matlab 加权回归估计_Matlab:地理加权回归基本操作
%--------------------------------------------------------------------------%计量经济学服务中心《空间计量经济学及Matlab应用》%--------------------------------------------------------------------------Vname=VariableGeometrically weighted regression estimatesDependent Variable = crimeR-squared = 0.9418Rbar-squared = 0.9393Bandwidth = 0.6518# iterations = 17Decay type = gaussianNobs, Nvars = 49, 3***************************************Obs = 1, x-coordinate= 42.3800, y-coordinate= 35.6200, sige= 3.4125Variable Coefficient t-statistic t-probabilityconstant 51.197363 9.212794 0.000000income -0.461038 -1.678857 0.099547hvalue -0.434237 -3.693955 0.000556Obs = 2, x-coordinate= 40.5200, y-coordinate= 36.5000, sige= 6.7847Variable Coefficient t-statistic t-probabilityconstant 63.564308 9.955778 0.000000income -0.369902 -0.991321 0.326399hvalue -0.683553 -4.656428 0.000025Obs = 3, x-coordinate= 38.7100, y-coordinate= 36.7100, sige= 8.6457Variable Coefficient t-statistic t-probabilityconstant 72.673672 9.395151 0.000000income -0.161106 -0.269269 0.788853hvalue -0.826921 -5.367996 0.000002Obs = 4, x-coordinate= 38.4100, y-coordinate= 33.3600, sige= 5.2400Variable Coefficient t-statistic t-probabilityconstant 81.381328 7.772343 0.000000income 0.149437 0.194405 0.846662hvalue -1.073198 -9.228621 0.000000Obs = 5, x-coordinate= 44.0700, y-coordinate= 38.8000, sige= 0.6985Variable Coefficient t-statistic t-probabilityconstant 46.737222 13.309854 0.000000income -0.689933 -2.949392 0.004869hvalue -0.223718 -4.843363 0.000013Obs = 6, x-coordinate= 41.1800, y-coordinate= 39.8200, sige= 2.7853Variable Coefficient t-statistic t-probabilityconstant 57.351504 10.979281 0.000000income -0.971958 -2.506024 0.015580hvalue -0.310679 -3.233765 0.002189Obs = 7, x-coordinate= 38.0000, y-coordinate= 40.0100, sige= 2.2903Variable Coefficient t-statistic t-probabilityconstant 79.683414 14.237667 0.000000income -1.990153 -3.856516 0.000336hvalue -0.402011 -2.423975 0.019088Obs = 8, x-coordinate= 39.2800, y-coordinate= 43.7500, sige= 0.6613Variable Coefficient t-statistic t-probabilityconstant 79.374676 10.227137 0.000000income -3.294825 -6.536725 0.000000hvalue 0.059876 0.936349 0.353686Obs = 9, x-coordinate= 34.9100, y-coordinate= 39.6100, sige= 2.8503Variable Coefficient t-statistic t-probabilityconstant 72.218154 10.454632 0.000000income -1.988247 -2.094491 0.041410hvalue -0.063618 -0.350051 0.727801Obs = 10, x-coordinate= 36.4200, y-coordinate= 47.6100, sige= 0.3660Variable Coefficient t-statistic t-probabilityconstant 54.058540 23.753628 0.000000income -1.719995 -13.667469 0.000000hvalue 0.033105 1.405730 0.166114Obs = 11, x-coordinate= 34.4600, y-coordinate= 48.5800, sige= 0.6241Variable Coefficient t-statistic t-probabilityconstant 55.363293 25.304369 0.000000income -1.767205 -17.192082 0.000000hvalue 0.019889 0.631315 0.530769Obs = 12, x-coordinate= 32.6500, y-coordinate= 49.6100, sige= 1.1183Variable Coefficient t-statistic t-probabilityconstant 54.800116 18.118969 0.000000income -1.673002 -11.485387 0.000000hvalue 0.000544 0.009703 0.992298Obs = 13, x-coordinate= 29.9100, y-coordinate= 50.1100, sige= 1.8016Variable Coefficient t-statistic t-probabilityconstant 49.090996 11.226397 0.000000income -1.206984 -4.006017 0.000209hvalue -0.061675 -0.484850 0.629943Obs = 14, x-coordinate= 27.8000, y-coordinate= 51.2400, sige= 1.1740Variable Coefficient t-statistic t-probabilityconstant 42.025898 8.693270 0.000000income -1.049190 -2.353344 0.022662hvalue 0.034076 0.168603 0.866803Obs = 15, x-coordinate= 25.2400, y-coordinate= 50.8900, sige= 0.5074Variable Coefficient t-statistic t-probabilityconstant 42.023487 9.931361 0.000000income -2.035189 -4.285382 0.000085hvalue 0.541132 2.264220 0.028021Obs = 16, x-coordinate= 27.9300, y-coordinate= 48.4400, sige= 1.8858Variable Coefficient t-statistic t-probabilityconstant 50.858338 11.840157 0.000000income -0.970544 -2.036648 0.047108hvalue -0.163386 -0.772214 0.443696Obs = 17, x-coordinate= 31.9100, y-coordinate= 46.7300, sige= 1.9074Variable Coefficient t-statistic t-probabilityconstant 63.935965 23.149085 0.000000income -1.851883 -8.000266 0.000000hvalue -0.065988 -0.739344 0.463225Obs = 18, x-coordinate= 35.9200, y-coordinate= 43.4400, sige= 1.0826Variable Coefficient t-statistic t-probabilityconstant 61.515865 14.934047 0.000000income -1.892916 -5.019187 0.000007hvalue 0.013062 0.157654 0.875377Obs = 19, x-coordinate= 33.4600, y-coordinate= 43.3700, sige= 2.7225Variable Coefficient t-statistic t-probabilityconstant 65.413374 17.271159 0.000000income -2.860764 -4.125718 0.000143hvalue 0.275876 1.222495 0.227368Obs = 20, x-coordinate= 33.1400, y-coordinate= 41.1300, sige= 5.0673Variable Coefficient t-statistic t-probabilityconstant 66.620907 8.186391 0.000000income -1.619154 -1.246106 0.218651hvalue -0.110761 -0.311593 0.756672Obs = 21, x-coordinate= 31.6100, y-coordinate= 43.9500, sige= 2.6677Variable Coefficient t-statistic t-probabilityconstant 68.176378 23.711500 0.000000income -3.351877 -5.596394 0.000001hvalue 0.449873 2.009607 0.049996Obs = 22, x-coordinate= 30.4000, y-coordinate= 44.1000, sige= 2.6080Variable Coefficient t-statistic t-probabilityconstant 68.744965 23.673393 0.000000income -3.282837 -5.969185 0.000000hvalue 0.438989 2.053138 0.045418Obs = 23, x-coordinate= 29.1800, y-coordinate= 43.7000, sige= 2.8861Variable Coefficient t-statistic t-probabilityconstant 69.068145 17.943117 0.000000income -3.326136 -5.847206 0.000000hvalue 0.468812 1.914810 0.061364Obs = 24, x-coordinate= 28.7800, y-coordinate= 41.0400, sige= 8.1087Variable Coefficient t-statistic t-probabilityconstant 77.271200 7.416531 0.000000income -3.000189 -3.431018 0.001230hvalue 0.167212 0.398976 0.691645Obs = 25, x-coordinate= 27.3100, y-coordinate= 43.2300, sige= 4.0434Variable Coefficient t-statistic t-probabilityconstant 67.368725 9.525528 0.000000income -3.069044 -3.468780 0.001099hvalue 0.363366 0.754567 0.454120Obs = 26, x-coordinate= 24.9600, y-coordinate= 42.6700, sige= 2.5678Variable Coefficient t-statistic t-probabilityconstant 61.306231 5.851086 0.000000income 0.006368 0.004423 0.996489hvalue -1.071954 -1.162870 0.250514Obs = 27, x-coordinate= 25.9000, y-coordinate= 41.2100, sige= 6.2344Variable Coefficient t-statistic t-probabilityconstant 59.819535 4.913992 0.000010income -1.697764 -1.212751 0.231040hvalue -0.138505 -0.170688 0.865172Obs = 28, x-coordinate= 25.8500, y-coordinate= 39.3200, sige= 5.2496Variable Coefficient t-statistic t-probabilityconstant 45.265068 2.954417 0.004803income -2.135825 -1.284057 0.205161hvalue 0.591982 0.883602 0.381226Obs = 29, x-coordinate= 27.4900, y-coordinate= 41.0900, sige= 8.3927Variable Coefficient t-statistic t-probabilityconstant 72.899979 6.290465 0.000000income -3.258441 -2.970188 0.004599hvalue 0.307426 0.555503 0.581078Obs = 30, x-coordinate= 28.8200, y-coordinate= 38.3200, sige= 6.0199Variable Coefficient t-statistic t-probabilityconstant 80.285094 7.449344 0.000000income -0.676605 -0.717337 0.476572hvalue -0.618717 -2.097025 0.041175Obs = 31, x-coordinate= 30.9000, y-coordinate= 41.3100, sige= 5.9421Variable Coefficient t-statistic t-probabilityconstant 68.118651 7.883805 0.000000income -1.803631 -2.133632 0.037905hvalue 0.059481 0.178766 0.858859Obs = 32, x-coordinate= 32.8800, y-coordinate= 39.3600, sige= 4.5678Variable Coefficient t-statistic t-probabilityconstant 58.637810 7.764366 0.000000income 0.495270 0.487439 0.628121hvalue -0.388646 -1.896549 0.063791Obs = 33, x-coordinate= 30.6400, y-coordinate= 39.7200, sige= 5.1218Variable Coefficient t-statistic t-probabilityconstant 70.568456 9.798923 0.000000income -0.218856 -0.335471 0.738702hvalue -0.448133 -1.933095 0.059014Obs = 34, x-coordinate= 30.3500, y-coordinate= 38.2900, sige= 3.1096Variable Coefficient t-statistic t-probabilityconstant 80.030552 12.784499 0.000000income 0.036213 0.068159 0.945936hvalue -0.786849 -5.179351 0.000004Obs = 35, x-coordinate= 32.0900, y-coordinate= 36.6000, sige= 3.5543Variable Coefficient t-statistic t-probabilityconstant 63.967857 8.009308 0.000000income 0.337987 0.382854 0.703484hvalue -0.492099 -3.556112 0.000846Obs = 36, x-coordinate= 34.0800, y-coordinate= 37.6000, sige= 2.7764Variable Coefficient t-statistic t-probabilityconstant 67.746908 11.590897 0.000000income -0.755463 -0.934476 0.354641hvalue -0.243619 -2.063643 0.044369Obs = 37, x-coordinate= 36.1200, y-coordinate= 37.1300, sige= 5.2909Variable Coefficient t-statistic t-probabilityconstant 65.979447 8.493093 0.000000income -0.082415 -0.089905 0.928729hvalue -0.420816 -3.386697 0.001402Obs = 38, x-coordinate= 36.3000, y-coordinate= 37.8500, sige= 4.1933Variable Coefficient t-statistic t-probabilityconstant 70.241135 9.853816 0.000000income -0.851484 -1.007494 0.318647hvalue -0.331039 -2.669386 0.010278Obs = 39, x-coordinate= 36.4000, y-coordinate= 35.9500, sige= 7.5290Variable Coefficient t-statistic t-probabilityconstant 60.058183 6.254403 0.000000income 1.346573 1.335110 0.188010hvalue -0.676333 -5.334379 0.000002Obs = 40, x-coordinate= 35.6000, y-coordinate= 35.7200, sige= 6.1315Variable Coefficient t-statistic t-probabilityconstant 59.441973 6.623725 0.000000income 1.197840 1.197762 0.236772hvalue -0.582346 -4.668150 0.000024Obs = 41, x-coordinate= 34.6600, y-coordinate= 35.7600, sige= 4.4315Variable Coefficient t-statistic t-probabilityconstant 64.924831 8.369141 0.000000income 0.077997 0.082850 0.934308hvalue -0.395195 -3.149189 0.002788Obs = 42, x-coordinate= 33.9200, y-coordinate= 36.1500, sige= 2.7971Variable Coefficient t-statistic t-probabilityconstant 68.995022 11.150905 0.000000income -0.721014 -0.903935 0.370452hvalue -0.276558 -2.460420 0.017450Obs = 43, x-coordinate= 30.4200, y-coordinate= 34.0800, sige= 1.6449Variable Coefficient t-statistic t-probabilityconstant 42.987174 3.069301 0.003493income -0.130118 -0.085542 0.932179hvalue -0.015665 -0.079318 0.937103Obs = 44, x-coordinate= 28.2600, y-coordinate= 30.3200, sige= 1.5262Variable Coefficient t-statistic t-probabilityconstant 38.427625 7.366893 0.000000income -0.618892 -1.442734 0.155458hvalue -0.192297 -0.847771 0.400688Obs = 45, x-coordinate= 29.8500, y-coordinate= 27.9400, sige= 1.2787Variable Coefficient t-statistic t-probabilityconstant 31.201319 5.045446 0.000007income -0.603071 -1.601338 0.115730hvalue -0.061387 -0.506869 0.614520Obs = 46, x-coordinate= 28.2100, y-coordinate= 27.2700, sige= 1.5429Variable Coefficient t-statistic t-probabilityconstant 27.113505 4.535402 0.000037income -0.413367 -1.327768 0.190407hvalue -0.043317 -0.586370 0.560318Obs = 47, x-coordinate= 26.6900, y-coordinate= 24.2500, sige= 0.5555Variable Coefficient t-statistic t-probabilityconstant 24.205091 3.701915 0.000542income -0.278691 -0.853556 0.397505hvalue -0.032273 -0.812649 0.420350Obs = 48, x-coordinate= 25.7100, y-coordinate= 25.4700, sige= 0.6629Variable Coefficient t-statistic t-probabilityconstant 24.211353 4.173486 0.000122income -0.271872 -0.991422 0.326350hvalue -0.034801 -0.854272 0.397112Obs = 49, x-coordinate= 26.5800, y-coordinate= 29.0200, sige= 1.4185Variable Coefficient t-statistic t-probabilityconstant 30.052990 5.675101 0.000001income -0.431664 -1.644271 0.106522hvalue -0.081504 -0.934733 0.354510
matlab 加权回归估计_Matlab:地理加权回归基本操作相关推荐
- 空间地理加权回归stata_xy妙妙屋丨地理加权回归和空间自相关
关于地理加权回归和空间自相关 菜鸡的我只是大神文章的搬运工orz,本意是想搞清楚双变量局部空间自相关和地理加权回归的区别,虽然依旧一知半解,但是,害.(下面网址我不会搞超链接,我发现有点麻烦,所以我懒 ...
- spgwr | R语言与地理加权回归(Ⅰ-2):广义线性地理加权回归
本篇来介绍基于广义线性模型的地理加权模型.广义线性模型包括Logistic模型.泊松模型等系列回归模型,具体内容请查看数学模型专辑的相关系列推文. 广义线性GWR的使用方法与线性GWR类似: ggwr ...
- spgwr | R语言与地理加权回归(Ⅰ-1):线性地理加权回归
地理加权回归(Geographically Weighted Regression, GWR)经过多年发展,已经具备了多种形式,在R语言中也对应着多个工具包,其中spgwr是一个开发较早.比较经典的工 ...
- 地理加权回归 | 模型如何应用于新数据的预测?
专注系列化.高质量的R语言教程 推文索引 | 联系小编 | 付费合集 有读者不知道如何用地理加权回归去预测新的数据.本篇以常用的两个工具包为例进行介绍. 本篇目录如下: 0 数据准备 1 spgwr工 ...
- R语言地理加权回归数据分析
在自然和社会科学领域有大量与地理或空间有关的数据,这一类数据一般具有严重的空间异质性,而通常的统计学方法并不能处理空间异质性,因而对此类型的数据无能为力.以地理加权回归为基础的一系列方法:经典地理加权 ...
- R语言GWR地理加权回归
最近需要用到GWR地理加权回归,数据量有5万条,使用了GIS.GWR4进行计算,但都没能成功.应该是数据量过大. 参考相关博客,还有一个方法是R语言的实现.因为没怎么接触过R语言,所有想请问一下各位, ...
- ArcGIS与地理加权回归【三】
开 工 大 急 原址链接: ArcGIS与地理加权回归[三]https://mp.weixin.qq.com/s/x85EXKImSHio1IZovW9qdA 接着5个月之前..... ...
- gis中的加权求和工具在哪里_干货分享 | 地理加权回归介绍及其arcgis软件操作
一.地理加权回归模型概述 橘生淮南则为橘,生于淮北则为枳,叶徒相似,其实味不同.所以然者何?水土异也.--<晏子春秋·内篇杂下>这段文字很好的描述了空间异质性.从地理空间的角度,经济发展尤 ...
- 白话空间统计二十四:地理加权回归(八)结果解读(一)
地理加权回归分析完成之后,与OLS不同的是会默认生成一张可视化图,像下面这张一样的: 这种图里面数值和颜色,主要是系数的标准误差.主要用来衡量每个系数估计值的可靠性.标准误差与实际系数值相比较小时,这 ...
- GWmodel | 地理加权模型(Ⅱ-1):地理加权主成分分析(GWPCA)
地理加权回归(GWR)相比于普通的回归模型能够考虑到回归系数的空间异质性.但实际上,地理加权并非GWR模型所独有,其他数据分析方法同样也能通过加入地理权重进行改进.本篇介绍的就是地理加权主成分分析(G ...
最新文章
- Real VNC 5.1.1新增实用的技能:VNC Address Book
- PyQt的Layout的比例化分块。
- 自律到极致-人生才精致「第5期」:领奖通知
- JVM规范阅读-instance of
- 知识图谱之语言计算与信息抽取
- 为什么阿里巴巴要求 POJO 中不能使用基本数据类型?
- centos 下安装 mysql 5.6
- 最长公共子串(10分)
- 软件安装下载的镜像站、国内源
- 分享一次在Windows Server2012 R2中安装SQL Server2008
- Markdown 编辑器 Editor.md 图片上传使用
- 硬盘分区 整G整数法(从1g到200g最精确的整数分区)
- FUP AMD300-27便携式拉曼食品安全分析仪 检测微痕量农兽药残留 非法添加
- composer require fxp/composer-asset-plugin 失败
- 盘点2014:10个词让你看懂今年的移动互联网
- 职场最高级的聪明是靠谱,到底一个人怎样才算真正靠谱?
- 电脑上如何登录华为云服务器地址,华为手机上的云服务备忘录如何在电脑上登录?...
- <STM32学习>--跑马灯实验
- 【转】【青春励志】当幸福来敲门——我的考研故事
- 1660_MIT 6.828 JOS初始化boot_alloc的初步实现