Add an exponential line in ggplot that goes through pre-specified points












2















The code below plots step functions of increasing accuracy toward the underlying polynomial or exponential curve. I am trying to add a curve to my plot that goes through the bottom of each step that I have added.



The commented lines are different attempts that I have tried, but nothing goes exactly through all of the bottom corners of each step down. Is anyone able to help me achieve this? Any help would be greatly appreciated.



 library(ggplot2)

X1 <- seq(1, 5, by=0.25)
Y1 <- (0.74 * X^(-2)+0.25)*100
sm <- data.frame(X1, Y1)

X2 <- sort(rep(seq(1, 5, by=0.5), 2))[-18]
Y2 <- sort(rep(Y1[1:17 %% 2 == 1], 2), decreasing = T)[-18]
med <- data.frame(X1, Y2)

X3 <- sort(rep(seq(1,5), 4))[1:17]
Y3 <- sort(rep(Y1[c(1, 5, 9, 13, 17)], 4), decreasing = T)[1:17]
lg <- data.frame(X1, Y3)

ggplot() +
#stat_function(data=sm, mapping = aes(x = X), fun = function(x) {exp(-1*x)*100+28}) +
#geom_curve(aes(x=1, xend=5, y=99, yend=28), ncp = 17) +
#geom_smooth(data = sm, aes(x=X1, y=Y1), method="lm", formula = y ~ poly(x,2), se=F, color= "black", fullrange=T) +
#geom_smooth(data = sm, aes(x=X1, y=Y1), method="lm", formula = (y ~ exp(-1.9*x)), se=F, color= "black", fullrange=T) +
scale_y_continuous(name="Overall Survival (%)", limits=c(0, 100)) +
scale_x_continuous(breaks = seq(from=1, to=5, by=0.25), name = "Survival Time (years)") +
geom_step(colour = "red", size = 1, data = lg, aes(x=X1, y=Y3)) +
geom_step(colour = "purple", size = 1, data = med, aes(x=X1, y=Y2)) +
geom_step(colour = "orange", size = 1, data = sm, aes(x=X1, y=Y1)) +
theme_classic()









share|improve this question



























    2















    The code below plots step functions of increasing accuracy toward the underlying polynomial or exponential curve. I am trying to add a curve to my plot that goes through the bottom of each step that I have added.



    The commented lines are different attempts that I have tried, but nothing goes exactly through all of the bottom corners of each step down. Is anyone able to help me achieve this? Any help would be greatly appreciated.



     library(ggplot2)

    X1 <- seq(1, 5, by=0.25)
    Y1 <- (0.74 * X^(-2)+0.25)*100
    sm <- data.frame(X1, Y1)

    X2 <- sort(rep(seq(1, 5, by=0.5), 2))[-18]
    Y2 <- sort(rep(Y1[1:17 %% 2 == 1], 2), decreasing = T)[-18]
    med <- data.frame(X1, Y2)

    X3 <- sort(rep(seq(1,5), 4))[1:17]
    Y3 <- sort(rep(Y1[c(1, 5, 9, 13, 17)], 4), decreasing = T)[1:17]
    lg <- data.frame(X1, Y3)

    ggplot() +
    #stat_function(data=sm, mapping = aes(x = X), fun = function(x) {exp(-1*x)*100+28}) +
    #geom_curve(aes(x=1, xend=5, y=99, yend=28), ncp = 17) +
    #geom_smooth(data = sm, aes(x=X1, y=Y1), method="lm", formula = y ~ poly(x,2), se=F, color= "black", fullrange=T) +
    #geom_smooth(data = sm, aes(x=X1, y=Y1), method="lm", formula = (y ~ exp(-1.9*x)), se=F, color= "black", fullrange=T) +
    scale_y_continuous(name="Overall Survival (%)", limits=c(0, 100)) +
    scale_x_continuous(breaks = seq(from=1, to=5, by=0.25), name = "Survival Time (years)") +
    geom_step(colour = "red", size = 1, data = lg, aes(x=X1, y=Y3)) +
    geom_step(colour = "purple", size = 1, data = med, aes(x=X1, y=Y2)) +
    geom_step(colour = "orange", size = 1, data = sm, aes(x=X1, y=Y1)) +
    theme_classic()









    share|improve this question

























      2












      2








      2








      The code below plots step functions of increasing accuracy toward the underlying polynomial or exponential curve. I am trying to add a curve to my plot that goes through the bottom of each step that I have added.



      The commented lines are different attempts that I have tried, but nothing goes exactly through all of the bottom corners of each step down. Is anyone able to help me achieve this? Any help would be greatly appreciated.



       library(ggplot2)

      X1 <- seq(1, 5, by=0.25)
      Y1 <- (0.74 * X^(-2)+0.25)*100
      sm <- data.frame(X1, Y1)

      X2 <- sort(rep(seq(1, 5, by=0.5), 2))[-18]
      Y2 <- sort(rep(Y1[1:17 %% 2 == 1], 2), decreasing = T)[-18]
      med <- data.frame(X1, Y2)

      X3 <- sort(rep(seq(1,5), 4))[1:17]
      Y3 <- sort(rep(Y1[c(1, 5, 9, 13, 17)], 4), decreasing = T)[1:17]
      lg <- data.frame(X1, Y3)

      ggplot() +
      #stat_function(data=sm, mapping = aes(x = X), fun = function(x) {exp(-1*x)*100+28}) +
      #geom_curve(aes(x=1, xend=5, y=99, yend=28), ncp = 17) +
      #geom_smooth(data = sm, aes(x=X1, y=Y1), method="lm", formula = y ~ poly(x,2), se=F, color= "black", fullrange=T) +
      #geom_smooth(data = sm, aes(x=X1, y=Y1), method="lm", formula = (y ~ exp(-1.9*x)), se=F, color= "black", fullrange=T) +
      scale_y_continuous(name="Overall Survival (%)", limits=c(0, 100)) +
      scale_x_continuous(breaks = seq(from=1, to=5, by=0.25), name = "Survival Time (years)") +
      geom_step(colour = "red", size = 1, data = lg, aes(x=X1, y=Y3)) +
      geom_step(colour = "purple", size = 1, data = med, aes(x=X1, y=Y2)) +
      geom_step(colour = "orange", size = 1, data = sm, aes(x=X1, y=Y1)) +
      theme_classic()









      share|improve this question














      The code below plots step functions of increasing accuracy toward the underlying polynomial or exponential curve. I am trying to add a curve to my plot that goes through the bottom of each step that I have added.



      The commented lines are different attempts that I have tried, but nothing goes exactly through all of the bottom corners of each step down. Is anyone able to help me achieve this? Any help would be greatly appreciated.



       library(ggplot2)

      X1 <- seq(1, 5, by=0.25)
      Y1 <- (0.74 * X^(-2)+0.25)*100
      sm <- data.frame(X1, Y1)

      X2 <- sort(rep(seq(1, 5, by=0.5), 2))[-18]
      Y2 <- sort(rep(Y1[1:17 %% 2 == 1], 2), decreasing = T)[-18]
      med <- data.frame(X1, Y2)

      X3 <- sort(rep(seq(1,5), 4))[1:17]
      Y3 <- sort(rep(Y1[c(1, 5, 9, 13, 17)], 4), decreasing = T)[1:17]
      lg <- data.frame(X1, Y3)

      ggplot() +
      #stat_function(data=sm, mapping = aes(x = X), fun = function(x) {exp(-1*x)*100+28}) +
      #geom_curve(aes(x=1, xend=5, y=99, yend=28), ncp = 17) +
      #geom_smooth(data = sm, aes(x=X1, y=Y1), method="lm", formula = y ~ poly(x,2), se=F, color= "black", fullrange=T) +
      #geom_smooth(data = sm, aes(x=X1, y=Y1), method="lm", formula = (y ~ exp(-1.9*x)), se=F, color= "black", fullrange=T) +
      scale_y_continuous(name="Overall Survival (%)", limits=c(0, 100)) +
      scale_x_continuous(breaks = seq(from=1, to=5, by=0.25), name = "Survival Time (years)") +
      geom_step(colour = "red", size = 1, data = lg, aes(x=X1, y=Y3)) +
      geom_step(colour = "purple", size = 1, data = med, aes(x=X1, y=Y2)) +
      geom_step(colour = "orange", size = 1, data = sm, aes(x=X1, y=Y1)) +
      theme_classic()






      r ggplot2






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      asked Nov 13 '18 at 2:59









      RABRAB

      874217




      874217
























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          1














          I ended up re-doing the initial functions and made it all match up:



          MyFunction <- function(x) {100*exp(-(1/4)*x)}

          Xyearly <- c(0:5)
          Yyearly <- MyFunction(Xyearly)
          Yearly <- data.frame(x=Xyearly, y=Yyearly)

          X6monthly <- c(0:10/2)
          Y6monthly <- MyFunction(X6monthly)
          Month6 <- data.frame(x=X6monthly, y=Y6monthly)

          X3monthly <- c(0:15/3)
          Y3monthly <- MyFunction(X3monthly)
          Month3 <- data.frame(x=X3monthly, y=Y3monthly)

          X1monthly <- c(0:60/12)
          Y1monthly <- MyFunction(X1monthly)
          Month1 <- data.frame(x=X1monthly, y=Y1monthly)

          ggplot() +
          stat_function(data=data.frame(x = 0), mapping = aes(x = x), fun = MyFunction, size=1.2) +
          scale_y_continuous(name="Overall Survival (%)", limits=c(0, 100)) +
          scale_x_continuous(breaks = seq(from=0, to=5, by=0.5), name = "Survival Time (years)") +
          geom_step(colour = "red", size = 1, data = Yearly, aes(x=x, y=y)) +
          geom_step(colour = "purple", size = 1, data = Month6, aes(x=x, y=y)) +
          geom_step(colour = "orange", size = 1, data = Month3, aes(x=x, y=y)) +
          geom_step(colour = "limegreen", size = 1, data = Month1, aes(x=x, y=y))





          share|improve this answer























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            1 Answer
            1






            active

            oldest

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            active

            oldest

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            active

            oldest

            votes









            1














            I ended up re-doing the initial functions and made it all match up:



            MyFunction <- function(x) {100*exp(-(1/4)*x)}

            Xyearly <- c(0:5)
            Yyearly <- MyFunction(Xyearly)
            Yearly <- data.frame(x=Xyearly, y=Yyearly)

            X6monthly <- c(0:10/2)
            Y6monthly <- MyFunction(X6monthly)
            Month6 <- data.frame(x=X6monthly, y=Y6monthly)

            X3monthly <- c(0:15/3)
            Y3monthly <- MyFunction(X3monthly)
            Month3 <- data.frame(x=X3monthly, y=Y3monthly)

            X1monthly <- c(0:60/12)
            Y1monthly <- MyFunction(X1monthly)
            Month1 <- data.frame(x=X1monthly, y=Y1monthly)

            ggplot() +
            stat_function(data=data.frame(x = 0), mapping = aes(x = x), fun = MyFunction, size=1.2) +
            scale_y_continuous(name="Overall Survival (%)", limits=c(0, 100)) +
            scale_x_continuous(breaks = seq(from=0, to=5, by=0.5), name = "Survival Time (years)") +
            geom_step(colour = "red", size = 1, data = Yearly, aes(x=x, y=y)) +
            geom_step(colour = "purple", size = 1, data = Month6, aes(x=x, y=y)) +
            geom_step(colour = "orange", size = 1, data = Month3, aes(x=x, y=y)) +
            geom_step(colour = "limegreen", size = 1, data = Month1, aes(x=x, y=y))





            share|improve this answer




























              1














              I ended up re-doing the initial functions and made it all match up:



              MyFunction <- function(x) {100*exp(-(1/4)*x)}

              Xyearly <- c(0:5)
              Yyearly <- MyFunction(Xyearly)
              Yearly <- data.frame(x=Xyearly, y=Yyearly)

              X6monthly <- c(0:10/2)
              Y6monthly <- MyFunction(X6monthly)
              Month6 <- data.frame(x=X6monthly, y=Y6monthly)

              X3monthly <- c(0:15/3)
              Y3monthly <- MyFunction(X3monthly)
              Month3 <- data.frame(x=X3monthly, y=Y3monthly)

              X1monthly <- c(0:60/12)
              Y1monthly <- MyFunction(X1monthly)
              Month1 <- data.frame(x=X1monthly, y=Y1monthly)

              ggplot() +
              stat_function(data=data.frame(x = 0), mapping = aes(x = x), fun = MyFunction, size=1.2) +
              scale_y_continuous(name="Overall Survival (%)", limits=c(0, 100)) +
              scale_x_continuous(breaks = seq(from=0, to=5, by=0.5), name = "Survival Time (years)") +
              geom_step(colour = "red", size = 1, data = Yearly, aes(x=x, y=y)) +
              geom_step(colour = "purple", size = 1, data = Month6, aes(x=x, y=y)) +
              geom_step(colour = "orange", size = 1, data = Month3, aes(x=x, y=y)) +
              geom_step(colour = "limegreen", size = 1, data = Month1, aes(x=x, y=y))





              share|improve this answer


























                1












                1








                1







                I ended up re-doing the initial functions and made it all match up:



                MyFunction <- function(x) {100*exp(-(1/4)*x)}

                Xyearly <- c(0:5)
                Yyearly <- MyFunction(Xyearly)
                Yearly <- data.frame(x=Xyearly, y=Yyearly)

                X6monthly <- c(0:10/2)
                Y6monthly <- MyFunction(X6monthly)
                Month6 <- data.frame(x=X6monthly, y=Y6monthly)

                X3monthly <- c(0:15/3)
                Y3monthly <- MyFunction(X3monthly)
                Month3 <- data.frame(x=X3monthly, y=Y3monthly)

                X1monthly <- c(0:60/12)
                Y1monthly <- MyFunction(X1monthly)
                Month1 <- data.frame(x=X1monthly, y=Y1monthly)

                ggplot() +
                stat_function(data=data.frame(x = 0), mapping = aes(x = x), fun = MyFunction, size=1.2) +
                scale_y_continuous(name="Overall Survival (%)", limits=c(0, 100)) +
                scale_x_continuous(breaks = seq(from=0, to=5, by=0.5), name = "Survival Time (years)") +
                geom_step(colour = "red", size = 1, data = Yearly, aes(x=x, y=y)) +
                geom_step(colour = "purple", size = 1, data = Month6, aes(x=x, y=y)) +
                geom_step(colour = "orange", size = 1, data = Month3, aes(x=x, y=y)) +
                geom_step(colour = "limegreen", size = 1, data = Month1, aes(x=x, y=y))





                share|improve this answer













                I ended up re-doing the initial functions and made it all match up:



                MyFunction <- function(x) {100*exp(-(1/4)*x)}

                Xyearly <- c(0:5)
                Yyearly <- MyFunction(Xyearly)
                Yearly <- data.frame(x=Xyearly, y=Yyearly)

                X6monthly <- c(0:10/2)
                Y6monthly <- MyFunction(X6monthly)
                Month6 <- data.frame(x=X6monthly, y=Y6monthly)

                X3monthly <- c(0:15/3)
                Y3monthly <- MyFunction(X3monthly)
                Month3 <- data.frame(x=X3monthly, y=Y3monthly)

                X1monthly <- c(0:60/12)
                Y1monthly <- MyFunction(X1monthly)
                Month1 <- data.frame(x=X1monthly, y=Y1monthly)

                ggplot() +
                stat_function(data=data.frame(x = 0), mapping = aes(x = x), fun = MyFunction, size=1.2) +
                scale_y_continuous(name="Overall Survival (%)", limits=c(0, 100)) +
                scale_x_continuous(breaks = seq(from=0, to=5, by=0.5), name = "Survival Time (years)") +
                geom_step(colour = "red", size = 1, data = Yearly, aes(x=x, y=y)) +
                geom_step(colour = "purple", size = 1, data = Month6, aes(x=x, y=y)) +
                geom_step(colour = "orange", size = 1, data = Month3, aes(x=x, y=y)) +
                geom_step(colour = "limegreen", size = 1, data = Month1, aes(x=x, y=y))






                share|improve this answer












                share|improve this answer



                share|improve this answer










                answered Nov 20 '18 at 3:27









                RABRAB

                874217




                874217






























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