Wenzl. Summary Bruges 2003

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summary函数

summary函数在使用summary(函数之前，我们首先需要安装和导入合适的库。

常用的库包括nltk（自然语言处理工具包），gensim（用于文本相关性分析的库），以及sumy（用于文本摘要的库）等。

一个简单的方法是使用sumy库中的summarizer模块。

以英文文本为例，我们可以使用sumy库中的LexRank算法来进行文本摘要生成。

下面是一个使用sumy库进行文本摘要生成的示例：```pythonfrom sumy.parsers.plaintext import PlaintextParserfrom sumy.nlp.tokenizers import Tokenizerfrom sumy.summarizers.lex_rank import LexRankSummarizerdef generate_summary(text, summary_length=3):parser = PlaintextParser.from_string(text,Tokenizer("english"))return ''.join([str(sentence) for sentence in summary])#调用函数生成摘要text = "这是一段文本..."print(summary)```在这段代码中，我们首先导入了需要的模块和函数。

然后，我们定义了一个generate_summary函数，该函数接受一个文本和一个可选参数summary_length（默认为3），并返回生成的摘要。

接下来，我们使用PlaintextParser将文本转化为可解析的文档，使用Tokenizer对文本进行分词处理，从而将文本准备好用于摘要生成。

然后，我们创建了一个LexRankSummarizer实例，并将其应用于解析后的文档，传入参数summary_length来指定要生成的摘要长度。

Influence of socioeconomic status, wealth and financial empowerment on gender differences in health

Inﬂuence of socioeconomic status,wealth and ﬁnancial empowerment on gender differences in health and healthcareutilization in later life:evidence from India *Kakoli Roy a ,*,Anoshua Chaudhuri baCenters for Disease Control and Prevention,1600Clifton Rd NE.,Mail Stop E-94,Atlanta,GA 30333,USAbSan Francisco State University,San Francisco,CA,USAAvailable online 3March 2008AbstractEmpirical studies from developed countries observe that women report worse health and higher healthcare utilization than men,but the health disadvantage diminishes with age;gender differences in self-rated health often vanish or are reversed in older parable assessments of health during later life from developing countries are limited because of the lack of large-scale surveys that include older women.Our study attempts to address the shortage of developing country studies by examining gender differ-ences in health and healthcare utilization among older adults in India.Both ordered and binary logit speciﬁcations were used to assess signiﬁcant gender differences in subjective and objective health,and healthcare utilization after controlling for demograph-ics,medical conditions,traditional indicators of socioeconomic status like education and income,and additional wealth indicators.The wealth indicators,measured by property ownership and economic independence,are regarded as ﬁnancially empowering older adults to exercise greater control over their health and well-being.Data are drawn from a nationally representative decennial socioeconomic and health survey of 120,942Indian households conducted during 1995e 1996.The study sample comprises 34,086older men and women aged !60years.Our results indicate that older women report worse self-rated health,higher prevalence of disabilities,marginally lower chronic conditions,and lower healthcare utilization than men.The health disadvantage and lower utilization among women cannot be explained by demographics and the differential distribution of medical conditions.While successive controls for education,income,and property ownership narrows the gender gap in both health and healthcare utilization,signiﬁcant differentials still persist.Upon controlling for economic independence,gender differentials disappear or are reversed,with older women having equal or better health than otherwise similar men.Financial empowerment might confer older women the health advantage reﬂected in developed societies by enhancing a woman’s ability to undertake primary and secondary prevention during the life course.Ó2008Elsevier Ltd.All rights reserved.Keywords:Elderly health;Gender;Empowerment;Health inequalities;Healthcare utilization;India;Socioeconomic status (SES)IntroductionRecent years have witnessed both population aging and gender emerging as prominent concerns in interna-tional forums (United Nations,2002;World Health*The ﬁndings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention (CDC).*Corresponding author.Tel.:þ14044986298.E-mail addresses:kjr3@ (K.Roy),anoshua@ (A.Chaudhuri).0277-9536/$-see front matter Ó2008Elsevier Ltd.All rights reserved.doi:10.1016/j.socscimed.2008.01.015Social Science &Medicine 66(2008)1951e1962/locate/socscimedOrganization,2003).The discourse frequently asserts that older women are universally more vulnerable to social,economic,and health disadvantages than men (United Nations,2005;World Health Organization, 1995).In general,most societies observe a nearly uni-versal pattern of longer life expectancy among women than men,increasing the ratio of women to men in older ages(Feachem,Kjellstrom,Murray,Over,&Phillips, 1992;Nathanson&Lopez,1987).Longer life,however, provides only limited information about the quality of life and burden of non-fatal disease during the extended years.Empirical studies related to developed countries tend to observe that although women live longer,they are sicker than men in that they report higher rates of morbidity,disability,and healthcare utilization than men(Nathanson,1977;Vergrubbe,1985;Wingard, Cohn,&Kaplan,1989).However,the health disadvan-tage among women diminishes with age(Manton,1990; Verbrugge,1984);gender differences in self-rated health often vanish or are reversed in older ages(Arber &Ginn,1993;Case&Paxton,2005;Strauss,Gertler, Rahman,&Fox,1993).This apparent paradox of lower mortality but higher rates of illness among women,compared with same-aged men,has attracted the attention of sociologists, economists,and epidemiologists alike.1Although there is no debate regarding the higher mortality rates among males(Marmot&Shipley,1997;Strauss et al.,1993), recent studies indicate that the pattern of gender differ-ences in morbidity are more complex,and women’s disadvantage relative to men might be smaller than previously assumed and they vary by measures of health and across the life course(Arber&Cooper, 1999;Hunt&Annandale,1999;Macintyre,Hunt,& Sweeting,1996;Williams,2003).Explanations on the differential health outcomes among men and women often highlight socioeconomic inequality as a funda-mental cause for variations in their well-being(Adler &Ostrove,1999;Huisman,Kunst,&Mackenbach, 2003;McDonough&Walters,2001).Hence,a possible interpretation of recentﬁndings might be that women’s health status has actually improved with increases in women’s socioeconomic status(SES)during previous decades.On conducting a critical review of the literature,Macintyre et al.(1996)concluded that the ‘‘whole topic warrants frequent reexamination,’’in-cluding systematic assessments across societies to examine whether gender differences described in one decade and in one context might be generalizable across societies at different stages of development and with different cultural attitudes towards appropri-ate gender roles.The majority of previous studies,however,have focused on developed parable assess-ments of gender disparities in health among older adults in developing countries are limited because of lack of large-scale surveys that include older women (Anson&Sun,2002;Zimmer&Kwong,2004). Among the limited number of studies from developing countries that include older adults,the majority focus on mortality rather than on morbidity indicators (Feachem et al.,1992;Strauss et al.,1993).The latter problem,however,is critical because the presumed ‘‘biological’’advantage of older women might be depleted by the economic disadvantage and gender biases that are pervasive in certain developing and often patriarchal societies.Many older women can also anticipate a period of widowhood withﬁnancial vulnerability,further compounding their physical and mental vulnerabilities.Given this backdrop,our study attempts to address the shortage of developing country studies by examining gender differences in health status and healthcare utilization for a nationally repre-sentative sample of older adults in India.In the current study,persons aged!60years are considered older adults according to the ofﬁcial deﬁnition for older persons used by the Indian government,which is also consistent with United Nations(UN)deﬁnition for an older person in the developing world,as adopted at the World Assembly on Aging convened in Vienna in 1982(Sharma&Xenos,1991;UN,1982,1993). Study context,purpose,and contributionIndia is a rapidly developing country in South Asia, but despite modernization,patriarchal relationships and traditional norms continue to be pervasive in many spheres of life(Dre`ze&Sen,1995;Gwatkin, Johnson,Wagstaff,Rustein,&Pande,2000).Although women in younger cohorts are becoming educated and urbanized and are climbing the occupational ladder,the majority of older women continue to be steeped in tradition and have substantially lower socioeconomic1Plausible explanations include(a)gender-speciﬁc causes ofill-health change during life course(e.g.,reproductive role of womenversus the higher incidence of smoking,alcohol-and tobacco-relatedmortality,and morbidity among men);(b)those who survive until oldage have better and more equal health(i.e.,mortality selection);(c)gender differentials in conditions that affect men and women(mentend to suffer more from conditions that have fatal outcomes versuswomen who suffer more from non-fatal conditions);and(d)differ-ences in reporting behavior(women tend to be less stoic abouttheir health and seek care more readily than men).(For an extensivereview,see Case&Paxton,2005.)1952K.Roy,A.Chaudhuri/Social Science&Medicine66(2008)1951e1962achievements than men(Dre`ze&Sen,2001).If wom-en’s lower SES were among the main reasons for poorer health outcomes in the developed world in earlier decades,it might be worthwhile to examine whether existing gender differences in morbidity among older adults in India can be explained by any persisting differences in SES.The latter effect is important from a policy perspective because it might be partly amena-ble to targeted interventions.Because gender has both a biological and social di-mension,gender-based health inequalities reﬂect both biological and social factors,including the interplay between them(Bird&Rieker,1999).In terms of social factors,social scientists pose two general hypotheses to account for gender-based inequalities.First,women might report higher levels of health problems as a result of differential exposure or reduced access to material and social factors that foster health and well-being (Arber&Cooper,1999).Second,women might report higher health problems because of differential vulnera-bility to material,behavioral,and psychosocial factors that foster health(McDonough&Walters,2001).The current paper explores both problems by examining four speciﬁc questions for older persons in India:First, do substantial gender differences exist in self-reported measures of health status and healthcare utilization? Second,can these gender differences be explained by the differential exposure to structural,material,and social factors among men and women?Third,how do the different determinants associate with the health status and healthcare indicators?Finally,do any gender differences exist among these associations?Techniques include estimating ordered and binary logit regression equations in both separate samples for men and women and in pooled samples with interaction terms being employed to test for gender effects.The current study makes a unique contribution in several different ways.First,although the existence of both SES-and gender-related inequalities in health is well-documented,only a limited number of studies from developing countries combine these two different strands of literature to assess how gender differences in SES shape differences in health among older men and women.In this sense,the current study contributes to an important enquiry in the social epidemiology literature.Second,the traditional approach generally measures socioeconomic inequalities in terms of consumption, education,or income,which are considered appropri-ate indicators of SES in middle age,but may not be sufﬁcient for measuring SES in older ages(Arber& Ginn,1993;Liberatos,Link,&Kesley,1988).Hence,the socioeconomic indicators used in this paper include those customarily used in these types of studies,as well as several supplemental wealth indicators, indexed by property ownership and economic indepen-dence,to provide a more accurate assessment of overall SES among older adults.The household economics literature predicts that bargaining power within a household is determined by the share of resources allocated to an individual. While bargaining power is hard to measure,it is affected by factors such as control over resources or ownership of assets.Thus,the wealth indicators used in this study can also be regarded as proxy measures ofﬁnancial autonomy or empowerment that provide older adults the ability to take greater control of their health and well-being in later life,as well as during the life course(Quisumbing,2003).The female disadvantage in health in developing societies,where patriarchal kinship and economic structure might limit women’s autonomy,is documented abundantly (Caldwell,1986;Das Gupta,1996;Santow,1995). However,the majority of these studies have focused on infant and/or child mortality,reproductive health-seeking behavior,or fertility outcomes among women of child-bearing age(Bloom,Wypij,&Das Gupta, 2001;Dharmalingam&Morgan,1996;Dyson& Moore,1983).To the best of our knowledge,this is theﬁrst nationally representative study that explores the association between direct measures ofﬁnancial empowerment and gender disparities in health status and healthcare utilization among older adults in India.Lastly,the study adds to the limited number of com-prehensive studies on the well-being of older adults in India,which is important as the country begins a period of rapid population aging(Chanana&Talwar,1987). The percentage of the population aged!60years, although constituting only a small percentage(6.9% in2001)of the Indian population,is the second largest older population in the world(71million in2001)and is projected to increase to12.4%by2026(Census, 2001).According to Census projections,older Indians, projected at173million by2026,will dominate the world’s older population.In the absence of broad-based pension or social security schemes,traditional living arrangements,where the majority of older adults are supported by their children or extended family, provide a form of social safety net(Irudaya Rajan, Mishra,&Sharma,1999).However,improvements in life expectancy coupled with reduction in fertility imply that older persons are living longer with fewer children or kin to support them.Because the health needs of this rapidly growing population segment are1953K.Roy,A.Chaudhuri/Social Science&Medicine66(2008)1951e1962much greater and because women,who have both worse health and lower SES,comprise a predominant proportion of the older population,developing innova-tive approaches to address challenges confronting the older population is critical,and an emerging policy imperative for both the government and civil society (Irudaya Rajan,Mishra,&Sharma,2001).MethodsDataThe study uses data from the52nd round of the National Sample Survey(NSS)conducted during July1995e June1996in India by the National Sample Survey Organization(NSSO)of the Government of India.Nationally representative data on morbidity were collected as part of the decennial surveys on social consumption during the NSS35th(July1980e June1981),42nd(July1986e June1987),52nd(July 1995e June1996)and60th(January e June2004) rounds.Although information on problems of the aged were included in the42nd,52nd,and60th rounds, the52nd round is employed in this analyses as it provides the most comprehensive coverage of older persons by including a large sample of older persons and containing multiple modules providing informa-tion on their health,retirement decisions,economic independence,social support,and living arrangements allowing possibilities for meaningful multivariate anal-yses(NSSO,1998).The survey design is a two-stage, stratiﬁed probability sample with census villages and urban blocks asﬁrst-stage sampling units for rural and urban areas,respectively,followed by sampling of households in the second stage.The survey covered 120,942households,yielding a sample of629,888 individuals with non-missing demographic informa-tion.We limit the analyses to a sample of34,086 (5.40%)older persons comprising men(49.4%)and women(50.6%)aged!60years.Dependent variablesDependent variables include two measures of health status and one measure of healthcare utilization.Health status is measured on both subjective and objective dimensions,because subjective health is predicted to increasingly deviate from objective health as people age(Pinquart,2001).Subjective,or self-rated health status,reﬂects how respondents rated their health by using a four-point scale ranging from excellent to poor,denoted by SRH(coded as a categorical variable that ranges from1equals excellent to4equals poor). This is a powerful measure,consistently identiﬁed as a powerful predictor of mortality(Idle&Benyamini, 1997).Objective health status is measured on the basis of the respondent’s response to a question regarding the presence of functional limitations that conﬁnes one to the bed or home,denoted by imobl(equals1 if physical immobility conﬁnes one to bed or home; equals0otherwise).Healthcare utilization is measured using self-reports of hospitalization during a1-year reference period preceding the survey,denoted by uhosp(equals1if hospitalized during the previous 365days;0otherwise).Independent variablesThe independent variables are grouped into demographic variables,social support factors,preexist-ing medical conditions,traditional SES measures,and supplemental wealth indicators as proxy measures of ﬁnancial empowerment.Demographic variables in-clude age,age-squared,gender,household size,social code(whether scheduled caste/tribe),and marital status.Old-age social support is measured by indica-tors of traditional living arrangements,number of children,and proportion of sons(i.e.,number of sons as proportion of total number of children),all of which might have both a direct and an indirect psychosocial inﬂuence on the mental health and well-being of older adults,including their ability to seek care when ill. Including the proportion of sons,in addition to number of children,is important because Indian society is predominantly patrilineal.Sons continue the patriline and are expected to provide old-age security,whereas daughters,including material goods given to her, belong to the patriline into which she marries(Das Gupta,1987).Preexisting medical conditions include indicators related to the presence of chronic and disabling conditions.Chronic conditions are measured on the basis of self-reports of one or more conditions, including cough,piles,joint pain,blood pressure,heart disease,urinary tract infection,and diabetes.Disabling conditions are measured based on self-reports of any impairments related to speech,vision,hearing,loco-motion,and amnesia.Traditional SES variables include dummies for education levels,household consumption quartiles, and urban residence.Supplemental measures of wealth include dummies for property ownership and economic independence,which can be viewed as proxies for ﬁnancial empowerment.An older person is considered economically independent if he/she does not take any1954K.Roy,A.Chaudhuri/Social Science&Medicine66(2008)1951e1962ﬁnancial help from others in order to lead a normal life. Economic independence is expected to have a direct effect on health and healthcare utilization by enhancing the ability of older adults to undertake primary and secondary prevention in old age as well as during the life course.In contrast,property(including land) ownership is expected to impact health and healthcare utilization indirectly by inducing children and kin to provide support for their older relatives,in lieu of potential bequests,which can be a key non-altruistic motivator for inherently rational individuals(Vlassof &Vlassof,1980).Analytical strategyMaximum likelihood multivariate regression analy-sis is used to examine gender differences in self-assessed health,functional limitation,and healthcare utilization in both separate samples for men and women and in pooled samples with interaction terms being employed to test for differential effects by gender.We estimate ordered logits for self-assessed health and dichotomous logits for functional limitation and healthcare utilization.Five different speciﬁcations with varying covariates are estimated for each dependent variable.For each health measure,theﬁrst model only controls for demographics and social support factors, and then we gradually include additional explanatory variables in order of prevalence of medical conditions, traditional SES,and supplemental wealth indicators. Model1controls for demographic factors,including age(age of the person in years),age2(age-squared), female(equals1if female;0if male),hhsize(house-hold size),curmar(0if currently married;0otherwise), scstcode(if person belongs to scheduled caste or tribe; 0otherwise),and social support offered by traditional living arrangements and support from offspring,in-cluding livalone(1if living alone or with spouse and no other kin;0otherwise),kids(number of children), and prson(proportion of sons).Model2adds controls on the prevalence of medical conditions,including chronic(equals1if person reports any chronic condi-tion;0otherwise)and disabl(equals1if person reports any disabling condition).Model3adds covariates on traditional socioeconomic factors,including nolit (equals1if not literate2;0otherwise),quart2e quart 4(three dummy variables on the second,third,and fourth quartile of household consumption expenditure per capita,with theﬁrst quartile being the excluded reference category).Model4adds a control for wealth measured by ownprop(equals1if the person own property;0otherwise).Model5(full model)adds another control for wealth measured by ecind(equals 1if the person is not economically dependent on others;0if partially or wholly dependent on others).As an additional robustness check of gender differ-ential effects,the predictors in Model5are followed by an interaction term between female*ecind to assess whether the effect of economic independence varies by gender.A positive and statistically signiﬁcant inter-action term would indicate that the effect is stronger for women and vice versa.In addition,we also added interaction terms between female and all other covari-ates and conducted Chow tests to examine whether the covariates have an equal effect between males and females(Chow,1960).Finally,we ran regressions sep-arately for males and females to examine whether the magnitude and direction of association between the covariates and the outcome variables differ by gender.All estimations control for complex survey design and rely on sampling weights to produce nationally representative estimates that adjust for variation in probabilities of selection and the variation in response rates by primary sampling units.The estimation method is weighted(pseudo)maximum likelihood with variances obtained by using implicit Taylor line-arization,which is a form of General Estimating Equa-tions(GEEs)that produces robust variance estimates (Binder,1983).ResultsDescriptive statisticsTable1presents for males and females sepa-rately the weighted means and percentages for se-lected demographic and socioeconomic variables. The results highlight the differential socioeconomic and cultural experiences of older men and women. As expected,older women report signiﬁcantly lower levels of literacy,property ownership,economic in-dependence,and social integration than men.The socioeconomic vulnerabilities of older women are further exacerbated by substantially higher rates of widowhood and dependent living arrangements(de-ﬁned as living with children or kin),compared with older men.Table1also presents the weighted means and per-centages for the selected health outcome and health-care utilization indicators.The results indicate that2A person who can read and write a simple message in any lan-guage with understanding is considered literate(NSSO codebook).1955K.Roy,A.Chaudhuri/Social Science&Medicine66(2008)1951e1962older women report worse health than men,as indicated by all three measures d self-rated health, functional limitations,and symptomatic disabling con-ditions.The only exception is the prevalence of chronic conditions,which is marginally higher among men than women but might remain undetected without ap-propriate medical diagnosis.Even though older women report poorer health than men,they have lower rates of healthcare utilization,as indicated by signiﬁcantly lower rates of hospitalization and outpatient encoun-ters.Although this might appear counterintuitive at ﬁrst glance,theﬁnding is consistent with the gender bias hypothesis,which posits that older women have worse health but also less access to the healthcare system(Fariyal&Omrana,2004).Regression resultsTables2e4present results from the regression analysis,conducted on the pooled sample,in which the different measures of health and healthcare are re-gressed on gender controlling for explanatory variables added in order of demographic and social support fac-tors,prevalence of medical conditions,traditional SES, and supplemental wealth indicators inﬁve different model speciﬁcations.If the gender differences in the dependent variables are caused by the differential exposure of men and women to these factors,we might expect these differences to disappear as we add succes-sive controls.Table2demonstrates that older women report signiﬁcantly poorer subjective health than men,when controlling for demographics and social support factors (Model1).The gender gaps cannot be explained by the differential distribution of chronic and symptomatic disabling conditions and widen further upon control-ling for these factors(Model2).Although successive controls for education,income,and property ownership narrow the gender gap,statistically signiﬁcant differen-tials still persist(Models3and4)which implies that the differential exposure of men and women to socio-economic factors cannot completely explain gender differences.However,the gender difference in subjec-tive health disappears or is no longer statistically sig-niﬁcant upon controlling for economic independence (Model5).Table3indicates that the unadjusted gender difference in functional health(Table2)can be com-pletely explained by the differential exposure of men and women to demographic and social support factors (Model1).The coefﬁcient for gender difference continues to be statistically insigniﬁcant as we place successive controls for the distribution of medical conditions(Model2)and traditional socioeconomic factors(Model3).However,controlling for property ownership reverses the gender gap(Model4),whichTable1Weighted means and percentages of selected demographic,socioeconomic and health variables by genderAll Male Female Male-femaleDifference P value Mean age67.7968.067.60.400.0000 Widowed39.8%20.1%58.9%À38.8%0.0000 Currently married58.2%77.3%39.5%37.8%0.0000 Illiterate70.3%55.3%85.9%À30.6%0.0000 Owning property62.8%80.3%45.6%34.7%0.0000 Financially independent31.0%50.2%12.3%37.9%0.0000 Living alone or with spouse only14.6%15.8%13.4% 2.4%0.0000 Integration in:Household chores78.9%79.3%78.5%0.010.3099 Religious matters82.3%85.7%79.0%0.070.0000 Social matters75.9%82.5%69.6%0.130.0000 Self-rated health reported as‘‘poor’’19.2%17.1%21.4%À4.3%0.0000 Functionally limited to bed/home10.3%9.8%11.3%À1.5%0.0198 Disabling conditions>139.0%36.9%41.0%À4.1%0.0000 Chronic conditions>152.4%52.5%52.3%0.3%0.7782 Number of disabling conditions0.640.600.69À0.090.0000 Number of chronic conditions0.770.780.750.030.0264 Hospitalized,last365days 3.8% 4.6% 3.0% 1.6%0.0000 Ailment,last15days16.3%16.8%15.8% 1.0%0.1530 1956K.Roy,A.Chaudhuri/Social Science&Medicine66(2008)1951e1962widens even further on controlling for economic inde-pendence,indicating that ﬁnancially empowered older women have better functional health than otherwise similar men (Model 5).Table 4indicates that the probability of hospitaliza-tion is signiﬁcantly lower among women,even after adjusting for demographic and social support factors (Model 1).Controlling for the distribution of medical conditions slightly reduces the gap in healthcare utilization usage (Model 2).Successive controls for traditional SES factors and property ownership further narrows the gender gap,possibly reﬂecting the direct effect of SES in increasing the ability to obtain needed care (Models 2e 4).However,controlling for economic independence substantially widens the gender gap in utilization,possibly as a result of an opposing indirect effect that decreases healthcare utilization,because ﬁnancially independent women might have better health and hence less need for inpatient care.Finally,we include multiple interaction terms and conduct robustness tests to examine different hypothe-ses.First,for each dependent variable,we include the interaction term between female *ecind to assess whether the effect of economic independence varies by gender (Table 2e 4,Model 5).The coefﬁcient in the ﬁrst two speciﬁcations is not statistically signiﬁcant (P value >0.10),indicating that the effect of economic independence on subjective and objective health is similar for men and women.The coefﬁcient in the third speciﬁcation is weakly signiﬁcant,indicating that the effect of economic independence on probability of hos-pitalization is stronger among women,compared with men.Table 2reﬂects that the proportion of women who are ﬁnancially independent is markedly lower,compared with men (12%versus 50%).Taken to-gether,these results imply that differences in economic independence and its effects can to a great extent explain (or ameliorate)the health disadvantage among females reported among older adults in India.We also conduct Chow tests to examine the equality in the effect of the covariates across males and females (Ta-ble 5).Adjusted Wald tests on hypotheses derived from speciﬁcations that include interaction terms between female and all other covariates indicate that the coefﬁcients for subjective and objective health are signiﬁcantly different (P value <0.01)by gender.For each outcome variable,we also examine the association with key covariates in the ﬁnal modelTable 2Coefﬁcients from the ordered logit regression on self-assessed healthModel 1Model 2Model 3Model 4Model 5age 0.2005***[0.0457]0.1527***[0.0471]0.1552***[0.0472]0.1497***[0.0474]0.1159**[0.0482]age2À0.0009***[0.0003]À0.0007**[0.0003]À0.0007**[0.0003]À0.0007**[0.0003]À0.0005[0.0003]curmar À0.1841***[0.0626]À0.0676[0.0648]À0.0073[0.0645]0.017[0.0649]0.0518[0.0659]scstcode 0.2043***[0.0648]0.1831***[0.0675]À0.0072[0.0695]À0.0222[0.0706]À0.0207[0.0705]female 0.2969***[0.0425]0.3243***[0.0446]0.2275***[0.0512]0.1258**[0.0567]À0.0972[0.0626]hhsz À0.0001[0.0081]À0.0008[0.0085]0.0345***[0.0110]0.0350***[0.0110]0.0292***[0.0111]livalone 0.0083[0.0894]0.022[0.0925]À0.1228[0.0947]À0.1036[0.0973]0.0204[0.0975]prson À0.0257[0.1094]0.0032[0.1156]À0.024[0.1150]À0.0499[0.1145]À0.0777[0.1152]kids 0.0037[0.0096]À0.004[0.0101]À0.0027[0.0101]À0.0014[0.0102]À0.002[0.0106]chronic 0.4802***[0.0312]0.5193***[0.0314]0.5204***[0.0317]0.4925***[0.0319]disability 0.4942***[0.0325]0.4741***[0.0324]0.4692***[0.0325]0.4552***[0.0331]nolit 0.3851***[0.0762]0.3728***[0.0757]0.3367***[0.0760]quart2À0.3336***[0.0895]À0.3317***[0.0906]À0.3482***[0.0895]quart3À0.4518***[0.1050]À0.4529***[0.1051]À0.4685***[0.1059]quart4À0.7266***[0.1240]À0.7134***[0.1240]À0.7021***[0.1238]urban À0.1728***[0.0659]À0.2014***[0.0661]À0.2091***[0.0671]ownprop À0.3290***[0.0630]À0.2057***[0.0643]ecindÀ0.7836***[0.0716]Adds to Model 5the interaction dummy:female *ecind 0.0965[0.1245]Observations 30,80930,80930,79930,51130,249F -statistic 56.2393.2672.1471.7073.11D.F.911161718Prob >F 0.00000.00000.00000.00000.0000Dependent variable:self-assessed health rated as ‘‘1’’¼excellent to .‘‘4’’¼poor.Standard errors in brackets.*Signiﬁcant at 10%;**signiﬁcant at 5%;***signiﬁcant at 1%.1957K.Roy,A.Chaudhuri /Social Science &Medicine 66(2008)1951e 1962。

python葡萄酒质量数据分类与回归

python葡萄酒质量数据分类与回归Python葡萄酒质量数据分类与回归在现代社会中，数据分析和机器学习已经成为了非常热门的话题。

Python作为一种强大的编程语言，在数据分析和机器学习方面也有着广泛的应用。

本文将介绍如何使用Python对葡萄酒质量数据进行分类和回归分析。

1. 数据集介绍本文使用的数据集是UCI Machine Learning Repository中的葡萄酒质量数据集。

该数据集包含了红葡萄酒和白葡萄酒的各种化学成分以及葡萄酒的质量评分。

该数据集共有1599个样本，其中红葡萄酒样本数量为1599个，白葡萄酒样本数量为4898个。

2. 数据预处理在进行机器学习任务之前，我们需要对数据进行预处理，以便更好地进行后续的分析。

首先，我们需要将数据集分为训练集和测试集。

训练集用于训练模型，测试集用于评估模型的性能。

其次，我们需要对数据进行标准化处理。

标准化处理可以使得数据的均值为0，方差为1，这样可以避免不同特征之间的数量级差异对模型的影响。

最后，我们需要对数据进行特征选择。

特征选择可以去除不相关或冗余的特征，从而提高模型的性能。

3. 分类任务在分类任务中，我们需要将葡萄酒分为好酒和差酒两类。

根据数据集中的质量评分，我们可以将质量评分大于等于7的葡萄酒定义为好酒，将质量评分小于7的葡萄酒定义为差酒。

在进行分类任务之前，我们需要选择合适的分类算法。

本文选择了支持向量机（SVM）算法进行分类。

SVM算法是一种非常优秀的分类算法，在处理高维数据和小样本数据方面有着很好的表现。

使用Python中的sklearn库，我们可以轻松地实现SVM算法。

首先，我们需要对训练集进行训练，然后使用测试集进行测试。

最后，我们可以计算模型的准确率、召回率、F1值等指标来评估模型的性能。

4. 回归任务在回归任务中，我们需要预测葡萄酒的质量评分。

同样地，我们需要选择合适的回归算法。

本文选择了多元线性回归算法进行回归分析。

详解RStudio_中使用lm_函数及summary_函数建模与模型检验的输出结果

DOI ：10.15913/ki.kjycx.2024.06.009详解RStudio中使用lm函数及summary函数建模与模型检验的输出结果廖海燕（韶关学院数学与统计学院，广东韶关 512005）摘要：使用RStudio ，通过各种随机函数生成样本数据，再使用stats 包的lm 函数及summary 函数建立线性回归模型，并对其输出结果的各项细则详细解读，叙述所用的理论与公式，并尝试用各种方法重新编程，从而对这个函数的建模原理得到更好的把握，能有助于更好地使用此函数建立合适的模型，并灵活地利用RStudio 编程实现各种建模需要的输出结果。

关键词：RStudio ；lm 函数；summary 函数；随机函数中图分类号：TP312.1 文献标志码：A 文章编号：2095-6835（2024）06-0036-03对于一份分析关于某变量影响因素的数据，倘若尝试拟合回归模型，可以考虑使用RStudio 中stats 包的lm 函数，但是经过研究发现，尚没有对于该函数各项输出结果的详细说明。

本文通过随机函数生成样本数据，再使用stats 包的lm 函数及summary 函数建立线性回归模型，并对其输出结果的各项细则详细解读，叙述所用的理论与公式。

问题为尝试拟合因变量Y 与自变量X 1，X 2，X 3，…，X p 之间的线性回归模型，模型如下所示：Y =β0+β1X 1+β2X 2+β3X 3+…+βp X p +ε （1）éëêêêêêêêêùûúúúúúúúúY 1 X 11 X 12 X 13 ⋯ X 1p Y 2 X 21 X 22 X 23 ⋯ X 2p ⋮Y n X n 1 X n 2 X n 3 ⋯ X np （2）将样本数据矩阵式（2）代入式（1），得到结果如式（3）所示：ìíîïïïïïY 1=β0+β1X 11+β2X 12+β3X 13+…+βp X 1p +ε1Y 2=β0+β1X 21+β2X 22+β3X 23+…+βp X 2p +ε2⋮Y n=β0+β1X n 1+β2X n 2+β3X n 3+…+βp X np +εn （3）式（3）的建模假定如下：误差ε1，ε2，ε3，…，εn ~iidN （0，σ2）。

To appear ICML-2003 Workshop on Learning from Imbalanced Data Sets (II) Learning When Data

1 0.8 True Positive Rate 0.6 0.
0.6
0.8
1
False Positive Rate
3. Basic Concepts of roc Analysis
Receiver Operating Characteristic (roc) analysis (Swets & Pickett, 1982) has its origin in signal detection theory, but most of the current work occurs in the medical decision making community. Researchers in the machine learning community have just recently become interested in roc analysis as a method for evaluating classiﬁers. Indeed, it is a method of analysis unconfounded by inductive bias, by unknown, but unequal error costs, and, as we describe in this paper, by the class distribution of examples (Metz, 1978; Provost et al., 1998; Maloof et al., to appear). Parametric roc analysis is based on a binormal assumption, meaning that the actually positive cases are normally distributed and the actually negative cases are normally distributed (Metz, 1978). Naturally, it is the overlap between these two distributions that results in the Bayes error rate (Duda et al., 2000). Once we have characterized in some way the training examples drawn from these two distributions, then we are free to set a decision threshold most anywhere. It is typically best to select the decision threshold that minimizes the Bayes error rate. Alternatively, if error costs are unequal and known, then we can adjust the decision threshold to minimize the overall cost of errors. As stated previously, there is a strong connection between the prior probability of a class and its error cost. If the class distribution of examples is consistent with the cost of errors, then building a classiﬁer consistent with those costs should pose little problem. However, when data sets are skewed in a manner that runs counter to the true cost of errors, then even if we know the cost of errors, it may be diﬃcult to build a classiﬁer that is consistent with those costs. To make matters worse, we often have only anecdotal evidence about the relationship between the class distribution and the cost of errors. For instance, on a rooftop detection task, which we discuss further in Section 5,

Anderson_Wincoop_AER_2003

Gravity with Gravitas:A Solution to the Border Puzzle By J AMES E.A NDERSON AND E RIC VAN W INCOOP*Gravity equations have been widely used to infer trade ow effects of variousinstitutional arrangements.We show that estimated gravity equations do not have atheoretical foundation.This implies both that estimation suffers from omittedvariables bias and that comparative statics analysis is unfounded.We develop amethod that(i)consistently and ef ciently estimates a theoretical gravity equation and(ii)correctly calculates the comparative statics of trade frictions.We apply themethod to solve the famous McCallum border puzzle.Applying our method,we ndthat national borders reduce trade between industrialized countries by moderateamounts of20–50percent.(JEL F10,F15)The gravity equation is one of the most em-pirically successful in economics.It relates bi-lateral trade ows to GDP,distance,and other factors that affect trade barriers.It has been widely used to infer trade ow effects of insti-tutions such as customs unions,exchange-rate mechanisms,ethnic ties,linguistic identity,and international borders.Contrary to what is often stated,the empirical gravity equations do not have a theoretical foundation.The theory, rst developed by Anderson(1979),tells us that after controlling for size,trade between two regions is decreasing in their bilateral trade bar-rier relative to the average barrier of the two regions to trade with all their partners.Intu-itively,the more resistant to trade with all others a region is,the more it is pushed to trade with a given bilateral partner.We will refer to the theoretically appropriate average trade barrier as“multilateral resistance.”The empirical grav-ity literature either does not include any form of multilateral resistance in the analysis or in-cludes an atheoretic“remoteness”variable re-lated to distance to all bilateral partners.The remoteness index does not capture any of the other trade barriers that are the focus of the analysis.Moreover,even if distance were the only bilateral barrier,its functional form in the remoteness index is at odds with the theory.1 The lack of theoretical foundation of empir-ical gravity equations has two important impli-cations.First,estimation results are biased due to omitted variables.Second,and perhaps even more important,one cannot conduct com-parative statics exercises,even though this is generally the purpose of estimating gravity equations.2In order to conduct a comparative statics exercise,such as asking what the effects are of removing certain trade barriers,one has to be able to solve the general-equilibrium model before and after the removal of trade barriers.In this paper we will(i)develop a method that consistently and ef ciently esti-mates a theoretical gravity equation,(ii)use the estimated general-equilibrium gravity model to conduct comparative statics exercises of the ef-*Anderson:Department of Economics,Boston College, Chestnut Hill,MA02467(e-mail:james.anderson@bc.edu),and NBER;van Wincoop:Department of Economics,University of Virginia,116Rouss Hall,Charlottesville,VA 22904(e-mail:vanwincoop@),and NBER.Wewould like to thank two referees,Carolyn Evans,Robert Feenstra,Jim Harrigan,John Helliwell,Russell Hillberry, David Hummels,Andy Rose,and Kei-Mu Yi for helpful comments.We also thank seminar participants at Boston College,Brandeis University,Harvard University,Prince-ton University,Tilburg University,the University of Cali-fornia at Davis,the University of Colorado,the University of North Carolina,the University of Virginia,the University of Wisconsin,Vanderbilt University,and the2000NBER ITI Fall meeting for helpful comments.1Jeffrey H.Bergstrand(1985,1989)acknowledges the multilateral resistance term and deals with its time-seriesimplications,but is unable to deal with the cross-section aspects which are crucial for proper treatment of bilateraltrade barriers.Anderson and Douglas Marcouiller(2002) use a To¨rnqvist approximation to the multilateral resistance term which handles the cross-section variation of bilateral barriers.2Recently,some authors(e.g.,David Hummels,1999) control for multilateral resistance in estimation with xed effects,but cannot consistently do comparative statics on this basis.170fect of trade barriers on trade ows,and(iii) apply the theoretical gravity model to resolve the“border puzzle.”One of the most celebrated inferences from the gravity literature is John McCallum’s (1995) nding that the U.S.–Canadian border led to1988trade between Canadian provinces that is a factor22(2,200percent)times trade between U.S.states and Canadian provinces. Maurice Obstfeld and Kenneth Rogoff(2001) pose it as one of their six puzzles of open economy macroeconomics.John F.Helliwell and McCallum(1995)document its violation of economists’prior beliefs.Gene Grossman (1998)says it is an unexpected result,even more surprising than Daniel Tre er’s(1995)“mystery of the missing trade.”A rapidly growing literature is aimed at measuring and understanding trade border effects.3So far none of the subsequent research has explained McCallum’s nding.We solve the border puzzle in this paper by applying the theory of the gravity equation seriously both to estima-tion and to the general-equilibrium compara-tive statics of borders.The rst step in solving the border puzzle is to estimate the gravity equation correctly based on the theory.In doing so we aim to stay as close as possible to McCallum’s(1995)gravity equation,in which bilateral trade ows between two regions depend on the output of both re-gions,their bilateral distance,and whether they are separated by a border.The theory modi es McCallum’s equation only by adding the mul-tilateral resistance variables.The second step in solving the border puzzle is to conduct the general-equilibrium comparative statics exer-cise of removing the U.S.–Canada border bar-rier in order to determine the effect of the border on trade ows.The primary concern of policy makers and macroeconomic analysts is the im-pact of borders on inter national trade.McCal-lum’s regression model(and the subsequent literature following him)cannot validly be used to infer such border effects.4In contrast,our theoretically grounded approach can be used to compute the impact of borders both on intra na-tional trade(within a country)and international trade.Applying our approach to1993data,we nd that borders reduce trade between the United States and Canada by44percent,while reducing trade among other industrialized coun-tries by29percent.While not negligible,we consider these to be plausibly moderate impacts of borders on international trade.Two factors contribute to making McCal-lum’s ceteris paribus ratio of interprovincial to province–state trade so large.First,his estimate is based on a regression with omitted variables, the multilateral resistance terms.Estimating McCallum’s regression for1993data we nd a ratio of16.4,while our calculation based on asymptotically unbiased structural estimation and the computed general-equilibrium compar-ative statics of border removal implies a ratio of 10.7.Second,the magnitude of both ratios largely re ects the small size of the Canadian economy.If we estimate McCallum’s regres-sion with U.S.data,we nd that trade between states is only a factor1.5times trade between states and provinces.The intuition is simple in the context of the model.Even a moderate bar-rier between Canada and the rest of the world has a large effect on multilateral resistance of the provinces because Canada it is a small open economy that trades a lot with the rest of the world(particularly the United States).This sig-ni cantly raises interprovincial trade,by a fac-tor6based on our estimated model.In contrast, the multilateral resistance of U.S.states is much less affected by a border barrier since it does not affect the barrier between a state and the rest of the large U.S.economy.Therefore trade between the states is not much increased by border barriers. To a large extent the contribution of this paper is methodological.Our speci cation can be applied in many different contexts in which various aspects of implicit trade barri-ers are the focus.Gravity equations similar to McCallum’s have been estimated to deter-mine the impact of trade unions,5monetary3See Hans Messinger(1993),Helliwell and McCallum (1995),Helliwell(1996,1997,1998),Shang-Jin Wei(1996),Russell Hillberry(1998,1999,2001),Michael A. Anderson and Stephen L.S.Smith(1999a,b),Jon Haveman and Hummels(1999),Hummels(1999),Natalie A.Chen (2000),Carolyn L.Evans(2000a,b),Holger Wolf(2000), Keith Head and John Ries(2001),Helliwell and Genevieve Verdier(2001),and Hillberry and Hummels(2002).4McCallum cautiously did not claim that his estimated factor22implied that removal of the border would raise Canada–U.S.trade relative to within-Canada trade by2,200 percent.5See Jeffrey Frankel et al.(1998).171VOL.93NO.1ANDERSON AND VAN WINCOOP:GRAVITY WITH GRAVITASunions,6different languages,adjacency,and a variety of other factors;all can be improved with our methods.Authors have,like McCal-lum,often hesitated to draw comparative static inferences from their ing our meth-ods,they can.Gravity equations have also been applied to migration ows,equity ows,and FDI ows.7Here there is no received theory to apply, consistently or not,but our results suggest the fruitfulness of theoretical foundations.The remainder of the paper is organized as follows.In Section I we will provide some results based on McCallum’s gravity equation. The main new aspect of this section is that we also report the results from the U.S.perspective, comparing interstate trade to state–province trade.In Section II we derive the theoretical gravity equation.The main innovation here is to rewrite it in a simple symmetric form,relating bilateral trade to size,bilateral trade barriers, and multilateral resistance variables.Section III discusses the procedure for estimating the the-oretical gravity equation,both for a two-country version of the model,consisting of the United States and Canada,and for a multicountry ver-sion that also includes all other industrialized countries.The results are discussed in Section IV.Section V performs sensitivity analysis,and the nal section concludes.I.The McCallum Gravity EquationMcCallum(1995)estimated the following equation:(1)ln x ij5a11a2ln y i1a3ln y j1a4ln d ij1a5d ij1«ij. Here x ij is exports from region i to region j,y i and y j are gross domestic production in regions i and j,d ij is the distance between regions i and j,and d ij is a dummy variable equal to one for interprovincial trade and zero for state–province trade.For the year1988McCallum estimated this equation using data for all10provinces and for30states that account for90percent of U.S.–Canada trade.In this section we will also report results when estimating equation(1)from the U.S.perspective.In that case the dummy vari-able is one for interstate trade and zero for state–province trade.We also report results when pooling all data,in which case there are two dummy variables.The rst is one for interprovin-cial trade and zero otherwise,while the second is one for interstate trade and zero otherwise.The data are discussed in Appendix A.With-out going into detail here,a couple of com-ments are useful.The interprovincial and state–province trade data are from different divisions of Statistics Canada,while the interstate trade data are from the Commodity Flow Survey con-ducted by the Bureau of the Census.We follow McCallum by applying adjustment factors to the original data in order to make them as closely comparable as possible.All results re-ported below are for the year1993,for which the interstate data are available.We follow Mc-Callum and others by using data for only30 states.The results from estimating(1)are reported in Table1.The rst three columns report results for,respectively,(i)state–province and inter-provincial trade,(ii)state–province and inter-state trade,(iii)state–province,interprovincial, and interstate trade.In the latter case there are separate border dummies for within-U.S.trade and within-Canada trade.The nal three col-umns report the same results after imposing unitary coef cients on the GDP variables.This makes comparison with our theoretically based gravity equation results easier because the the-ory imposes unitary coef cients.Border–Canada is the exponential of the Ca-nadian dummy variable coef cient,a5,which gives us the effect of the border on the ratio of interprovincial trade to state–province trade af-ter controlling for distance and size.Similarly, Border–U.S.is the exponential of the coef -cient on the U.S.dummy variable,which gives the effect of the border on the ratio of interstate trade to state–province trade after controlling for distance and size.Four conclusions can be reached from the6Andrew K.Rose(2000) nds that trade among coun-tries in a monetary union is three times the size of tradeamong countries that are not in a monetary union,holdingother trade costs constant.Rose and van Wincoop(2001)apply the theory developed in this paper to compute theeffect of monetary unions on bilateral trade.7The rst application to migration ows dates from thenineteenth-century writings by Ernst G.Ravenstein(1885).For a more recent application see Helliwell(1997).RichardPortes and Helene Rey(1998)applied a gravity equation tobilateral equity ows.Paul Brenton et al.(1999)apply thegravity equation to FDI ows.172THE AMERICAN ECONOMIC REVIEW MARCH2003table.First,we con rm a very large border coef cient for Canada.The rst column shows that,after controlling for distance and size,in-terprovincial trade is 16.4times state–province trade.This is only somewhat lower than the border effect of 22that McCallum estimated based on 1988data.Second,the U.S.border coef cient is much smaller.The second column tells us that interstate trade is a factor 1.50times state–province trade after controlling for dis-tance and size.We will show below that this large difference between the Canadian and U.S.border coef cients is exactly what the theory predicts.Third,these border coef cients are very similar when pooling all the data.Fi-nally,the border coef cients are also similarwhen unitary income coef cients are im-posed.With pooled data and unitary income coef cients (last column),the Canadian bor-der coef cient is 14.2and the U.S.border coef cient is 1.62.The bottom of the table reports results when remoteness variables are added.We use the de nition of remoteness that has been com-monly used in the literature following McCal-lum’s paper.The regression then becomes (2)ln x ij 5a 11a 2ln y i 1a 3ln y j 1a 4ln d ij1a 5ln REM i 1a 6ln REM j 1a 7d ij 1«ijT ABLE 1—M C C ALLUM REGRESSIONS Notes:The table reports the results of estimating a McCallum gravity equation for the year 1993for 30U.S.states and 10Canadian provinces.In all regressions the dependent variable is the log of exports from region i to region j .The independent variables are de ned as follows:y i and y j are gross domestic production in regions i and j ;d ij is the distance between regions i and j ;Dummy–Canada and Dummy–U.S.are dummy variables that are one when both regions are located in respectively Canada and the United States,and zero otherwise.The rst three columns report results based on nonunitary income elasticities (as in the original McCallum regressions),while the last three columns assume unitary income elasticities.Results are reported for three different sets of data:(i)state–province and interprovincial trade,(ii)state–province and interstate trade,(iii)state–province,interprovincial,and interstate trade.The border coef cients Border–U.S.and Border–Canada are theexponentials of the coef cients on the respective dummy variables.The nal three rows report the border coef cients and R#2when the remoteness indices (3)are added.Robust standard errors are in parentheses.173VOL.93NO.1ANDERSON AND VAN WINCOOP:GRAVITY WITH GRAVITASwhere the remoteness of region i is(3)REM i5O mÞj d im/y m.This variable is intended to re ect the average distance of region i from all trading partners other than j.Although these remoteness vari-ables are commonly used in the literature,we will show in the next section that they are en-tirely disconnected from the theory.Table 1shows that adding remoteness indices for both regions changes the border coef cient estimates very little and also has very little additional explanatory power based on the adjusted R2.II.The Gravity ModelThe empirical literature cited above pays no more than lip service to theoretical justi cation. We show in this section how taking the existing gravity theory seriously provides a different model to estimate with a much more useful interpretation.Anderson(1979)presented a theoretical foundation for the gravity model based on con-stant elasticity of substitution(CES)prefer-ences and goods that are differentiated by region of origin.Subsequent extensions(Berg-strand,1989,1990;Alan V.Deardoff,1998) have preserved the CES preference structure and added monopolistic competition or a Heckscher-Ohlin structure to explain special-ization.A contribution of this paper is our manip-ulation of the CES expenditure system to derive an operational gravity model with an elegantly simple form.On this basis we derive a decompo-sition of trade resistance into three intuitive com-ponents:(i)the bilateral trade barrier between region i and region j,(ii)i’s resistance to trade with all regions,and(iii)j’s resistance to trade with all regions.The rst building block of the gravity model is that all goods are differentiated by place of origin.We assume that each region is special-ized in the production of only one good.8The supply of each good is xed.The second building block is identical,ho-mothetic preferences,approximated by a CES utility function.If c ij is consumption by region j consumers of goods from region i,consumers in region j maximize(4)X O i b i~12s!/s c ij~s21!/s D s/~s21! subject to the budget constraint (5)O i p ij c ij5y j.Here s is the elasticity of substitution between all goods,b i is a positive distribution parame-ter,y j is the nominal income of region j resi-dents,and p ij is the price of region i goods for region j consumers.Prices differ between loca-tions due to trade costs that are not directly observable,and the main objective of the em-pirical work is to identify these costs.Let p i denote the exporter’s supply price,net of trade costs,and let t ij be the trade cost factor between i and j.Then p ij5p i t ij.We assume that the trade costs are borne by the exporter.We have in mind information costs,design costs,and various legal and regu-latory costs as well as transport costs.The new empirical literature on the export behavior of rms(Mark Roberts and James Tybout,1997; Andrew Bernard and Joachim Wagner,2001) emphasizes the large costs facing exporters. Formally,we assume that for each good shipped from i to j the exporter incurs export costs equal to t ij21of country i goods.The exporter passes on these trade costs to the importer.The nominal value of exports from i to j(j’s pay-ments to i)is x ij5p ij c ij,the sum of the value of production at the origin,p i c ij and the trade cost(t ij21)p i c ij that the exporter passes on to the importer.Total income of region i is there-fore y i5¥j x ij.98With this assumption we suppress ner classi cations of goods.Our purpose is to reveal resistance to trade on average, with special reference to the proper treatment of international borders.Resistance to trade does differ among goods,so there is something to be learned from disaggregation.9The model is essentially the same when adopting the “iceberg melting”structure of the economic geography lit-erature,whereby a fraction(t ij21)/t ij of goods shipped is lost in transport.The only small difference is that observedfree on board(f.o.b.)trade data do not include transportation costs,while they do include costs that are borne by the exporter and passed on to the importer.When transportation costs are the only trade costs,the observed f.o.b.trade ows174THE AMERICAN ECONOMIC REVIEW MARCH2003The nominal demand for region i goods by region j consumers satisfying maximization of (4)subject to(5)is(6)x ij5X b i p i t ij jD~12s!y j,where P j is the consumer price index of j,given by(7)P j5O i~b i p i t ij!12s1/~12s!.The general-equilibrium structure of the model imposes market clearance,which im-plies:(8)y i5O j x ij5O j~b i t ij p i/P j!12s y j5~b i p i!12s O j~t ij/P j!12s y j,@i.To derive the gravity equation,Deardorff (1998)followed Anderson(1979)in using mar-ket clearance(8)to solve for the coef cients {b i}while imposing the choice of units such that all supply prices p i are equal to one and then substituting into the import demand equa-tion.10Because we are interested in the general-equilibrium determination of prices and in comparative statics where these will change,we apply the same technique to solve for the scaled prices{b i p i}from the market-clearing condi-tions(8)and substitute them in the demand equation(6).De ne world nominal income by y W[¥j y j and income shares by u j[y j/y W. The technique yields (9)x ij5yiyjW X tiji j D12swhere(10)i;X O j~t ij/P j!12s u j D1/~12s!.Substituting the equilibrium scaled prices into (7),we obtain(11)P j5X O i~t ij/i!12s u i D1/~12s!. Taken together,(10)and(11)can be solved forall i’s and P i’s in terms of income shares {u i},bilateral trade barriers{t ij}and s.We achieve a very useful simpli cation by assuming that the trade barriers are symmetric, that is,t ij5t ji.11Under symmetry it is easily veri ed that a solution to(10)–(11)is i5P i with:(12)P j12s5O i P i s21u i t ij12s@j.This provides an implicit solution to the price indices as a function of all bilateral trade barri-ers and income shares.12The gravity equation then becomes(13)x ij5yiyjy WX t ijPiPj D12s.are equal to p i c ij.The same is the case when the costs are borne by the importer.While we believe that most trade costs are borne by the exporter,particularly for U.S.–Canada trade where formal import barriers are very low,this is not critical to the ndings of the paper;the results would be similar when assuming that observed trade ows are equal to p i c ij.10Deardorff simpli ed by abstracting from the multiple goods classes which Anderson allowed in his Appendix on the CES case.11There are many equilibria with asymmetric barriers that lead to the same equilibrium trade ows as with sym-metric barriers,so that empirically they are impossible to distinguish.In particular,if l i and l j are region-speci cconstants,multiplying t ij by l j/l i@i,j leads to the same equilibrium trade ows[p i is multiplied by l i and P j is multiplied by l j in(8)].The product of the trade barriers in different directions remains the same though.If the l’s are country speci c,but differ across countries,we have intro-duced asymmetric border barriers across countries,while the product of border barriers remains the same.We can therefore interpret the border barriers we estimate in this paper as an average of the barriers in both directions.Our analysis suggests that inferential identi cation of the asym-metry is problematic.12The solution for the equilibrium price indexes from (12)can be shown to be unique.If we denote by P#i 5#i the solution to(12),the general solution to(10)–(11)is P i5 l P#iand i 5#i/l for any nonzero l.The solution(12) therefore implicitly adopts a particular normalization.175VOL.93NO.1ANDERSON AND VAN WINCOOP:GRAVITY WITH GRAVITASOur basic gravity model is(13)subject to(12). Equation(13)signi cantly simpli es expres-sions derived by Anderson(1979)and Dear-dorff(1998),while our simultaneous use of the market-clearing constraints to obtain the equi-librium price indexes in(12)is a signi cant inno-vation that will allow us to estimate the gravity equation and therefore make it operational.We will refer to the price indices{P i}as “multilateral resistance”variables as they de-pend on all bilateral resistances{t ij},including those not directly involving i.A rise in trade barriers with all trading partners will raise the index.For example,in the absence of trade barri-ers(all t ij51)it follows immediately from(12) that all price indices are equal to1.Below we will show that a marginal increase in cross-country trade barriers will raise all price indices above1. While the P i are consumer price indices in the model,that would not be a proper interpre-tation of these indices more generally.One can derive exactly the same gravity equation and solution to the P i when trade costs are nonpe-cuniary.An example is home bias in prefer-ences,whereby c ij in the utility function is replaced by c ij/t ij.In that case P i no longer represents the consumer price index and the border barrier includes home bias.The gravity equation tells us that bilateral trade,after controlling for size,depends on the bilateral trade barrier between i and j,relative to the product of their multilateral resistance indices.It is easy to see why higher multilateral resistance of the importer j raises its trade with i.For a given bilateral barrier between i and j, higher barriers between j and its other trading partners will reduce the relative price of goods from i and raise imports from i.Higher multi-lateral resistance of the exporter i also raises trade.Higher trade barriers faced by an exporter will lower the demand for its goods and there-fore its supply price p i.For a given bilateral barrier between i and j,this raises the level of trade between them.The gravity model(13),subject to(12),im-plies that bilateral trade is homogeneous of de-gree zero in trade costs,where these include the costs of shipping within a region,t ii.This fol-lows because the equilibrium multilateral resis-tances P i are homogeneous of degree1 2in the trade costs.The economics behind the formal result is that the constant vector of real products must be distributed despite higher trade costs.The rise in trade costs is offset by the fall in supply prices[they are homogeneous of degree minus1 2in trade costs,based on(7)and the homogeneity of the equilibrium multilateral re-sistances]required to achieve shipment of the same volume.The invariance of trade to uni-form decreases in trade costs may offer a clue as to why the usual gravity model estimation has not found trade becoming less sensitive to dis-tance over time(Barry Eichengreen and Doug-las A.Irwin,1998).The key implication of the theoretical gravity equation is that trade between regions is deter-mined by relative trade barriers.Trade between two regions depends on the bilateral barrier between them relative to average trade barriers that both regions face with all their trading partners.This insight has many implications for the impact of trade barriers on trade ows.Here we will focus on one important set of implica-tions related to the size of countries because they are useful in interpreting the ndings in Section IV.Consider the simple thought exper-iment of a uniform rise in border barriers be-tween all countries.For simplicity we assume that each region i is a frictionless country.We will discuss three general-equilibrium compar-ative static implications of this experiment, which are listed below.IMPLICATION1:Trade barriers reduce size-adjusted trade between large countries more than between small countries.IMPLICATION2:Trade barriers raise size-adjusted trade within small countries more than within large countries.IMPLICATION3:Trade barriers raise the ratio of size-adjusted trade within country1 relative to size-adjusted trade between coun-tries1and2by more the smaller is country1 and the larger is country2.The experiment amounts to a marginal in-crease in trade barriers across all countries,so dt ij5dt,iÞj;dt ii50.Frictionless initial equilibrium implies t ij51@i,j3P i51. Differentiating(12)at t ij51,@i,j yields1313To obtain this expression we differentiate totally at t ij515P i to obtain176THE AMERICAN ECONOMIC REVIEW MARCH2003。

model.summary()用法

Model.summary()用法1.介绍在深度学习中，模型的搭建和调试是一个重要的环节。

当我们完成了一个模型的构建后，我们需要对模型的架构进行检查和总结。

K e ra s中的`mo de l.su mm ar y()`方法可以帮助我们快速查看和理解模型的结构和参数情况。

本文将介绍`mo de l.su mm ar y()`方法的使用方法和输出结果的解读。

2. `m odel.summary()`方法的调用方式在使用K er as搭建模型后，我们可以使用`mo de l.su mm ar y()`方法来输出模型的概述信息。

其调用方式如下：m o de l.su mm ar y()3.输出结果解读`m od el.s um ma ry()`方法的输出结果包含了模型的层信息和参数统计。

下面我们逐一解读输出结果的各个部分。

3.1.模型总结输出结果的开头部分是模型的概要信息，包括模型类型和模型输入的形状。

示例：M o de l:"s eq ue nt ial"_________________________________________________________________L a ye r(ty pe)O ut put S ha pe Pa ra m#=================================================================3.2.层信息接下来是模型的层信息，包括每一层的名称、类型和输出形状。

示例：d e ns e(De ns e)(N one,64)9472_________________________________________________________________d e ns e_1(De ns e)(No n e,10)650对每一层的输出解读如下：-`de ns e`是该层的名称。

-`(N on e,64)`表示该层的输出形状为(N o ne,64)。

R语言-summary（）函数的用法解读

R语⾔-summary（）函数的⽤法解读summary()：获取描述性统计量，可以提供最⼩值、最⼤值、四分位数和数值型变量的均值，以及因⼦向量和逻辑型向量的频数统计等。

结果解读如下：1. 调⽤：Calllm(formula = DstValue ~ Month + RecentVal1 + RecentVal4 + RecentVal6 + RecentVal8 + RecentVal12, data = trainData)当创建模型时，以上代码表明lm是如何被调⽤的。

2. 残差统计量：ResidualsMin 1Q Median 3Q Max-4806.5 -1549.1 -171.8 1368.7 6763.3残差第⼀四分位数（1Q）和第三分位数（Q3）有⼤约相同的幅度，意味着有较对称的钟形分布。

3. 系数：CoefficientsEstimate Std. Error t value Pr(>|t|)(Intercept) 1.345e+06 5.659e+05 2.377 0.01879 *Month 8.941e+02 2.072e+02 4.316 3.00e-05 ***分别表⽰：估值标准误差 T值 P值Intercept：表⽰截距Month：影响因⼦/特征Estimate的列：包含由普通最⼩⼆乘法计算出来的估计回归系数。

Std. Error的列：估计的回归系数的标准误差。

P值估计系数不显著的可能性，有较⼤P值的变量是可以从模型中移除的候选变量。

t 统计量和P值：从理论上说，如果⼀个变量的系数是0，那么该变量是⽆意义的，它对模型毫⽆贡献。

然⽽，这⾥显⽰的系数只是估计，它们不会正好为0。

因此，我们不禁会问：从统计的⾓度⽽⾔，真正的系数为0的可能性有多⼤？这是t统计量和P值的⽬的，在汇总中被标记为t value和Pr(>|t|)。

其中，我们可以直接通过P值与我们预设的0.05进⾏⽐较，来判定对应的解释变量的显著性，我们检验的原假设是：该系数显著为0；若P<0.05，则拒绝原假设，即对应的变量显著不为0。

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Intraluminal ImpedanceDr. med. Tobias G. Wenzl, MRCPCHKinderklinik, Universitätsklinikum AachenPauwelsstr. 30, D 52074 Aachen, Germanytel +49-241-8089240fax +49-241-8082484email chef@The intraluminal impedance technique (IMP) is a new method for the pH-independent detection of gastrointestinal motility. The principle of IMP is a change of electrical impedance during the passage of a bolus through a measuring segment, i.e. between two adjacent electrodes. The use of multiple segments along a catheter allows the analysis of the direction of the bolus transport. Thus, anterograde (e.g. swallowing) and retrograde (e.g. gastroesophageal reflux) bolus movements can be distinguished. In infants, a catheter with one or two pH sensitive antimony electrodes and 7 integrated impedance electrodes (Helmholtz-Institut für Biomedizinische Technik, Aachen, Germany), representing 6 measuring channels, is used. The total measuring length reaches from the cardia (channel 6) to the pharynx (channel 1), with the pH sensor being situated at the level of channel 5, approximately 3 cm above the gastroesophageal junction. Impedance and pH signals are sampled at a rate of 50 Hz per channel, as compared to 0.25 Hz in conventional pH monitoring.Situations presumably associated with esophageal motility events, such as oxygen desaturations, apneas, apparent life threatening events (ALTE), but also recurrent aspiration and bronchitis, irritability and sleep disturbances, can apparentely be induced by both an acidic or a non-acidic bolus. pH monitoring, although being a standard tool, is only able to detect acidic (pH < 4) and alkaline (pH > 7.5) gastroesophageal reflux (GER), leaving a method inherent diagnostic gap. Many refluxes are likely to happen in the physiological esophageal pH range (pH 5 - 6.8), and are therefore undetectable by pH monitoring.The pH-independent intraluminal impedance technique proves to be a valuable tool for this approach. GER episodes with the unique pattern of retrograde bolus movement are reliably detected by IMP. The impedance values decrease in all channels reached by the refluxate, and the change in impedance begins in the mostdistal channel and proceedes to more proximal channels, indicating a retrograde flow of gastric contents, representing a GER (Fig. 1). There are no side effects, and only few technical drop outs. Our study results show, that IMP for the registration and evaluation of gastroesophageal reflux provides important supplemental information to pH monitoring data. This new method is especially usefull in phases of gastric hypoacidity, as most common in the postprandial time, because of neutralization of gastric content by milk formula feeds. Exclusive use of pH monitoring is unable to detect the major amount of these reflux episodes. IMP is also a reliable tool for GER associated symptoms, that are not only associated with acidic reflux. Additionally, information about the height reached by the refluxate in the esophagus and about the clearance process of GER can be obtained and analysed. With the development of automated analysis, impedance recording will become the new standard tool for gastroesophageal motility detection in infants and children.ReferencesWenzl TG, Silny J, Schenke S, Peschgens T, Heimann G, Skopnik H (1999). Gastroesophageal reflux and respiratory symptoms in infants: Status of the intraluminal impedance technique. J Pediatr Gastroenterol Nutr 28: 423-8.Wenzl TG, Skopnik H (2000). Intraluminal impedance: an ideal technique for evaluation of pediatric gastroesophageal reflux disease. Curr Gastroenterol Rep 2: 259-64.Wenzl TG, Schenke S, Peschgens T, Silny J, Heimann G. Skopnik H (2001). Association of apnea and nonacid gastroesophageal reflux in infants: investigations with the intraluminal impedance technique. Pediatr Pulmonol 31: 144-9.Wenzl TG (2002). Investigating esophageal reflux with the intraluminal impedance technique. J Pediatr Gastroenterol Nutr 34: 261-8.Wenzl TG (2003). Evaluation of gastroesophageal reflux events in children using multichannel intraluminal electrical impedance. Am J Med (in print)Figure 1 : Original tracing of non-acidic gastroesophageal reflux. Retrograde esophageal bolus passage with sequential decrease of impedance over time (impedance channel 1, proximal; channel 6, distal) and pH remaining > 4. Arrow indicates bolus passage from distal to proximal.p。