Rcmdr.kr: An R Commander User in Korea

모델 > 가설 검정 > 분산분석표...

Models > Hypothesis tests > ANOVA table...

Statistics > Fit models > Linear model... (통계 > 적합성 모델 > 선형 모델...) 기능을 통하여 LinearModel.1라는 모델을 만들었다고 하자. 위의 화면 상단에 <모델: LinearModel.1>이라고 보일 것이다. 

https://rcmdr.tistory.com/131

Models > Hypothesis tests > ANOVA table... (모델 > 가설 검정 > 분산분산표...) 기능은 앞선 모델 만들기 작업 위에서 후속적으로 가능함을 명심하자.

Anova(LinearModel.1, type="II")

Rcmdr > etc > Nations.txt (컴파일 이후)

Rcmdr > inst > etc > Nations.txt (소스에서)

Rcmdr 패키지에는 Getting-Started-with-the-Rcmdr.pdf 라는 사용법 소개 파일이 포함되어 있다. 이 파일에서 예제용으로 활용하는 데이터셋이 Nations.txt 이다. 이 파일 또한 Rcmdr 패키지에 포함되어 있다.

아래와 같이 내부 구성을 볼 수 있다.

Nations 데이터셋의 요약 정보는 아래와 같다:

TFR (출생율, the total fertility rate, expressed as number of children per woman),

contraception (피임율, the rate of contraceptive use among married women, in percent),

infant.mortality (영유아 사망율, the infant-mortality rate per 1000 live births),

GDP (국내 총생산, gross domestic product per capita, in U.S. dollars),

region (대륙, one line per country)

통계 > 차원 분석 > 요인 분석...

Statistics > Dimensional analysis > factor analysis...

통계 > 차원 분석 > 주-성분 분석...

Statistics > Dimensional analysis > Principal-components analysis...

<주성분 분석> 메뉴 창에서 <변수 (두개 이상 선택)> 에서 4개의 변수를 모두 선택해보자.

<선택기능> 창에서 기본 설정되어 있는 기능을 기억하자.

local({
  .PC <- princomp(~Assault+Murder+Rape+UrbanPop, cor=TRUE, data=USArrests)
  cat("\nComponent loadings:\n")
  print(unclass(loadings(.PC)))
  cat("\nComponent variances:\n")
  print(.PC$sd^2)
  cat("\n")
  print(summary(.PC))
})

.PC <- princomp(~Assault+Murder+Rape+UrbanPop, cor=TRUE, data=USArrests)
plot(.PC)

biplot(.PC)

datasets::USArrests()

data(USArrests, package="datasets")

R Commander 화면 상단에서 <데이터셋 보기> 버튼을 누르면 아래와 같은 내부 구성을 확인할 수 있다.

help("USArrests")

Violent Crime Rates by US State

Description

This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

Usage

USArrests

Format

A data frame with 50 observations on 4 variables.

Note

USArrests contains the data as in McNeil's monograph. For the UrbanPop percentages, a review of the table (No. 21) in the Statistical Abstracts 1975 reveals a transcription error for Maryland (and that McNeil used the same “round to even” rule that R's round() uses), as found by Daniel S Coven (Arizona).

See the example below on how to correct the error and improve accuracy for the ‘<n>.5’ percentages.

Source

World Almanac and Book of facts 1975. (Crime rates).

Statistical Abstracts of the United States 1975, p.20, (Urban rates), possibly available as https://books.google.ch/books?id=zl9qAAAAMAAJ&pg=PA20.

References

McNeil, D. R. (1977) Interactive Data Analysis. New York: Wiley.

See Also

The state data sets.

Examples

summary(USArrests)

require(graphics)
pairs(USArrests, panel = panel.smooth, main = "USArrests data")

## Difference between 'USArrests' and its correction
USArrests["Maryland", "UrbanPop"] # 67 -- the transcription error
UA.C <- USArrests
UA.C["Maryland", "UrbanPop"] <- 76.6

## also +/- 0.5 to restore the original  <n>.5  percentages
s5u <- c("Colorado", "Florida", "Mississippi", "Wyoming")
s5d <- c("Nebraska", "Pennsylvania")
UA.C[s5u, "UrbanPop"] <- UA.C[s5u, "UrbanPop"] + 0.5
UA.C[s5d, "UrbanPop"] <- UA.C[s5d, "UrbanPop"] - 0.5

## ==> UA.C  is now a *C*orrected version of  USArrests

carData > Prestige

data(Prestige, package="carData")

help("Prestige")

Prestige of Canadian Occupations

Description

The Prestige data frame has 102 rows and 6 columns. The observations are occupations.

Usage

Prestige

Format

This data frame contains the following columns:

education

Average education of occupational incumbents, years, in 1971.

income

Average income of incumbents, dollars, in 1971.

women

Percentage of incumbents who are women.

prestige

Pineo-Porter prestige score for occupation, from a social survey conducted in the mid-1960s.

census

Canadian Census occupational code.

type

Type of occupation. A factor with levels (note: out of order): bc, Blue Collar; prof, Professional, Managerial, and Technical; wc, White Collar.

Source

Canada (1971) Census of Canada. Vol. 3, Part 6. Statistics Canada [pp. 19-1–19-21].

Personal communication from B. Blishen, W. Carroll, and C. Moore, Departments of Sociology, York University and University of Victoria.

References

Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

carData > Moore

data(Moore, package="carData")

help("Moore")

Status, Authoritarianism, and Conformity

Description

The Moore data frame has 45 rows and 4 columns. The data are for subjects in a social-psychological experiment, who were faced with manipulated disagreement from a partner of either of low or high status. The subjects could either conform to the partner's judgment or stick with their own judgment.

Usage

Moore

Format

This data frame contains the following columns:

partner.status

Partner's status. A factor with levels: high, low.

conformity

Number of conforming responses in 40 critical trials.

fcategory

F-Scale Categorized. A factor with levels (note levels out of order): high, low, medium.

fscore

Authoritarianism: F-Scale score.

Source

Moore, J. C., Jr. and Krupat, E. (1971) Relationship between source status, authoritarianism and conformity in a social setting. Sociometry 34, 122–134.

Personal communication from J. Moore, Department of Sociology, York University.

References

Fox, J. (2016) Applied Regression Analysis and Generalized Linear Models, Third Edition. Sage.

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

carData::DavisThin()

help("DavisThin")

Davis's Data on Drive for Thinness

Description

The DavisThin data frame has 191 rows and 7 columns. This is part of a larger dataset for a study of eating disorders. The seven variables in the data frame comprise a "drive for thinness" scale, to be formed by summing the items.

Usage

DavisThin

Format

This data frame contains the following columns:

DT1

a numeric vector

DT2

a numeric vector

DT3

a numeric vector

DT4

a numeric vector

DT5

a numeric vector

DT6

a numeric vector

DT7

a numeric vector

Source

Davis, C., G. Claridge, and D. Cerullo (1997) Personality factors predisposing to weight preoccupation: A continuum approach to the association between eating disorders and personality disorders. Journal of Psychiatric Research 31, 467–480. [personal communication from the authors.]

References

Fox, J. and Weisberg, S. (2019) An R Companion to Applied Regression, Third Edition, Sage.

통계 > 차원 분석 > 척도 신뢰성...

Statistics > Dimensional analysis > Scale reliability...

carData 패키지에서 제공하는 DavisThin 데이터셋을 활성화시키자. DavisThin 데이터셋에는 DT1, DT2, DT3, ..., DT7 이라는 7개의 수치형 변수가 있다. 이 변수들은 "drive for thinness" 척도를 구성한다.

<척도 신뢰성> 창에서 <변수 (세개 이상 선택)> 기능에서 DT1부터 DT7까지 일곱개의 변수를 선택하고,  예(OK) 버튼을 누른다.

reliability(cov(DavisThin[,c("DT1","DT2","DT3","DT4","DT5","DT6","DT7")], 
	use="complete.obs"))

통계 > 분산 > Levene의 검정...

Statistics > Variances > Levene's test...

carData 패키지에서 제공하는 Prestige 데이터셋을 활성화 시키자. Prestige 데이터셋에는 type 이라는 세개의 수준을 가진 요인형 변수가 있다. 그 수준 이름은 bc, prof, wc 이다. 직업유형(type)별로 사회적인 권위가 다른지를 확인하는 문제의식이 있다고 하자. 집단별(직업유형, type)로 직업의 사회적 권위(prestige)에 대한 분산의 차이가 있는지를 통계적으로 살펴본다. <중앙(센터)>에서 중앙값과 평균을 선택할 수 있다. 중앙값이 기본 설정이다.

Tapply(prestige ~ type, var, na.action=na.omit, data=Prestige) # variances by group
leveneTest(prestige ~ type, data=Prestige, center="median")