R Scaling a vector between -1 and 1 - r

I have a vector with 100 elements.
vec <- c(58.12, 51.97, 61.83, 53.46, 30.67, 38.8, 48.79, 56.82, 20.19,
53.1, 54.95, 46.45, 41.09, 51.76, 52.56, 44.63, 52.95, 30, 50.7,
56.33, 64.72, 39.99, 39.37, 33.82, 47.62, 51.28, 37.38, 50.55,
68.39, 53.88, 33.37, 29.69, 30.74, 47.51, 72.64, 47.88, 42.28,
62.71, 47.47, 71.45, 55.94, 39.5, 32.97, 28.81, 56.59, 49.79,
43.49, 41.97, 43.61, 30.09, 50.18, 63.88, 57.77, 41.57, 27.52,
38.47, 46.13, 41.85, 39.14, 46.38, 47.73, 61.51, 66.73, 56.28,
59.89, 47.38, 27.27, 17.41, 36.8, 27.21, 43.13, 43.68, 29.33,
53.76, 74.69, 29.56, 63.41, 31.61, 56.32, 49.68, 48.65, 46.81,
51.23, 65.23, 54.79, 84.64, 63.55, 32.4, 47.93, 68.13, 33.05,
30.21, 40.62, 48.28, 38.69, 31.72, 52.01, 64.17, 53.12, 35.03)
I want to scale this vector vec so that all the numbers between 0 to 50 are scaled from -1 to 0 and all number between 50 to 100 are scaled from 0 to 1.
I have written the following code to do this -
newvec = ifelse(vec < 50, -(vec/min(vec, na.rm = T)), vec/max(vec, na.rm = T))
plot(vec, newvec)
The output looks like (see the black circles) -
For the numbers above 50, scaling is fine, however, for the numbers below 50, the scaling is working in reverse order and is incorrect (as showing in the graph).
I have drawn a red line in this graph showing the correct scaling.
Can someone show, what I am doing wrong?
Thanks.


looks like a perfect linear equation!
m <- lm( y~x, data=data.frame( x=c(0,50,100), y=c(-1,0,1) ) )
coef(m)
gives:
(Intercept) x
-1.00 0.02
So multiply by 0.02 and subtract 1


You could do it by first rescaling it to 0-1 to then multiply it by two and subtracting one:
vec <- c(58.12, 51.97, 61.83, 53.46, 30.67, 38.8, 48.79, 56.82, 20.19,
53.1, 54.95, 46.45, 41.09, 51.76, 52.56, 44.63, 52.95, 30, 50.7,
56.33, 64.72, 39.99, 39.37, 33.82, 47.62, 51.28, 37.38, 50.55,
68.39, 53.88, 33.37, 29.69, 30.74, 47.51, 72.64, 47.88, 42.28,
62.71, 47.47, 71.45, 55.94, 39.5, 32.97, 28.81, 56.59, 49.79,
43.49, 41.97, 43.61, 30.09, 50.18, 63.88, 57.77, 41.57, 27.52,
38.47, 46.13, 41.85, 39.14, 46.38, 47.73, 61.51, 66.73, 56.28,
59.89, 47.38, 27.27, 17.41, 36.8, 27.21, 43.13, 43.68, 29.33,
53.76, 74.69, 29.56, 63.41, 31.61, 56.32, 49.68, 48.65, 46.81,
51.23, 65.23, 54.79, 84.64, 63.55, 32.4, 47.93, 68.13, 33.05,
30.21, 40.62, 48.28, 38.69, 31.72, 52.01, 64.17, 53.12, 35.03)
rescale_minMax <- function(x){
1 - (x - max(x)) / (min(x) - max(x))
}
newvec = rescale_minMax(vec) * 2 - 1
plot(vec, newvec)
Created on 2021-03-12 by the reprex package (v0.3.0)

Related

R: Extract single month data from a monthly time-series

Question
I have a monthly time series (object axts in the code below) for the period Jan 1993 - Dec 2019 and I would like to create a time series that contains all the data for a single month, e.g. february.
Details
This is its structure:
str(axts)
An ‘xts’ object on Jan 1993/Dec 2019 containing:
Data: num [1:324, 1] -28.3 -22.8 -67.1 -122.5 -72.7 ...
- attr(*, "dimnames")=List of 2
..$ : NULL
..$ : chr "SSH"
Indexed by objects of class: [yearmon] TZ: UTC
xts Attributes:
NULL
Here is its creation:
axts<-structure(c(-28.3, -22.8, -67.1, -122.5, -72.7, -102.6, 49.3, 107.2,
62.6, 98.8, 51.6, -12.8, -36.5, -22.6, -53.6, -16.3, -83.6,
-127.5, -40.2, -80.8, -71.4, -89.6, -78, -24.6, -41.8, -79.2,
-88.9, -111.3, -127.6, -97.6, -41.6, -6.3, -8, -15.8, -21.6,
1.5, 33.4, 71.5, 36, -18.8, 78.5, -20.4, 111.1, 93.5, 142.5,
110.4, 60, 78.6, 72.6, -5.2, -65.7, -102.1, -61, 109.8, 69.8,
67.7, 47.6, 64.7, 52.7, -20, -58, 41.7, 5.8, -3.1, 120.1, 180.3,
149.4, 213.7, 166.4, 78.5, 65.6, 12.1, -2.7, -46.1, -27.2,
72.8, 11.3, 46.4, 50.3, 40, 78.5, 154.8, 178.1, 99.9, 66.5,
-47.1, -76.7, -6.9, -23.6, 113.7, 37.6, 120.6, 136.9, 116.1,
79.6, 33.3, 66.8, -5.3, 8.3, 95, 81.7, 82.9, 118.6, 140, 139.8,
126.2, 157.6, 95.1, 95.7, 26.8, 116.6, 77.3, 106.7, -53.3, -104.4,
-88.1, -58.9, -17.8, 159.1, 179.8, 27.1, -88.5, -84.2, -34.3, 61,
102, 133.1, 162, 130, 124.8, 133.7, 109.6, 21.9, 123, 113.5, 102.4,
117.2, 24.9, 45.6, 71.2, 163.8, 118, 54.2, 87.4, 97.9, 127.2, 11.6,
-84.1, 15.1, 5.9, 71, 140.6, 47.4, 26.4, 36.2, 5.4, -5.4, -46.8, -25.3,
-79.4, -45.8, 40.1, -52.8, -95.5, -56.5, 175.6, 140.6, -10.5, -77.7, -26.8,
73.6, 13.4, 15.4, 128.1, 129.2, 50.6, 7.5, -27.6, 19.2, 61, 84.3, -31.2,
-19.1, 31.8, 22.4, -31, -93.9, -25.8, -4.1, 87.3, -12.8, -125.8, -58.1,
74.5, 41, -141.6, -142.1, -92.9, 25.6, 50.8, 139.3, 126.1, -10.4, 45.1,
92.2, 47.4, -1.6, -46.9, 69.4, -52.3, 18.2, 194.8, 157.6, 37.3, 28.3, -18,
-46.8, -95.8, -146, -172.4, -14.7, -33.4, -46.4, 5.9, 146.3, 124.5, 82.8,
-5.9, -124.7, -108.4, -66.3, -61.8, -62.6, 29.6, 38.5, 8.3, 158.4, 80.2, 10.5,
27.1, 21.2, 21.2, 121.8, 94.3, -86.3, -49.4, 178.4, 100.3, 68.6, 106.5, 72.1, 58.6,
-42.5, -74.6, -85.3, -100.1, -122.5, -125.7, -109.3, -100.6, -17, 2.8, -58, -32.4,
-63.2, 28.4, -99.8, -198.3, -103, 36.3, 47.5, 85.4, 124.9, 61.6, -150.2, -174.6,
-182.3, -69.3, -48.8, -10.3, 82.3, 13.9, -40, 64.1, 126.1, 15.5, 49.8, 12.3, -105,
-23.9, -51.7, -25.6, -140.1, -73.1, -66.1, -57, -20.1, -50.1, 25.3, -67.9, -47.8,
6.3, 13.2, 48.1, -50.8, 26.5, -58.4, -70.6, 81.2, 41.9, -32.6, 25.1, 118.7, 82.6,
62.6, 13.9, -149.5, 8, -32.8, 70.8, 96.9, 146.7, 106.2, -21.3),
.Dim = c(324L, 1L), index = structure(c(725846400, 728524800, 730944000, 733622400, 736214400,
738892800, 741484800, 744163200, 746841600, 749433600,
752112000, 754704000, 757382400, 760060800, 762480000,
765158400, 767750400, 770428800, 773020800, 775699200,
778377600, 780969600, 783648000, 786240000, 788918400, 791596800,
794016000, 796694400, 799286400, 801964800, 804556800, 807235200,
809913600, 812505600, 815184000, 817776000, 820454400, 823132800,
825638400, 828316800, 830908800, 833587200, 836179200, 838857600,
841536000, 844128000, 846806400, 849398400, 852076800, 854755200,
857174400, 859852800, 862444800, 865123200, 867715200, 870393600,
873072000, 875664000, 878342400, 880934400, 883612800, 886291200,
888710400, 891388800, 893980800, 896659200, 899251200, 901929600,
904608000, 907200000, 909878400, 912470400, 915148800, 917827200,
920246400, 922924800, 925516800, 928195200, 930787200, 933465600,
936144000, 938736000, 941414400, 944006400, 946684800, 949363200,
951868800, 954547200, 957139200, 959817600, 962409600, 965088000,
967766400, 970358400, 973036800, 975628800, 978307200, 980985600,
983404800, 986083200, 988675200, 991353600, 993945600, 996624000,
999302400, 1001894400, 1004572800, 1007164800, 1009843200, 1012521600,
1014940800, 1017619200, 1020211200, 1022889600, 1025481600, 1028160000,
1030838400, 1033430400, 1036108800, 1038700800, 1041379200, 1044057600,
1046476800, 1049155200, 1051747200, 1054425600, 1057017600, 1059696000,
1062374400, 1064966400, 1067644800, 1070236800, 1072915200, 1075593600,
1078099200, 1080777600, 1083369600, 1086048000, 1088640000, 1091318400,
1093996800, 1096588800, 1099267200, 1101859200, 1104537600, 1107216000,
1109635200, 1112313600, 1114905600, 1117584000, 1120176000, 1122854400,
1125532800, 1128124800, 1130803200, 1133395200, 1136073600, 1138752000,
1141171200, 1143849600, 1146441600, 1149120000, 1151712000, 1154390400,
1157068800, 1159660800, 1162339200, 1164931200, 1167609600, 1170288000,
1172707200, 1175385600, 1177977600, 1180656000, 1183248000, 1185926400,
1188604800, 1191196800, 1193875200, 1196467200, 1199145600, 1201824000,
1204329600, 1207008000, 1209600000, 1212278400, 1214870400, 1217548800,
1220227200, 1222819200, 1225497600, 1228089600, 1230768000, 1233446400,
1235865600, 1238544000, 1241136000, 1243814400, 1246406400, 1249084800,
1251763200, 1254355200, 1257033600, 1259625600, 1262304000, 1264982400,
1267401600, 1270080000, 1272672000, 1275350400, 1277942400, 1280620800,
1283299200, 1285891200, 1288569600, 1291161600, 1293840000, 1296518400,
1298937600, 1301616000, 1304208000, 1306886400, 1309478400, 1312156800,
1314835200, 1317427200, 1320105600, 1322697600, 1325376000, 1328054400,
1330560000, 1333238400, 1335830400, 1338508800, 1341100800, 1343779200,
1346457600, 1349049600, 1351728000, 1354320000, 1356998400, 1359676800,
1362096000, 1364774400, 1367366400, 1370044800, 1372636800, 1375315200,
1377993600, 1380585600, 1383264000, 1385856000, 1388534400, 1391212800,
1393632000, 1396310400, 1398902400, 1401580800, 1404172800, 1406851200,
1409529600, 1412121600, 1414800000, 1417392000, 1420070400, 1422748800,
1425168000, 1427846400, 1430438400, 1433116800, 1435708800, 1438387200,
1441065600, 1443657600, 1446336000, 1448928000, 1451606400, 1454284800,
1456790400, 1459468800, 1462060800, 1464739200, 1467331200, 1470009600,
1472688000, 1475280000, 1477958400, 1480550400, 1483228800, 1485907200,
1488326400, 1491004800, 1493596800, 1496275200, 1498867200, 1501545600,
1504224000, 1506816000, 1509494400, 1512086400, 1514764800, 1517443200,
1519862400, 1522540800, 1525132800, 1527811200, 1530403200, 1533081600,
1535760000, 1538352000, 1541030400, 1543622400, 1546300800, 1548979200,
1551398400, 1554076800, 1556668800, 1559347200, 1561939200, 1564617600,
1567296000, 1569888000, 1572566400, 1575158400),
tzone = "UTC", tclass = "yearmon", tformat = "%m/%Y"), class = c("xts", "zoo"), .Dimnames = list(NULL, "SSH"))

Because the index of axts is of yearmon class we can use cycle to get the month number so for February (2nd month of the year):
axts[cycle(axts) == 2]
giving:
SSH
02/1993 -22.8
02/1994 -22.6
02/1995 -79.2
02/1996 71.5
02/1997 -5.2
02/1998 41.7
## ...snip...
If the reason you want this is to create a plot for each month then we don't even need that as it can be done simply using monthplot which produces 12 side by side plots, one for each month.
monthplot(as.ts(axts), ylab = "SSH")

I edited your dput because there were some minor errors in it but should be good now.
I done some transformations of your data so that it is only taking the value from on specific month of each year; in this case, February.
#do some transformations first
axts_df<-as.data.frame(axts)
library(data.table)
library(lubridate)
axts_df<-setDT(axts_df, keep.rownames = T)[]
axts_df$MonthYear<-my(axts_df$rn)
axts_df$Month<-month(axts_df$MonthYear)
#for February only:-
library(dplyr)
axts_df_feb<-axts_df%>%
filter(Month==2)
#convert back into time series
axts_df_feb$rn<-NULL
axts_df_feb$Month<-NULL
axts_df_feb_ts<-ts(axts_df_feb$SSH)
plot(axts_df_feb_ts)
The plot below gives you the value for every February of each year.
However, there is a way to do this in ggplot2, you could do it like this:-
ggplot(axts_df_feb, aes(x=MonthYear,y=SSH))+geom_line()+ggtitle("Values for every February (1993-2019)")

Problem to find the Maximum likelihood estimates of parameters of generalized normal distribution in R

First I estimated the parameters of exponentiated generalized normal distribution using the dataset (data1: generated from normal distribution). Then I used real dataset (data2) and tried to find the maximum likelihood estimates (MLE) using the AdequacyModel packages. It gives the error message (mentioned right below the code).
Why does the same code estimate for first dataset (data1), but it doesn't estimate for the second dataset (data2)?
Is my way of plugging the baseline distribution (normal) in generalized class of distributions F(x)= [1-(1-G(x))^beta]^gamma introduced by Cordeiro et al.(2013) in R environment right? Am I defining the cdf and pdf of an generalized normal distribution in R in right way?
# For first dataset
data1 <- rnorm(100)
pdf_exps <- function(par,x){
beta = par[1]
gamma= par[2]
mean = par[3]
sd = par[4]
( beta*gamma* ((1-(1-(pnorm(x,mean,sd)))^beta)^(gamma-1)) * ((1-
(pnorm(x,mean,sd)))^(beta-1)) ) * (dnorm(x,mean,sd))
}
cdf_exps <- function(par,x){
beta = par[1]
gamma= par[2]
mean = par[3]
sd = par[4]
( 1-(1-(pnorm(x,mean,sd)))^beta)^gamma
}
set.seed(1)
result = goodness.fit(pdf = pdf_exps, cdf = cdf_exps,
starts = c(1,1,1,1),data = data1 , method = "BFGS",
domain = c(-Inf,Inf), lim_inf = c(0,0,0,0),
lim_sup = c(2,2,2,2), S = 250, prop=0.1, N=50)
result$mle
[1] 0.5976990 0.1541794 1.0073006 0.4689187
# For second data set
data2 <-c( 20.56, 20.67, 21.86, 21.88, 18.96, 21.04, 21.69, 20.62, 22.64, 19.44, 25.75, 21.20,
22.03, 25.44, 22.63, 21.86, 22.27, 21.27, 23.47, 23.19, 23.17, 24.54, 22.96, 19.76,
23.36, 22.67, 24.24, 24.21, 20.46, 20.81, 20.17, 23.06, 24.40, 23.97, 22.62, 19.16,
21.15, 21.40, 21.03, 21.77, 21.38, 21.47, 24.45, 22.63, 22.80, 23.58, 20.06, 23.01,
24.64, 18.26, 24.47, 23.99, 26.24, 20.04, 25.72, 25.64, 19.87, 23.35, 22.42, 20.42,
22.13, 25.17, 23.72, 21.28, 20.87, 19.00, 22.04, 20.12, 21.35, 28.57, 26.95, 28.13,
26.85, 25.27, 31.93, 16.75, 19.54, 20.42, 22.76, 20.12, 22.35, 19.16, 20.77, 19.37,
22.37, 17.54, 19.06, 20.30, 20.15, 25.36, 22.12, 21.25, 20.53, 17.06, 18.29, 18.37,
18.93, 17.79, 17.05, 20.31, 22.46, 23.88, 23.68, 23.15, 22.32, 24.02, 23.29, 25.11,
22.81, 26.25, 21.38, 22.52, 26.73, 23.57, 25.84, 24.06, 23.85, 25.09, 23.84, 25.31,
19.69, 26.07, 25.50, 23.69, 26.79, 25.61, 25.06, 24.93, 22.96, 20.69, 23.97, 24.64,
25.93, 23.69, 25.38, 22.68, 23.36, 22.44, 22.57, 19.81, 21.19, 20.39, 21.12, 21.89,
29.97, 27.39, 23.11, 21.75, 20.89, 22.83, 22.02, 20.07, 20.15, 21.24, 19.63, 23.58,
21.65, 25.17, 23.25, 32.52, 22.59, 30.18, 34.42, 21.86, 23.99, 24.81, 21.68, 21.04,
23.12, 20.76, 23.13, 22.35, 22.28, 23.55, 19.85, 26.51, 24.78, 33.73, 30.18, 23.31,
24.51, 25.37, 23.67, 24.28, 25.82, 21.93, 23.38, 23.07, 25.21, 23.25, 22.93, 26.86,
21.26, 25.43, 24.54, 27.79, 23.58, 27.56, 23.76, 22.01, 22.34, 21.07)
pdf_exps <- function(par,x){
beta = par[1]
gamma= par[2]
mean = par[3]
sd = par[4]
( beta*gamma* ((1-(1-(pnorm(x,mean,sd)))^beta)^(gamma-1)) *
((1-(pnorm(x,mean,sd)))^(beta-1)) ) * (dnorm(x,mean,sd))
}
cdf_exps <- function(par,x){
beta = par[1]
gamma= par[2]
mean = par[3]
sd = par[4]
( 1-(1-(pnorm(x,mean,sd)))^beta)^gamma
}
set.seed(1)
result = goodness.fit(pdf = pdf_exps, cdf = cdf_exps,
starts = c(1,1,1,1),data = data2 , method = "BFGS",
domain = c(-Inf,Inf), lim_inf = c(0,0,0,0),
lim_sup = c(2,2,2,2), S = 250, prop=0.1, N=50)
result$mle
Error in optim(par = starts, fn = likelihood, x = data, method = "BFGS",
:non-finite finite-difference value [1]

How do I store values after for() loop function?

I've been struggling for a few days with this problem, so help would be much appreciated. I have a line in a function that goes like this:
sig <- which(!ma.sig[1,]==0)&which(!pricebreak[1,]==0) ## Retrieves signals
sig <- which(sig)
for (i in 1:length(sig))
cursig[i] <- cur[i]
}
Basically what I'm doing is calculating signals, and if these signals are 0 or 1 I want R to save the corresponding variables. So for example, here's what the signal table might look like:
>ma.sig(MergedData)
Eur JPY GBP CHF AUD CAD NZD SEK NOK
1 0 1 0 1 1 0 0 0
>price.break(MergedData)
Eur JPY GBP CHF AUD CAD NZD SEK NOK
1 0 1 0 1 1 0 1 1
And so on for 29 variables. And here's the original currency, cur, vector:
> cur
[1] "EUR" "JPY" "GBP" "CHF" "AUD" "CAD" "NZD" "SEK" "NOK" "CZK" "HUF" "ILS" "PLN" "RUB" "TRY"
[16] "ZAR" "BRL" "CLP" "COP" "MXN" "PEN" "CNY" "IDR" "INR" "KRW" "MYR" "SGD" "PHP" "THB"
So basically I want to save a variable cursig such that it displays only the currencies with a signal of 1. So I'll have something like this:
cursig
[1] "GBP" " AUD" "CAD"....
And so on for whichever currency displays 1 for both signal functions. My problem is that
cursig[i] <- cur[i]
Returns the very last currency for which the signals are true. I know this is probably a very rookie mistake, but a nudge in the right direction would be much appreciated.
EDIT:
Here's the full function:
new.trend <- function(MergedData)
{
ret <- as.list(rep(NA, length(MergedData)))
ma.sig <- ma.crossover(MergedData)
pricebreak <- price.channel(MergedData)
sig <- which(!ma.sig[1,]==0)&which(!pricebreak[1,]==0) ## Retrieves signals
sig <- which(sig)
cursig <- character() #Preparing to save currency variables
for (i in 1:length(sig)) { #Calculates output variables based on active signals
x <- MergedData[[i]]
x <- xts(x[,-1], order.by=x[,1])
dev20 <- (x[,5]-SMA(x[,5], n=20))/x[,5]*100
dev50 <- (x[,5]-SMA(x[,5], n=50))/x[,5]*100
RSI <- RSI(x[,5], n=14)
cursig <- c(cursig, cur[1])
ret[[i]] <- na.omit(merge(tail(dev20, n=1L), tail(dev50, n=1L), tail(RSI, n=1L)))
}
na.omit(do.call(merge,ret))
}
print(new.trend(MergedData))
The reason I'm using a for() loop is because MergedData consits of 29 dataframes such that MergedData[[1] returns something like:
Date EUR.OPEN EUR.HIGH EUR.LOW EUR.CLOSE EUR.LAST
1 2016-08-30 1.1153 1.1192 1.1153 1.1161 1.1161
2 2016-08-29 1.1158 1.1208 1.1158 1.1189 1.1189
3 2016-08-28 1.1181 1.1341 1.1181 1.1198 1.1198
4 2016-08-27 1.1181 1.1341 1.1181 1.1198 1.1198
5 2016-08-26 1.1181 1.1341 1.1181 1.1198 1.1198
And so on for 29 currencies listed in cur.
Sample dput is here:
tzone = "", class = c("POSIXct",
"POSIXt")), PHP.OPEN = c(46.385, 46.283, 46.283, 46.283,
46.283, 46.33, 46.45, 46.45, 46.5, 46.29, 46.29, 46.29, 46.235,
46.28, 46.225, 46.445, 46.455, 46.455, 46.455, 46.365, 46.73,
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35.787, 35.706, 35.736, 35.736, 35.736, 35.8, 35.625,
35.557, 35.461, 35.466, 35.466, 35.466, 35.34, 35.294,
35.267, 35.166, 35.193, 35.193, 35.193, 35.19, 35.184,
34.94, 34.934, 35.009, 35.009, 35.009, 35.16, 35.195,
35.222, 35.17, 35.095, 35.095, 35.095, 35.095, 34.949,
35.02, 35.11, 35.12, 35.12, 35.12, 35.211, 35.055, 35.07,
35.112, 35.234, 35.234, 35.234, 35.253, 35.31, 35.39,
35.29, 35.175, 35.175, 35.175, 35.262, 35.365, 35.44,
35.408, 35.365, 35.365, 35.365, 35.395, 35.13, 34.963,
34.952, 34.925, 34.925, 34.925, 34.975, 35.145, 35.15,
35.151, 35.26, 35.26, 35.26, 35.31, 35.393, 35.47, 35.495,
35.46, 35.46, 35.46, 35.559, 35.656, 35.655, 35.763,
35.707, 35.707, 35.707, 35.714, 35.759, 35.776, 35.781,
35.75, 35.75, 35.75, 35.63, 35.68, 35.671, 35.69, 35.64,
35.64, 35.64, 35.325, 35.428, 35.539, 35.612, 35.651,
35.651, 35.651, 35.755, 35.87, 35.771, 35.77, 35.861,
35.861, 35.861, 35.885, 35.874, 36.045, 36.055, 36.253,
36.253, 36.253, 36.29, 36.327, 36.325, 36.382, 36.374,
36.374, 36.374, 36.345, 36.338, 36.375, 36.345, 36.327,
36.327, 36.327, 36.35, 36.257, 36.177, 36.19, 36.033,
36.033, 36.033), THB.LOW = c(34.53, 34.596, 34.506, 34.506,
34.506, 34.539, 34.573, 34.564, 34.607, 34.528, 34.528,
34.528, 34.531, 34.577, 34.52, 34.577, 34.696, 34.696,
34.696, 34.731, 34.738, 34.911, 34.901, 34.796, 34.796,
34.796, 34.886, 34.739, 34.74, 34.72, 34.8, 34.8, 34.8,
34.84, 34.951, 34.98, 34.905, 34.87, 34.87, 34.87, 34.95,
34.97, 34.931, 34.918, 34.907, 34.907, 34.907, 35.029,
35.13, 35.103, 35.049, 35.169, 35.169, 35.169, 35.145,
35.149, 35.012, 35.011, 35.06, 35.06, 35.06, 35.097,
35.175, 35.238, 35.257, 35.114, 35.114, 35.114, 35.146,
35.173, 35.15, 35.162, 35.225, 35.225, 35.225, 35.151,
35.24, 35.12, 35.16, 35.147, 35.147, 35.147, 35.085,
35.189, 35.217, 35.26, 35.545, 35.545, 35.545, 35.57,
35.64, 35.691, 35.69, 35.56, 35.56, 35.56, 35.571, 35.625,
35.664, 35.56, 35.622, 35.622, 35.622, 35.641, 35.486,
35.32, 35.341, 35.311, 35.311, 35.311, 35.217, 35.19,
35.193, 35.09, 35.126, 35.126, 35.126, 35.075, 34.99,
34.83, 34.83, 34.885, 34.885, 34.885, 35.007, 35.09,
35.08, 35.052, 35.02, 35.02, 35.02, 34.883, 34.825, 34.88,
35.01, 35.051, 35.051, 35.051, 35.024, 34.951, 35.005,
35.04, 35.09, 35.09, 35.09, 35.15, 35.214, 35.237, 35.12,
35.09, 35.09, 35.09, 35.159, 35.189, 35.323, 35.274,
35.265, 35.265, 35.265, 35.188, 34.917, 34.88, 34.85,
34.745, 34.745, 34.745, 34.77, 34.99, 35.07, 35.05, 35.064,
35.064, 35.064, 35.239, 35.287, 35.365, 35.349, 35.385,
35.385, 35.385, 35.476, 35.54, 35.588, 35.618, 35.63,
35.63, 35.63, 35.65, 35.69, 35.715, 35.717, 35.54, 35.54,
35.54, 35.537, 35.6, 35.59, 35.552, 35.228, 35.228, 35.228,
35.228, 35.3, 35.388, 35.495, 35.48, 35.48, 35.48, 35.55,
35.77, 35.579, 35.591, 35.684, 35.684, 35.684, 35.775,
35.77, 35.89, 35.925, 36.026, 36.026, 36.026, 36.235,
36.272, 36.26, 36.29, 36.28, 36.28, 36.28, 36.256, 36.237,
36.235, 36.25, 36.17, 36.17, 36.17, 36.226, 36.103, 36.08,
36.033, 36.02, 36.02, 36.02), THB.CLOSE = c(34.558, 34.601,
34.565, 34.565, 34.565, 34.565, 34.618, 34.64, 34.67,
34.635, 34.635, 34.635, 34.605, 34.7, 34.575, 34.61,
34.81, 34.81, 34.81, 34.76, 34.751, 34.965, 34.974, 34.822,
34.822, 34.822, 34.927, 34.94, 34.75, 34.77, 34.83, 34.83,
34.83, 34.868, 34.97, 35.01, 35.05, 34.93, 34.93, 34.93,
34.995, 34.989, 35.01, 35.005, 34.94, 34.94, 34.94, 35.03,
35.16, 35.138, 35.11, 35.25, 35.25, 35.25, 35.173, 35.214,
35.134, 35.048, 35.086, 35.086, 35.086, 35.138, 35.23,
35.29, 35.35, 35.339, 35.339, 35.339, 35.173, 35.24,
35.173, 35.21, 35.235, 35.235, 35.235, 35.24, 35.25,
35.3, 35.18, 35.26, 35.26, 35.26, 35.21, 35.21, 35.23,
35.28, 35.629, 35.629, 35.629, 35.61, 35.67, 35.705,
35.75, 35.658, 35.658, 35.658, 35.6, 35.71, 35.73, 35.66,
35.679, 35.679, 35.679, 35.71, 35.6, 35.49, 35.37, 35.43,
35.43, 35.43, 35.32, 35.27, 35.236, 35.13, 35.149, 35.149,
35.149, 35.179, 35.184, 34.92, 34.841, 34.92, 34.92,
34.92, 35.02, 35.15, 35.16, 35.11, 35.04, 35.04, 35.04,
35.01, 34.86, 34.91, 35.03, 35.065, 35.065, 35.065, 35.12,
35.036, 35.02, 35.07, 35.12, 35.12, 35.12, 35.205, 35.256,
35.32, 35.263, 35.105, 35.105, 35.105, 35.185, 35.235,
35.426, 35.36, 35.27, 35.27, 35.27, 35.35, 35.118, 34.91,
34.89, 34.85, 34.85, 34.85, 34.78, 35.02, 35.132, 35.08,
35.16, 35.16, 35.16, 35.25, 35.32, 35.425, 35.42, 35.403,
35.403, 35.403, 35.51, 35.57, 35.625, 35.64, 35.64, 35.64,
35.64, 35.68, 35.7, 35.74, 35.755, 35.745, 35.745, 35.745,
35.548, 35.61, 35.61, 35.65, 35.58, 35.58, 35.58, 35.245,
35.36, 35.489, 35.51, 35.497, 35.497, 35.497, 35.56,
35.841, 35.76, 35.605, 35.73, 35.73, 35.73, 35.825, 35.79,
35.908, 36.021, 36.037, 36.037, 36.037, 36.263, 36.3,
36.27, 36.32, 36.366, 36.366, 36.366, 36.313, 36.25,
36.33, 36.28, 36.307, 36.307, 36.307, 36.285, 36.22,
36.15, 36.14, 36.02, 36.02, 36.02), THB.LAST = c(34.558,
34.601, 34.565, 34.565, 34.565, 34.565, 34.618, 34.64,
34.67, 34.635, 34.635, 34.635, 34.605, 34.7, 34.575,
34.61, 34.81, 34.81, 34.81, 34.76, 34.751, 34.965, 34.974,
34.822, 34.822, 34.822, 34.927, 34.94, 34.75, 34.77,
34.83, 34.83, 34.83, 34.868, 34.97, 35.01, 35.05, 34.93,
34.93, 34.93, 34.995, 34.989, 35.01, 35.005, 34.94, 34.94,
34.94, 35.03, 35.16, 35.138, 35.11, 35.25, 35.25, 35.25,
35.173, 35.214, 35.134, 35.048, 35.086, 35.086, 35.086,
35.138, 35.23, 35.29, 35.35, 35.339, 35.339, 35.339,
35.173, 35.24, 35.173, 35.21, 35.235, 35.235, 35.235,
35.24, 35.25, 35.3, 35.18, 35.26, 35.26, 35.26, 35.21,
35.21, 35.23, 35.28, 35.629, 35.629, 35.629, 35.61, 35.67,
35.705, 35.75, 35.658, 35.658, 35.658, 35.6, 35.71, 35.73,
35.66, 35.679, 35.679, 35.679, 35.71, 35.6, 35.49, 35.37,
35.43, 35.43, 35.43, 35.32, 35.27, 35.236, 35.13, 35.149,
35.149, 35.149, 35.179, 35.184, 34.92, 34.841, 34.92,
34.92, 34.92, 35.02, 35.15, 35.16, 35.11, 35.04, 35.04,
35.04, 35.01, 34.86, 34.91, 35.03, 35.065, 35.065, 35.065,
35.12, 35.036, 35.02, 35.07, 35.12, 35.12, 35.12, 35.205,
35.256, 35.32, 35.263, 35.105, 35.105, 35.105, 35.185,
35.235, 35.426, 35.36, 35.27, 35.27, 35.27, 35.35, 35.118,
34.91, 34.89, 34.85, 34.85, 34.85, 34.78, 35.02, 35.132,
35.08, 35.16, 35.16, 35.16, 35.25, 35.32, 35.425, 35.42,
35.403, 35.403, 35.403, 35.51, 35.57, 35.625, 35.64,
35.64, 35.64, 35.64, 35.68, 35.7, 35.74, 35.755, 35.745,
35.745, 35.745, 35.548, 35.61, 35.61, 35.65, 35.58, 35.58,
35.58, 35.245, 35.36, 35.489, 35.51, 35.497, 35.497,
35.497, 35.56, 35.841, 35.76, 35.605, 35.73, 35.73, 35.73,
35.825, 35.79, 35.908, 36.021, 36.037, 36.037, 36.037,
36.263, 36.3, 36.27, 36.32, 36.366, 36.366, 36.366, 36.313,
36.25, 36.33, 36.28, 36.307, 36.307, 36.307, 36.285,
36.22, 36.15, 36.14, 36.02, 36.02, 36.02)), .Names = c("Date",
"THB.OPEN", "THB.HIGH", "THB.LOW", "THB.CLOSE", "THB.LAST"
), row.names = c(NA, -243L), class = "data.frame"))
And the two functions listed within the original function is here:
price.channel <- function(MergedData)
{
ret <- as.list(rep(NA, length(MergedData)))
for (i in 1:length(MergedData)) {
x<- MergedData[[i]]
x <- xts(x[,-1], order.by=x[,1])
high <- runMax(x[,2], 20)
low <- runMin(x[,3], 20)
mid <- (high+low)/2
channel <- cbind(high, mid, low)
sig <- ifelse(sign(high-x[,5])>=0, sign(high-x[,5]), sign(low-x[,5]))
ret[[i]] <- sig
}
na.omit(do.call(merge, ret))
}
ma.crossover<- function(MergedData){
ma <- as.list(rep(NA, length(MergedData)))
for(i in 1:length(MergedData)) {
x <- MergedData[[i]]
x <- xts(x[,-1], order.by=x[,1])
ma20 <- SMA(x[,3], n=20)
ma50 <- SMA(x[,3], n=50)
ma[[i]] <- sign(ma20 - ma50)
}
na.omit(do.call(merge, ma))
}
And I want to create a table from new.trend with the following:
out.trend <- matrix(new.trend(MergedData), ncol = 3, byrow = TRUE, dimnames = list(paste0(cursig), c("% dev from 20MA", "% dev from 20MA", "RSI")))

I think manotheshark's answer is right about not needing a for loop and using intersect(), you could do it like this without relying on names():
The dummy data:
ma.sig <- read.table(text = "Eur JPY GBP CHF AUD CAD NZD SEK NOK
1 0 1 0 1 1 0 0 0", header = T)
pricebreak <- read.table(text = "Eur JPY GBP CHF AUD CAD NZD SEK NOK
1 0 1 0 1 1 0 1 1", , header = T)
cur <- c("EUR", "JPY","GBP", "CHF", "AUD", "CAD", "NZD", "SEK", "NOK", "CZK", "HUF", "ILS")
Manotheshark's method, without names():
sig <- intersect(which(!ma.sig[1,]==0), which(!pricebreak[1,]==0)) ## Retrieves signals
sig
[1] 1 3 5 6
cur[sig]
"EUR" "GBP" "AUD" "CAD"
Or your idea for a for loop with my comment:
cursig <- character()
for (i in sig) { #not 1:length(sig) thats always 1,2,3... in order
cursig <- c(cursig, cur[i])
}
cursig
[1] "EUR" "GBP" "AUD" "CAD"

If I understand what you're trying to get, a for loop should not be required. You can index the vectors on the conditions you're looking for
names(ma.sig[ma.sig == 1])
names(price.break[price.break == 1])
This will return the names that meet both conditions
intersect(names(ma.sig[ma.sig == 1]), names(price.break[price.break == 1]))
[1] "Eur" "GBP" "AUD" "CAD"

How to have R selectively calculate values for certain variables?

My dataset is a list of 29 dataframes, such that one data frame looks like this:
>MergedData[[1]]
Date EUR.HIGH EUR.LOW EUR.CLOSE
1 2016-08-30 1.1192 1.1153 1.1161
2 2016-08-29 1.1208 1.1158 1.1189
3 2016-08-28 1.1341 1.1181 1.1198
4 2016-08-27 1.1341 1.1181 1.1198
5 2016-08-26 1.1341 1.1181 1.1198
6 2016-08-25 1.1298 1.1259 1.1285
7 2016-08-24 1.1312 1.1245 1.1264
So using this list, I calculated 'signals' in R with values of -2,1 and 0. I want R to do calculations and leave them in an output for variables that have values that aren't zero. Here's what my signal looks like in R:
>price.break
EUR JPY GBP CHF AUD CAD NZD SEK
2016-02-19 NA NA NA NA NA NA NA NA
2016-02-20 0 -2 0 0 -2 0 0 0
2016-02-21 0 0 0 0 0 0 0 0
>ma.sig
EUR JPY GBP CHF AUD CAD NZD SEK
2016-02-19 NA NA NA NA NA NA NA NA
2016-02-20 0 1 0 0 1 0 0 0
2016-02-21 0 0 0 0 0 0 0 0
Since we have that both JPY and AUD have returned non-zero signals, I want R to calculate an output table that looks like this:
Currency Deviation from MA RSI
JPY
AUD
Here is my (rather terrible) attempt at trying to have R do this:
new.trend <- function(x)
{
ret <- as.list(rep(NA, length(x)))
for (i in 1:length(MergedData)){
x <- MergedData[[i]]
x <- xts(x[,-1], order.by=x[,1])
ma.sig <- ma.crossover(x)
pricebreak <- price.channel(x)
sig.col <- col(x)[which(!ma.sig[,i]&ma.sig[,i-1)]==0)&which(!pricebreak[,i]&pricebreak[,i-1]==0)]
sig.row <- row(x)[which(!ma.sig[,i]&ma.sig[,i-1)]==0)&which(!pricebreak[,i]&pricebreak[,i-1]==0)]
ma20 <- SMA(x[sig.row, sig.col], n=20)
ma50 <- SMA(x[sig.row, sig.col], n=50)
RSI <- RSI(x[sig.row, sig.col], n=14)
dev20 <- x[sig.row, sig.col]-ma20[sig.row, sig.col]/x[sig.row, sig.col]*100
dev50 <- x[sig.row, sig.col]-ma50[sig.row, sig.col]/x[sig.row, sig.col]*100
ret[[i]] <- merge(RSI, 20dev, 50dev)
}
na.omit(do.call(merge,ret))
}
With sig.col and sig.row, I'm hoping to extract the exact row/column number in order to selectively calculate the instances where we get nonnegative signals. I get the error saying 'incorrect number dimensions', and I have a strong feeling what I'm doing isn't correct anyway.
ma.crossover and pricebreak are just functions that calculate the following:
ma.crossover<- function(MergedData){
ma <- as.list(rep(NA, length(MergedData)))
for(i in 1:length(MergedData)) {
x <- MergedData[[i]]
x <- xts(x[,-1], order.by=x[,1])
ma20 <- SMA(x[,3], n=20)
ma50 <- SMA(x[,3], n=50)
ma[[i]] <- sign(ma20 - ma50)
}
na.omit(do.call(merge, ma))
}
##price channel
price.channel <- function(MergedData)
{
ret <- as.list(rep(NA, length(MergedData)))
for (i in 1:length(MergedData)) {
x<- MergedData[[i]]
x <- xts(x[,-1], order.by=x[,1])
high <- runMax(x[,2], 20)
low <- runMin(x[,3], 20)
mid <- (high+low)/2
channel <- cbind(high, mid, low)
ret[[i]] <- merge(sign(high-x[,5]), sign(x[,5]-low))
}
na.omit(do.call(merge, ret))
}
I've been stuck on this for some time, so comments or feedback would be very much appreciated. Here's a sample of my dput, if that helps:
tzone = "", class = c("POSIXct",
"POSIXt")), PHP.OPEN = c(46.385, 46.283, 46.283, 46.283,
46.283, 46.33, 46.45, 46.45, 46.5, 46.29, 46.29, 46.29, 46.235,
46.28, 46.225, 46.445, 46.455, 46.455, 46.455, 46.365, 46.73,
46.85, 46.423, 46.05, 46.05, 46.05, 46.975, 46.03, 46.035,
46.915, 47.017, 47.017, 47.017, 47.05, 46.825, 47.105, 47.12,
47, 47, 47, 47.055, 46.015, 46.725, 46.675, 46.671, 46.671,
46.671, 47.019, 47, 47.055, 47.033, 47, 47, 47, 46.85, 46.765,
46.765, 46.753, 46.465, 46.465, 46.465, 46.035, 46.49, 45.93,
46.955, 46.51, 46.51, 46.51, 46.475, 46.365, 46.31, 46.3,
44.623, 44.623, 44.623, 46.285, 46.31, 46.073, 46.07, 46.06,
46.06, 46.06, 45.859, 46, 45.728, 46.2, 46.452, 46.452, 46.452,
46.53, 46.64, 46.653, 46.69, 46.614, 46.614, 46.614, 46.429,
46.63, 46.788, 46.69, 46.714, 46.714, 46.714, 46.705, 46.465,
46.325, 46.055, 46.55, 46.55, 46.55, 46.458, 46.53, 46.753,
47.085, 47.085, 47.085, 47.085, 47.118, 46.595, 46.845, 46.225,
46.66, 46.66, 46.66, 46.722, 46.64, 46.728, 46.683, 46.285,
46.285, 46.285, 46.224, 46.04, 46.055, 46.06, 46.049, 46.049,
46.049, 46.154, 45.903, 46.017, 46, 46.12, 46.12, 46.12,
46.07, 46.165, 46.153, 45, 45.95, 45.95, 45.95, 45.91, 46.02,
46.235, 46.325, 46.227, 46.227, 46.227, 46.227, 46.227, 46.16,
46, 46.168, 46.168, 46.168, 46.357, 46.475, 46.61, 46.439,
46.595, 46.595, 46.595, 46.653, 46.835, 46.285, 46.772, 46.861,
46.861, 46.861, 47.02, 47.225, 47.3, 47.525, 47.076, 47.076,
47.076, 47.62, 47.613, 47.575, 47.585, 47.552, 47.552, 47.552,
47.51, 47.45, 47.403, 47.435, 47.45, 47.45, 47.45, 47.375,
47.445, 47, 47.665, 47.665, 47.665, 47.665, 47.719, 47.825,
47.67, 47.623, 47.615, 47.615, 47.615, 47.717, 47.809, 47.869,
47.72, 47.8, 47.8, 47.8, 47.695, 47.67, 47.586, 47.635, 47.63,
47.63, 47.63, 47.55, 47.335, 47.2, 47.233, 46.92, 46.92,
46.92, 46.98, 46.82, 46.875, 47, 47.17, 47.17, 47.17), PHP.HIGH = c(46.535,
46.438, 46.438, 46.438, 46.438, 46.583, 46.645, 46.59, 46.702,
46.488, 46.488, 46.488, 46.41, 46.88, 46.88, 46.77, 46.865,
46.865, 46.865, 46.8, 46.865, 46.975, 47.02, 47.04, 47.04,
47.04, 47.075, 47.358, 47.487, 47.03, 47.135, 47.135, 47.135,
47.18, 47.228, 47.24, 47.228, 47.155, 47.155, 47.155, 47.23,
47.158, 46.96, 46.89, 47.055, 47.055, 47.055, 47.22, 47.435,
47.49, 47.32, 47.585, 47.585, 47.585, 47.112, 47.345, 47.345,
46.895, 47.065, 47.065, 47.065, 47.935, 47.98, 47.95, 47.183,
46.975, 46.975, 46.975, 46.587, 46.64, 46.388, 46.445, 46.475,
46.475, 46.475, 46.455, 46.479, 47.065, 46.222, 46.155, 46.155,
46.155, 46.388, 46.12, 46.265, 46.45, 46.982, 46.982, 46.982,
46.615, 46.79, 47.065, 46.825, 46.71, 46.71, 46.71, 46.788,
46.889, 46.925, 46.86, 46.86, 46.86, 46.86, 46.947, 46.705,
46.457, 46.655, 46.8, 46.8, 46.8, 46.715, 46.75, 47.205,
47.385, 47.385, 47.385, 47.385, 47.245, 47.132, 47.75, 47.041,
46.964, 46.964, 46.964, 46.878, 46.842, 46.94, 46.874, 46.68,
46.68, 46.68, 46.46, 46.225, 46.16, 46.2, 46.25, 46.25, 46.25,
46.306, 46.487, 46.14, 46.18, 46.266, 46.266, 46.266, 46.17,
46.338, 46.295, 46.105, 47.02, 47.02, 47.02, 46.085, 46.262,
46.439, 46.537, 46.463, 46.463, 46.463, 46.463, 46.463, 46.39,
46.577, 46.379, 46.379, 46.379, 46.58, 46.925, 46.83, 46.607,
46.795, 46.795, 46.795, 46.855, 47.025, 47.035, 46.925, 47.125,
47.125, 47.125, 47.252, 47.307, 47.5, 47.605, 47.603, 47.603,
47.603, 47.62, 47.717, 47.66, 47.705, 47.905, 47.905, 47.905,
47.595, 47.695, 47.59, 47.555, 47.74, 47.74, 47.74, 47.488,
47.632, 47.795, 47.757, 47.757, 47.757, 47.757, 47.8, 47.925,
47.79, 47.74, 47.775, 47.775, 47.775, 47.945, 47.995, 48.005,
47.9, 47.911, 47.911, 47.911, 47.958, 47.895, 47.755, 47.885,
47.78, 47.78, 47.78, 47.748, 47.55, 47.46, 47.335, 47.173,
47.173, 47.173, 47.14, 47.062, 47.188, 47.158, 47.17, 47.17,
47.17), PHP.LOW = c(46.385, 46.283, 46.283, 46.283, 46.283,
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35.3, 35.18, 35.26, 35.26, 35.26, 35.21, 35.21, 35.23,
35.28, 35.629, 35.629, 35.629, 35.61, 35.67, 35.705,
35.75, 35.658, 35.658, 35.658, 35.6, 35.71, 35.73, 35.66,
35.679, 35.679, 35.679, 35.71, 35.6, 35.49, 35.37, 35.43,
35.43, 35.43, 35.32, 35.27, 35.236, 35.13, 35.149, 35.149,
35.149, 35.179, 35.184, 34.92, 34.841, 34.92, 34.92,
34.92, 35.02, 35.15, 35.16, 35.11, 35.04, 35.04, 35.04,
35.01, 34.86, 34.91, 35.03, 35.065, 35.065, 35.065, 35.12,
35.036, 35.02, 35.07, 35.12, 35.12, 35.12, 35.205, 35.256,
35.32, 35.263, 35.105, 35.105, 35.105, 35.185, 35.235,
35.426, 35.36, 35.27, 35.27, 35.27, 35.35, 35.118, 34.91,
34.89, 34.85, 34.85, 34.85, 34.78, 35.02, 35.132, 35.08,
35.16, 35.16, 35.16, 35.25, 35.32, 35.425, 35.42, 35.403,
35.403, 35.403, 35.51, 35.57, 35.625, 35.64, 35.64, 35.64,
35.64, 35.68, 35.7, 35.74, 35.755, 35.745, 35.745, 35.745,
35.548, 35.61, 35.61, 35.65, 35.58, 35.58, 35.58, 35.245,
35.36, 35.489, 35.51, 35.497, 35.497, 35.497, 35.56,
35.841, 35.76, 35.605, 35.73, 35.73, 35.73, 35.825, 35.79,
35.908, 36.021, 36.037, 36.037, 36.037, 36.263, 36.3,
36.27, 36.32, 36.366, 36.366, 36.366, 36.313, 36.25,
36.33, 36.28, 36.307, 36.307, 36.307, 36.285, 36.22,
36.15, 36.14, 36.02, 36.02, 36.02), THB.LAST = c(34.558,
34.601, 34.565, 34.565, 34.565, 34.565, 34.618, 34.64,
34.67, 34.635, 34.635, 34.635, 34.605, 34.7, 34.575,
34.61, 34.81, 34.81, 34.81, 34.76, 34.751, 34.965, 34.974,
34.822, 34.822, 34.822, 34.927, 34.94, 34.75, 34.77,
34.83, 34.83, 34.83, 34.868, 34.97, 35.01, 35.05, 34.93,
34.93, 34.93, 34.995, 34.989, 35.01, 35.005, 34.94, 34.94,
34.94, 35.03, 35.16, 35.138, 35.11, 35.25, 35.25, 35.25,
35.173, 35.214, 35.134, 35.048, 35.086, 35.086, 35.086,
35.138, 35.23, 35.29, 35.35, 35.339, 35.339, 35.339,
35.173, 35.24, 35.173, 35.21, 35.235, 35.235, 35.235,
35.24, 35.25, 35.3, 35.18, 35.26, 35.26, 35.26, 35.21,
35.21, 35.23, 35.28, 35.629, 35.629, 35.629, 35.61, 35.67,
35.705, 35.75, 35.658, 35.658, 35.658, 35.6, 35.71, 35.73,
35.66, 35.679, 35.679, 35.679, 35.71, 35.6, 35.49, 35.37,
35.43, 35.43, 35.43, 35.32, 35.27, 35.236, 35.13, 35.149,
35.149, 35.149, 35.179, 35.184, 34.92, 34.841, 34.92,
34.92, 34.92, 35.02, 35.15, 35.16, 35.11, 35.04, 35.04,
35.04, 35.01, 34.86, 34.91, 35.03, 35.065, 35.065, 35.065,
35.12, 35.036, 35.02, 35.07, 35.12, 35.12, 35.12, 35.205,
35.256, 35.32, 35.263, 35.105, 35.105, 35.105, 35.185,
35.235, 35.426, 35.36, 35.27, 35.27, 35.27, 35.35, 35.118,
34.91, 34.89, 34.85, 34.85, 34.85, 34.78, 35.02, 35.132,
35.08, 35.16, 35.16, 35.16, 35.25, 35.32, 35.425, 35.42,
35.403, 35.403, 35.403, 35.51, 35.57, 35.625, 35.64,
35.64, 35.64, 35.64, 35.68, 35.7, 35.74, 35.755, 35.745,
35.745, 35.745, 35.548, 35.61, 35.61, 35.65, 35.58, 35.58,
35.58, 35.245, 35.36, 35.489, 35.51, 35.497, 35.497,
35.497, 35.56, 35.841, 35.76, 35.605, 35.73, 35.73, 35.73,
35.825, 35.79, 35.908, 36.021, 36.037, 36.037, 36.037,
36.263, 36.3, 36.27, 36.32, 36.366, 36.366, 36.366, 36.313,
36.25, 36.33, 36.28, 36.307, 36.307, 36.307, 36.285,
36.22, 36.15, 36.14, 36.02, 36.02, 36.02)), .Names = c("Date",
"THB.OPEN", "THB.HIGH", "THB.LOW", "THB.CLOSE", "THB.LAST"
), row.names = c(NA, -243L), class = "data.frame"))

How to create a table of cumulative distribution in R?

I have plotted cumulative distribution of speeds using ecdf but I also want to get the output of cumulative probability as a table like this:
Speed Cumulative Probability
40 0.20
45 0.45
55 0.51
60 0.70
70 0.90
80 1.00
For my data, when I use ecdf it gives me following (Note that 'cc' is my original data frame):
> ccf <- subset(cc, cc$svel>=55 & cc$Headway>=4)
> cdf<- ecdf(ccf$svel)
> cdf
Empirical CDF
Call: ecdf(ccf$svel)
x[1:356] = 55, 55.01, 55.02, ..., 76.76, 76.8
How can I get the table like the example above? Please note that I tried 'cumsum' but it only gives the cumulative frequency whereas I need cumulative probability.
EDIT
Here's my data:
dput(ccf$svel)
c(67.9, 67.62, 67.37, 67.19, 67.04, 66.93, 66.83, 66.74, 66.65,
66.55, 66.46, 66.36, 66.25, 66.12, 65.97, 61.12, 61.2, 61.29,
61.39, 61.49, 61.58, 61.66, 61.73, 61.79, 57.98, 57.73, 57.5,
57.29, 57.1, 56.92, 56.75, 56.59, 56.45, 56.32, 56.19, 58, 58.18,
58.36, 58.52, 58.69, 56.28, 56.19, 56.08, 55.96, 55.83, 55.68,
55.52, 55.34, 55.15, 58.58, 58.89, 59.17, 59.4, 59.58, 55.01,
55.14, 55.23, 55.3, 55.36, 55.41, 55.47, 55.53, 55.59, 55.66,
55.74, 55.83, 55.92, 56.03, 56.16, 56.3, 56.44, 56.58, 56.71,
56.82, 56.91, 56.98, 57.03, 57.06, 57.07, 57.07, 57.06, 57.04,
57.02, 55.05, 55.22, 55.39, 55.56, 55.73, 55.92, 56.11, 56.31,
56.53, 56.77, 57.02, 57.28, 57.54, 57.79, 58, 58.18, 58.32, 58.43,
58.5, 58.56, 58.6, 58.64, 58.68, 58.73, 58.8, 58.86, 58.92, 58.97,
59.01, 59.03, 59.05, 59.05, 59.04, 59.02, 58.99, 58.97, 58.95,
55.1, 55.39, 55.68, 55.97, 56.24, 56.48, 56.68, 56.82, 56.9,
56.94, 56.96, 56.97, 56.99, 57.02, 57.07, 57.14, 57.22, 57.3,
57.37, 57.41, 57.45, 57.48, 57.51, 57.56, 57.62, 57.69, 57.77,
57.86, 57.95, 58.06, 58.17, 58.29, 58.42, 58.53, 58.64, 58.74,
58.83, 58.91, 58.98, 55.01, 55.08, 55.15, 55.22, 55.3, 55.37,
55.45, 55.53, 55.62, 55.73, 55.85, 55.99, 56.14, 56.31, 56.49,
56.67, 56.87, 57.05, 57.22, 57.37, 57.51, 57.65, 57.79, 57.95,
58.13, 58.3, 58.47, 58.63, 58.78, 58.91, 59.03, 59.14, 59.24,
59.34, 59.43, 59.53, 59.62, 59.72, 59.81, 59.9, 59.98, 60.07,
60.15, 60.22, 60.31, 60.39, 60.47, 60.56, 60.65, 60.75, 60.86,
60.98, 61.11, 61.24, 61.39, 61.54, 61.71, 61.89, 62.09, 62.31,
62.56, 62.84, 63.14, 63.46, 63.78, 64.08, 64.81, 64.84, 64.85,
64.87, 64.89, 64.92, 64.94, 64.97, 65, 65.02, 65.04, 65.07, 65.11,
65.15, 65.17, 65.18, 65.17, 65.15, 65.13, 65.1, 65.06, 65.01,
64.96, 64.9, 64.84, 64.79, 64.76, 55.04, 55.15, 55.25, 55, 55.23,
55.45, 55.68, 55.9, 56.69, 56.74, 55, 55, 55, 55, 55, 55.01,
55.26, 55.51, 55.77, 56.02, 56.28, 56.56, 56.84, 57.13, 57.42,
57.7, 57.98, 58.25, 58.49, 58.73, 58.94, 59.13, 59.29, 59.4,
59.48, 59.5, 59.48, 59.42, 59.31, 59.17, 59, 58.8, 58.6, 58.38,
58.17, 57.96, 57.77, 57.59, 57.44, 57.31, 57.21, 57.13, 57.07,
57.04, 57.03, 57.04, 57.07, 57.11, 57.18, 57.26, 57.34, 57.43,
57.51, 57.59, 57.68, 57.78, 57.88, 57.99, 58.08, 58.16, 58.22,
58.27, 58.3, 58.31, 58.31, 58.3, 58.27, 58.25, 58.22, 58.18,
58.14, 58.08, 58.01, 57.93, 57.84, 57.72, 57.59, 57.43, 57.27,
57.1, 56.93, 56.77, 56.63, 56.5, 56.38, 56.28, 56.19, 56.12,
56.05, 55.99, 55.94, 55.9, 55.88, 55.86, 55.85, 55.86, 55.87,
55.89, 55.9, 55.91, 55.91, 55.88, 55.84, 55.78, 55.71, 55.63,
55.56, 55.5, 55.45, 55.4, 55.37, 55.34, 55.32, 55.3, 55.29, 55.27,
55.26, 55.26, 55.25, 55.25, 55.26, 55.26, 55.27, 55.28, 55.29,
55.31, 55.33, 55.36, 55.39, 55.02, 55.07, 55.12, 55.16, 55.21,
55.26, 55.31, 55.04, 55.21, 55.38, 55.54, 55.71, 55.88, 56.05,
56.21, 56.38, 56.54, 56.71, 56.88, 57.04, 57.2, 57.35, 55.46,
55.59, 55.74, 55.92, 56.11, 56.32, 56.54, 56.77, 57.02, 57.28,
55.22, 55.28, 55.35, 55.42, 55.5, 55.58, 55.68, 55.78, 55.88,
56, 55.15, 55.45, 55.72, 55.94, 56.11, 56.22, 56.29, 56.33, 56.36,
56.4, 56.45, 56.51, 56.59, 56.69, 56.81, 56.95, 57.11, 57.27,
57.44, 57.61, 57.78, 57.95, 58.12, 58.29, 58.46, 58.63, 58.79,
58.94, 59.08, 59.21, 59.32, 59.41, 55.13, 55.3, 55.47, 55.65,
55.83, 56.02, 56.22, 56.43, 56.66, 56.9, 55.17, 56.02, 56.11,
56.21, 56.32, 56.42, 56.52, 57.18, 57.29, 57.42, 76.27, 76.28,
76.3, 76.33, 76.37, 76.41, 76.47, 76.54, 76.62, 76.7, 76.76,
76.8, 76.8, 55.08, 55.16, 55.24, 55.32, 55.4, 55.48, 55.12, 55.39,
55.67, 55.94, 56.21, 56.47, 56.72, 56.97, 57.19, 57.4, 57.58,
57.73, 57.87, 57.99, 58.11)

Here is a function that will do it:
cumprob <- function(y) {
fun <- function(y, x) length(y[y<x])/length(y)
prob<-sapply(y, fun, y=y)
data<- data.frame(value=unique(y[order(y)]), prob=unique(prob[order(prob)]))
}
Testing in your data (here I called it data):
cp<-cumprob(data)
head(cp)
value prob
1 55.00 0.00000000
2 55.01 0.01156069
3 55.02 0.01734104
4 55.04 0.01926782
5 55.05 0.02312139
6 55.07 0.02504817
Plot:
plot(cp)
Another quick and informal way I find pretty handy is using the hist function to automatically cut the data and get the midpoints.
Using your data as data:
h <- hist(data)
cum.prob <- data.frame(value=h$mids, prob=cumsum(h$counts)/sum(h$counts))
That gives you:
cum.prob
value prob
1 55 0.2793834
2 57 0.6319846
3 59 0.8285164
4 61 0.8786127
5 63 0.8921002
6 65 0.9479769
7 67 0.9749518
8 69 0.9749518
9 71 0.9749518
10 73 0.9749518
11 75 0.9749518
12 77 1.0000000

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