df<-read.csv("Clotinidina.csv")
df
## Conc SolvArea MatArea
## 1 0.1 1962.409 2319.3244
## 2 0.5 1089.431 900.0077
## 3 2.0 2006.795 3711.4262
## 4 5.0 4994.264 7642.4762
## 5 20.0 21642.742 26881.7529
## 6 50.0 55763.590 71111.6268
## 7 100.0 111901.946 132072.1672
Curvas del clotinidina: Efecto de la Matriz
ysolv<-df$SolvArea
ymat<-df$MatArea
x<-df$Conc
modelo<-lm(ymat~x)
s<-summary(modelo)
LOD<-s$coefficients[1,2]/s$coefficients[1]*3.3*8
b0mat=s$coefficients[1]
b1mat=s$coefficients[2]
xs <- seq(0, 100, by=0.1)
ycalc1=b0mat+b1mat*xs
yexpMat=modelo$fitted.values
modelo<-lm(ysolv~x)
s<-summary(modelo)
b0solv=s$coefficients[1]
b1solv=s$coefficients[2]
xs <- seq(0, 100, by=0.1)
ycalc2=b0solv+b1solv*xs
yexpMat=modelo$fitted.values
print(s)
##
## Call:
## lm(formula = ysolv ~ x)
##
## Residuals:
## 1 2 3 4 5 6 7
## 1632.6 314.0 -439.5 -793.8 -854.1 -150.8 291.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 218.42 440.49 0.496 0.641
## x 1113.92 10.25 108.681 1.25e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 940.7 on 5 degrees of freedom
## Multiple R-squared: 0.9996, Adjusted R-squared: 0.9995
## F-statistic: 1.181e+04 on 1 and 5 DF, p-value: 1.251e-09
n=length(x)
plot(x,ymat,main="Clotinidina",xlab="[Clotinidina](ug/Kg)",ylab="Area", ylim=c(0,140000))
points(x,ysolv,col="blue")
lines(xs,ycalc1,lty=1)
lines(xs,ycalc2,lty=1,col="blue")
yr=ycalc1/ycalc2*100
plot(xs,yr,type="l",col="orange",main="Clotinidina: %Efecto de Matriz",xlab="[Clotinidina](ug/Kg)",ylab="%EM")
