1 개요
- R lm()
- "linear model"
R
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x <- c(151, 174, 138, 186, 128, 136, 179, 163, 152, 131)
y <- c(63, 81, 56, 91, 47, 57, 76, 72, 62, 48)
relation <- lm(y~x)
relation
##
## Call:
## lm(formula = y ~ x)
##
## Coefficients:
## (Intercept) x
## -38.4551 0.6746
summary(relation)
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.3002 -1.6629 0.0412 1.8944 3.9775
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -38.45509 8.04901 -4.778 0.00139 **
## x 0.67461 0.05191 12.997 1.16e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.253 on 8 degrees of freedom
## Multiple R-squared: 0.9548, Adjusted R-squared: 0.9491
## F-statistic: 168.9 on 1 and 8 DF, p-value: 1.164e-06
R
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library(MASS)
data(hills)
attach(hills)
md <- lm( time ~ dist + climb)
summary(md)
##
## Call:
## lm(formula = time ~ dist + climb)
##
## Residuals:
## Min 1Q Median 3Q Max
## -16.215 -7.129 -1.186 2.371 65.121
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8.992039 4.302734 -2.090 0.0447 *
## dist 6.217956 0.601148 10.343 9.86e-12 ***
## climb 0.011048 0.002051 5.387 6.45e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 14.68 on 32 degrees of freedom
## Multiple R-squared: 0.9191, Adjusted R-squared: 0.914
## F-statistic: 181.7 on 2 and 32 DF, p-value: < 2.2e-16
R
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fit4 <- lm(Fertility ~ Agriculture + Education + Catholic + Infant.Mortality, data = swiss)
summary(fit4)
##
## Call:
## lm(formula = Fertility ~ Agriculture + Education + Catholic +
## Infant.Mortality, data = swiss)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14.6765 -6.0522 0.7514 3.1664 16.1422
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 62.10131 9.60489 6.466 8.49e-08 ***
## Agriculture -0.15462 0.06819 -2.267 0.02857 *
## Education -0.98026 0.14814 -6.617 5.14e-08 ***
## Catholic 0.12467 0.02889 4.315 9.50e-05 ***
## Infant.Mortality 1.07844 0.38187 2.824 0.00722 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.168 on 42 degrees of freedom
## Multiple R-squared: 0.6993, Adjusted R-squared: 0.6707
## F-statistic: 24.42 on 4 and 42 DF, p-value: 1.717e-10##
R
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data(state)
statdata<-data.frame(state.x77,row.names=state.abb)
g3<-lm(Life.Exp ~ Illiteracy, data=statdata)
summary(g3)
##
## Call:
## lm(formula = Life.Exp ~ Illiteracy, data = statdata)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7169 -0.8063 -0.0349 0.7674 3.6675
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 72.3949 0.3383 213.973 < 2e-16 ***
## Illiteracy -1.2960 0.2570 -5.043 6.97e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.097 on 48 degrees of freedom
## Multiple R-squared: 0.3463, Adjusted R-squared: 0.3327
## F-statistic: 25.43 on 1 and 48 DF, p-value: 6.969e-06
R
Copy
m <- lm(dist ~ speed, data=cars)
summary(m)
##
## Call:
## lm(formula = dist ~ speed, data = cars)
##
## Residuals:
## Min 1Q Median 3Q Max
## -29.069 -9.525 -2.272 9.215 43.201
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -17.5791 6.7584 -2.601 0.0123 *
## speed 3.9324 0.4155 9.464 1.49e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 15.38 on 48 degrees of freedom
## Multiple R-squared: 0.6511, Adjusted R-squared: 0.6438
## F-statistic: 89.57 on 1 and 48 DF, p-value: 1.49e-12
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