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R에서 지원되는 분류방법 - rpart / rpartOrdinal, randomForest, party, Tree, marginTree, MapTree 랜덤포레스트 일단 다음과 같이 샘플을 나누는건 기본 > ind rf table(predict(rf),trainData$Species) > plot(rf) > importance(rf) >vzrImpPlot(rf)

R에서 SQL 기능을 아래와 같이 이용가능하다. > install.packages("sqldf")> library(sqldf)> data("iris")> sqldf::sqldf("select * from iris")> sqldf::sqldf("select * from iris limit 5")> sqldf::sqldf("select count(*) from iris where Species like 'S%')

주성분 분석또한 가능하다. > library(datasets) > data(USArrests) > summary(USArrests) Murder Assault UrbanPop Rape Min. : 0.800 Min. : 45.0 Min. :32.00 Min. : 7.30 1st Qu.: 4.075 1st Qu.:109.0 1st Qu.:54.50 1st Qu.:15.07 Median : 7.250 Median :159.0 Median :66.00 Median :20.10 Mean : 7.788 Mean :170.8 Mean :65.54 Mean :21.23 3rd Qu.:11.250 3rd Qu.:249.0 3rd Qu.:77.75 3rd Qu.:26.18 > fit fit Call: princomp(x..

> data("eurodist")> eurodist Athens Barcelona Brussels Calais Cherbourg Cologne Copenhagen Geneva Gibraltar Hamburg Hook of Holland Barcelona 3313 Brussels 2963 1318 Calais 3175 1326 204 Cherbourg 3339 1294 583 460 Cologne 2762 1498 206 409 785 Copenhagen 3276 2218 966 1136 1545 760 Geneva 2610 803 677 747 853 1662 1418 Gibraltar 4485 1172 2256 2224 2047 2436 3196 1975 Hamburg 2977 2018 597 714 ..

ARIMA 모델 (Autoregressive integrated moving average) ARIMA (p,d,q) 에서 AR - p MA - q I - 차분 횟수 변태는 아니지만, 연도에 따른 여자들 치마길이 분석을 ARIMA(Autoregressive integrated moving average)를 해본다. > skirts sts1 sts2 acf(sts2, lag.max=20) arima 자동 > auto.arima(skirts) Series: skirts ARIMA(1,2,0) Coefficients: ar1 -0.2997 s.e. 0.1424 sigma^2 estimated as 388.7: log likelihood=-193.66 AIC=391.33 AICc=391.62 BIC=394.9 ..

> kings kingsts kingsts Time Series: Start = 1 End = 42 Frequency = 1 [1] 60 43 67 50 56 42 50 65 68 43 65 34 47 34 49 41 13 35 53 56 16 43 69 59 48 59 86 55 68 51 33 49 67 77 81 67 71 81 68 70 [41] 77 56 > births birthts souvenir plot.ts(kingsts) lot() 함수를 취해서 plot할수도 있다. 평탄화 (TTR패키지 SMA 함수) > SMA(kingsts, n=3) 시계열 요소 죄다 decompose > result result plot()을 그리면 예쁘다.

> set.seed(2) > x = runif(10,0,11) 0~11 까지 랜덤하게 10개를 뽑는다. > y = runif(10,11,20) > d = data.frame(x,y) > d x y 1 2.033705 15.97407 2 7.726114 13.15005 3 6.306590 17.84462 4 1.848571 12.62738 5 10.382233 14.64754 6 10.378225 18.68194 7 1.420749 19.78759 8 9.167937 13.03243 9 5.148204 15.00328 10 6.049821 11.67481 > result = lm(y~x, data=d)> result Call: lm(formula = y ~ x, data = d) Coefficients: ..

> library(Hmisc)> data(mtcars) > mtcars 데이터 mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 Valiant 18.1 6 225.0 105 2.76 3...

> install.packages("RGtk2")> install.packages("rattle")> library(rattle) 위와같이 쓰면 됨.

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