I am working on my modeling skills and am having a go at Black Scholes. The idea is to use the current option prices (x) to get an estimate for volatility (b).
When I run nlsLM, I get this error:
Error in assign(i, data[[i]], envir = env) :
attempt to use zero-length variable name
I cannot see where the error is, I have removed all of the variables from the formula and replaced them with their numerical values for this specific case. x and b remain as variables. I have run this without d1 and d2, in other words, placing the long formulas for each inside the respective pnorm() but no luck there either.
x is the strike price, y is the price of the GOOG call expiring 12/20/14 as of 12/12/14.
Here is the code to reproduce the error.
df <- structure(list(x = c(505, 510, 512.5, 515, 517.5, 520, 522.5,
525, 527.5, 530, 532.5), y = c(17.7, 12.5, 12, 9.2, 7.82, 6.3,
5.1, 4.1, 3.21, 2.45, 1.9)), .Names = c("x", "y"),
row.names = c(NA, -11L), class = "data.frame")
f <- function(x, b){
d1 = (1/(b*sqrt(0.021918)))*(log(518.66/x) + (0.0025+b^2/2) * 0.021918)
d2 = (1/(b*sqrt(0.021918)))*(log(518.66/x) - (0.0025+b^2/2) * 0.021918)
return (pnorm(d1)*518.66 - pnorm(d2)*x*exp(-0.0025*0.021918))
}
library(minpack.lm)
test <- nlsLM(y~f(x, b), data=df, start=list((b=0.50)))
Thank you.
Found the answer...There is a line in the nlsLM function:
for (i in names(start)) assign(i, start[[i]], envir = startEnv)
The problem was the start provided in my formula did not have a name, b. I thought by using the list command that would resolve the issue.
I have corrected it as follows:
test <- nlsLM(y~f(x, b), data=df, start=c(b=0.50))
Related
I hope somebody will be able to help me with this chordDiagram visualisation I am trying to create. I am well aware that maybe this visualization type was not suitable for this particular data, but somehow it was something I had in my head (or how I wanted to visualize this data) and what I wanted to create, and now I think it is too late to give it up :) too curious how one can fix it. It is my first real post here, though I am an active user of stackoverflow and I genuinely admire the audience here.
So I have this data on the change in the size of area in km2 over time (d0) and I am trying to create a GIF out of it using example here: https://guyabel.com/post/animated-directional-chord-diagrams/
The data "d0":
Time <- as.numeric(c(10,10,10,100,100,100,200,200,200,5,5,5,50,50,50,0,0,0))
Year <- as.character(c(2050,2100,2200,2050,2100,2200,2050,2100,2200,2050,2100,2200,2050,2100,2200,2050,2100,2200))
Area_km2 <- as.numeric(c(4.3075211,7.1672926,17.2780622,5.9099250,8.2909189,16.9748961,6.5400554,8.9036313,16.5627228,3.0765610,6.3929883,18.0708108,5.3520782,8.4503856,16.7938196,0.5565978,1.8415855,12.5089476))
(d0 <- as.data.frame(cbind(Time,Year,Area_km2)))
I also have the color codes stored in a separate dataframe (d1) following the above mentioned example.
The data "d1":
year <- as.numeric(c(2050,2100,2200))
order1 <- as.character(c(1,2,3))
col1 <- c("#40A4D8","#33BEB7","#0C5BCE")
(d1 <- as.data.frame(cbind(year,order1,col1)))
So the idea was to have self-linking flows within each sector increasing in size over time, which will look like just growing segments in a final animated GIF (or like growing pie segments), but I noticed that regardless how hard I try I can't seem to manage to constrain the axis of each segment to limits of that particular year in an every single frame. It seems that the axis is being added on and keeps on adding over time, which is not what I want.
Like for example in the first figure (figure0) or "starting frame" the size of the links matches well the dataframe:
figure0
So it is
orig_year
Area_km2
.frame
2050
0.557
0
2100
1.84
0
2200
12.5
0
But when one plots next figure (figure1), the axis seems to have taken the values from the starting frame and added on the current values (4, 7.4 and 19 respectively) instead of (3.08, 6.39 and 18.1) or what should have been the values according the data frame:
figure1
orig_year
Area_km2
.frame
2050
3.08
1
2100
6.39
1
2200
18.1
1
And it keep on doing so as one loops through the data and creates new plots for the next frames. I wonder whether it is possible to constrain the axis and create the visualization in a way that the links just gradually increase over time and the axis is, so to say, following the increase or does also increase gradually following the data???
Any help is highly appreciated!
Thanks.
My code:
Sort decreasing
(d0 <- arrange(d0,Time))
Copy columns
(d0$Dest_year <- d0$Year)
Re-arrange data
library(tweenr)
(d2 <- d0 %>%
mutate(corridor=paste(Year,Dest_year,sep="->")) %>%
dplyr::select(Time,corridor,Area_km2) %>%
mutate(ease="linear") %>%
tweenr::tween_elements('Time','corridor','ease',nframes=30) %>%
tibble::as_tibble())
(d2 <- d2 %>%
separate(col=.group,into=c("orig_year","dest_year"),sep="->") %>%
dplyr::select(orig_year,dest_year,Area_km2,everything()))
d2$Time <- NULL
Create a directory to store the individual plots
dir.create("./plot-gif/")
Fixing scales
scale_gap <- function(Area_km2_m,Area_km2_max,gap_at_max=1,gaps=NULL) {
p <- Area_km2_m/Area_km2_max
if(length(gap_at_max)==1 & !is.null(gaps)) {
gap_at_max <- rep(gap_at_max,gaps)
}
gap_degree <- (360-sum(gap_at_max))*(1-p)
gap_m <- (gap_degree + sum(gap_at_max))/gaps
return(gap_m)
}
Function to derive the size of gaps in each frame for an animated GIF
(d3 <- d2 %>% group_by(orig_year) %>% mutate(gaps=scale_gap(Area_km2_m=Area_km2,Area_km2_max=max(.$Area_km2),gap_at_max=4,gaps=9)))
library(magrittr)
Get the values for axis limits
(axmax <- d2 %>% group_by(orig_year,.frame) %>% mutate(max=mean(Area_km2)))
Creating unique chordDiagrams for each frame
library(circlize)
for(f in unique(d2$.frame)){
png(file=paste0("./plot-gif/figure",f,".png"),height=7,width=7,units="in",res=500)
circos.clear()
par(mar=rep(0,4),cex=1)
circos.par(start.degree=90,track.margin=c(-0.1,0.1),
gap.degree=filter(d3,.frame==f)$gaps,
points.overflow.warning=FALSE)
chordDiagram(x=filter(d2,.frame==f),directional=2,order=d1$year,
grid.col=d1$col1,annotationTrack=c("grid","name","axis"),
transparency=0.25,annotationTrackHeight=c(0.05,0.1),
direction.type=c("diffHeight"),
diffHeight=-0.04,link.sort=TRUE,
xmax=axmax$max)
dev.off()
}
Now make a GIF
library(magick)
img <- image_read(path="./plot-gif/figure0.png")
for(f in unique(d2$.frame)[-1]){
img0 <- image_read(path=paste0("./plot-gif/figure",f,".png"))
img <- c(img,img0)
message(f)
}
img1 <- image_scale(image=img,geometry="720x720")
ani0 <- image_animate(image=img1,fps=10)
image_write(image=ani0,path="./plot-gif/figure.gif")
I will start with your d0 object. I first construct the d0 object but I do not convert everything to characters, just put them as the original numeric format. Also I reorder d0 by Time and Year:
Time = c(10,10,10,100,100,100,200,200,200,5,5,5,50,50,50,0,0,0)
Year = c(2050,2100,2200,2050,2100,2200,2050,2100,2200,2050,2100,2200,2050,2100,2200,2050,2100,2200)
Area_km2 = c(4.3075211,7.1672926,17.2780622,5.9099250,8.2909189,16.9748961,6.5400554,8.9036313,16.5627228,3.0765610,6.3929883,18.0708108,5.3520782,8.4503856,16.7938196,0.5565978,1.8415855,12.5089476)
d0 = data.frame(Time = Time,
Year = Year,
Area_km2 = Area_km2,
Dest_year = Year)
d0 = d0[order(d0$Time, d0$Year), ]
The key thing is to calculate proper values for "gaps" between sectors so that the same unit from data corresponds to the same degree in different plots.
We first calculate the maximal total width of the circular plot:
width = tapply(d0$Area_km2, d0$Time, sum)
max_width = max(width)
We assume there are n sectors (where n = 3 in d0). We let the first n-1 gaps to be 2 degrees and we dynamically adjust the last gap according to the total amount of values in each plot. For the plot with the largest total value, the last gap is also set to 2 degrees.
n = 3
degree_per_unit = (360 - n*2)/max_width
Now degree_per_unit can be shared between multiple plots. Every time we calculate the value for last_gap:
for(t in sort(unique(Time))) {
l = d0$Time == t
d0_current = d0[l, c("Year", "Dest_year", "Area_km2")]
last_gap = 360 - (n-1)*2 - sum(d0_current$Area_km2)*degree_per_unit
circos.par(gap.after = c(rep(2, n-1), last_gap))
chordDiagram(d0_current, grid.col = c("2050" = "red", "2100" = "blue", "2200" = "green"))
circos.clear()
title(paste0("Time = ", t, ", Sum = ", sum(d0_current$Area_km2)))
Sys.sleep(1)
}
I tried running the following code and encountered several errors with the simstudy package.
library(simstudy)
clusterDef <- defData(varname = "u_3", dist = "normal", formula = 0,
variance = 25.77, id="clus") #cluster-level random effect
clusterDef <- defData(clusterDef, varname = "error", dist = "normal", formula = 0,
variance = 38.35) #error termeriod
clusterDef <- defData(clusterDef, varname = "ind", dist = "nonrandom",
formula = 25) #individuals per cluster
#Generate individual-level random effect and treatment variable
indDef <- defDataAdd(varname = "u_2", dist = "normal", formula = 0,
variance = 120.62)
#Generate clusters of data
set.seed(12345)
cohortsw <- genData(3, clusterDef)
cohortswTm <- addPeriods(cohortsw, nPeriods = 6, idvars = "clus", perName = "period")
cohortswTm <- trtStepWedge(cohortswTm, "clus", nWaves = 3, lenWaves = 1, startPer = 1, grpName = "trt")
cohortswTm <- genCluster(cohortswTm, cLevelVar = "clus", numIndsVar = "ind", level1ID = "id")
Error in vecseq(f__, len__, if (allow.cartesian || notjoin ||
!anyDuplicated(f__, : Join results in 2700 rows; more than 468 =
nrow(x)+nrow(i). Check for duplicate key values in i each of which
join to the same group in x over and over again. If that's ok, try
by=.EACHI to run j for each group to avoid the large allocation. If
you are sure you wish to proceed, rerun with allow.cartesian=TRUE.
Otherwise, please search for this error message in the FAQ, Wiki,
Stack Overflow and data.table issue tracker for advice.
cohortswTm <- addColumns(indDef, cohortswTm)
#Define coefficients for time as a categorical variable
timecoeff1 <- -5.42
timecoeff2 <- -5.72
timecoeff3 <- -7.03
timecoeff4 <- -6.13
timecoeff5 <- -9.13
#Generate outcome y
y <- defDataAdd(varname = "Y", formula = "17.87 + 5.0*trt + timecoeff1*I(period == 1) + timecoeff2*I(period == 2) + timecoeff3*I(period == 3) + timecoeff4*I(period == 4) + timecoeff5*I(period == 5) + u_3 + u_2 + error", dist = "normal")
#Add outcome to dataset
cohortswTm <- addColumns(y, cohortswTm)
Error: Variable(s) referenced not previously defined: timecoeff1,
timecoeff2, timecoeff3, timecoeff4, timecoeff5
Does anybody know why I am getting the errors that were highlighted above? How would I fix the code to prevent them from occuring?
Any help is much appreciated.
The first error is generated because you are trying to create individual level data within each cluster, but each cluster appears repeatedly (over 6 periods). genCluster is expecting that cLevelVar is a unique id. In this case, you can generate 6 individuals per cluster per time period by modifying the genCluster command to be
cohortswTm <- genCluster(cohortswTm, cLevelVar = "timeID",
numIndsVar = "ind", level1ID = "id")
This code creates a "closed" cohort, individuals are observed only in a single period. Generating an open cohort, where individuals might be observed over time as well, is a bit more involved, and is described here.
The second error is generated because simstudy data definitions can only include variables that have been defined in the context of the data definition. So, any constants need to be in the formula. (The formula itself can be updated using updateDef and updateDefAdd if you want to explore the effects of different covariate levels.)
This is how y should be defined:
y <- defDataAdd(varname = "Y", formula = "17.87 + 5.0*trt -
5.42*I(period == 1) - 5.72*I(period == 2) - 7.03*I(period == 3) -
6.13*I(period == 4) - 9.13*I(period == 5) + u_3 + u_2 + error",
dist = "normal")
Hello I am trying to create a program that has a function main_function() that holds two int variables and then passes the variables to two other functions difference() and sum(). I want the two functions perform the computation and display the results. In turn calling each of the two functions from the main_function(). However I am currently having an issue with my program only outputting the bottom most function that is being called in the main_function()
Here is what I have
-module(numbers).
-export([main_function/2]).
main_function(X,Y)->
sum(X,Y),
difference(X,Y).
sum(X,Y)->
X + Y.
difference(X,Y)->
X - Y.
My output for this would be 2 if I was to pass 5 and 3 would for X and Y respectively and my program seems to be only using the difference() function and not sum(). I am looking for an output of 8 and 2.
Any help is greatly appreciated
Thanks
You can change main_function/2 like below
main_function(X,Y)->
A = sum(X,Y),
B = difference(X,Y),
{A, B}.
The result in shell when X = 5, Y = 3 is:
{8, 2}
Or like this
main_function(X,Y)->
A = sum(X,Y),
B = difference(X,Y),
io:format("A = ~p~nB = ~p~n", [A, B]).
The result in shell when X = 5, Y = 3 is:
A = 8
B = 2
Hello experienced pythoners.
The goal is simply to read in my own files which have the following format, and to then apply mathematical operations to these values and polynomials. The files have the following format:
m1:=10:
m2:=30:
Z1:=1:
Z2:=-1:
...
Some very similar variables, next come the laguerre polynomials
...
F:= (12.58295)*L(0,x)*L(1,y)*L(6,z) + (30.19372)*L(0,x)*L(2,y)*L(2,z) - ...:
Where L stands for a laguerre polynomial and takes two arguments.
I have written a procedure in Python which splits apart each line into a left and right hand side split using the "=" character as a divider. The format of these files is always the same, but the number of laguerre polynomials in F can vary.
import re
linestring = open("file.txt", "r").read()
linestring = re.sub("\n\n","\n",str(linestring))
linestring = re.sub(",\n",",",linestring)
linestring = re.sub("\\+\n","+",linestring)
linestring = re.sub(":=\n",":=",linestring)
linestring = re.sub(":\n","\n",linestring)
linestring = re.sub(":","",linestring)
LINES = linestring.split("\n")
for LINE in LINES:
LINE = re.sub(" ","",LINE)
print "LINE=", LINE
if len(LINE) <=0:
next
PAIR = LINE.split("=")
print "PAIR=", PAIR
LHS = PAIR[0]
RHS = PAIR[1]
print "LHS=", LHS
print "RHS=", RHS
The first re.sub block just deals with formatting the file and discarding characters that python will not be able to process; then a loop is performed to print 4 things, LINE, PAIR, LHS and RHS, and it does this nicely. using the example file from above the procedure will print the following:
LINE= m1=1
PAIR= ['m1', '1']
LHS= m1
RHS= 1
LINE= m2=1
PAIR= ['m2', '1']
LHS= m2
RHS= 1
LINE= Z1=-1
PAIR= ['Z1', '-1']
LHS= Z1
RHS= -1
LINE= Z2=-1
PAIR= ['Z2', '-1']
LHS= Z2
RHS= -1
LINE= F= 12.5*L(0,x)L(1,y) + 30*L(0,x)L(2,y)L(2,z)
PAIR=['F', '12.5*L(0,x)L(1,y) + 30*L(0,x)L(2,y)L(2,z)']
LHS= F
RHS= 12.5*L(0,x)L(1,y) + 30*L(0,x)L(2,y)L(2,z)
My question is what is the next best step to process this output and use it in a mathematical script, especially assigning the L to mean a laguerre polynomial? I tried putting the LHS and RHS into a dictionary, but found it troublesome to put F in it due to the laguerre polynomials.
Any ideas are welcome. Perhaps I am overcomplicating this and there is a much simpler way to parse this file.
Many thanks in advance
Your parsing algorithm doesn't seem to work correctly, as the RHS of your variables dont produce the expected result.
Also the first re.sub block where you want to format the file seems overly complicated. Assuming every statement in your input file is terminated by a colon, you could get rid of all whitespace and newlines and seperate the statements using the following code:
linestring = open('file.txt','r').read()
strippedstring = linestring.replace('\n','').replace(' ','')
statements = re.split(':(?!=)',strippedstring)[:-1]
Then you iterate over the statements and split each one in LHS and RHS:
for st in statements:
lhs,rhs = re.split(':=',st)
print 'lhs=',lhs
print 'rhs=',rhs
In the next step, try to distinguish normal float variables and polynomials:
#evaluate rhs
try:
#interpret as numeric constant
f = float(rhs)
print " ",f
except ValueError:
#interpret as laguerre-polynomial
summands = re.split('\+', re.sub('-','+-',rhs))
for s in summands:
m = re.match("^(?P<factor>-?[0-9]*(\.[0-9]*)?)(?P<poly>(\*?L\([0-9]+,[a-z]\))*)", s)
if not m:
print ' polynomial misformatted'
continue
f = m.group('factor')
print ' factor: ',f
p = m.group('poly')
for l in re.finditer("L\((?P<a>[0-9]+),(?P<b>[a-z])\)",p):
print ' poly: L(%s,%s)' % (l.group("a"),l.group("b"))
This should work for your given example file.
I wanna combine three graphics on one graph. The data from inside of R which is " nottem ". Can someone help me to write code to put a seasonal mean and harmonic (cosine model) and its time series plots together by using different colors? I already wrote model code just don't know how to combine them together to compare.
Code :library(TSA)
nottem
month.=season(nottem)
model=lm(nottem~month.-1)
summary(nottem)
har.=harmonic(nottem,1)
model1=lm(nottem~har.)
summary(model1)
plot(nottem,type="l",ylab="Average monthly temperature at Nottingham castle")
points(y=nottem,x=time(nottem), pch=as.vector(season(nottem)))
Just put your time series inside a matrix:
x = cbind(serie1 = ts(cumsum(rnorm(100)), freq = 12, start = c(2013, 2)),
serie2 = ts(cumsum(rnorm(100)), freq = 12, start = c(2013, 2)))
plot(x)
Or configure the plot region:
par(mfrow = c(2, 1)) # 2 rows, 1 column
serie1 = ts(cumsum(rnorm(100)), freq = 12, start = c(2013, 2))
serie2 = ts(cumsum(rnorm(100)), freq = 12, start = c(2013, 2))
require(zoo)
plot(serie1)
lines(rollapply(serie1, width = 10, FUN = mean), col = 'red')
plot(serie2)
lines(rollapply(serie2, width = 10, FUN = mean), col = 'blue')
hope it helps.
PS.: zoo package is not needed in this example, you could use the filter function.
You can extract the seasonal mean with:
s.mean = tapply(serie, cycle(serie), mean)
# January, assuming serie is monthly data
print(s.mean[1])
This graph is pretty hard to read, because your three sets of values are so similar. Still, if you want to simply want to graph all of these on the sample plot, you can do it pretty easily by using the coefficients generated by your models.
Step 1: Plot the raw data. This comes from your original code.
plot(nottem,type="l",ylab="Average monthly temperature at Nottingham castle")
Step 2: Set up x-values for the mean and cosine plots.
x <- seq(1920, (1940 - 1/12), by=1/12)
Step 3: Plot the seasonal means by repeating the coefficients from the first model.
lines(x=x, y=rep(model$coefficients, 20), col="blue")
Step 4: Calculate the y-values for the cosine function using the coefficients from the second model, and then plot.
y <- model1$coefficients[2] * cos(2 * pi * x) + model1$coefficients[1]
lines(x=x, y=y, col="red")
ggplot variant: If you decide to switch to the popular 'ggplot2' package for your plot, you would do it like so:
x <- seq(1920, (1940 - 1/12), by=1/12)
y.seas.mean <- rep(model$coefficients, 20)
y.har.cos <- model1$coefficients[2] * cos(2 * pi * x) + model1$coefficients[1]
plot_Data <- melt(data.frame(x=x, temp=nottem, seas.mean=y.seas.mean, har.cos=y.har.cos), id="x")
ggplot(plot_Data, aes(x=x, y=value, col=variable)) + geom_line()