library(wooldridge) Data verisini içinden çekmek için kullanılır library(rmarkdown)page_table komutuyla veri setini rmarkdown üzerinde gösterebilmek içinrequire(dplyr)## Zorunlu paket yükleniyor: dplyr## Warning: package 'dplyr' was built under R version 4.2.2##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary komutu yerine kullanacağınız bir komutturdata("BWGHT")## Warning in data("BWGHT"): data set 'BWGHT' not foundhead(bwght)## faminc cigtax cigprice bwght fatheduc motheduc parity male white cigs
## 1 13.5 16.5 122.3 109 12 12 1 1 1 0
## 2 7.5 16.5 122.3 133 6 12 2 1 0 0
## 3 0.5 16.5 122.3 129 NA 12 2 0 0 0
## 4 15.5 16.5 122.3 126 12 12 2 1 0 0
## 5 27.5 16.5 122.3 134 14 12 2 1 1 0
## 6 7.5 16.5 122.3 118 12 14 6 1 0 0
## lbwght bwghtlbs packs lfaminc
## 1 4.691348 6.8125 0 2.6026897
## 2 4.890349 8.3125 0 2.0149031
## 3 4.859812 8.0625 0 -0.6931472
## 4 4.836282 7.8750 0 2.7408400
## 5 4.897840 8.3750 0 3.3141861
## 6 4.770685 7.3750 0 2.0149031ilk altı datayı gösteriri
paged_table(bwght)guruplamaya yarar
sınav <-bwght %>%
group_by(cigprice) %>%
summarise("ortalama erkek sayısı (male)" = mean(male))%>% kullanarak cümle yazar gibi komut yazabilirsiniz. yazdıktan sonra bu veri setini sigara fiyatına (cigprice) göre gruplandırdık. Bunun için group_by komutunu kullandık. mean ortalama olmaya yarıyor Sonuçlarımızı göstermek için, summarise komutunu kullandık
summary(sınav)## cigprice ortalama erkek sayısı (male)
## Min. :103.8 Min. :0.0000
## 1st Qu.:122.5 1st Qu.:0.4638
## Median :131.0 Median :0.5000
## Mean :130.8 Mean :0.4981
## 3rd Qu.:138.0 3rd Qu.:0.5755
## Max. :152.5 Max. :1.0000summary özet demek bize kısa bir özet tablo çıkartıyor
summary(lm(male ~ cigtax + fatheduc + white + parity, data = bwght))##
## Call:
## lm(formula = male ~ cigtax + fatheduc + white + parity, data = bwght)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.6059 -0.5140 0.4250 0.4812 0.5653
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.510713 0.086190 5.925 4.08e-09 ***
## cigtax 0.002604 0.001860 1.400 0.162
## fatheduc 0.001600 0.005325 0.300 0.764
## white -0.051243 0.040423 -1.268 0.205
## parity -0.013097 0.016599 -0.789 0.430
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4999 on 1187 degrees of freedom
## (196 observations deleted due to missingness)
## Multiple R-squared: 0.003214, Adjusted R-squared: -0.0001451
## F-statistic: 0.9568 on 4 and 1187 DF, p-value: 0.4303Lm formülü kullanarak Interceptli regresyon oluşmuştur.
summary(lm(male ~ cigtax + fatheduc + -1 + white + parity, data = bwght))##
## Call:
## lm(formula = male ~ cigtax + fatheduc + -1 + white + parity,
## data = bwght)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7712 -0.4968 0.3450 0.4980 0.7661
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## cigtax 0.006077 0.001791 3.393 0.000713 ***
## fatheduc 0.025496 0.003527 7.228 8.74e-13 ***
## white 0.016431 0.039328 0.418 0.676183
## parity 0.021636 0.015751 1.374 0.169831
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.507 on 1188 degrees of freedom
## (196 observations deleted due to missingness)
## Multiple R-squared: 0.5058, Adjusted R-squared: 0.5041
## F-statistic: 304 on 4 and 1188 DF, p-value: < 2.2e-16Lm formülü kullanarak Intercepsiz regresyon oluşmuştur.
odev <- bwght %>%
mutate(kola = 1) %>%
mutate(odev = ifelse(fatheduc == 1, 5, 2))odev altında data oluşturduk kola karşılığında tüm gözlemleri 1 olan değişken oluşturduk.mutate formulune ifelse ekliyerek fatheduc’un 1 değerinde 0 0 değerinde ise 3’ eşit olduğunu gösterdik.
benim <- bwght %>% select(cigprice, male, fatheduc)
benim## cigprice male fatheduc
## 1 122.3 1 12
## 2 122.3 1 6
## 3 122.3 0 NA
## 4 122.3 1 12
## 5 122.3 1 14
## 6 122.3 1 12
## 7 122.3 0 16
## 8 122.3 0 12
## 9 122.3 0 12
## 10 122.3 0 16
## 11 122.3 1 12
## 12 122.3 1 16
## 13 122.3 1 NA
## 14 122.3 1 12
## 15 122.3 1 7
## 16 122.3 1 13
## 17 122.3 1 18
## 18 122.3 0 NA
## 19 122.3 1 16
## 20 122.3 0 NA
## 21 122.3 0 16
## 22 122.3 1 12
## 23 122.3 1 8
## 24 122.3 0 12
## 25 122.3 0 11
## 26 122.3 1 11
## 27 122.3 1 NA
## 28 122.3 1 12
## 29 122.3 1 12
## 30 125.6 1 NA
## 31 125.6 0 12
## 32 125.6 1 16
## 33 125.6 1 16
## 34 125.6 0 11
## 35 125.6 0 5
## 36 125.6 1 16
## 37 125.6 1 12
## 38 125.6 0 16
## 39 125.6 1 12
## 40 125.6 0 10
## 41 125.6 1 12
## 42 125.6 1 15
## 43 125.6 1 18
## 44 125.6 1 16
## 45 125.6 1 14
## 46 125.6 0 18
## 47 125.6 1 18
## 48 125.6 1 12
## 49 125.6 1 13
## 50 125.6 1 16
## 51 125.6 1 12
## 52 125.6 1 13
## 53 125.6 0 12
## 54 125.6 0 16
## 55 125.6 1 12
## 56 125.6 1 11
## 57 125.6 0 16
## 58 125.6 0 12
## 59 125.6 1 12
## 60 125.6 0 14
## 61 125.6 1 16
## 62 125.6 1 11
## 63 125.6 0 12
## 64 125.6 0 NA
## 65 125.6 0 NA
## 66 125.6 0 12
## 67 125.6 0 11
## 68 127.7 1 12
## 69 127.7 1 12
## 70 127.7 0 12
## 71 127.7 0 12
## 72 127.7 1 16
## 73 127.7 0 7
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## 75 127.7 0 3
## 76 127.7 1 16
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## 82 127.7 1 16
## 83 127.7 1 18
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## 89 127.7 1 NA
## 90 127.7 1 14
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## 105 126.4 1 8
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## 114 126.4 0 11
## 115 126.4 1 17
## 116 126.4 0 11
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## 128 126.4 1 17
## 129 126.4 0 12
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## 134 126.4 0 NA
## 135 126.4 1 16
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## 140 126.4 1 15
## 141 126.4 1 12
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## 148 126.4 0 16
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## 201 126.4 1 17
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## 215 126.4 0 12
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## 229 126.4 0 13
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## 231 126.4 0 13
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## 273 142.0 1 NA
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## 1352 138.3 1 12
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## 1356 138.3 1 12
## 1357 138.3 1 13
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## 1361 138.3 0 14
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## 1365 138.3 1 16
## 1366 138.3 0 18
## 1367 138.3 1 14
## 1368 138.3 1 16
## 1369 138.3 0 NA
## 1370 138.3 0 14
## 1371 138.3 1 NA
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## 1373 138.3 1 12
## 1374 138.3 0 16
## 1375 138.3 0 12
## 1376 138.3 1 11
## 1377 138.3 1 18
## 1378 138.3 0 15
## 1379 138.3 1 12
## 1380 138.3 0 10
## 1381 138.3 1 NA
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## 1384 138.3 1 12
## 1385 138.3 1 NA
## 1386 118.6 0 18
## 1387 118.6 0 NA
## 1388 118.6 0 16selecet komutu ile sütünları filirtrelerdik
benim2 <- bwght %>% filter(fatheduc== 1)
benim2## faminc cigtax cigprice bwght fatheduc motheduc parity male white cigs
## 1 32.5 26 137 112 1 9 3 0 1 0
## lbwght bwghtlbs packs lfaminc
## 1 4.718499 7 0 3.48124bwght paketinde fatheduc paketindeki verileri sabit 1 yaptık
tablo <- lm(benim2$fatheduc ~ benim2$cigprice)
summary(tablo)##
## Call:
## lm(formula = benim2$fatheduc ~ benim2$cigprice)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## benim2$cigprice NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedombenim3 <-bwght %>% filter(fatheduc == 0 )
benim3## [1] faminc cigtax cigprice bwght fatheduc motheduc parity male
## [9] white cigs lbwght bwghtlbs packs lfaminc
## <0 rows> (or 0-length row.names)aynı regresyonu 0 eşitledik
tablo2 <- lm(benim2$fatheduc ~ benim2$male)
summary(tablo2)##
## Call:
## lm(formula = benim2$fatheduc ~ benim2$male)
##
## Residuals:
## ALL 1 residuals are 0: no residual degrees of freedom!
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1 NaN NaN NaN
## benim2$male NA NA NA NA
##
## Residual standard error: NaN on 0 degrees of freedomiki datayı karşılaştırdık
require(stargazer)## Zorunlu paket yükleniyor: stargazer##
## Please cite as:## Hlavac, Marek (2022). stargazer: Well-Formatted Regression and Summary Statistics Tables.## R package version 5.2.3. https://CRAN.R-project.org/package=stargazerindirilmemiş paketi indirir ve library yapar
stargazer(list(benim , benim2), type = "text")##
## ================================================
## Statistic N Mean St. Dev. Min Max
## ------------------------------------------------
## cigprice 1,388 130.559 10.244 103.800 152.500
## male 1,388 0.521 0.500 0 1
## fatheduc 1,192 13.186 2.746 1 18
## ------------------------------------------------
##
## ==========================================
## Statistic N Mean St. Dev. Min Max
## ------------------------------------------
## faminc 1 32.500 32.500 32.500
## cigtax 1 26.000 26 26
## cigprice 1 137.000 137 137
## bwght 1 112.000 112 112
## fatheduc 1 1.000 1 1
## motheduc 1 9.000 9 9
## parity 1 3.000 3 3
## male 1 0.000 0 0
## white 1 1.000 1 1
## cigs 1 0.000 0 0
## lbwght 1 4.718 4.718 4.718
## bwghtlbs 1 7.000 7 7
## packs 1 0.000 0 0
## lfaminc 1 3.481 3.481 3.481
## ------------------------------------------