Kapitel 53 Einfachste Graphiken erstellen (ohne ggplot2)
53.1 Wir können auch einen Vektor in ein Objekt speichern
R bietet unterschiedliche Formen an http://www.endmemo.com/program/R/pchsymbols.php
x <- c(3,4,2,5,4,3,2,6,5,3,1,2,3,4,5)
hist(x, col="PINK")
y<-c(2,3,4,2,6,5,3,2,4,5,7,8,6,3,1)
plot(x,y, col="red")
## oder etwas schöner...
plot(x,y,pch=1, cex=4)
## ah, das ist zu gross...
plot(x,y,pch=1, cex=0.5)
## ah, das ist jetzt zu klein
plot(x,y,pch=1, cex=2)
## ah, das ist zu hässlich
plot(x,y,pch=1, cex=4, col="PINK")
## ah, da sieht man ja nichts
plot(x,y,pch=20, cex=3, col="PINK")
53.1.1 Wir können Grafiken kombinieren, z.B. um eine Regressionslinie hinzuzufügen, man kann sogar den Determinationskoeffizienten (r2) hinzufügen.
http://www.ats.ucla.edu/stat/r/faq/scatter.htm
plot.new()
plot(x,y)
reg1 <- lm(y~x) ## si le plot est (a,b), il faut mettre faire la regression b~a; parce que a = axe horizontale, b = axe verticale; et par definition on dit que a explique b
abline(reg1)
summary(reg1)##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4952 -1.0361 0.2596 1.0048 2.5865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.2500 1.1693 6.200 3.22e-05 ***
## x -0.9183 0.3140 -2.924 0.0118 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.654 on 13 degrees of freedom
## Multiple R-squared: 0.3968, Adjusted R-squared: 0.3504
## F-statistic: 8.552 on 1 and 13 DF, p-value: 0.01184cor(y,x)^2## [1] 0.3968086legend("topright", bty="n", legend=paste("r-squared: ", format(summary(reg1)$adj.r.squared, digits=3)))
53.1.2 Wir können auch die Regressionsformel anfügen:
fit <- lm(y ~ x)
summary(fit)##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.4952 -1.0361 0.2596 1.0048 2.5865
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.2500 1.1693 6.200 3.22e-05 ***
## x -0.9183 0.3140 -2.924 0.0118 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.654 on 13 degrees of freedom
## Multiple R-squared: 0.3968, Adjusted R-squared: 0.3504
## F-statistic: 8.552 on 1 and 13 DF, p-value: 0.01184plot.new()
plot(y ~ x, xlab = "X", ylab = "Y")
abline(coef(fit)[1:2])
cf <- round(coef(fit), 2) # round = runden, hier auf zwei Nachkommastellen.
## sign check to avoid having plus followed by minus for negative coefficients
eq <- paste0("Y = ", cf[1],
ifelse(sign(cf[2])==1, " + ", " - "), abs(cf[2]), "\u00D7x ")
## printing of the equation
mtext(eq, 3, line=-2)
53.1.3 Im nächsten Beispiel eine Regression mit mehreren unabhängigen Variablen.
plot.new()
covariable<-c(5,2,1,4,7,3,2,7,2,5,7,3,9,3,1)
fit <- lm(y ~ x+covariable)
summary(fit)##
## Call:
## lm(formula = y ~ x + covariable)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.7629 -0.6534 -0.1574 0.5317 2.9236
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.9980 1.2855 4.666 0.000545 ***
## x -0.9027 0.2902 -3.110 0.009019 **
## covariable 0.2946 0.1639 1.797 0.097534 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.528 on 12 degrees of freedom
## Multiple R-squared: 0.5247, Adjusted R-squared: 0.4455
## F-statistic: 6.624 on 2 and 12 DF, p-value: 0.01153plot.new()
plot(y ~ x, xlab = "X", ylab = "Y")
abline(coef(fit)[1:2])
## rounded coefficients for better output
cf <- round(coef(fit), 2)
## sign check to avoid having plus followed by minus for negative coefficients
eq <- paste0("Y = ", cf[1],
ifelse(sign(cf[2])==1, " + ", " - "), abs(cf[2]), " \u00D7x ",
ifelse(sign(cf[3])==1, " + ", " - "), abs(cf[3]), " \u00D7Covariable")
## printing of the equation
mtext(eq, 3, line=-2)