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Pediatrics Paper.ipynb
{
"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 0x112e35048>"
]
},
"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": 31,
"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 = 'ident_by_3'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Proportion of missing values for each variable"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 0.01\n",
"school 0.00\n",
"score 0.00\n",
"male 0.00\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",
"ident_by_3 0.00\n",
"dtype: float64"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"covs = ['age_years', 'school', 'score', 'male', \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": 33,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"receptive_language_dataset = receptive_language_dataset[receptive_language_dataset.age.notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x10d34b9b0>"
]
},
"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": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10d358f98>"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"/w9vxZ5R1b0CjAAAAABJRU5ErkJggg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x1131375f8>"
]
},
"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": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(823, 80)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"mother_hs 32.1\n",
"family_inv 19.4\n",
"synd_or_disab 13.1\n",
"sib 6.3\n",
"deg_hl_below6 4.6\n",
"ident_by_3 0.0\n",
"male 0.0\n",
"score 0.0\n",
"school 0.0\n",
"age_years 0.0\n",
"dtype: float64"
]
},
"execution_count": 37,
"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": 38,
"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": 39,
"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": 39,
"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": 40,
"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": [
"<matplotlib.figure.Figure at 0x113a93c88>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x113b43b38>"
]
},
"metadata": {},
"output_type": "display_data"
},
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x113b60d30>"
]
},
"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": 41,
"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": 42,
"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": 42,
"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": 43,
"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 0x1142afdd8>"
]
},
"metadata": {},
"output_type": "display_data"
},
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"text/plain": [
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},
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],
"text/plain": [
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]
},
"metadata": {},
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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 0x114222a90>"
]
},
"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": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.3378898339408671"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_language_dataset.age_years.mean()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"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",
"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",
" jcih = dataset.jcih.values\n",
" study_id = dataset.index.values\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",
" 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]*8)\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",
" x_crit=x_crit,\n",
" a=alpha_child):\n",
" \n",
" # Complete predictors\n",
" X = [age_std, male]\n",
" # Partially-imputed predictors\n",
" X += [x_family_inv, x_sib, x_hl, x_mother_hs, x_synd_or_disab, x_crit]\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, normal family involvement, 2 siblings, non-profound hearing loss,\n",
" # mother with HS, no previous disability, JCIH\n",
" x1 = [0, 0, 0, 2, 1, 1, 0, 1]\n",
" \n",
" # Boy, 6 months younger than average, normal family involvement, no siblings, profound hearing loss,\n",
" # mother without HS diploma, previous disability, JCIH\n",
" x2 = [-0.5, 1, 0, 0, 0, 0, 1, 1]\n",
" \n",
" # Boy, 1.5 years older than average, poor family involvement, 1 sibling, non-profound hearing loss,\n",
" # mother without HS diploma, previous disability, no JCIH\n",
" x3 = [1.5, 1, 2, 1, 1, 0, 1, 0]\n",
" \n",
" # Girl, 6 months older than average, impaired family involvement, 3 siblings, profound hearing loss,\n",
" # mother with HS diploma, no previous disability, JCIH\n",
" x4 = [0.5, 1, 1, 3, 0, 1, 0, 1]\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": 46,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_reclang = MCMC(generate_model(receptive_language_dataset), db='sqlite', name='reclang', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iterations = 100000\n",
"burn = 90000"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 365.7 sec"
]
}
],
"source": [
"M_reclang.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"labels = ['Age of Enrollment', 'Male', \n",
" 'Reduced Family Involvement', 'Sibling Count', 'Non-profound Hearing Loss',\n",
" 'Mother with HS Diploma','Additional Disability', \n",
" criterion_labels[criterion]]"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"YII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x113cf4748>"
]
},
"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": 51,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"YII=\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x1141fceb8>"
]
},
"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": 52,
"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-3.428 0.517 0.03 [-4.475 -2.486]\n",
"\t-0.081 1.283 0.098 [-2.444 2.36 ]\n",
"\t-5.33 0.577 0.034 [-6.443 -4.235]\n",
"\t-0.62 0.662 0.043 [-2.034 0.524]\n",
"\t7.259 1.301 0.103 [ 4.596 9.55 ]\n",
"\t5.071 1.323 0.109 [ 2.73 7.842]\n",
"\t-6.063 1.329 0.106 [-8.643 -3.514]\n",
"\t2.887 1.271 0.096 [ 0.31 5.279]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-4.512 -3.761 -3.409 -3.047 -2.495\n",
"\t-2.427 -1.03 -0.041 0.784 2.461\n",
"\t-6.436 -5.742 -5.318 -4.93 -4.206\n",
"\t-1.97 -1.067 -0.602 -0.142 0.617\n",
"\t4.676 6.444 7.306 8.13 9.703\n",
"\t2.456 4.186 5.089 5.885 7.668\n",
"\t-8.623 -6.904 -6.023 -5.169 -3.493\n",
"\t0.31 2.066 2.898 3.731 5.279\n",
"\t\n"
]
}
],
"source": [
"M_reclang.beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x113cc97f0>"
]
},
"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": 54,
"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": 54,
"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": 55,
"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": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 0.01\n",
"school 0.00\n",
"score 0.00\n",
"male 0.00\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",
"ident_by_3 0.00\n",
"dtype: float64"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(expressive_language_dataset[covs].isnull().sum() / expressive_language_dataset.shape[0]).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"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": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"gg==\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x113c9a390>"
]
},
"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": 59,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_explang = MCMC(generate_model(expressive_language_dataset), db='sqlite', name='explang', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 370.3 sec"
]
}
],
"source": [
"M_explang.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"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-3.754 0.517 0.026 [-4.78 -2.784]\n",
"\t-0.515 1.266 0.097 [-2.813 2.076]\n",
"\t-4.479 0.64 0.04 [-5.71 -3.163]\n",
"\t-0.738 0.645 0.039 [-2.021 0.469]\n",
"\t8.289 1.216 0.091 [ 6.023 10.813]\n",
"\t7.108 1.332 0.105 [ 4.38 9.771]\n",
"\t-6.047 1.36 0.104 [-8.309 -3.109]\n",
"\t4.425 1.439 0.119 [ 1.759 7.434]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-4.766 -4.112 -3.755 -3.397 -2.746\n",
"\t-2.834 -1.372 -0.626 0.36 2.066\n",
"\t-5.802 -4.887 -4.468 -4.083 -3.208\n",
"\t-2.067 -1.168 -0.708 -0.301 0.461\n",
"\t5.807 7.501 8.288 9.149 10.611\n",
"\t4.236 6.336 7.139 7.956 9.728\n",
"\t-8.518 -7.026 -6.126 -5.147 -3.285\n",
"\t1.763 3.468 4.351 5.366 7.444\n",
"\t\n"
]
}
],
"source": [
"M_explang.beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x113c96518>"
]
},
"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",
"**Student 1** Girl, 1 year older than average, normal family involvement, 2 siblings, non-profound hearing loss,\n",
"mother with HS, no previous disability, JCIH\n",
"\n",
"**Student 2** Boy, 6 months younger than average, normal family involvement, no siblings, profound hearing loss,\n",
"mother without HS diploma, previous disability, JCIH\n",
"\n",
"**Student 3** Boy, 1.5 years older than average, poor family involvement, 1 sibling, non-profound hearing loss, mother without HS diploma, previous disability, no JCIH\n",
"\n",
"**Student 4** Girl, 6 months older than average, impaired family involvement, 3 siblings, profound hearing loss, mother with HS diploma, no previous disability, JCIH"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"predictions:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t101.115 16.217 0.197 [ 69.935 133.267]\n",
"\t86.021 16.319 0.247 [ 54.886 117.944]\n",
"\t72.165 16.292 0.224 [ 39.335 103.286]\n",
"\t86.637 16.299 0.222 [ 54.807 117.918]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t69.967 90.362 101.032 111.894 133.345\n",
"\t54.939 74.795 85.932 97.033 118.039\n",
"\t40.229 61.379 72.064 83.122 104.486\n",
"\t55.07 75.482 86.847 97.641 118.27\n",
"\t\n"
]
}
],
"source": [
"M_reclang.predictions.summary()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11ac50908>"
]
},
"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": 65,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"predictions:\n",
" \n",
"\tMean SD MC Error 95% HPD interval\n",
"\t------------------------------------------------------------------\n",
"\t97.373 16.974 0.225 [ 65.488 131.935]\n",
"\t79.11 16.863 0.284 [ 47.281 113.033]\n",
"\t65.741 16.895 0.27 [ 32.002 97.796]\n",
"\t81.518 16.802 0.233 [ 50.735 116.322]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t63.681 85.855 97.487 108.804 130.158\n",
"\t46.146 67.758 79.102 90.373 112.017\n",
"\t32.47 54.364 65.759 77.228 98.611\n",
"\t48.715 69.992 81.759 92.8 114.343\n",
"\t\n"
]
}
],
"source": [
"M_explang.predictions.summary()"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"Matplot.summary_plot([M_explang.predictions], rhat=False, custom_labels=['Student {}'.format(i) for i in range(1,5)])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Articulation 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",
"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": 67,
"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": 68,
"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": 69,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 0.00\n",
"school 0.00\n",
"score 0.00\n",
"male 0.00\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",
"ident_by_3 0.00\n",
"dtype: float64"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(articulation_dataset[covs].isnull().sum() / articulation_dataset.shape[0]).round(2)"
]
},
{
"cell_type": "code",
"execution_count": 70,
"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": 71,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x113d007f0>"
]
},
"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": 72,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"M_articulation = MCMC(generate_model(articulation_dataset), db='sqlite', name='articulation', dbmode='w')"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 372.0 sec"
]
}
],
"source": [
"M_articulation.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x113cd0fd0>"
]
},
"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": 75,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x114343208>"
]
},
"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": 76,
"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-3.055 0.564 0.032 [-4.118 -1.936]\n",
"\t1.027 1.38 0.101 [-1.463 3.706]\n",
"\t-4.268 0.651 0.039 [-5.461 -2.953]\n",
"\t0.009 0.713 0.04 [-1.475 1.327]\n",
"\t6.518 1.331 0.104 [ 4.096 9.31 ]\n",
"\t5.219 1.631 0.134 [ 2.221 8.196]\n",
"\t-7.6 1.416 0.109 [-10.425 -4.826]\n",
"\t2.372 1.489 0.114 [-0.427 5.113]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-4.138 -3.437 -3.061 -2.679 -1.949\n",
"\t-1.463 0.065 1.032 1.916 3.73\n",
"\t-5.51 -4.69 -4.267 -3.836 -2.966\n",
"\t-1.394 -0.459 0.013 0.474 1.448\n",
"\t3.845 5.591 6.518 7.404 9.134\n",
"\t2.16 4.011 5.228 6.365 8.182\n",
"\t-10.545 -8.55 -7.533 -6.612 -4.838\n",
"\t-0.363 1.293 2.367 3.432 5.299\n",
"\t\n"
]
}
],
"source": [
"M_articulation.beta.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Expressive Vocabulary Model"
]
},
{
"cell_type": "code",
"execution_count": 77,
"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": 77,
"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": 78,
"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": 79,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 6\n",
"school 0\n",
"score 0\n",
"male 0\n",
"family_inv 219\n",
"sib 70\n",
"deg_hl_below6 123\n",
"mother_hs 351\n",
"synd_or_disab 115\n",
"ident_by_3 0\n",
"dtype: int64"
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"expressive_vocab_dataset[covs].isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 80,
"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": 81,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"QmCC\n"
],
"text/plain": [
"<matplotlib.figure.Figure at 0x113c9a668>"
]
},
"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": 82,
"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": 83,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 408.6 sec"
]
}
],
"source": [
"M_expressive_vocab.sample(iterations, burn)"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x114663cf8>"
]
},
"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": 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",
"\t-3.21 0.521 0.029 [-4.197 -2.212]\n",
"\t1.747 1.092 0.082 [-0.097 3.974]\n",
"\t-5.124 0.658 0.046 [-6.45 -3.894]\n",
"\t-1.359 0.656 0.042 [-2.706 -0.152]\n",
"\t6.911 1.242 0.098 [ 4.565 9.378]\n",
"\t6.528 1.472 0.127 [ 3.497 9.217]\n",
"\t-5.709 1.259 0.098 [-8.155 -3.362]\n",
"\t3.273 1.211 0.088 [ 0.916 5.556]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-4.227 -3.587 -3.196 -2.85 -2.226\n",
"\t-0.275 0.963 1.748 2.506 3.886\n",
"\t-6.366 -5.577 -5.145 -4.67 -3.77\n",
"\t-2.648 -1.802 -1.356 -0.911 -0.086\n",
"\t4.595 6.016 6.874 7.759 9.418\n",
"\t3.331 5.593 6.634 7.529 9.152\n",
"\t-8.225 -6.587 -5.64 -4.841 -3.37\n",
"\t0.916 2.438 3.27 4.133 5.588\n",
"\t\n"
]
}
],
"source": [
"M_expressive_vocab.beta.summary()"
]
},
{
"cell_type": "code",
"execution_count": 86,
"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 0x1142ed358>"
]
},
"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": 87,
"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 0x113bef3c8>"
]
},
"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": 88,
"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": 88,
"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": 89,
"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": 90,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"age_years 5\n",
"school 0\n",
"score 0\n",
"male 0\n",
"family_inv 224\n",
"sib 69\n",
"deg_hl_below6 126\n",
"mother_hs 357\n",
"synd_or_disab 115\n",
"ident_by_3 0\n",
"dtype: int64"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_vocab_dataset[covs].isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 91,
"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": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x10ef79860>"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x1145a9a90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"receptive_vocab_dataset.age_int.hist(bins=40)"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(961, 80)"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"receptive_vocab_dataset.shape"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": false
},
"outputs": [
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x11138fd68>"
]
},
"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": 95,
"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": 96,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" [-----------------100%-----------------] 100000 of 100000 complete in 436.2 sec"
]
}
],
"source": [
"M_receptive_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 0x114588748>"
]
},
"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": 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-3.27 0.475 0.025 [-4.204 -2.341]\n",
"\t0.595 1.053 0.076 [-1.559 2.641]\n",
"\t-4.967 0.565 0.037 [-6.055 -3.866]\n",
"\t-1.312 0.664 0.042 [-2.55 0.018]\n",
"\t5.93 1.118 0.084 [ 3.613 8.03 ]\n",
"\t4.919 1.445 0.124 [ 2.007 7.778]\n",
"\t-6.64 1.166 0.089 [-8.751 -4.289]\n",
"\t1.876 1.324 0.109 [-0.558 4.444]\n",
"\t\n",
"\t\n",
"\tPosterior quantiles:\n",
"\t\n",
"\t2.5 25 50 75 97.5\n",
"\t |---------------|===============|===============|---------------|\n",
"\t-4.157 -3.584 -3.291 -2.954 -2.258\n",
"\t-1.662 -0.002 0.635 1.281 2.601\n",
"\t-6.038 -5.338 -4.976 -4.594 -3.829\n",
"\t-2.559 -1.753 -1.349 -0.877 0.018\n",
"\t3.646 5.177 5.998 6.674 8.117\n",
"\t2.079 3.977 4.878 5.816 7.97\n",
"\t-8.965 -7.455 -6.645 -5.789 -4.463\n",
"\t-0.731 0.913 1.868 2.835 4.364\n",
"\t\n"
]
}
],
"source": [
"M_receptive_vocab.beta.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Database queries"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sqlite3"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"db_files = !ls *sqlite"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"articulation = sqlite3.connect(\"articulation.sqlite\")"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c_artic = articulation.cursor()"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<sqlite3.Cursor at 0x114568110>"
]
},
"execution_count": 103,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c_artic.execute('SELECT v1 FROM sigma_school WHERE trace==0')"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sigma_school_artic = np.ravel(c_artic.fetchall())"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"5.7395983710999996"
]
},
"execution_count": 105,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sigma_school_artic.mean()"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"articulation.sqlite\n",
"5.606214\n",
"[ 3.668585 8.544194]\n",
"\n",
"explang.sqlite\n",
"5.3168805\n",
"[ 3.607359 7.595157]\n",
"\n",
"expressive_vocab.sqlite\n",
"8.525305\n",
"[ 6.582717 11.287013]\n",
"\n",
"receptive_vocab.sqlite\n",
"6.660398\n",
"[ 5.19439 8.751019]\n",
"\n",
"reclang.sqlite\n",
"5.473053\n",
"[ 4.032111 7.406457]\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)]])"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"expressive_vocab_data = lsl_dr[lsl_dr.domain=='Expressive Vocabulary']"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1146446d8>"
]
},
"execution_count": 108,
"metadata": {},
"output_type": "execute_result"
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x114644208>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"expressive_vocab_data.score.hist()"
]
}
],
"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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