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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pediatrics Paper Analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"> All studies with successful outcomes reported for early-identified children who are deaf or hard of hearing have intervention provided by specialists who are trained in parent-infant intervention services.[12,90,97] Early intervention programs should develop mechanisms to ensure that early intervention professionals have special skills necessary for providing families with the highest quality of service specific to children with hearing loss. Professionals with a background in deaf education, audiology, and speech-language pathology will typically have the skills needed for providing intervention services. Professionals should be highly qualified in their respective fields and should be skilled communicators who are knowledgeable and sensitive to the importance of enhancing families' strengths and supporting their priorities. When early intervention professionals have knowledge of the principles of adult learning, it increases their success with parents and other professionals.\n",
"\n",
"TJC must be competent to provide services.\n",
"\n",
"**References**:\n",
"1. Yoshinaga-Itano C, Sedey AL, Coulter DK, Mehl AL. Language of early- and later-identified children with hearing loss. Pediatrics.1998;102 :1161– 1171\n",
"2. Moeller MP. Early intervention and language development in children who are deaf and hard of hearing. Pediatrics.2000;106(3) :e43 . Available at: www.pediatrics.org/cgi/content/full/106/3/e43\n",
"3. Calderon R. Parental involvement in deaf children's education programs as a predictor of child's language, early reading, and social-emotional development. J Deaf Stud Deaf Educ.2000;5 :140– 155\n",
"\n",
"\n",
"Purpose of the study is to examine the relationship between highly qualified providers, age of enrollment in intervention, and speech and language outcomes at 4 years of age in a group of children enrolled in school programs that specialize in the development of listening and spoken language.\n",
"\n",
"- Does age of enrollment in intervention impact articulation, vocabulary, and language scores? (age_int)\n",
"- Does place of enrollment in intervention impact articulation, vocabulary, and language scores? (age and age_int)\n",
"- Is there an interaction effect between the place and age?\n",
"- What other factors contribute to the variance?\n",
"- Parental involvement\n",
"- Degree of hearing loss\n",
"- Mother’s education level (mother_ed)\n",
"- Prematurity (premature_weeks)\n",
"- Gender (gender)\n",
"- Language (prim_lang)\n",
"- Number of children in the home (sib)\n",
"- Age when first amplified (age_amp)\n",
"- Additional disability (secondary_diagnosis, known_synd)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"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": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/site-packages/pandas/io/parsers.py:1159: DtypeWarning: Columns (31,33,41) 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": 3,
"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 0 0 0 0 \n",
"1 initial_assessment_arm_1 2007-2008 0 0 0 0 2 \n",
"2 initial_assessment_arm_1 NaN 0 NaN NaN NaN NaN \n",
"3 initial_assessment_arm_1 2014 0 NaN NaN NaN NaN \n",
"4 initial_assessment_arm_1 2008-2009 0 0 0 0 1 \n",
"\n",
" _mother_ed father_ed premature_age ... known_synd \\\n",
"0 6 6 8 ... 0 \n",
"1 6 6 2 ... NaN \n",
"2 NaN NaN NaN ... 0 \n",
"3 NaN NaN NaN ... 0 \n",
"4 2 4 9 ... NaN \n",
"\n",
" synd_or_disab race age_test domain school score test_name \\\n",
"0 1 0 NaN NaN NaN NaN NaN \n",
"1 NaN 0 NaN NaN NaN NaN NaN \n",
"2 0 NaN NaN NaN NaN NaN NaN \n",
"3 0 NaN NaN NaN NaN NaN NaN \n",
"4 NaN 0 62 Articulation 522 40 NaN \n",
"\n",
" test_type academic_year_start \n",
"0 NaN 2005 \n",
"1 NaN 2007 \n",
"2 NaN NaN \n",
"3 NaN 2014 \n",
"4 Goldman 2008 \n",
"\n",
"[5 rows x 73 columns]"
]
},
"execution_count": 3,
"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": 4,
"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": 5,
"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": 6,
"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": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 21. , 0. , nan, 25. , 1. , 3. , 12. , 24. ,\n",
" 7. , 46. , 9. , 2. , 15. , 17. , 36. , 18. ,\n",
" 28. , 10. , 48. , 29. , 23. , 30. , 6. , 4. ,\n",
" 20. , 19. , 11. , 8. , 14. , 31. , 16. , 27. ,\n",
" 33. , 22. , 35. , 5. , 13. , 26. , 60. , 32. ,\n",
" 44. , 40. , 1.5, 52. , 43. , 38. , 34. , 54. ,\n",
" 41. , 50. , 51. , 116. , 72. , 42. , 57. , 65. ,\n",
" 78. , 56. , 59. , 83. , 61. , 107. , 64. , 74. ,\n",
" 37. , 77. , 62. , 119. , 88. , 84. , 0.5, 66. ,\n",
" 80. , 58. , 53. , 47. , 81. , 39. , 63. , 140. ,\n",
" 45. , 67. , 49. , 2.5, 86. , 126. , 85. , 133. ,\n",
" 103. , 98. , 87. , 76. , 68. , 92. , 97. , 71. ,\n",
" 55. , 70. , 75. , 152. , 89. , 154. ])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.onset_1.unique()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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": 9,
"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": 10,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lsl_dr = lsl_dr[(lsl_dr.age_test>=48) & (lsl_dr.age_test<60)]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(5847, 80)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lsl_dr.shape"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1369,)"
]
},
"execution_count": 12,
"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": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"unique_students = lsl_dr.drop_duplicates(subset='study_id')"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(1369, 80)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.shape"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1351.00000\n",
"mean 29.73131\n",
"std 15.62684\n",
"min 1.00000\n",
"25% 18.00000\n",
"50% 32.00000\n",
"75% 40.50000\n",
"max 68.00000\n",
"Name: age, dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.age.describe()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.52126099706744866"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.male.mean()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1261.000000\n",
"mean 1.175258\n",
"std 0.930211\n",
"min 0.000000\n",
"25% 1.000000\n",
"50% 1.000000\n",
"75% 2.000000\n",
"max 3.000000\n",
"Name: sib, dtype: float64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.sib.describe()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1369\n",
"mean 0.4704164\n",
"std 0.4993064\n",
"min False\n",
"25% 0\n",
"50% 0\n",
"75% 1\n",
"max True\n",
"Name: degree_hl, dtype: object"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(unique_students.degree_hl == 6).describe()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1369\n",
"mean 0.3798393\n",
"std 0.485524\n",
"min False\n",
"25% 0\n",
"50% 0\n",
"75% 1\n",
"max True\n",
"Name: degree_hl, dtype: object"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"((unique_students.degree_hl < 6) & (unique_students.degree_hl > 1)).describe()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 846.000000\n",
"mean 0.569740\n",
"std 0.495405\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": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.mother_hs.describe()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"count 1207.000000\n",
"mean 0.218724\n",
"std 0.413552\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": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_students.synd_or_disab.describe()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10cc593c8>"
]
},
"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": "markdown",
"metadata": {},
"source": [
"## Receptive Language Test Score Model"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"24 initial_assessment_arm_1 2007-2008 0 0 0 0 0 \n",
"77 initial_assessment_arm_1 2010-2011 0 1 0 0 0 \n",
"126 initial_assessment_arm_1 2011-2012 0 1 0 0 2 \n",
"139 initial_assessment_arm_1 2011-2012 0 1 0 0 0 \n",
"156 initial_assessment_arm_1 2011-2012 0 1 1 0 2 \n",
"\n",
" _mother_ed father_ed premature_age ... test_name test_type \\\n",
"24 6 6 8 ... PLS receptive \n",
"77 3 1 8 ... PLS receptive \n",
"126 6 6 8 ... CELF-P2 receptive \n",
"139 6 6 3 ... PLS receptive \n",
"156 0 6 8 ... PLS receptive \n",
"\n",
" academic_year_start deg_hl_below6 int_outside_option mother_hs \\\n",
"24 2007 0 1 NaN \n",
"77 2010 0 1 0 \n",
"126 2011 0 1 NaN \n",
"139 2011 0 1 NaN \n",
"156 2011 0 1 0 \n",
"\n",
" ident_by_3 program_by_6 jcih age_years \n",
"24 False False 0 2.250000 \n",
"77 False False 0 4.166667 \n",
"126 True False 0 4.166667 \n",
"139 False False 0 3.666667 \n",
"156 True False 0 3.333333 \n",
"\n",
"[5 rows x 80 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Filter domain and English-only, and appropriate bilateral subset\n",
"receptive_language_dataset = lsl_dr[(lsl_dr.domain=='Language') & (lsl_dr.test_type=='receptive') &\n",
" (lsl_dr.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": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(901, 80)"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mean_score = receptive_language_dataset.groupby('study_id')['score'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset = receptive_language_dataset.drop_duplicates('study_id')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset = receptive_language_dataset.set_index('study_id')"
]
},
{
"cell_type": "code",
"execution_count": 28,
"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": 29,
"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": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 0.01\n",
"school 0.00\n",
"score 0.00\n",
"male 0.00\n",
"time 0.06\n",
"family_inv 0.19\n",
"sib 0.07\n",
"deg_hl_below6 0.05\n",
"mother_hs 0.32\n",
"synd_or_disab 0.13\n",
"jcih 0.27\n",
"dtype: float64"
]
},
"execution_count": 30,
"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": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset = receptive_language_dataset[receptive_language_dataset.age.notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1071b2ba8>"
]
},
"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": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10d35d320>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"/w9vxZ5R1b0CjAAAAABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x1071b2a58>"
]
},
"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": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(823, 80)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"mother_hs 32.1\n",
"jcih 27.0\n",
"family_inv 19.4\n",
"synd_or_disab 13.1\n",
"sib 6.3\n",
"time 5.7\n",
"deg_hl_below6 4.6\n",
"male 0.0\n",
"score 0.0\n",
"school 0.0\n",
"age_years 0.0\n",
"dtype: float64"
]
},
"execution_count": 35,
"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": 36,
"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": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" male synd_or_disab deg_hl_below6 mother_hs\n",
"age_amp_missing \n",
"False 0.511986 0.238748 0.483304 0.600985\n",
"True 0.514644 0.308824 0.458333 0.607843"
]
},
"execution_count": 37,
"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": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/site-packages/pandas/tools/plotting.py:2625: FutureWarning: \n",
"The default value for 'return_type' will change to 'axes' in a future release.\n",
" To use the future behavior now, set return_type='axes'.\n",
" To keep the previous behavior and silence this warning, set return_type='dict'.\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
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"text/plain": [
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10d6e27f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10d879b38>"
]
},
"metadata": {},
"output_type": "display_data"
},
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10da02e80>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for col in ['age', 'score', 'degree_hl', 'family_inv', 'age_int']:\n",
" try:\n",
" _ = by_age_amp_missing.boxplot(column=col)\n",
" except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" male synd_or_disab deg_hl_below6 mother_hs\n",
"jcih_missing \n",
"False 0.517471 0.237094 0.477002 0.610048\n",
"True 0.500000 0.317708 0.474747 0.581560"
]
},
"execution_count": 40,
"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": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.4/site-packages/pandas/tools/plotting.py:2625: FutureWarning: \n",
"The default value for 'return_type' will change to 'axes' in a future release.\n",
" To use the future behavior now, set return_type='axes'.\n",
" To keep the previous behavior and silence this warning, set return_type='dict'.\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e0cd0f0>"
]
},
"metadata": {},
"output_type": "display_data"
},
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"text/plain": [
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],
"text/plain": [
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]
},
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{
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"text/plain": [
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},
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},
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10d89ff98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for col in ['age', 'score', 'degree_hl', 'family_inv', 'age_int']:\n",
" _ = by_jcih_missing.boxplot(column=col)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.3378898339408671"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_dataset.age_years.mean()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"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 = 9\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",
" \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), 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, observed=True)\n",
" \n",
" # Imputation of hearing loss\n",
"# p_crit = Beta(\"p_%s\" % criterion, 1, 1, value=0.5)\n",
"# crit_masked = masked_values(dataset[criterion].values, value=np.nan)\n",
"# x_crit = Bernoulli('x_%s' % criterion, p_crit, value=crit_masked, 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, 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",
" # Imputation of time\n",
"# n_time_cats = len(dataset.time.unique())\n",
"# p_time = Dirichlet(\"p_time\", np.ones(n_time_cats), value=[1./n_time_cats]*(n_time_cats-1))\n",
"# time_masked = masked_values(dataset.time.fillna(0.5), value=0.5)\n",
"# x_time = Categorical('x_time', p_time, value=time_masked, observed=True)\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, value=np.zeros(len(unique_schools)))\n",
" \n",
" # Indices to child random effects\n",
" unique_child = np.unique(study_id)\n",
" child_index = [list(unique_child).index(s) for s in study_id]\n",
" \n",
" # Robust individual random effect\n",
" nu_child = Exponential(\"nu_child\", 0.1, value=10)\n",
" alpha_child = T(\"alpha_child\", nu=nu_child, value=np.zeros(len(unique_child)))\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, a=alpha_child):\n",
" \n",
" # Complete predictors\n",
" X = [age_std, male, ident_by_3, program_by_6]\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",
" # Predictions:\n",
" # Girl, average age, ident by 3 prog by 6, normal family involvement, \n",
" # 2 siblings, non-profound hearing loss,\n",
" # mother with HS, no previous disability, JCIH\n",
" x1 = [0, 0, 1, 1, 0, 2, 1, 1, 0]\n",
" \n",
" # Boy, 6 months younger than average, ident by 3 prog by 6, normal family involvement, \n",
" # ident by 3 prog by 6, no siblings, profound hearing loss,\n",
" # mother without HS diploma, previous disability\n",
" x2 = [-0.5, 1, 1, 1, 0, 0, 0, 0, 1]\n",
" \n",
" # Boy, 1.5 years older than average, not ident by 3 or prog by 6, poor family involvement, \n",
" # 1 sibling, non-profound hearing loss,\n",
" # mother without HS diploma, previous disability\n",
" x3 = [1.5, 1, 0, 0, 2, 1, 1, 0, 1]\n",
" \n",
" # Girl, 6 months older than average, ident by 3 prog by 6, impaired family involvement, \n",
" # 3 siblings, profound hearing loss,\n",
" # mother with HS diploma, no previous disability\n",
" x4 = [0.5, 1, 1, 1, 1, 3, 0, 1, 0]\n",
" \n",
" X = np.c_[x1, x2, x3, x4]\n",
"\n",
" theta_pred = Lambda('theta_pred', lambda b=beta, mu=mean_score: mu + np.dot(X.T, b))\n",
" predictions = Normal('predictions', mu=theta_pred, tau=tau)\n",
" \n",
" \n",
" return locals()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"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": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iterations = 100000\n",
"burn = 90000"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 732.0 sec"
]
}
],
"source": [
"M_reclang.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"labels = ['Age of Enrollment', 'Male', 'Identified by 3 mo.', \n",
" 'In Program by 6 mo.', '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": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10daba0f0>"
]
},
"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": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10c0f4dd8>"
]
},
"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": 63,
"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-3.572 0.631 0.045 [-4.74 -2.369]\n",
"\t-0.187 1.215 0.091 [-2.634 2.285]\n",
"\t3.164 1.322 0.104 [ 0.57 5.665]\n",
"\t-0.648 2.703 0.252 [-5.237 4.756]\n",
"\t-5.278 0.586 0.036 [-6.401 -4.159]\n",
"\t-0.436 0.628 0.037 [-1.64 0.795]\n",
"\t7.596 1.195 0.088 [ 5.329 9.906]\n",
"\t5.091 1.325 0.104 [ 2.395 7.535]\n",
"\t-5.846 1.286 0.101 [-8.533 -3.454]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-4.738 -4.024 -3.57 -3.125 -2.317\n",
"\t-2.676 -0.978 -0.127 0.59 2.271\n",
"\t0.464 2.319 3.214 4.052 5.612\n",
"\t-5.503 -2.643 -0.755 1.232 4.599\n",
"\t-6.414 -5.68 -5.281 -4.875 -4.167\n",
"\t-1.635 -0.857 -0.468 0.005 0.822\n",
"\t5.369 6.757 7.566 8.435 9.991\n",
"\t2.43 4.228 5.126 5.948 7.609\n",
"\t-8.533 -6.648 -5.817 -5.0 -3.441\n",
"\t\n"
]
}
],
"source": [
"M_reclang.beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10d362400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_reclang.predictions], rhat=False, custom_labels=['Student {}'.format(i) for i in range(1,5)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expressive Language 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",
"25 initial_assessment_arm_1 2007-2008 0 0 0 0 0 \n",
"78 initial_assessment_arm_1 2010-2011 0 1 0 0 0 \n",
"127 initial_assessment_arm_1 2011-2012 0 1 0 0 2 \n",
"140 initial_assessment_arm_1 2011-2012 0 1 0 0 0 \n",
"157 initial_assessment_arm_1 2011-2012 0 1 1 0 2 \n",
"\n",
" _mother_ed father_ed premature_age ... test_name test_type \\\n",
"25 6 6 8 ... PLS expressive \n",
"78 3 1 8 ... PLS expressive \n",
"127 6 6 8 ... CELF-P2 expressive \n",
"140 6 6 3 ... PLS expressive \n",
"157 0 6 8 ... PLS expressive \n",
"\n",
" academic_year_start deg_hl_below6 int_outside_option mother_hs \\\n",
"25 2007 0 1 NaN \n",
"78 2010 0 1 0 \n",
"127 2011 0 1 NaN \n",
"140 2011 0 1 NaN \n",
"157 2011 0 1 0 \n",
"\n",
" ident_by_3 program_by_6 jcih age_years \n",
"25 False False 0 2.250000 \n",
"78 False False 0 4.166667 \n",
"127 True False 0 4.166667 \n",
"140 False False 0 3.666667 \n",
"157 True False 0 3.333333 \n",
"\n",
"[5 rows x 80 columns]"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_language_dataset = lsl_dr[(lsl_dr.domain=='Language') & (lsl_dr.test_type=='expressive') &\n",
" (lsl_dr.non_english==False)]\n",
"\n",
"expressive_language_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"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": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 0.01\n",
"school 0.00\n",
"score 0.00\n",
"male 0.00\n",
"time 0.06\n",
"family_inv 0.19\n",
"sib 0.07\n",
"deg_hl_below6 0.05\n",
"mother_hs 0.32\n",
"synd_or_disab 0.14\n",
"jcih 0.27\n",
"dtype: float64"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(expressive_language_dataset[covs].isnull().sum() / expressive_language_dataset.shape[0]).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"gg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x1186ec198>"
]
},
"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": 72,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_explang = MCMC(generate_model(expressive_language_dataset), db='sqlite', name='explang', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 733.9 sec"
]
}
],
"source": [
"M_explang.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"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-4.011 0.622 0.043 [-5.216 -2.829]\n",
"\t-0.51 1.154 0.088 [-2.802 1.763]\n",
"\t4.319 1.44 0.117 [ 1.876 7.414]\n",
"\t-1.337 2.391 0.22 [-5.54 3.443]\n",
"\t-4.682 0.577 0.036 [-5.806 -3.566]\n",
"\t-0.774 0.671 0.042 [-2.203 0.412]\n",
"\t8.344 1.297 0.102 [ 6.004 10.943]\n",
"\t6.726 1.453 0.121 [ 4.156 9.883]\n",
"\t-6.165 1.268 0.098 [-8.719 -3.751]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-5.142 -4.449 -4.041 -3.589 -2.666\n",
"\t-2.91 -1.289 -0.462 0.268 1.721\n",
"\t1.332 3.385 4.271 5.303 7.144\n",
"\t-5.446 -3.096 -1.529 0.257 3.607\n",
"\t-5.79 -5.083 -4.696 -4.312 -3.496\n",
"\t-2.129 -1.222 -0.755 -0.309 0.506\n",
"\t6.029 7.371 8.28 9.269 11.006\n",
"\t3.971 5.717 6.671 7.642 9.825\n",
"\t-8.588 -7.028 -6.221 -5.338 -3.553\n",
"\t\n"
]
}
],
"source": [
"M_explang.beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"RK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x1186dfb00>"
]
},
"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": [
"### Making predictions\n",
"\n",
"Though these models were constructed primarily for inference, that is, to provide insights on the relative effects of a suite of factors on expected test scores, there is the potential for using them to predict speech-language outcomes. To explore this, we contrived four predictive scenarios, whereby children with different values of the set of predictor variables were specified, and predictions for them were generated by our models. \n",
"\n",
"The Bayesian hierarchical modeling approach makes generating predictions straightforward, via the posterior predictive distribution, which generates predicted values of outcomes conditional on the outcomes observed in the dataset. The variation of these predictions includes both the residual uncertainty in the model parameters and the aleatory sampling variability of the test outcomes. Our test cases were specified as follows:\n",
"\n",
"**Case 1** 👧\n",
"\n",
"* female\n",
"* average age\n",
"* normal family involvement\n",
"* 2 siblings\n",
"* non-profound hearing loss\n",
"* mother with HS\n",
"* no previous disability\n",
"* JCIH criterion met\n",
"\n",
"**Case 2** 👦\n",
"\n",
"* male \n",
"* 6 months younger than average\n",
"* normal family involvement\n",
"* no siblings\n",
"* profound hearing loss\n",
"* mother without HS diploma\n",
"* previous disability\n",
"* JCIH criterion met\n",
"\n",
"**Case 3** 👶\n",
"\n",
"* male \n",
"* 1.5 years older than average\n",
"* poor family involvement\n",
"* 1 sibling\n",
"* non-profound hearing loss\n",
"* mother without HS diploma\n",
"* previous disability\n",
"* no JCIH\n",
"\n",
"**Case 4** 🙎\n",
"\n",
"* female \n",
"* 6 months older than average\n",
"* impaired family involvement\n",
"* 3 siblings\n",
"* profound hearing loss\n",
"* mother with HS diploma\n",
"* no previous disability\n",
"* JCIH criterion met"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"predictions:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t101.062 16.35 0.276 [ 70.02 134.248]\n",
"\t84.974 16.468 0.315 [ 52.47 116.848]\n",
"\t71.965 16.203 0.221 [ 40.308 103.237]\n",
"\t85.678 16.497 0.312 [ 53.156 117.479]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t68.889 90.112 101.054 111.965 133.313\n",
"\t52.577 73.922 84.952 96.241 117.034\n",
"\t40.89 60.91 71.838 83.054 104.141\n",
"\t53.344 74.444 85.7 96.975 117.713\n",
"\t\n"
]
}
],
"source": [
"M_reclang.predictions.summary()"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10da67048>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_reclang.predictions], rhat=False, custom_labels=['Student {}'.format(i) for i in range(1,5)])"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"predictions:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t96.338 16.855 0.274 [ 62.645 128.481]\n",
"\t77.998 16.99 0.33 [ 43.675 110.062]\n",
"\t65.469 16.694 0.205 [ 33.268 97.793]\n",
"\t79.681 16.86 0.326 [ 46.457 112.541]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t63.171 84.967 96.431 107.714 129.437\n",
"\t44.753 66.441 77.97 89.619 111.348\n",
"\t33.318 53.8 65.582 76.824 97.859\n",
"\t46.426 68.564 79.686 91.087 112.511\n",
"\t\n"
]
}
],
"source": [
"M_explang.predictions.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Articulation Model"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"15 initial_assessment_arm_1 2011-2012 1 1 0 0 NaN \n",
"21 initial_assessment_arm_1 2007-2008 0 0 0 0 0 \n",
"26 initial_assessment_arm_1 2011-2012 0 0 0 0 2 \n",
"123 initial_assessment_arm_1 2011-2012 0 1 0 0 2 \n",
"136 initial_assessment_arm_1 2011-2012 0 1 0 0 0 \n",
"\n",
" _mother_ed father_ed premature_age ... test_name test_type \\\n",
"15 6 6 9 ... NaN Goldman \n",
"21 6 6 8 ... NaN Goldman \n",
"26 6 6 9 ... NaN Goldman \n",
"123 6 6 8 ... NaN Goldman \n",
"136 6 6 3 ... NaN Goldman \n",
"\n",
" academic_year_start deg_hl_below6 int_outside_option mother_hs \\\n",
"15 2011 NaN 0 NaN \n",
"21 2007 0 1 NaN \n",
"26 2011 1 1 NaN \n",
"123 2011 0 1 NaN \n",
"136 2011 0 1 NaN \n",
"\n",
" ident_by_3 program_by_6 jcih age_years \n",
"15 False False NaN 4.166667 \n",
"21 False False 0 2.250000 \n",
"26 True False 0 4.833333 \n",
"123 True False 0 4.166667 \n",
"136 False False 0 3.666667 \n",
"\n",
"[5 rows x 80 columns]"
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"articulation_dataset = lsl_dr[(lsl_dr.domain=='Articulation') & \n",
" (lsl_dr.non_english==False)]\n",
"articulation_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 81,
"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": 82,
"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 0.06\n",
"family_inv 0.22\n",
"sib 0.07\n",
"deg_hl_below6 0.13\n",
"mother_hs 0.36\n",
"synd_or_disab 0.12\n",
"jcih 0.36\n",
"dtype: float64"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(articulation_dataset[covs].isnull().sum() / articulation_dataset.shape[0]).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 83,
"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": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e156da0>"
]
},
"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": 85,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_articulation = MCMC(generate_model(articulation_dataset), db='sqlite', name='articulation', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 722.3 sec"
]
}
],
"source": [
"M_articulation.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10d9e1a58>"
]
},
"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": 88,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10d832240>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_articulation.predictions], rhat=False, custom_labels=['Student {}'.format(i) for i in range(1,5)])"
]
},
{
"cell_type": "code",
"execution_count": 89,
"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-3.511 0.666 0.042 [-4.892 -2.244]\n",
"\t0.903 1.399 0.107 [-1.989 3.376]\n",
"\t2.571 1.533 0.127 [-0.36 5.526]\n",
"\t-3.006 2.355 0.208 [-7.835 0.979]\n",
"\t-4.149 0.653 0.043 [-5.357 -2.845]\n",
"\t-0.025 0.709 0.041 [-1.453 1.301]\n",
"\t6.457 1.514 0.125 [ 3.639 9.264]\n",
"\t5.163 1.88 0.163 [ 1.422 8.984]\n",
"\t-7.967 1.365 0.103 [-10.579 -5.177]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-4.883 -3.939 -3.494 -3.045 -2.217\n",
"\t-1.891 0.005 0.947 1.851 3.56\n",
"\t-0.292 1.488 2.548 3.624 5.624\n",
"\t-7.558 -4.515 -2.966 -1.584 1.804\n",
"\t-5.366 -4.601 -4.18 -3.674 -2.846\n",
"\t-1.376 -0.482 -0.046 0.423 1.389\n",
"\t3.532 5.371 6.509 7.567 9.192\n",
"\t1.398 3.92 5.215 6.388 8.984\n",
"\t-10.856 -8.796 -7.883 -7.076 -5.407\n",
"\t\n"
]
}
],
"source": [
"M_articulation.beta.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expressive Vocabulary Model"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"16 initial_assessment_arm_1 2011-2012 1 1 0 0 NaN \n",
"22 initial_assessment_arm_1 2007-2008 0 0 0 0 0 \n",
"27 initial_assessment_arm_1 2011-2012 0 0 0 0 2 \n",
"51 initial_assessment_arm_1 2012-2013 0 0 0 0 2 \n",
"75 initial_assessment_arm_1 2010-2011 0 1 0 0 0 \n",
"\n",
" _mother_ed father_ed premature_age ... test_name test_type \\\n",
"16 6 6 9 ... NaN EVT \n",
"22 6 6 8 ... NaN EOWPVT \n",
"27 6 6 9 ... NaN EVT \n",
"51 6 6 8 ... NaN EVT \n",
"75 3 1 8 ... NaN EVT \n",
"\n",
" academic_year_start deg_hl_below6 int_outside_option mother_hs \\\n",
"16 2011 NaN 0 NaN \n",
"22 2007 0 1 NaN \n",
"27 2011 1 1 NaN \n",
"51 2012 1 1 NaN \n",
"75 2010 0 1 0 \n",
"\n",
" ident_by_3 program_by_6 jcih age_years \n",
"16 False False NaN 4.166667 \n",
"22 False False 0 2.250000 \n",
"27 True False 0 4.833333 \n",
"51 False False 0 4.000000 \n",
"75 False False 0 4.166667 \n",
"\n",
"[5 rows x 80 columns]"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_vocab_dataset = lsl_dr[(lsl_dr.domain=='Expressive Vocabulary') & \n",
" (lsl_dr.non_english==False)]\n",
"expressive_vocab_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"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": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 6\n",
"school 0\n",
"score 0\n",
"male 0\n",
"time 61\n",
"family_inv 219\n",
"sib 70\n",
"deg_hl_below6 123\n",
"mother_hs 351\n",
"synd_or_disab 115\n",
"jcih 344\n",
"dtype: int64"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_vocab_dataset[covs].isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 93,
"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": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"QmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e3ad0f0>"
]
},
"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": 95,
"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": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 774.0 sec"
]
}
],
"source": [
"M_expressive_vocab.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e2cf518>"
]
},
"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": 98,
"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-3.351 0.557 0.033 [-4.512 -2.339]\n",
"\t1.915 1.106 0.081 [-0.095 4.035]\n",
"\t3.932 1.413 0.114 [ 1.381 6.62 ]\n",
"\t-1.507 2.12 0.192 [-5.212 3.226]\n",
"\t-5.021 0.626 0.042 [-6.091 -3.698]\n",
"\t-1.189 0.646 0.038 [-2.531 -0.007]\n",
"\t7.019 1.187 0.089 [ 4.722 9.439]\n",
"\t6.978 1.432 0.118 [ 4.374 9.931]\n",
"\t-5.563 1.333 0.103 [-8.11 -2.944]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-4.488 -3.726 -3.362 -2.975 -2.286\n",
"\t-0.177 1.162 1.868 2.664 4.025\n",
"\t1.263 2.919 3.919 4.954 6.551\n",
"\t-5.384 -2.985 -1.613 -0.231 3.167\n",
"\t-6.236 -5.475 -5.032 -4.566 -3.79\n",
"\t-2.474 -1.623 -1.192 -0.741 0.097\n",
"\t4.546 6.239 7.06 7.79 9.345\n",
"\t4.266 6.055 6.918 7.953 9.876\n",
"\t-8.304 -6.462 -5.52 -4.668 -3.062\n",
"\t\n"
]
}
],
"source": [
"M_expressive_vocab.beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e2cf4a8>"
]
},
"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": 100,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Could not calculate Gelman-Rubin statistics. Requires multiple chains of equal length.\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e1aee10>"
]
},
"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": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
" redcap_event_name academic_year hl male _race prim_lang sib \\\n",
"17 initial_assessment_arm_1 2011-2012 1 1 0 0 NaN \n",
"23 initial_assessment_arm_1 2007-2008 0 0 0 0 0 \n",
"28 initial_assessment_arm_1 2011-2012 0 0 0 0 2 \n",
"52 initial_assessment_arm_1 2012-2013 0 0 0 0 2 \n",
"76 initial_assessment_arm_1 2010-2011 0 1 0 0 0 \n",
"\n",
" _mother_ed father_ed premature_age ... test_name test_type \\\n",
"17 6 6 9 ... NaN PPVT \n",
"23 6 6 8 ... NaN PPVT \n",
"28 6 6 9 ... NaN PPVT \n",
"52 6 6 8 ... NaN PPVT \n",
"76 3 1 8 ... NaN PPVT \n",
"\n",
" academic_year_start deg_hl_below6 int_outside_option mother_hs \\\n",
"17 2011 NaN 0 NaN \n",
"23 2007 0 1 NaN \n",
"28 2011 1 1 NaN \n",
"52 2012 1 1 NaN \n",
"76 2010 0 1 0 \n",
"\n",
" ident_by_3 program_by_6 jcih age_years \n",
"17 False False NaN 4.166667 \n",
"23 False False 0 2.250000 \n",
"28 True False 0 4.833333 \n",
"52 False False 0 4.000000 \n",
"76 False False 0 4.166667 \n",
"\n",
"[5 rows x 80 columns]"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_vocab_dataset = lsl_dr[(lsl_dr.domain=='Receptive Vocabulary') & \n",
" (lsl_dr.non_english==False)]\n",
"receptive_vocab_dataset.head()"
]
},
{
"cell_type": "code",
"execution_count": 102,
"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": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 5\n",
"school 0\n",
"score 0\n",
"male 0\n",
"time 56\n",
"family_inv 224\n",
"sib 69\n",
"deg_hl_below6 126\n",
"mother_hs 357\n",
"synd_or_disab 115\n",
"jcih 347\n",
"dtype: int64"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_vocab_dataset[covs].isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"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": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10e3917f0>"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"/bUAAAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x10dabce10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"receptive_vocab_dataset.age_int.hist(bins=40)"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(961, 80)"
]
},
"execution_count": 106,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_vocab_dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1186c4e80>"
]
},
"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": 108,
"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": 109,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 781.0 sec"
]
}
],
"source": [
"M_receptive_vocab.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"6vZ+dmKytoqIecC8ktXXAa8AziN7gc9IZQNWk+f/vwAkvR6YDrx7Z8a1kwy6/dlKRAyF/VrU7f08\n",
"qIeUNijkL2PyCNm0QK38NRExmD9dA7wA/L7dQfSTobg/awbzfq2pvXe7vZ89YrJKknQB8E7gjyS9\n",
"PCLOjYitki4Bfkw25XVJW4PsR5JuBA4km/o5v83h9IuhtD9rhsJ+rffelXQp3djPvlaemZlViqfy\n",
"zMysUpyYzMysUpyYzMysUpyYzMysUpyYzMysUpyYzMysUpyYzMysUpyYzMysUv4/mEp0voAGrVoA\n",
"AAAASUVORK5CYII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x10e41ba58>"
]
},
"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": 111,
"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-3.496 0.582 0.041 [-4.591 -2.317]\n",
"\t0.549 1.039 0.077 [-1.466 2.593]\n",
"\t2.375 1.271 0.103 [-0.181 4.735]\n",
"\t-1.439 2.046 0.182 [-5.365 2.937]\n",
"\t-4.668 0.567 0.04 [-5.715 -3.521]\n",
"\t-1.288 0.641 0.04 [-2.64 -0.143]\n",
"\t6.2 1.18 0.096 [ 4.156 8.845]\n",
"\t5.286 1.279 0.104 [ 2.847 7.767]\n",
"\t-6.816 1.149 0.088 [-8.899 -4.483]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-4.62 -3.9 -3.502 -3.103 -2.333\n",
"\t-1.442 -0.134 0.491 1.188 2.636\n",
"\t-0.042 1.51 2.391 3.226 4.948\n",
"\t-5.3 -2.77 -1.454 -0.21 3.009\n",
"\t-5.699 -5.073 -4.675 -4.299 -3.489\n",
"\t-2.619 -1.694 -1.276 -0.828 -0.103\n",
"\t3.92 5.394 6.179 6.977 8.723\n",
"\t2.909 4.431 5.277 6.134 7.852\n",
"\t-9.075 -7.636 -6.799 -5.97 -4.567\n",
"\t\n"
]
}
],
"source": [
"M_receptive_vocab.beta.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Database queries"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sqlite3"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"db_files = !ls *sqlite"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"articulation = sqlite3.connect(\"articulation.sqlite\")"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c_artic = articulation.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<sqlite3.Cursor at 0x10cad7500>"
]
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c_artic.execute('SELECT v1 FROM sigma_school WHERE trace==0')"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sigma_school_artic = np.ravel(c_artic.fetchall())"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"6.1150142553000002"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sigma_school_artic.mean()"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"articulation.sqlite\n",
"6.101879\n",
"[ 3.601835 8.942658]\n",
"\n",
"explang.sqlite\n",
"5.363171\n",
"[ 3.465128 7.299587]\n",
"\n",
"expressive_vocab.sqlite\n",
"8.680978\n",
"[ 6.714001 11.222219]\n",
"\n",
"receptive_vocab.sqlite\n",
"6.7558135\n",
"[ 5.057159 8.978382]\n",
"\n",
"reclang.sqlite\n",
"5.214563\n",
"[ 3.906563 7.246824]\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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