value=np.nan)\n",
" x_synd_or_disab = Bernoulli('x_synd_or_disab', p_synd_or_disab, value=synd_or_disab_masked, \n",
" observed=True)\n",
" \n",
" # Imputation of siblings\n",
" n_sib_cats = len(dataset.sib.unique())\n",
" p_sib = Dirichlet(\"p_sib\", np.ones(n_sib_cats), value=[1./n_sib_cats]*(n_sib_cats-1))\n",
" sib_masked = masked_values(dataset.sib.fillna(0.5), value=0.5)\n",
" x_sib = Categorical('x_sib', p_sib, value=sib_masked, observed=True)\n",
" \n",
" \n",
" mean_score = Normal(\"mean_score\", 100, 0.001, value=80)\n",
" \n",
" # Indices to school random effects\n",
" unique_schools = np.unique(school)\n",
" school_index = [list(unique_schools).index(s) for s in school]\n",
"\n",
" # School random effect\n",
" sigma_school = Uniform(\"sigma_school\", 0, 1000, value=10)\n",
" tau_school = sigma_school**-2\n",
" alpha_school = Normal(\"alpha_school\", mu=0, tau=tau_school, \n",
" value=np.zeros(len(unique_schools)))\n",
" \n",
" @deterministic\n",
" def intercept(a0=mean_score, a1=alpha_school):\n",
" \"\"\"Random intercepts\"\"\"\n",
" return a0 + a1[school_index]\n",
" \n",
" # Fixed effects\n",
" beta = Normal(\"beta\", 0, 0.001, value=[0]*n_covs)\n",
"\n",
" @deterministic\n",
" def theta(b0=intercept, b=beta, x_family_inv=x_family_inv, x_sib=x_sib, \n",
" x_hl=x_hl, x_mother_hs=x_mother_hs, x_synd_or_disab=x_synd_or_disab):\n",
" \n",
" # Complete predictors\n",
" X = [male, ident_by_3, program_by_6, bilateral_ci, bimodal, unimodal_ci, unimodal_ha]\n",
" # Partially-imputed predictors\n",
" X += [x_family_inv, x_sib, x_hl, x_mother_hs, x_synd_or_disab]\n",
" return b0 + np.dot(b, X) #+ a[child_index]\n",
" \n",
" sigma = Uniform(\"sigma\", 0, 1000, value=10)\n",
" tau = sigma**-2\n",
" \n",
" # Data likelihood\n",
" score_like = Normal(\"score_like\", mu=theta, tau=tau, value=score, observed=True)\n",
" #score_like_pred = Normal(\"score_like_pred\", mu=theta, tau=tau, value=score)\n",
" \n",
" \n",
" return locals()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_reclang = MCMC(generate_model(receptive_language_dataset), \n",
" db='sqlite', name='reclang', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iterations = 100000\n",
"burn = 90000"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 1140.6 sec"
]
}
],
"source": [
"M_reclang.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"labels = ['Male', 'Identified by 3 mo.', \n",
" 'In Program by 6 mo.', \n",
" 'Bilateral CI', 'Bimodal', 'Unimodal CI', 'Unimodal HA',\n",
" 'Reduced Family Involvement', \n",
" 'Sibling Count', 'Non-profound Hearing Loss',\n",
" 'Mother with HS Diploma','Additional Disability']\n",
"# criterion_labels[criterion]]"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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jY1VUitJ1d6+lu3stkydPx38CYu2umXpMfyHpl8DuwNkAqRz3VcAJZMlqkaRb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\nxYnJzMxK5f8HDFMhtUbTkqoAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x119499cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_reclang.beta], rhat=False,\n",
" custom_labels=labels, x_range=(-15,15), main='Receptive Language')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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/2wsraMaMqa+Us2K2l63A24C70kxtnouSmbVaaWdMZrZzkDQtIqa1Og8rDxcm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\nMjOzUnFhMjOzUvn/AUaHivDZQ1wAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111611a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_reclang.beta], rhat=False,\n",
" custom_labels=labels, x_range=(-10,10), main='Receptive Language')"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"beta:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t-0.68 1.176 0.08 [-2.799 1.596]\n",
"\t4.252 1.453 0.114 [ 1.429 7.149]\n",
"\t7.113 2.032 0.173 [ 3.463 11.173]\n",
"\t-5.486 0.646 0.044 [-6.73 -4.247]\n",
"\t-0.373 0.648 0.035 [-1.568 0.994]\n",
"\t6.699 1.233 0.086 [ 4.563 9.32 ]\n",
"\t5.317 1.432 0.117 [ 2.44 7.92]\n",
"\t-6.645 1.238 0.086 [-9.174 -4.309]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-2.889 -1.506 -0.727 0.177 1.559\n",
"\t1.461 3.244 4.191 5.207 7.248\n",
"\t2.998 5.68 7.118 8.611 10.876\n",
"\t-6.735 -5.94 -5.487 -5.046 -4.247\n",
"\t-1.657 -0.813 -0.367 0.055 0.92\n",
"\t4.527 5.905 6.608 7.481 9.303\n",
"\t2.347 4.342 5.442 6.319 7.868\n",
"\t-8.994 -7.506 -6.679 -5.81 -4.12\n",
"\t\n"
]
}
],
"source": [
"M_reclang.beta.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expressive Language Model"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"3 initial_assessment_arm_1 2005-2006 0 1 0 0 3 \n",
"188 initial_assessment_arm_1 2014-2015 0 0 2 0 2 \n",
"211 initial_assessment_arm_1 2014-2015 0 0 0 0 1 \n",
"221 initial_assessment_arm_1 2012-2013 0 1 0 0 2 \n",
"254 initial_assessment_arm_1 2011-2012 0 1 0 0 2 \n",
"\n",
" _mother_ed father_ed premature_age ... academic_year_start \\\n",
"3 2 2 8 ... 2005 \n",
"188 6 6 8 ... 2014 \n",
"211 5 6 7 ... 2014 \n",
"221 4 6 7 ... 2012 \n",
"254 6 6 8 ... 2011 \n",
"\n",
" deg_hl_below6 int_outside_option mother_hs ident_by_3 program_by_6 \\\n",
"3 0 1 0 False False \n",
"188 1 1 NaN False False \n",
"211 1 1 1 False False \n",
"221 1 1 1 False False \n",
"254 0 1 NaN False False \n",
"\n",
" jcih age_years unimodal_ci unimodal_ha \n",
"3 0 4.500000 False False \n",
"188 0 3.583333 False False \n",
"211 0 4.083333 False False \n",
"221 0 4.666667 True False \n",
"254 0 4.416667 False False \n",
"\n",
"[5 rows x 83 columns]"
]
},
"execution_count": 98,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_dataset = technology_subset[(technology_subset.domain=='Language') & (technology_subset.test_type=='expressive') &\n",
" (technology_subset.non_english==False)]\n",
"\n",
"expressive_language_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mean_score = expressive_language_dataset.groupby('study_id')['score'].mean()\n",
"expressive_language_dataset = expressive_language_dataset.drop_duplicates('study_id').set_index('study_id')\n",
"expressive_language_dataset['mean_score'] = mean_score"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 0.00\n",
"school 0.00\n",
"score 0.00\n",
"male 0.00\n",
"time 0.04\n",
"family_inv 0.16\n",
"sib 0.06\n",
"deg_hl_below6 0.01\n",
"mother_hs 0.34\n",
"synd_or_disab 0.15\n",
"jcih 0.20\n",
"dtype: float64"
]
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(expressive_language_dataset[covs].isnull().sum() / expressive_language_dataset.shape[0]).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"expressive_language_dataset = expressive_language_dataset[expressive_language_dataset.age.notnull() \n",
" & expressive_language_dataset.int_outside_option.notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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BADoTyAAAOhPIAAA6E8gAADoTyAAAOhPIAAA6E8gAADoTyAAAOhPIAAA6E8gAADoTyAAA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\n9v8BM2122F/VMtYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f637e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expressive_language_dataset.score.hist(grid=False, bins=25, figsize=(10, 6))\n",
"plt.xlabel('Standard Scores'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_explang = MCMC(generate_model(expressive_language_dataset), db='sqlite', name='explang', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 1111.8 sec"
]
}
],
"source": [
"M_explang.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"beta:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t-1.001 1.226 0.09 [-3.454 1.158]\n",
"\t5.961 1.332 0.096 [ 3.41 8.534]\n",
"\t6.543 2.242 0.198 [ 2.959 11.565]\n",
"\t-5.016 0.669 0.044 [-6.33 -3.655]\n",
"\t-0.745 0.69 0.041 [-2.149 0.568]\n",
"\t7.448 1.205 0.085 [ 5.245 9.872]\n",
"\t7.015 1.297 0.101 [ 4.598 9.655]\n",
"\t-6.837 1.442 0.109 [-9.326 -3.917]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-3.349 -1.826 -0.976 -0.189 1.341\n",
"\t3.423 5.013 5.938 6.874 8.551\n",
"\t2.68 4.801 6.358 8.081 11.348\n",
"\t-6.33 -5.485 -5.022 -4.579 -3.655\n",
"\t-2.125 -1.212 -0.729 -0.273 0.609\n",
"\t5.23 6.616 7.425 8.267 9.869\n",
"\t4.459 6.145 7.04 7.88 9.585\n",
"\t-9.64 -7.75 -6.82 -5.854 -4.071\n",
"\t\n"
]
}
],
"source": [
"M_explang.beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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cz3dI2qXJWItxLpM0I81YL86Nf3218VOcC9NXcUZSLc5a42xx/Bscn1r/Xvn4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\nyczMSsWJyczMSsWJyczMSuX/A16jCae/GxOAAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1194d2710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_explang.beta], custom_labels=labels, rhat=False, main='Expressive Language', x_range=(-15,15))"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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PkzSf7FDf8y3ErxW70XYHADMlvUo2wxlfozgW41wlaSPZnP/ERuMp6Z+Bd5Md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\ns0r5/zTeiojD96AGAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115835710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_explang.beta], custom_labels=labels, rhat=False, main='Expressive Language', x_range=(-10,10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Articulation Model"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"0 initial_assessment_arm_1 2005-2006 0 1 0 0 3 \n",
"184 initial_assessment_arm_1 2014-2015 0 0 2 0 2 \n",
"207 initial_assessment_arm_1 2014-2015 0 0 0 0 1 \n",
"286 initial_assessment_arm_1 2014-2015 0 0 0 0 1 \n",
"412 initial_assessment_arm_1 NaN 0 0 0 0 1 \n",
"\n",
" _mother_ed father_ed premature_age ... academic_year_start \\\n",
"0 2 2 8 ... 2005 \n",
"184 6 6 8 ... 2014 \n",
"207 5 6 7 ... 2014 \n",
"286 6 6 8 ... 2014 \n",
"412 6 6 9 ... NaN \n",
"\n",
" deg_hl_below6 int_outside_option mother_hs ident_by_3 program_by_6 \\\n",
"0 0 1 0 False False \n",
"184 1 1 NaN False False \n",
"207 1 1 1 False False \n",
"286 0 1 NaN True False \n",
"412 0 0 NaN False False \n",
"\n",
" jcih age_years unimodal_ci unimodal_ha \n",
"0 0 4.500000 False False \n",
"184 0 3.583333 False False \n",
"207 0 4.083333 False False \n",
"286 0 3.916667 False False \n",
"412 NaN 3.750000 False False \n",
"\n",
"[5 rows x 83 columns]"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_dataset = technology_subset[(technology_subset.domain=='Articulation') & \n",
" (technology_subset.non_english==False)]\n",
"articulation_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mean_score = articulation_dataset.groupby('study_id')['score'].mean()\n",
"articulation_dataset = articulation_dataset.drop_duplicates('study_id').set_index('study_id')\n",
"articulation_dataset['mean_score'] = mean_score"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 0.00\n",
"school 0.00\n",
"score 0.00\n",
"male 0.00\n",
"time 0.04\n",
"family_inv 0.17\n",
"sib 0.05\n",
"deg_hl_below6 0.01\n",
"mother_hs 0.32\n",
"synd_or_disab 0.14\n",
"jcih 0.21\n",
"dtype: float64"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(articulation_dataset[covs].isnull().sum() / articulation_dataset.shape[0]).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"articulation_dataset = articulation_dataset[articulation_dataset.age.notnull()\n",
" & articulation_dataset.int_outside_option.notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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hW2itmR0DAObaqEuWVfV7Sb4+ydEkr22tnayqXUn2ZhLQXJ4JAJh7Y3/K8mXLtN2Y5MYx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\nV0DROUAAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f4fa128>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"articulation_dataset.score.hist(grid=False, bins=25, figsize=(10, 6))\n",
"plt.xlabel('Standard Scores'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_articulation = MCMC(generate_model(articulation_dataset), db='sqlite', name='articulation', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 1003.0 sec"
]
}
],
"source": [
"M_articulation.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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pG/BAEzEi6XLgI8CE9FpdmQBR9RRoOuX2c7Ij2krbDklfAFampr6IeKxBnIPG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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11946af28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_articulation.beta], \n",
" custom_labels=labels, rhat=False, main='Articulation', x_range=(-15,15))"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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njV3AWcDvAp+LiHUd4rySi6TPA9+OiBWtcpXUDUwFdgB3A6dFxDNp3VxgfUTc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\nZllxYTIzs6z8f/KvxTkOn1gPAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1156f58d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_articulation.beta], \n",
" custom_labels=labels, rhat=False, main='Articulation', x_range=(-10,10))"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"beta:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t0.842 1.276 0.087 [-1.699 3.333]\n",
"\t4.066 1.714 0.142 [ 0.955 7.342]\n",
"\t3.812 2.447 0.214 [-0.984 8.727]\n",
"\t-4.393 0.66 0.042 [-5.713 -3.201]\n",
"\t0.048 0.77 0.047 [-1.486 1.531]\n",
"\t5.537 1.507 0.116 [ 2.681 8.384]\n",
"\t5.125 1.572 0.123 [ 2.074 8.148]\n",
"\t-8.13 1.489 0.117 [-11.165 -5.307]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-1.565 -0.043 0.826 1.679 3.548\n",
"\t0.955 2.834 3.962 5.29 7.373\n",
"\t-1.285 2.392 3.897 5.403 8.56\n",
"\t-5.698 -4.85 -4.384 -3.921 -3.179\n",
"\t-1.532 -0.442 0.065 0.556 1.486\n",
"\t2.69 4.476 5.591 6.588 8.406\n",
"\t2.171 3.999 5.132 6.195 8.345\n",
"\t-11.128 -9.092 -8.05 -7.171 -5.227\n",
"\t\n"
]
}
],
"source": [
"M_articulation.beta.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expressive Vocabulary Model"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"1 initial_assessment_arm_1 2005-2006 0 1 0 0 3 \n",
"10 initial_assessment_arm_1 2007-2008 0 0 0 0 3 \n",
"185 initial_assessment_arm_1 2014-2015 0 0 2 0 2 \n",
"208 initial_assessment_arm_1 2014-2015 0 0 0 0 1 \n",
"228 initial_assessment_arm_1 2012-2013 0 0 0 0 2 \n",
"\n",
" _mother_ed father_ed premature_age ... academic_year_start \\\n",
"1 2 2 8 ... 2005 \n",
"10 4 4 8 ... 2007 \n",
"185 6 6 8 ... 2014 \n",
"208 5 6 7 ... 2014 \n",
"228 6 6 8 ... 2012 \n",
"\n",
" deg_hl_below6 int_outside_option mother_hs ident_by_3 program_by_6 \\\n",
"1 0 1 0 False False \n",
"10 0 1 1 False False \n",
"185 1 1 NaN False False \n",
"208 1 1 1 False False \n",
"228 1 1 NaN False False \n",
"\n",
" jcih age_years unimodal_ci unimodal_ha \n",
"1 0 4.500000 False False \n",
"10 0 4.583333 False False \n",
"185 0 3.583333 False False \n",
"208 0 4.083333 False False \n",
"228 0 4.000000 False False \n",
"\n",
"[5 rows x 83 columns]"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_vocab_dataset = technology_subset[(technology_subset.domain=='Expressive Vocabulary') & \n",
" (technology_subset.non_english==False)]\n",
"expressive_vocab_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mean_score = expressive_vocab_dataset.groupby('study_id')['score'].mean()\n",
"expressive_vocab_dataset = expressive_vocab_dataset.drop_duplicates('study_id').set_index('study_id')\n",
"expressive_vocab_dataset['mean_score'] = mean_score"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 4\n",
"school 0\n",
"score 0\n",
"male 0\n",
"time 40\n",
"family_inv 153\n",
"sib 49\n",
"deg_hl_below6 8\n",
"mother_hs 312\n",
"synd_or_disab 124\n",
"jcih 200\n",
"dtype: int64"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_vocab_dataset[covs].isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"expressive_vocab_dataset = expressive_vocab_dataset[expressive_vocab_dataset.age.notnull() \n",
" & expressive_vocab_dataset.male.notnull()\n",
" & expressive_vocab_dataset.int_outside_option.notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x10bcc0358>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expressive_vocab_dataset.score.hist(grid=False, bins=25, figsize=(10, 6))\n",
"plt.xlabel('Standard Scores'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_expressive_vocab = MCMC(generate_model(expressive_vocab_dataset), db='sqlite', name='expressive_vocab', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 1128.2 sec"
]
}
],
"source": [
"M_expressive_vocab.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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+npXXOpdfsLIZjll5lQH3Bl7a0iidJGmJpEWSTsu1T5G0TNK1wMhc+/Iaj98m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\nI5lvlgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10f75d748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_expressive_vocab.beta], \n",
" custom_labels=labels, rhat=False, main='Expressive Vocabulary', x_range=(-15,15))"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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pl3OkOC9ImgYsAB5KOTdTL+a5wDRJATwdEaem9jmS5pKd6nuxhfi1Yjda7wBg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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115977668>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_expressive_vocab.beta], \n",
" custom_labels=labels, rhat=False, main='Expressive Vocabulary', x_range=(-10,10))"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"beta:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t1.343 1.236 0.088 [-0.896 3.726]\n",
"\t4.361 1.337 0.099 [ 1.641 6.673]\n",
"\t6.004 2.078 0.182 [ 2.042 10.622]\n",
"\t-5.315 0.611 0.039 [-6.396 -3.969]\n",
"\t-1.217 0.677 0.039 [-2.495 0.116]\n",
"\t5.996 1.353 0.104 [ 3.464 8.742]\n",
"\t5.991 1.717 0.15 [ 2.596 9.108]\n",
"\t-5.925 1.275 0.093 [-8.425 -3.711]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-1.027 0.481 1.334 2.205 3.651\n",
"\t1.677 3.446 4.394 5.334 6.802\n",
"\t1.964 4.643 5.97 7.341 10.556\n",
"\t-6.473 -5.72 -5.344 -4.942 -4.004\n",
"\t-2.54 -1.672 -1.213 -0.765 0.083\n",
"\t3.464 5.052 5.995 6.892 8.742\n",
"\t2.632 4.831 6.057 7.19 9.215\n",
"\t-8.301 -6.813 -5.973 -4.975 -3.486\n",
"\t\n"
]
}
],
"source": [
"M_expressive_vocab.beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1157d9be0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot(M_explang.alpha_school, custom_labels=[' ']*42, main='Expressive language school effects', \n",
" x_range=(-25, 25))"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
"data": {
"image/png": 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BBCArFCu0D0GEpNDIAN3DWnNIxtZhl3OvLH4OvbC3nlvK2rjWHDBP9IgwdalMbs9rspjv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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1158ede80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot(M_expressive_vocab.alpha_school, custom_labels=[' ']*42, main='Expressive vocabulary school effects',\n",
" x_range=(-25, 25))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Receptive Vocabulary Model"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"11 initial_assessment_arm_1 2007-2008 0 0 0 0 3 \n",
"186 initial_assessment_arm_1 2014-2015 0 0 2 0 2 \n",
"209 initial_assessment_arm_1 2014-2015 0 0 0 0 1 \n",
"229 initial_assessment_arm_1 2012-2013 0 0 0 0 2 \n",
"288 initial_assessment_arm_1 2014-2015 0 0 0 0 1 \n",
"\n",
" _mother_ed father_ed premature_age ... academic_year_start \\\n",
"11 4 4 8 ... 2007 \n",
"186 6 6 8 ... 2014 \n",
"209 5 6 7 ... 2014 \n",
"229 6 6 8 ... 2012 \n",
"288 6 6 8 ... 2014 \n",
"\n",
" deg_hl_below6 int_outside_option mother_hs ident_by_3 program_by_6 \\\n",
"11 0 1 1 False False \n",
"186 1 1 NaN False False \n",
"209 1 1 1 False False \n",
"229 1 1 NaN False False \n",
"288 0 1 NaN True False \n",
"\n",
" jcih age_years unimodal_ci unimodal_ha \n",
"11 0 4.583333 False False \n",
"186 0 3.583333 False False \n",
"209 0 4.083333 False False \n",
"229 0 4.000000 False False \n",
"288 0 3.916667 False False \n",
"\n",
"[5 rows x 83 columns]"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_vocab_dataset = technology_subset[(technology_subset.domain=='Receptive Vocabulary') & \n",
" (technology_subset.non_english==False)]\n",
"receptive_vocab_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mean_score = receptive_vocab_dataset.groupby('study_id')['score'].mean()\n",
"receptive_vocab_dataset = receptive_vocab_dataset.drop_duplicates('study_id').set_index('study_id')\n",
"receptive_vocab_dataset['mean_score'] = mean_score"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 4\n",
"school 0\n",
"score 0\n",
"male 0\n",
"time 37\n",
"family_inv 158\n",
"sib 49\n",
"deg_hl_below6 10\n",
"mother_hs 318\n",
"synd_or_disab 125\n",
"jcih 202\n",
"dtype: int64"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_vocab_dataset[covs].isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_vocab_dataset = receptive_vocab_dataset[receptive_vocab_dataset.age.notnull() & \n",
" receptive_vocab_dataset.male.notnull() &\n",
" receptive_vocab_dataset.int_outside_option.notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10bc0dc18>"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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T+A3gU1PbwgplnwM6X70yZ4P8+UpNOqLpApeBvcDpiLgYEReAVUmLE2+dmZmNbdLDJO8D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\nV0IAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x10bc0b320>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"receptive_vocab_dataset.age_int.hist(bins=40)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(922, 83)"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_vocab_dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x1193ba5f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"receptive_vocab_dataset.score.hist(grid=False, bins=25, figsize=(10, 6))\n",
"plt.xlabel('Standard Scores'); plt.ylabel('Frequency');"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_receptive_vocab = MCMC(generate_model(receptive_vocab_dataset), db='sqlite', name='receptive_vocab', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 1251.2 sec"
]
}
],
"source": [
"M_receptive_vocab.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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SYkkLJZ2ea58saZmkG4CRufblNR4fLWl+OqK5ob/jN4h/u+cp6WxJcyStlHRh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\n7cV7DXCUpEp8xwLX5JbvLEn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"text/plain": [
"<matplotlib.figure.Figure at 0x11935de48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#order = np.argsort(M.beta.stats()['mean'])[::-1]\n",
"Matplot.summary_plot([M_receptive_vocab.beta], rhat=False,\n",
" custom_labels=labels, main='Receptive Vocabulary', x_range=(-15,15))"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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sBR5OMbfSqM1zgdmSAng6Ij6RyhdKWkQ21fdCG+3Xa7vZdgcBcyS9RjbCObtO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\ncWIyM7NKcWIyM7NKcWIyM7NK+f9iFk1T5ypTCAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11588c470>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#order = np.argsort(M.beta.stats()['mean'])[::-1]\n",
"Matplot.summary_plot([M_receptive_vocab.beta], rhat=False,\n",
" custom_labels=labels, main='Receptive Vocabulary', x_range=(-10,10))"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"beta:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t0.309 1.085 0.076 [-1.857 2.387]\n",
"\t3.26 1.406 0.113 [ 0.746 6.212]\n",
"\t6.076 2.06 0.181 [ 2.363 10.008]\n",
"\t-4.999 0.563 0.035 [-6.112 -3.92 ]\n",
"\t-1.181 0.578 0.031 [-2.359 -0.128]\n",
"\t5.245 1.139 0.083 [ 2.797 7.29 ]\n",
"\t4.356 1.361 0.108 [ 1.676 7.031]\n",
"\t-6.92 1.236 0.088 [-9.163 -4.642]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-1.809 -0.41 0.28 1.029 2.451\n",
"\t0.402 2.339 3.268 4.256 5.909\n",
"\t2.378 4.543 5.985 7.562 10.045\n",
"\t-6.064 -5.394 -5.019 -4.635 -3.857\n",
"\t-2.344 -1.57 -1.165 -0.815 -0.078\n",
"\t2.974 4.471 5.269 6.044 7.483\n",
"\t1.475 3.504 4.359 5.265 6.956\n",
"\t-9.03 -7.824 -7.002 -6.073 -4.409\n",
"\t\n"
]
}
],
"source": [
"M_receptive_vocab.beta.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Database queries"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sqlite3"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"db_files = !ls *sqlite"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"articulation = sqlite3.connect(\"articulation.sqlite\")"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c_artic = articulation.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<sqlite3.Cursor at 0x1151881f0>"
]
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c_artic.execute('SELECT v1 FROM sigma_school WHERE trace==0')"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sigma_school_artic = np.ravel(c_artic.fetchall())"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.3893053284999999"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sigma_school_artic.mean()"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"articulation.sqlite\n",
"5.468853\n",
"[ 2.593277 8.103165]\n",
"\n",
"explang.sqlite\n",
"5.3118985\n",
"[ 3.703665 7.366459]\n",
"\n",
"expressive_vocab.sqlite\n",
"8.231873\n",
"[ 6.286766 11.080385]\n",
"\n",
"receptive_vocab.sqlite\n",
"6.559168\n",
"[ 4.745076 9.006935]\n",
"\n",
"reclang.sqlite\n",
"5.470786\n",
"[ 3.816387 7.789314]\n"
]
}
],
"source": [
"for dbname in db_files:\n",
" db = sqlite3.connect(dbname)\n",
" cur = db.cursor()\n",
" cur.execute('SELECT v1 FROM sigma_school WHERE trace==0')\n",
" trace = np.ravel(cur.fetchall())\n",
" trace.sort()\n",
" print('\\n'+dbname)\n",
" print(np.median(trace))\n",
" print(trace[[int(len(trace)*0.025), int(len(trace)*0.975)]])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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