Factor analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 因子分析\n",
    "\n",
    "黄荣贵（复旦大学社会学系）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'st1'</li>\n",
       "\t<li>'q34a'</li>\n",
       "\t<li>'q34b'</li>\n",
       "\t<li>'q35b'</li>\n",
       "\t<li>'q36b'</li>\n",
       "\t<li>'q18b'</li>\n",
       "\t<li>'q19'</li>\n",
       "\t<li>'q34c'</li>\n",
       "\t<li>'q34d'</li>\n",
       "\t<li>'q39b'</li>\n",
       "\t<li>'q511'</li>\n",
       "\t<li>'q516'</li>\n",
       "\t<li>'q50n'</li>\n",
       "\t<li>'q42an'</li>\n",
       "\t<li>'q42bn'</li>\n",
       "\t<li>'q45an'</li>\n",
       "\t<li>'q45bn'</li>\n",
       "\t<li>'q25an'</li>\n",
       "\t<li>'q57n'</li>\n",
       "\t<li>'q52n'</li>\n",
       "\t<li>'q54bn'</li>\n",
       "\t<li>'q467'</li>\n",
       "\t<li>'q468'</li>\n",
       "\t<li>'q469'</li>\n",
       "\t<li>'q4615'</li>\n",
       "\t<li>'q4621'</li>\n",
       "\t<li>'q461'</li>\n",
       "\t<li>'q462'</li>\n",
       "\t<li>'q463'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'st1'\n",
       "\\item 'q34a'\n",
       "\\item 'q34b'\n",
       "\\item 'q35b'\n",
       "\\item 'q36b'\n",
       "\\item 'q18b'\n",
       "\\item 'q19'\n",
       "\\item 'q34c'\n",
       "\\item 'q34d'\n",
       "\\item 'q39b'\n",
       "\\item 'q511'\n",
       "\\item 'q516'\n",
       "\\item 'q50n'\n",
       "\\item 'q42an'\n",
       "\\item 'q42bn'\n",
       "\\item 'q45an'\n",
       "\\item 'q45bn'\n",
       "\\item 'q25an'\n",
       "\\item 'q57n'\n",
       "\\item 'q52n'\n",
       "\\item 'q54bn'\n",
       "\\item 'q467'\n",
       "\\item 'q468'\n",
       "\\item 'q469'\n",
       "\\item 'q4615'\n",
       "\\item 'q4621'\n",
       "\\item 'q461'\n",
       "\\item 'q462'\n",
       "\\item 'q463'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'st1'\n",
       "2. 'q34a'\n",
       "3. 'q34b'\n",
       "4. 'q35b'\n",
       "5. 'q36b'\n",
       "6. 'q18b'\n",
       "7. 'q19'\n",
       "8. 'q34c'\n",
       "9. 'q34d'\n",
       "10. 'q39b'\n",
       "11. 'q511'\n",
       "12. 'q516'\n",
       "13. 'q50n'\n",
       "14. 'q42an'\n",
       "15. 'q42bn'\n",
       "16. 'q45an'\n",
       "17. 'q45bn'\n",
       "18. 'q25an'\n",
       "19. 'q57n'\n",
       "20. 'q52n'\n",
       "21. 'q54bn'\n",
       "22. 'q467'\n",
       "23. 'q468'\n",
       "24. 'q469'\n",
       "25. 'q4615'\n",
       "26. 'q4621'\n",
       "27. 'q461'\n",
       "28. 'q462'\n",
       "29. 'q463'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] \"st1\"   \"q34a\"  \"q34b\"  \"q35b\"  \"q36b\"  \"q18b\"  \"q19\"   \"q34c\"  \"q34d\" \n",
       "[10] \"q39b\"  \"q511\"  \"q516\"  \"q50n\"  \"q42an\" \"q42bn\" \"q45an\" \"q45bn\" \"q25an\"\n",
       "[19] \"q57n\"  \"q52n\"  \"q54bn\" \"q467\"  \"q468\"  \"q469\"  \"q4615\" \"q4621\" \"q461\" \n",
       "[28] \"q462\"  \"q463\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "load(\"data/GuiHuang2008.Rdata\")\n",
    "\n",
    "CSC = GuiHuang2008$Fsc\n",
    "\n",
    "names(CSC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用最大似然估计进行因子分析\n",
    "\n",
    "通过比较不同因子数的模型来确定最终的因子数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "factanal(x = ~st1 + q34a + q34b + q35b + q36b + q461 + q462 +     q463 + q468 + q469 + q4621 + q467 + q4615 + q34c + q34d +     q18b + q19 + q39b + q42bn + q45an + q45bn + q50n + q57n +     q52n + q511 + q42an + q25an + q54bn + q516, factors = 5,     data = CSC)\n",
       "\n",
       "Uniquenesses:\n",
       "  st1  q34a  q34b  q35b  q36b  q461  q462  q463  q468  q469 q4621  q467 q4615 \n",
       "0.442 0.396 0.302 0.399 0.377 0.228 0.005 0.316 0.565 0.628 0.561 0.621 0.548 \n",
       " q34c  q34d  q18b   q19  q39b q42bn q45an q45bn  q50n  q57n  q52n  q511 q42an \n",
       "0.117 0.150 0.533 0.634 0.607 0.391 0.539 0.075 0.683 0.430 0.444 0.898 0.895 \n",
       "q25an q54bn  q516 \n",
       "0.476 0.639 0.835 \n",
       "\n",
       "Loadings:\n",
       "      Factor1 Factor2 Factor3 Factor4 Factor5\n",
       "st1            0.670   0.174   0.240  -0.120 \n",
       "q34a           0.772                         \n",
       "q34b           0.675   0.480                 \n",
       "q35b           0.592   0.466           0.148 \n",
       "q36b           0.748   0.213   0.103         \n",
       "q461   0.842   0.235                         \n",
       "q462   0.954   0.233  -0.158                 \n",
       "q463   0.789  -0.152                  -0.182 \n",
       "q468   0.430   0.241   0.328   0.129   0.261 \n",
       "q469   0.476   0.333           0.173         \n",
       "q4621  0.599  -0.103          -0.120   0.234 \n",
       "q467   0.201   0.117   0.477   0.117   0.290 \n",
       "q4615  0.445           0.399   0.157   0.264 \n",
       "q34c           0.433   0.833                 \n",
       "q34d  -0.159   0.173   0.885                 \n",
       "q18b   0.519  -0.413                  -0.165 \n",
       "q19    0.475  -0.297  -0.135   0.181         \n",
       "q39b   0.445  -0.264  -0.139          -0.325 \n",
       "q42bn                          0.681   0.366 \n",
       "q45an          0.131           0.651   0.103 \n",
       "q45bn  0.143           0.178   0.899  -0.252 \n",
       "q50n  -0.187  -0.177          -0.259   0.427 \n",
       "q57n           0.118   0.175           0.719 \n",
       "q52n                   0.311           0.664 \n",
       "q511           0.120           0.266         \n",
       "q42an -0.121           0.177   0.183   0.145 \n",
       "q25an -0.330   0.195   0.530   0.206   0.232 \n",
       "q54bn  0.134   0.181   0.497   0.228   0.108 \n",
       "q516   0.347           0.165          -0.125 \n",
       "\n",
       "               Factor1 Factor2 Factor3 Factor4 Factor5\n",
       "SS loadings      4.337   3.442   3.326   2.204   1.959\n",
       "Proportion Var   0.150   0.119   0.115   0.076   0.068\n",
       "Cumulative Var   0.150   0.268   0.383   0.459   0.526\n",
       "\n",
       "Test of the hypothesis that 5 factors are sufficient.\n",
       "The chi square statistic is 336.75 on 271 degrees of freedom.\n",
       "The p-value is 0.004 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factanal(~ st1 + q34a + q34b + q35b + q36b + \n",
    "         q461 + q462 + q463 + q468 + q469 +q4621 +\n",
    "         q467 + q4615 + q34c + q34d + \n",
    "         q18b + q19 + q39b + \n",
    "         q42bn + q45an + q45bn + \n",
    "         q50n + q57n + q52n +\n",
    "         q511 + q42an + q25an + q54bn + q516,\n",
    "         factors=5, data=CSC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "factanal(x = ~st1 + q34a + q34b + q35b + q36b + q461 + q462 +     q463 + q468 + q469 + q4621 + q467 + q4615 + q34c + q34d +     q18b + q19 + q39b + q42bn + q45an + q45bn + q50n + q57n +     q52n + q511 + q42an + q25an + q54bn + q516, factors = 6,     data = CSC)\n",
       "\n",
       "Uniquenesses:\n",
       "  st1  q34a  q34b  q35b  q36b  q461  q462  q463  q468  q469 q4621  q467 q4615 \n",
       "0.428 0.365 0.223 0.414 0.348 0.226 0.005 0.295 0.550 0.548 0.586 0.583 0.577 \n",
       " q34c  q34d  q18b   q19  q39b q42bn q45an q45bn  q50n  q57n  q52n  q511 q42an \n",
       "0.119 0.161 0.005 0.507 0.519 0.355 0.454 0.120 0.648 0.242 0.499 0.877 0.890 \n",
       "q25an q54bn  q516 \n",
       "0.474 0.627 0.695 \n",
       "\n",
       "Loadings:\n",
       "      Factor1 Factor2 Factor3 Factor4 Factor5 Factor6\n",
       "st1    0.144   0.141   0.679           0.228  -0.106 \n",
       "q34a                   0.785  -0.127                 \n",
       "q34b           0.463   0.746                         \n",
       "q35b   0.217   0.465   0.532  -0.168                 \n",
       "q36b           0.187   0.759  -0.135                 \n",
       "q461   0.779           0.228   0.318  -0.104         \n",
       "q462   0.869  -0.200   0.215   0.379                 \n",
       "q463   0.586                   0.556          -0.170 \n",
       "q468   0.500   0.335   0.174           0.136   0.193 \n",
       "q469   0.595           0.203           0.207         \n",
       "q4621  0.565          -0.126   0.210  -0.115   0.146 \n",
       "q467   0.310   0.496                   0.131   0.223 \n",
       "q4615  0.407   0.377           0.212   0.139   0.226 \n",
       "q34c           0.819   0.429  -0.130                 \n",
       "q34d           0.880   0.179  -0.154                 \n",
       "q18b                  -0.189   0.957  -0.187         \n",
       "q19    0.207  -0.155  -0.153   0.614   0.126   0.100 \n",
       "q39b   0.168  -0.176  -0.112   0.582          -0.261 \n",
       "q42bn                                  0.709   0.351 \n",
       "q45an                         -0.157   0.710         \n",
       "q45bn          0.190           0.288   0.837  -0.239 \n",
       "q50n  -0.106          -0.218  -0.175  -0.228   0.459 \n",
       "q57n           0.195   0.159                   0.831 \n",
       "q52n   0.154   0.321                           0.598 \n",
       "q511                   0.109  -0.115   0.294         \n",
       "q42an          0.201                   0.189   0.131 \n",
       "q25an -0.206   0.558   0.170  -0.243   0.198   0.211 \n",
       "q54bn  0.145   0.504   0.194           0.206   0.109 \n",
       "q516   0.452   0.161  -0.149                  -0.207 \n",
       "\n",
       "               Factor1 Factor2 Factor3 Factor4 Factor5 Factor6\n",
       "SS loadings      3.374   3.368   3.175   2.636   2.231   1.878\n",
       "Proportion Var   0.116   0.116   0.109   0.091   0.077   0.065\n",
       "Cumulative Var   0.116   0.232   0.342   0.433   0.510   0.575\n",
       "\n",
       "Test of the hypothesis that 6 factors are sufficient.\n",
       "The chi square statistic is 286.09 on 247 degrees of freedom.\n",
       "The p-value is 0.0443 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factanal(~st1 + q34a + q34b + q35b + q36b + \n",
    "         q461 + q462 + q463 + q468 + q469 +q4621 +\n",
    "         q467 + q4615 + q34c + q34d + \n",
    "         q18b + q19 + q39b + \n",
    "         q42bn + q45an + q45bn + \n",
    "         q50n + q57n + q52n +\n",
    "         q511 + q42an + q25an + q54bn + q516,\n",
    "         factors=6, data=CSC)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Call:\n",
       "factanal(x = ~st1 + q34a + q34b + q35b + q36b + q461 + q462 +     q463 + q468 + q469 + q4621 + q467 + q4615 + q34c + q34d +     q18b + q19 + q39b + q42bn + q45an + q45bn + q50n + q57n +     q52n + q511 + q42an + q25an + q54bn + q516, factors = 7,     data = CSC)\n",
       "\n",
       "Uniquenesses:\n",
       "  st1  q34a  q34b  q35b  q36b  q461  q462  q463  q468  q469 q4621  q467 q4615 \n",
       "0.432 0.340 0.204 0.415 0.372 0.196 0.005 0.261 0.549 0.545 0.587 0.549 0.377 \n",
       " q34c  q34d  q18b   q19  q39b q42bn q45an q45bn  q50n  q57n  q52n  q511 q42an \n",
       "0.141 0.087 0.005 0.492 0.511 0.289 0.351 0.197 0.534 0.270 0.487 0.680 0.570 \n",
       "q25an q54bn  q516 \n",
       "0.324 0.549 0.519 \n",
       "\n",
       "Loadings:\n",
       "      Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7\n",
       "st1    0.681   0.165                   0.158           0.175 \n",
       "q34a   0.786                  -0.142                  -0.140 \n",
       "q34b   0.805           0.346                           0.147 \n",
       "q35b   0.568   0.176   0.411  -0.164                   0.157 \n",
       "q36b   0.743           0.179  -0.153                         \n",
       "q461   0.193   0.827           0.274                         \n",
       "q462   0.170   0.904           0.318                  -0.185 \n",
       "q463           0.636           0.541          -0.155         \n",
       "q468   0.208   0.421   0.391           0.193   0.176  -0.100 \n",
       "q469   0.214   0.584   0.100  -0.100   0.173           0.114 \n",
       "q4621 -0.143   0.563   0.111   0.183           0.150         \n",
       "q467           0.201   0.563           0.175   0.203         \n",
       "q4615          0.318   0.560   0.162   0.247   0.200  -0.285 \n",
       "q34c   0.505           0.724                           0.250 \n",
       "q34d   0.245  -0.179   0.885  -0.128                   0.139 \n",
       "q18b  -0.187   0.134           0.954  -0.133          -0.114 \n",
       "q19   -0.193   0.254  -0.101   0.589   0.197                 \n",
       "q39b  -0.141   0.215  -0.125   0.568          -0.272  -0.107 \n",
       "q42bn                                  0.738   0.330   0.210 \n",
       "q45an                  0.136  -0.183   0.767                 \n",
       "q45bn                  0.149   0.271   0.755  -0.256   0.249 \n",
       "q50n  -0.224          -0.102  -0.149  -0.269   0.506   0.224 \n",
       "q57n   0.156           0.177                   0.816         \n",
       "q52n           0.104   0.348           0.124   0.595         \n",
       "q511   0.128                           0.182           0.498 \n",
       "q42an                                          0.178   0.625 \n",
       "q25an  0.234  -0.220   0.388  -0.194           0.241   0.571 \n",
       "q54bn  0.244   0.130   0.411           0.117   0.115   0.411 \n",
       "q516  -0.159   0.476   0.147                  -0.216   0.390 \n",
       "\n",
       "               Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Factor7\n",
       "SS loadings      3.464   3.424   3.028   2.432   2.123   1.893   1.801\n",
       "Proportion Var   0.119   0.118   0.104   0.084   0.073   0.065   0.062\n",
       "Cumulative Var   0.119   0.238   0.342   0.426   0.499   0.564   0.626\n",
       "\n",
       "Test of the hypothesis that 7 factors are sufficient.\n",
       "The chi square statistic is 237.12 on 224 degrees of freedom.\n",
       "The p-value is 0.261 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "factanal(~st1 + q34a + q34b + q35b + q36b + \n",
    "         q461 + q462 + q463 + q468 + q469 +q4621 +\n",
    "         q467 + q4615 + q34c + q34d + \n",
    "         q18b + q19 + q39b + \n",
    "         q42bn + q45an + q45bn + \n",
    "         q50n + q57n + q52n +\n",
    "         q511 + q42an + q25an + q54bn + q516,\n",
    "         factors=7, data=CSC)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过简单相加计算各因子对应变量的取值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "CSC$F1 <- with(CSC, st1 + q34a + q34b + q35b + q36b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "compute factor scores using regression method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "fam1 = factanal(~st1 + q34a + q34b + q35b + q36b + \n",
    "         q461 + q462 + q463 + q468 + q469 +q4621 +\n",
    "         q467 + q4615 + q34c + q34d + \n",
    "         q18b + q19 + q39b + \n",
    "         q42bn + q45an + q45bn + \n",
    "         q50n + q57n + q52n +\n",
    "         q511 + q42an + q25an + q54bn + q516,\n",
    "         factors=7, data=CSC,\n",
    "                score=\"regression\",\n",
    "                na.action=na.exclude)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Factor1</th><th scope=col>Factor2</th><th scope=col>Factor3</th><th scope=col>Factor4</th><th scope=col>Factor5</th><th scope=col>Factor6</th><th scope=col>Factor7</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td> 0.3017597 </td><td> 2.04215352</td><td>-0.3482691 </td><td> 0.28722011</td><td> 1.6301046 </td><td> 0.1457525 </td><td> 0.7539039 </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>-1.1037553 </td><td> 2.05827983</td><td>-0.2874146 </td><td>-0.73725014</td><td>-3.5644406 </td><td> 1.2139073 </td><td>-2.0245428 </td></tr>\n",
       "\t<tr><th scope=row>3</th><td> 0.8833915 </td><td>-1.59676295</td><td>-0.7922272 </td><td> 0.53131012</td><td> 0.2638491 </td><td>-0.9154511 </td><td>-0.6206264 </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>-0.7602429 </td><td>-0.64195998</td><td> 0.5093196 </td><td> 0.03373962</td><td> 0.8454736 </td><td>-0.3062409 </td><td>-1.1194940 </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>-0.4936209 </td><td> 0.08061677</td><td>-0.4800328 </td><td>-0.25677144</td><td> 0.6328250 </td><td>-1.7148408 </td><td>-0.5825947 </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>-0.2948417 </td><td> 0.07865925</td><td>-0.6375411 </td><td>-0.66331030</td><td>-0.6605949 </td><td>-0.5186293 </td><td>-0.9317354 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllll}\n",
       "  & Factor1 & Factor2 & Factor3 & Factor4 & Factor5 & Factor6 & Factor7\\\\\n",
       "\\hline\n",
       "\t1 &  0.3017597  &  2.04215352 & -0.3482691  &  0.28722011 &  1.6301046  &  0.1457525  &  0.7539039 \\\\\n",
       "\t2 & -1.1037553  &  2.05827983 & -0.2874146  & -0.73725014 & -3.5644406  &  1.2139073  & -2.0245428 \\\\\n",
       "\t3 &  0.8833915  & -1.59676295 & -0.7922272  &  0.53131012 &  0.2638491  & -0.9154511  & -0.6206264 \\\\\n",
       "\t4 & -0.7602429  & -0.64195998 &  0.5093196  &  0.03373962 &  0.8454736  & -0.3062409  & -1.1194940 \\\\\n",
       "\t5 & -0.4936209  &  0.08061677 & -0.4800328  & -0.25677144 &  0.6328250  & -1.7148408  & -0.5825947 \\\\\n",
       "\t6 & -0.2948417  &  0.07865925 & -0.6375411  & -0.66331030 & -0.6605949  & -0.5186293  & -0.9317354 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | Factor1 | Factor2 | Factor3 | Factor4 | Factor5 | Factor6 | Factor7 | \n",
       "|---|---|---|---|---|---|\n",
       "| 1 |  0.3017597  |  2.04215352 | -0.3482691  |  0.28722011 |  1.6301046  |  0.1457525  |  0.7539039  | \n",
       "| 2 | -1.1037553  |  2.05827983 | -0.2874146  | -0.73725014 | -3.5644406  |  1.2139073  | -2.0245428  | \n",
       "| 3 |  0.8833915  | -1.59676295 | -0.7922272  |  0.53131012 |  0.2638491  | -0.9154511  | -0.6206264  | \n",
       "| 4 | -0.7602429  | -0.64195998 |  0.5093196  |  0.03373962 |  0.8454736  | -0.3062409  | -1.1194940  | \n",
       "| 5 | -0.4936209  |  0.08061677 | -0.4800328  | -0.25677144 |  0.6328250  | -1.7148408  | -0.5825947  | \n",
       "| 6 | -0.2948417  |  0.07865925 | -0.6375411  | -0.66331030 | -0.6605949  | -0.5186293  | -0.9317354  | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  Factor1    Factor2     Factor3    Factor4     Factor5    Factor6   \n",
       "1  0.3017597  2.04215352 -0.3482691  0.28722011  1.6301046  0.1457525\n",
       "2 -1.1037553  2.05827983 -0.2874146 -0.73725014 -3.5644406  1.2139073\n",
       "3  0.8833915 -1.59676295 -0.7922272  0.53131012  0.2638491 -0.9154511\n",
       "4 -0.7602429 -0.64195998  0.5093196  0.03373962  0.8454736 -0.3062409\n",
       "5 -0.4936209  0.08061677 -0.4800328 -0.25677144  0.6328250 -1.7148408\n",
       "6 -0.2948417  0.07865925 -0.6375411 -0.66331030 -0.6605949 -0.5186293\n",
       "  Factor7   \n",
       "1  0.7539039\n",
       "2 -2.0245428\n",
       "3 -0.6206264\n",
       "4 -1.1194940\n",
       "5 -0.5825947\n",
       "6 -0.9317354"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "head(fam1$scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>q463</th><th scope=col>F1</th><th scope=col>F2</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>3.428571   </td><td> 0.3017597 </td><td> 2.04215352</td></tr>\n",
       "\t<tr><td>3.058824   </td><td>-1.1037553 </td><td> 2.05827983</td></tr>\n",
       "\t<tr><td>2.900000   </td><td> 0.8833915 </td><td>-1.59676295</td></tr>\n",
       "\t<tr><td>2.678571   </td><td>-0.7602429 </td><td>-0.64195998</td></tr>\n",
       "\t<tr><td>2.882353   </td><td>-0.4936209 </td><td> 0.08061677</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " q463 & F1 & F2\\\\\n",
       "\\hline\n",
       "\t 3.428571    &  0.3017597  &  2.04215352\\\\\n",
       "\t 3.058824    & -1.1037553  &  2.05827983\\\\\n",
       "\t 2.900000    &  0.8833915  & -1.59676295\\\\\n",
       "\t 2.678571    & -0.7602429  & -0.64195998\\\\\n",
       "\t 2.882353    & -0.4936209  &  0.08061677\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "q463 | F1 | F2 | \n",
       "|---|---|---|---|---|\n",
       "| 3.428571    |  0.3017597  |  2.04215352 | \n",
       "| 3.058824    | -1.1037553  |  2.05827983 | \n",
       "| 2.900000    |  0.8833915  | -1.59676295 | \n",
       "| 2.678571    | -0.7602429  | -0.64195998 | \n",
       "| 2.882353    | -0.4936209  |  0.08061677 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "  q463     F1         F2         \n",
       "1 3.428571  0.3017597  2.04215352\n",
       "2 3.058824 -1.1037553  2.05827983\n",
       "3 2.900000  0.8833915 -1.59676295\n",
       "4 2.678571 -0.7602429 -0.64195998\n",
       "5 2.882353 -0.4936209  0.08061677"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "CSC$F1 <- fam1$scores[,1]\n",
    "CSC$F2 <- fam1$scores[,2]\n",
    "\n",
    "CSC[1:5, 29:31]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "两点注意：\n",
    "1. 原始数据包含缺失值，因此增加na.action参数\n",
    "2. fam1$scores是矩阵，因此提取各因子得分时使用基于位置的索引"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  信度分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: MASS\n",
      "Loading required package: msm\n",
      "Loading required package: polycor\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "Standardized Cronbach's alpha for the 'CSC[, c(\"st1\", \"q34a\", \"q34b\", \"q35b\", \"q36b\")]' data-set\n",
       "\n",
       "Items: 5\n",
       "Sample units: 50\n",
       "alpha: 0.746\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(ltm)\n",
    "\n",
    "cronbach.alpha(CSC[,c('st1','q34a','q34b','q35b','q36b')], stand=TRUE) # 第一个因子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "Cronbach's alpha for the 'CSC[, c(\"st1\", \"q34a\", \"q34b\", \"q35b\", \"q36b\")]' data-set\n",
       "\n",
       "Items: 5\n",
       "Sample units: 50\n",
       "alpha: 0.313\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cronbach.alpha(CSC[,c('st1','q34a','q34b','q35b','q36b')], stand=FALSE) # 第一个因子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 使用psych程序包进行因子分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Factor Analysis using method =  pa\n",
       "Call: fa(r = CSC[, c(\"st1\", \"q34a\", \"q34b\", \"q35b\", \"q36b\", \"q461\", \n",
       "    \"q462\", \"q463\", \"q468\", \"q469\", \"q4621\")], nfactors = 2, \n",
       "    rotate = \"varimax\", fm = \"pa\")\n",
       "Standardized loadings (pattern matrix) based upon correlation matrix\n",
       "        PA1   PA2    h2   u2 com\n",
       "st1    0.11  0.71 0.516 0.48 1.0\n",
       "q34a   0.02  0.75 0.567 0.43 1.0\n",
       "q34b  -0.09  0.13 0.024 0.98 1.8\n",
       "q35b   0.11  0.66 0.449 0.55 1.1\n",
       "q36b   0.03  0.84 0.708 0.29 1.0\n",
       "q461   0.83  0.11 0.704 0.30 1.0\n",
       "q462   0.94  0.08 0.896 0.10 1.0\n",
       "q463   0.77 -0.28 0.679 0.32 1.3\n",
       "q468   0.48  0.33 0.343 0.66 1.8\n",
       "q469   0.55  0.32 0.406 0.59 1.6\n",
       "q4621  0.64 -0.09 0.412 0.59 1.0\n",
       "\n",
       "                       PA1  PA2\n",
       "SS loadings           3.16 2.55\n",
       "Proportion Var        0.29 0.23\n",
       "Cumulative Var        0.29 0.52\n",
       "Proportion Explained  0.55 0.45\n",
       "Cumulative Proportion 0.55 1.00\n",
       "\n",
       "Mean item complexity =  1.2\n",
       "Test of the hypothesis that 2 factors are sufficient.\n",
       "\n",
       "The degrees of freedom for the null model are  55  and the objective function was  6.05 with Chi Square of  269.3\n",
       "The degrees of freedom for the model are 34  and the objective function was  1.09 \n",
       "\n",
       "The root mean square of the residuals (RMSR) is  0.07 \n",
       "The df corrected root mean square of the residuals is  0.08 \n",
       "\n",
       "The harmonic number of observations is  50 with the empirical chi square  23.75  with prob <  0.91 \n",
       "The total number of observations was  50  with Likelihood Chi Square =  47.12  with prob <  0.067 \n",
       "\n",
       "Tucker Lewis Index of factoring reliability =  0.897\n",
       "RMSEA index =  0.108  and the 90 % confidence intervals are  0 0.146\n",
       "BIC =  -85.89\n",
       "Fit based upon off diagonal values = 0.97\n",
       "Measures of factor score adequacy             \n",
       "                                                   PA1  PA2\n",
       "Correlation of (regression) scores with factors   0.97 0.93\n",
       "Multiple R square of scores with factors          0.94 0.86\n",
       "Minimum correlation of possible factor scores     0.88 0.72"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(psych)\n",
    "\n",
    "fam2pa <- fa(CSC[,c('st1', 'q34a', 'q34b', 'q35b', 'q36b',  \n",
    "                    'q461', 'q462', 'q463', 'q468', 'q469', 'q4621')],\n",
    "             nfactors=2, rotate=\"varimax\", fm=\"pa\")\n",
    "\n",
    "fam2pa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Factor Analysis using method =  minres\n",
       "Call: fa(r = CSC[, c(\"st1\", \"q34a\", \"q34b\", \"q35b\", \"q36b\", \"q461\", \n",
       "    \"q462\", \"q463\", \"q468\", \"q469\", \"q4621\")], nfactors = 2, \n",
       "    rotate = \"varimax\", fm = \"minres\")\n",
       "Standardized loadings (pattern matrix) based upon correlation matrix\n",
       "        MR1   MR2    h2   u2 com\n",
       "st1    0.11  0.71 0.516 0.48 1.0\n",
       "q34a   0.02  0.75 0.567 0.43 1.0\n",
       "q34b  -0.09  0.13 0.024 0.98 1.8\n",
       "q35b   0.11  0.66 0.449 0.55 1.1\n",
       "q36b   0.03  0.84 0.709 0.29 1.0\n",
       "q461   0.83  0.11 0.703 0.30 1.0\n",
       "q462   0.94  0.08 0.897 0.10 1.0\n",
       "q463   0.77 -0.28 0.679 0.32 1.3\n",
       "q468   0.48  0.33 0.343 0.66 1.8\n",
       "q469   0.55  0.32 0.406 0.59 1.6\n",
       "q4621  0.64 -0.09 0.412 0.59 1.0\n",
       "\n",
       "                       MR1  MR2\n",
       "SS loadings           3.16 2.55\n",
       "Proportion Var        0.29 0.23\n",
       "Cumulative Var        0.29 0.52\n",
       "Proportion Explained  0.55 0.45\n",
       "Cumulative Proportion 0.55 1.00\n",
       "\n",
       "Mean item complexity =  1.2\n",
       "Test of the hypothesis that 2 factors are sufficient.\n",
       "\n",
       "The degrees of freedom for the null model are  55  and the objective function was  6.05 with Chi Square of  269.3\n",
       "The degrees of freedom for the model are 34  and the objective function was  1.09 \n",
       "\n",
       "The root mean square of the residuals (RMSR) is  0.07 \n",
       "The df corrected root mean square of the residuals is  0.08 \n",
       "\n",
       "The harmonic number of observations is  50 with the empirical chi square  23.75  with prob <  0.91 \n",
       "The total number of observations was  50  with Likelihood Chi Square =  47.07  with prob <  0.067 \n",
       "\n",
       "Tucker Lewis Index of factoring reliability =  0.897\n",
       "RMSEA index =  0.108  and the 90 % confidence intervals are  0 0.145\n",
       "BIC =  -85.94\n",
       "Fit based upon off diagonal values = 0.97\n",
       "Measures of factor score adequacy             \n",
       "                                                   MR1  MR2\n",
       "Correlation of (regression) scores with factors   0.97 0.93\n",
       "Multiple R square of scores with factors          0.94 0.86\n",
       "Minimum correlation of possible factor scores     0.88 0.72"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fam2minres <- fa(CSC[,c('st1', 'q34a', 'q34b', 'q35b', 'q36b',  \n",
    "                    'q461', 'q462', 'q463', 'q468', 'q469', 'q4621')],\n",
    "             nfactors=2, rotate=\"varimax\", fm=\"minres\")\n",
    "\n",
    "fam2minres"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通过平行分析确定因子数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parallel analysis suggests that the number of factors =  2  and the number of components =  2 \n"
     ]
    },
    {
     "data": {
      "image/png": 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uhKpEx7ogK3Ph7pQaKcAvXmH+fkpvwg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\nCIiACIiACIiACIiACOQ1AuEaSPRX9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ah7VxXlasMWEFbcwcfI57Ga7/4h3v3mVv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",
      "text/plain": [
       "Plot with title “Parallel Analysis Scree Plots”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fa.parallel(CSC[,c('st1', 'q34a', 'q34b', 'q35b', 'q36b',  \n",
    "                    'q461', 'q462', 'q463', 'q468', 'q469', 'q4621')])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}