Commit 3d056470 authored by Hao-Ting Wang's avatar Hao-Ting Wang

Manuscript ready

parent f16af875
# project folder
data/raw/
data/interim/
data/external/
# Byte-compiled / optimized / DLL files
__pycache__/
......
......@@ -6,12 +6,12 @@
"metadata": {},
"outputs": [],
"source": [
"cd -q ~/TaskSCCA_craddock/"
"cd -q ~/research/Project_TaskSCCA/"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
......@@ -36,7 +36,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
......@@ -65,7 +65,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
......@@ -95,7 +95,7 @@
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{
"cell_type": "code",
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"execution_count": 6,
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{
......@@ -112,7 +112,7 @@
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{
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"execution_count": 7,
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{
......@@ -129,7 +129,7 @@
},
{
"cell_type": "code",
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"execution_count": 8,
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"source": [
......@@ -141,7 +141,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
......@@ -154,7 +154,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
......@@ -164,7 +164,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"outputs": [
{
......@@ -173,7 +173,7 @@
"18.0"
]
},
"execution_count": 10,
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
......@@ -184,7 +184,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
......@@ -205,7 +205,7 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 13,
"metadata": {},
"outputs": [
{
......@@ -226,7 +226,7 @@
" 'MWQ_Words']"
]
},
"execution_count": 12,
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
......@@ -237,7 +237,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
......@@ -249,160 +249,6 @@
"df_stats = pd.concat([df_diff, df_stats], axis=1, join='inner')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['x0', 'x1'])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"manova = MANOVA(endog=df_stats[es],\n",
" exog=df_stats[['component_1', 'component_2']] )\n",
"results = manova.mv_test().results\n",
"results.keys()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.04820690926975146\n"
]
}
],
"source": [
"f_val = results['x0']['stat']['F Value'][0]\n",
"den_df = results['x0']['stat']['Den DF'][0]\n",
"num_df = results['x0']['stat']['Num DF'][0]\n",
"\n",
"par_eta_sqr = num_df * f_val / (num_df *f_val + den_df)\n",
"print(par_eta_sqr)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Value</th>\n",
" <th>Num DF</th>\n",
" <th>Den DF</th>\n",
" <th>F Value</th>\n",
" <th>Pr &gt; F</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Wilks' lambda</th>\n",
" <td>0.814694</td>\n",
" <td>13</td>\n",
" <td>164</td>\n",
" <td>2.86943</td>\n",
" <td>0.000937898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pillai's trace</th>\n",
" <td>0.185306</td>\n",
" <td>13</td>\n",
" <td>164</td>\n",
" <td>2.86943</td>\n",
" <td>0.000937898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hotelling-Lawley trace</th>\n",
" <td>0.227455</td>\n",
" <td>13</td>\n",
" <td>164</td>\n",
" <td>2.86943</td>\n",
" <td>0.000937898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Roy's greatest root</th>\n",
" <td>0.227455</td>\n",
" <td>13</td>\n",
" <td>164</td>\n",
" <td>2.86943</td>\n",
" <td>0.000937898</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Value Num DF Den DF F Value Pr > F\n",
"Wilks' lambda 0.814694 13 164 2.86943 0.000937898\n",
"Pillai's trace 0.185306 13 164 2.86943 0.000937898\n",
"Hotelling-Lawley trace 0.227455 13 164 2.86943 0.000937898\n",
"Roy's greatest root 0.227455 13 164 2.86943 0.000937898"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"results['x1']['stat']"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.18530595082886725\n"
]
}
],
"source": [
"f_val = results['x1']['stat']['F Value'][0]\n",
"den_df = results['x1']['stat']['Den DF'][0]\n",
"num_df = results['x1']['stat']['Num DF'][0]\n",
"\n",
"par_eta_sqr = num_df * f_val / (num_df *f_val + den_df)\n",
"print(par_eta_sqr)"
]
},
{
"cell_type": "code",
"execution_count": 14,
......@@ -477,7 +323,7 @@
"source": [
"# Predict thoguhts with CCA components\n",
"\n",
"When using the two CCA scores to predict the expereince sampling questions scores, component 2 is significant in all the mutivariate models of all the questions, 0-back or 1-back questions. The difference score of 0-back and 1-back, however, didn't show significant relationships in the MANOVA. See bellow to find the detailed MANOVA tables and the correlation coefficients and Bonferroni adjusted p-values of the univariate model in heat map format.\n",
"When using the two CCA scores to predict the expereince sampling questions scores, component 2 is significant in all the mutivariate models of all the questions, 0-back or 1-back questions. The difference score of 0-back and 1-back, however, didn't show significant relationships in the Multivariate Multiple Regression. See bellow to find the detailed MANOVA tables and the correlation coefficients and Bonferroni adjusted p-values of the univariate model in heat map format.\n",
"\n",
"When predicting PCA scores, there's no significant results at the multivariate level."
]
......@@ -493,7 +339,7 @@
"cell_type": "code",
"execution_count": 15,
"metadata": {
"scrolled": true
"scrolled": false
},
"outputs": [
{
......@@ -2594,7 +2440,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
"version": "3.6.8"
}
},
"nbformat": 4,
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......@@ -976,7 +976,7 @@
"name": "python",
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......@@ -1065,7 +1065,7 @@
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......
......@@ -111,7 +111,7 @@
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</DataArray>
</GIFTI>
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Digit Span -0.0
Category Fluency 0.0
Picture Naming 0.38589501525733855
TS-Flexibility 0.8023016448947715
TS-Inhibition 0.297452819102795
Four Mountains 0.18352956171711482
Unusual Uses 0.05880000561696846
RAPM 0.1130238231070786
Paired Associate 0.0
Semantics - Strength -0.06881579697630226
Semantics - Modality 0.0
Semantics - Specificity -0.2535121082288314
Semantics - Feature Matching 0.0
This diff is collapsed.
Digit Span 0.3993663804243405
Category Fluency 0.5005175571236369
Picture Naming -0.5925534548987045
TS-Flexibility 0.4557530048362001
TS-Inhibition 0.0
Four Mountains 0.0
Unusual Uses 0.0
RAPM -0.044263072431619324
Paired Associate 0.0
Semantics - Strength -0.0
Semantics - Modality 0.0
Semantics - Specificity 0.0
Semantics - Feature Matching 0.17087729892248607
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