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I want to fit my points with logarithmic curve. Here is my data which contains x and y. I desire to plot x and y and the add a logarithmic fitting curve.
x<-structure(list(X2.y = c(39.99724745, 29.55541525, 23.39578201,
15.46797044, 10.52063652, 7.296161198, 6.232038434, 4.811851132,
4.641281547, 4.198523289, 3.325515839, 2.596563723, 1.894902523,
1.556380314), X5.y = c(62.76037622, 48.54726084, 37.71302646,
24.93942365, 17.71060023, 13.31130267, 10.36341862, 7.706914722,
7.170517624, 6.294292013, 4.917428837, 3.767836298, 2.891519878,
2.280974128), X10.y = c(77.83154815, 61.12151516, 47.19228808,
31.21034981, 22.47098182, 17.29384973, 13.09875178, 9.623698726,
8.845091983, 7.681873268, 5.971413758, 4.543320659, 3.551367285,
2.760718282), X25.y = c(96.87401383, 77.00911883, 59.16936025,
39.13368164, 28.48573658, 22.32580849, 16.55485248, 12.0455604,
10.96092113, 9.435085861, 7.303126501, 5.523147205, 4.385086234,
3.366876291), X50.y = c(111.0008027, 88.79545082, 68.05463659,
45.01166182, 32.94782526, 26.05880295, 19.11878542, 13.84223574,
12.53056405, 10.73571912, 8.291067088, 6.25003851, 5.003586577,
3.81655893), X100.y = c(125.0232816, 100.4947544, 76.87430545,
50.84623991, 37.37696657, 29.76423356, 21.66378667, 15.6256447,
14.08861698, 12.0267487, 9.271712877, 6.971562563, 5.61752001,
4.262921183)), class = "data.frame", row.names = c(NA, -14L))
I tried this:
single_idf<-function(x) {
idf<-x
durations = c(5/60, 10/60, 15/60, 30/60, 1, 2, 3, 4, 5, 6, 8, 12, 18, 24)
nd = length(durations)
Tp = c(2, 5, 10, 25, 50, 100)
nTp = length(Tp)
psym = seq(1, nTp)
# open new window for this graph, set plotting parameters for a single graph panel
windows()
par(mfrow = c(1,1), mar = c(5, 5, 5, 5), cex = 1)
# set up custom axis labels and grid line locations
ytick = c(1,2,3,4,5,6,7,8,9,10,20,30,40,50,60,70,80,90,100,
200,300,400,500,600,700,800,900,1000,1100,1200,1300,1400)
yticklab = as.character(ytick)
xgrid = c(5,6,7,8,9,10,15,20,30,40,50,60,120,180,240,300,360,
420,480,540,600,660,720,840,960,1080,1200,1320,1440)
xtick = c(5,10,15,20,30,60,120,180,240,300,360,480,720,1080,1440)
xticklab = c("5","10","15","20","30","60","2","3","4","5","6","8","12","18","24")
ymax1 = max(idf)
durations = durations*60
plot(durations, col=c("#FF00FF") ,lwd=c(1), idf[, 1],
xaxt="n",yaxt="n",
pch = psym[1], log = "xy",
xlim = c(4, 24*60), ylim = range(c(1,idf+150)),
xlab = "(min) Duration (hr)",
ylab = "Intensity (mm/hr)"
)
for (iT in 2:nTp) {
points(durations, idf[, iT], pch = psym[iT], col="#FF00FF",lwd=1)
}
for (iT in 1:nTp) {
mod.lm = lm(log10(idf[, iT]) ~ log10(durations))
b0 = mod.lm$coef[1]
b1 = mod.lm$coef[2]
yfit = log(10^(b0 + b1*log10(durations)))
lines(durations,col=c("#FF00FF"),yfit, lty = psym[iT],lwd=1)
}
}
But when I run this, the curves stands far away from the points. I want to see curves over the points. How can I arrange this?
single_idf(x)
Consider this as an option for you using ggplot2 and dplyr. Also added method='lm' to match OP expected output (Many thanks and credits to #AllanCameron for his magnificent advice):
library(ggplot2)
library(dplyr)
#Data
df <- data.frame(x,y)
#Plot
df %>%
pivot_longer(-y) %>%
ggplot(aes(x=log(y),y=log(value),color=name,group=name))+
geom_point()+
stat_smooth(geom = 'line',method = 'lm')
Output:
The main problem is that you were plotting the natural log of the fit rather than the fit itself.
If you change the line
yfit = log(10^(b0 + b1*log10(durations)))
To
yfit = 10^(b0 + b1*log10(durations))
And rerun your code, you get
> dput(head(inputData))
structure(list(Date = c("2018:07:00", "2018:06:00", "2018:05:00",
"2018:04:00", "2018:03:00", "2018:02:00"), IIP = c(125.8, 127.5,
129.7, 122.6, 140.3, 127.4), CPI = c(139.8, 138.5, 137.8, 137.1,
136.5, 136.4), `Term Spread` = c(1.580025, 1.89438, 2.020112,
1.899074, 1.470544, 1.776862), RealMoney = c(142713.9916, 140728.6495,
140032.2762, 139845.5215, 139816.4682, 139625.865), NSE50 = c(10991.15682,
10742.97381, 10664.44773, 10472.93333, 10232.61842, 10533.10526
), CallMoneyRate = c(6.161175, 6.10112, 5.912088, 5.902226, 5.949956,
5.925538), STCreditSpread = c(-0.4977, -0.3619, 0.4923, 0.1592,
0.3819, -0.1363)), row.names = c(NA, -6L), class = c("tbl_df",
"tbl", "data.frame"))
I want to make my autoregressive plot like this plot:
#------> importing all libraries
library(readr)
install.packages("lubridtae")
library("lubridate")
install.packages("forecast")
library('ggplot2')
library('fpp')
library('forecast')
library('tseries')
#--------->reading data
inputData <- read_csv("C:/Users/sanat/Downloads/exercise_1.csv")
#--------->calculating the lag=1 for NSE50
diff_NSE50<-(diff(inputData$NSE50, lag = 1, differences = 1)/lag(inputData$NSE50))
diff_RealM2<-(diff(inputData$RealMoney, lag = 1, differences = 1)/lag(inputData$RealMoney))
plot.ts(diff_NSE50)
#--------->
lm_fit = dynlm(IIP ~ CallMoneyRate + STCreditSpread + diff_NSE50 + diff_RealM2, data = inputData)
summary(lm_fit)
#--------->
inputData_ts = ts(inputData, frequency = 12, start = 2012)
#--------->area of my doubt is here
VAR_data <- window(ts.union(ts(inputData$IIP), ts(inputData$CallMoneyRate)))
VAR_est <- VAR(y = VAR_data, p = 12)
plot(VAR_est)
I want to my plots to get plotted together in same plot. How do I serparate the var() plots to two separate ones.
Current plot:
My dataset :
dataset
Okay, so this still needs some work, but it should set the right framework for you. I would look more into working with the ggplot2 for future.
Few extra packages needed, namely library(vars) and library(dynlm).
Starting from,
VAR_est <- VAR(y = VAR_data, p = 12)
Now we extract the values we want from the VAR_est object.
y <- as.numeric(VAR_est$y[,1])
z <- as.numeric(VAR_est$y[,2])
x <- 1:length(y)
## second data set on a very different scale
par(mar = c(5, 4, 4, 4) + 0.3) # Leave space for z axis
plot(x, y, type = "l") # first plot
par(new = TRUE)
plot(x, z, type = "l", axes = FALSE, bty = "n", xlab = "", ylab = "")
axis(side=4, at = pretty(range(z)))
mtext("z", side=4, line=3)
I will leave you to add the dotted lines on etc...
Hint: Decompose the VAR_est object, for example, VAR_est$datamat, then see which bit of data corresponds to the part of the plot you want.
Used some of this
Complete beginner at R here trying to perform nonmetric multidimensional scaling on a 95x95 matrix of similarities where 8 corresponds to very similar and 1 corresponds to very dissimilar. I also have an additional column (96th) signifying type and ranging from 0 to 1.
First I load the data:
dsimilarity <- read.table("d95x95matrix.txt",
header = T,
row.names = c("Y1", "Y2", "Y3", "Y4", "Y5", "Y6", "Y7", "Y8", "Y9", "Y10", "Y11", "Y12", "Y13", "Y14", "Y15", "Y16", "Y17", "Y18", "Y19", "Y20",
"Y21", "Y22", "Y23", "Y24", "Y25", "Y26", "Y27", "Y28", "Y29", "Y30", "Y31", "Y32", "Y33", "Y34", "Y35", "Y36", "Y37", "Y38", "Y39", "Y40",
"Y41", "Y42", "Y43", "Y44", "Y45", "Y46", "Y47", "Y48", "Y49", "Y50", "Y51", "Y52", "Y53", "Y54", "Y55", "Y56", "Y57", "Y58", "Y59", "Y60",
"Y61", "Y62", "Y63", "Y64", "Y65", "Y66", "Y67", "Y68", "Y69", "Y70", "Y71", "Y72", "Y73", "Y74", "Y75", "Y76", "Y77", "Y78", "Y79", "Y80",
"Y81", "Y82", "Y83", "Y84", "Y85", "Y86", "Y87", "Y88", "Y89", "Y90", "Y91", "Y92", "Y93", "Y94", "Y95"))
I convert the matrix of similarities into a matrix of dissimilarities, and exclude the 96th column:
ddissimilarity <- dsimilarity; ddissimilarity[1:95, 1:95] = 8 - ddissimilarity[1:95, 1:95]
Then I perform the nonmetric MDS using the Smacof function:
ordinal.mds.results <- smacofSym(ddissimilarity[1:95, 1:95],
type = c("ordinal"),
ndim = 2,
ties = "primary",
verbose = T )
I create a new data frame (I'm following a guide and don't really know what's going on here):
mds.config <- as.data.frame(ordinal.mds.results$conf)
All well and good thus far (to my knowledge). However at this point I will try to create an xyplot of the data and get a good result using this code:
xyplot(D2 ~ D1, data = mds.config,
aspect = 1,
main = "Figure 1. MDS solution",
panel = function (x, y) {
panel.xyplot(x, y, col = "black")
panel.text(x, y-.03, labels = rownames(mds.config),
cex = .75)
},
xlab = "MDS Axis 1",
ylab = "MDS Axis 2",
xlim = c(-1.1, 1.1),
ylim = c(-1.1, 1.1))
Now I want to create a figure that incorporates the type in column 96th and assigns different colors to observations of the two different types. However, can't quite figure out how to do so. Does anyone have any ideas of where I'm going wrong here?
xyplot(D2 ~ D1, data = mds.config ~ ddissimilarity[96:96, 96:96],
aspect = 1,
main = "Figure 1. MDS solution",
panel = function (x, y) {
panel.xyplot(x, y, col = "black")
panel.text(x, y-.03, labels = rownames(mds.config),
cex = .75)
},
xlab = "MDS Axis 1",
ylab = "MDS Axis 2",
xlim = c(-1.1, 1.1),
ylim = c(-1.1, 1.1),
group = "Type")
I'm using boxplot() in R. My code is:
#rm(list=ls())
#B2
fps_error <- c(0.058404273, 0.028957446, 0.026276044, 0.07084294, 0.078438563, 0.024000178, 0.120678965, 0.081774358, 0.025644741, 0.02931614)
fps_error = fps_error *100
fps_qp_error <-c(1.833333333, 1.69047619, 1.666666667, 3.095238095, 2.738095238, 1.714285714, 3.634146341, 5.142857143, 1.238095238, 2.30952381)
bit_error <- c(0.141691737, 0.136173785, 0.073808209, 0.025057931, 0.165722097, 0.004276999, 0.365353752, 0.164757488, 0.003362543, 0.022423845)
bit_error = bit_error *100
bit_qp_error <-c(0.666666667, 0.785714286, 0.428571429, 0.142857143, 0.785714286, 0.023809524, 1.523809524, 0.976190476, 0.023809524, 0.142857143)
ssim_error <-c(0.01193773, 0.015151569, 0.003144532, 0.003182908, 0.008125274, 0.013796366, 0.00359078, 0.019002591, 0.005031524, 0.004370175)
ssim_error = ssim_error * 100
ssim_qp_error <-c(3.833333333, 1.80952381, 0.69047619, 0.571428571, 2, 1.904761905, 0.761904762, 2.119047619, 0.857142857, 0.976190476)
all_errors = cbind(fps_error, bit_error, ssim_error)
all_qp_errors = cbind(fps_qp_error, bit_qp_error, ssim_qp_error)
modes = cbind(rep("FPS error",10), rep("Bitrate error",10), rep("SSIM error",10))
journal_linear_data <-data.frame(fps_error, fps_qp_error,bit_error,bit_qp_error,ssim_error,ssim_qp_error )
yvars <- c("fps_error","bit_error","ssim_error")
yvars_qp <-c("fps_qp_error","bit_qp_error","ssim_qp_error")
xvars <- c("FPS", "Bitrate", "SSIM")
graphics.off()
bmp(filename="boxplot_B2_error.bmp")
op <- par(mfrow = c(1, 3), #matrix of plots
oma = c(0,0,2,0),mar=c(5.1, 7.1, 2.1, 2.1),mgp=c(4,1,0)) #outer margins
par(cex.lab=3)
par(cex.axis=3)
for (i in 1:3) {boxplot(journal_linear_data[,yvars[i]], xlab=xvars[i], ylab="Percentage error", outcex = 2)}
par(op)
mtext(text="Percentage error per mode for B2",side=3, line=1.5, font=2, cex=2,adj=0.95, col='black')
dev.off()
The image output is shown below. As you can see the y-axis does not have the same range for all graphs. How can I fix this? For example starting in 0.5 or 0.
You can simply put an ylim = c(0, 5) in all your boxplot() call. This sets y-axis range (roughly) between 0 and 5.
Perhaps you did not see ylim argument in ?boxplot; the "Arguments" section also does not mention it. But ylim is just a trivial graphical parameter passed via "...". You can also find such example in the "Examples" session of ?boxplot:
boxplot(len ~ dose, data = ToothGrowth,
boxwex = 0.25, at = 1:3 - 0.2,
subset = supp == "VC", col = "yellow",
main = "Guinea Pigs' Tooth Growth",
xlab = "Vitamin C dose mg",
ylab = "tooth length",
xlim = c(0.5, 3.5), ylim = c(0, 35), yaxs = "i")
I have this dataset
structure(list(Year = 1988:2012, A = c(1415.6, 1345.3, 1321.7,
1234.5, 1567.8, 1476.6, 1610.1, 1422.6, 1209.1, 1249.3, 1377.5,
1525.7, 1683.7, 1500.1, 1565.3, 1737.4, 1321, 1477.8, 1642, 1608.1,
1427.8, 1608.2, 1404.4, 1688.3, 1795.4), B = c(76, 359, 299,
215, 177, 3112, 12047, 26307, 27173, 6514, 4190, 1776, 1708,
1335, 1012, 8170, 4306, 3716, 23531.986, 34803.012, 22758.7645,
29140.16304, 36369.3619, 56975.62256, 33516.95628)), .Names = c("Year",
"A", "B"), class = "data.frame", row.names = c(NA, -25L))
and I created this plot with the twoord.plot function:
install.packages("plotrix")
library(plotrix)
twoord.plot(Example$Year, Example$B, Example$Year, Example$A, xlim = range(1988:2012))
abline(lm(B ~ Year, data = Example), col="black")
abline(lm(A ~ Year, data = Example), col="red")
How should I tell to the second trendline (the red one) that it belongs to the righ hand y-axis? Is it possible to do it in r?
I guess R knows only one scale for x and one for y. If you have a look into the function twoord.plot you can see that it adds the right hand plot with a scaling-offest operation :
points(rx, ry * ymult + yoff, col = rcol, pch = rpch,
type = type[2], ...)
my guess is that you have to do the same to add extra lines. Just selecting some lines into the function, it should be (ly = Example$B and ry = Example$A) :
lylim <- range(ly, na.rm = TRUE)
rylim <- range(ry, na.rm = TRUE)
ymult <- diff(lylim)/diff(rylim)
yoff <- lylim[1] - rylim[1] * ymult
coef = lm(A ~ Year, data = Example)$coefficients
abline(a = coef[1]*ymult + yoff, b = coef[2]*ymult, col="red")