I have a data.frame df with matching columns that are also paired. The matching columns are defined in the factor patient. I would like to devide the matching columns by each other. Any suggestions how to do this?
I tried this, but this does not take the pairing from patient into account.
m1 <- m1[sort(colnames(df)]
m1_g <- m1[,grep("^n",colnames(df))]
m1_r <- m1[,grep("^t",colnames(df))]
m1_new <- m1_g/m1_r
m1_new
head(df)
na-008 ta-008 nc012 tb012 na020 na-018 ta-018 na020 tc020 tc093 nc093
hsa-let-7b-5p_TGAGGTAGTAGGTTGTGT 56 311 137 242 23 96 113 106 41 114
hsa-let-7b-5p_TGAGGTAGTAGGTTGTGTGG 208 656 350 713 49 476 183 246 157 306
hsa-let-7b-5p_TGAGGTAGTAGGTTGTGTGGT 631 1978 1531 2470 216 1906 732 850 665 909
hsa-let-7b-5p_TGAGGTAGTAGGTTGTGTGGTT 2760 8159 6067 9367 622 4228 2931 3031 2895 2974
hsa-let-7b-5p_TGAGGTAGTAGGTTGTGTGGTTT 1698 4105 3737 3729 219 1510 1697 1643 1527 1536
> head(patient)
$`008`
[1] "na-008" "ta-008"
$`012`
[1] "nc012" "tb012"
$`018`
[1] "na-018" "ta-018"
$`020`
[1] "na020" "tc020"
$`045`
[1] "nb045" "tc045"
$`080`
[1] "nb-080" "ta-080"
Related
I would like estimate the parameters of the Gompert-Makeham distribution, but I haven't got a result.
I would like a method in R, like this Weibull parameter estimation code:
weibull_loglik <- function(parm){
gamma <- parm[1]
lambda <- parm[2]
loglik <- sum(dweibull(vec, shape=gamma, scale=lambda, log=TRUE))
return(-loglik)
}
weibull <- nlm(weibull_loglik,parm<-c(1,1), hessian = TRUE, iterlim=100)
weibull$estimate
c=weibull$estimate[1];b=weibull$estimate[2]
My data:
[1] 872 52 31 26 22 17 11 17 17 8 20 12 25 14 17
[16] 20 17 23 32 37 28 24 43 40 34 29 26 32 34 51
[31] 50 67 84 70 71 137 123 137 172 189 212 251 248 272 314
[46] 374 345 411 494 461 505 506 565 590 535 639 710 733 795 786
[61] 894 963 1019 1149 1185 1356 1354 1460 1622 1783 1843 2049 2262 2316 2591
[76] 2730 2972 3187 3432 3438 3959 3140 3612 3820 3478 4054 3587 3433 3150 2881
[91] 2639 2250 1850 1546 1236 966 729 532 375 256 168 107 65 39 22
[106] 12 6 3 2 1 1
summary(vec)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.0 32.0 314.0 900.9 1355.0 4054.0
It would be nice to have a reproducible example, but something like:
library(bbmle)
library(eha)
set.seed(101)
vec <- rmakeham(1000, shape = c(2,3), scale = 2)
dmwrap <- function(x, shape1, shape2, scale, log) {
res <- try(dmakeham(x, c(shape1, shape2), scale, log = log), silent = TRUE)
if (inherits(res, "try-error")) return(NA)
res
}
m1 <- mle2(y ~ dmwrap(shape1, shape2, scale),
start = list(shape1=1,shape2=1, scale=1),
data = data.frame(y = vec),
method = "Nelder-Mead"
)
Define a wrapper that (1) takes shape parameters as separate values; (2) returns NA rather than throwing an error when e.g. parameters are negative
Use Nelder-Mead rather than default BFGS for robustness
the fitdistrplus package might help too
if you're going to do a lot of this it may help to fit parameters on the log scale (i.e. use parameters logshape1, etc., and use exp(logshape1) etc. in the fitting formula)
I had to work a little harder to fit your data; I scaled the variable by 1000 (and found that I could only compute the log-likelihood; the likelihood gave an error that I didn't bother trying to track down). Unfortunately, it doesn't look like a great fit (too many small values).
x <- scan(text = "872 52 31 26 22 17 11 17 17 8 20 12 25 14 17
20 17 23 32 37 28 24 43 40 34 29 26 32 34 51
50 67 84 70 71 137 123 137 172 189 212 251 248 272 314
374 345 411 494 461 505 506 565 590 535 639 710 733 795 786
894 963 1019 1149 1185 1356 1354 1460 1622 1783 1843 2049 2262 2316 2591
2730 2972 3187 3432 3438 3959 3140 3612 3820 3478 4054 3587 3433 3150 2881
2639 2250 1850 1546 1236 966 729 532 375 256 168 107 65 39 22
12 6 3 2 1 1")
m1 <- mle2(y ~ dmwrap(shape1, shape2, scale),
start = list(shape1=1,shape2=1, scale=10000),
data = data.frame(y = x/1000),
method = "Nelder-Mead"
)
cc <- as.list(coef(m1))
png("gm.png")
hist(x,breaks = 25, freq=FALSE)
with(cc,
curve(exp(dmwrap(x/1000, shape1, shape2, scale, log = TRUE))/1000, add = TRUE)
)
dev.off()
I am trying to write a function to calculate Williams %R on data in R. Here is my code:
getSymbols('AMD', src = 'yahoo', from = '2018-01-01')
wr = function(high, low, close, n) {
highh = runMax((high),n)
lowl = runMin((low),n)
-100 * ((highh - close) / (highh - lowl))
}
williampr = wr(AMD$AMD.High, AMD$AMD.Low, AMD$AMD.Close, n = 10)
After implementing a buy/sell/hold signal, it returns integer(0):
## 1 = BUY, 0 = HOLD, -1 = SELL
## implement Lag to shift the time back to the previous day
tradingSignal = Lag(
## if wpr is greater than 0.8, BUY
ifelse(Lag(williampr) > 0.8 & williampr < 0.8,1,
## if wpr signal is less than 0.2, SELL, else, HOLD
ifelse(Lag(williampr) > 0.2 & williampr < 0.2,-1,0)))
## make all missing values equal to 0
tradingSignal[is.na(tradingSignal)] = 0
## see how many SELL signals we have
which(tradingSignal == "-1")
What am I doing wrong?
It would have been a good idea to identify that you were using the package quantmod in your question.
There are two things preventing this from working.
You didn't inspect what you expected! Your results in williampr are all negative. Additionally, you multiplied the values by 100, so 80% is 80, not .8. I removed -100 *.
I have done the same thing so many times.
wr = function(high, low, close, n) {
highh = runMax((high),n)
lowl = runMin((low),n)
((highh - close) / (highh - lowl))
}
That's it. It works now.
which(tradingSignal == "-1")
# [1] 13 15 19 22 39 71 73 84 87 104 112 130 134 136 144 146 151 156 161 171 175
# [22] 179 217 230 255 268 288 305 307 316 346 358 380 386 404 449 458 463 468 488 492 494
# [43] 505 510 515 531 561 563 570 572 574 594 601 614 635 642 644 646 649 666 668 672 691
# [64] 696 698 719 729 733 739 746 784 807 819 828 856 861 872 877 896 900 922 940 954 968
# [85] 972 978 984 986 1004 1035 1048 1060
The following is my code,
auto<-auto.arima(x)
auto_for<-forecast(auto,h=30)
> auto_for$x
Time Series:
Start = 1
End = 74
Frequency = 1
[1] 151 151 151 151 151 219 465 465 465 465 465 743 743 743 743 743 743 743 743 743 743 743
[23] 743 743 743 743 743 743 743 829 829 829 829 829 829 1004 1004 1004 1424 1424 1424 1822 1941 1941
[45] 1941 1941 1941 1941 1941 2076 2076 2252 2252 2252 2252 2252 2252 2252 2252 2252 2252 2252 2252 2940 2940 2940
[67] 2940 2940 3134 3134 3134 3207 3207 3465
> auto_for
Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
75 3510.397 3359.577 3661.217 3279.738 3741.056
76 3555.795 3342.503 3769.086 3229.594 3881.996
77 3601.192 3339.964 3862.419 3201.679 4000.705
78 3646.589 3344.949 3948.229 3185.271 4107.907
79 3691.986 3354.743 4029.230 3176.217 4207.755
80 3737.384 3367.952 4106.815 3172.387 4302.380
81 3782.781 3383.749 4181.812 3172.515 4393.047
82 3828.178 3401.595 4254.761 3175.776 4480.580
83 3873.575 3421.116 4326.035 3181.599 4565.552
84 3918.973 3442.039 4395.907 3189.565 4648.380
85 3964.370 3464.157 4464.582 3199.361 4729.379
86 4009.767 3487.312 4532.222 3210.741 4808.793
87 4055.164 3511.376 4598.953 3223.512 4886.817
88 4100.562 3536.246 4664.878 3237.515 4963.608
89 4145.959 3561.836 4730.081 3252.621 5039.297
90 4191.356 3588.077 4794.635 3268.720 5113.992
91 4236.753 3614.908 4858.599 3285.722 5187.785
I have the forecasted value, but I am not able to get the dates from the model. The dates are not present in the graph either and it has changed from 0 to 91, instead of my actual dates. I have used xts variable at the starting.
Update:
> a<-ts(ana)
> a
Time Series:
Start = 1
End = 68
Frequency = 1
final.day final.cumsum135
1 16535 318
2 16536 318
3 16537 318
4 16538 318
5 16539 318
6 16540 318
7 16541 318
8 16542 318
9 16543 318
10 16544 318
11 16545 318
12 16546 318
13 16547 318
14 16548 318
15 16549 318
16 16550 318
17 16551 318
18 16552 318
19 16553 318
20 16554 318
21 16555 318
22 16556 318
23 16557 318
24 16558 318
25 16559 318
26 16560 369
27 16561 369
28 16562 369
29 16563 369
30 16564 369
31 16565 369
32 16566 369
33 16567 369
34 16568 369
35 16569 369
> auto<-arima(a)
Error in arima(a) : only implemented for univariate time series
Is there any way I can get back the dates here?
Whit daily series, sometimes fitted and forecast "lost" dates. You could get dates by hand, using index:
y=x # x is your xts series
n=length(y)
model_a1 <- auto.arima(y)
# the plot
plot(x=1:n,y,xaxt="n",xlab="")
axis(1,at=seq(1,n,length.out=20),labels=index(y)[seq(1,n,length.out=20)],
las=2,cex.axis=.5)
lines(fitted(model_a1), col = 2)
#the forecast
auto_for<-forecast(model_a1,h=30)
fcs=xts(auto_for$mean,seq.Date(as.Date(index(y)[n]),by=1,length.out=30))
fcs
The data in the table is given below:
Year NSW Vic. Qld SA WA Tas. NT ACT Aust.
1 1917 1904 1409 683 440 306 193 5 3 4941
2 1927 2402 1727 873 565 392 211 4 8 6182
3 1937 2693 1853 993 589 457 233 6 11 6836
4 1947 2985 2055 1106 646 502 257 11 17 7579
5 1957 3625 2656 1413 873 688 326 21 38 9640
6 1967 4295 3274 1700 1110 879 375 62 103 11799
7 1977 5002 3837 2130 1286 1204 415 104 214 14192
8 1987 5617 4210 2675 1393 1496 449 158 265 16264
9 1997 6274 4605 3401 1480 1798 474 187 310 18532
I want to plot a graph with (Year) on my x-axis and (total value) on my Y-axis. The barplot should depicting the ACT and NT value for the respective (Years).
I tried the following command:
barplot(as.matrix(r_data$ACT, r_data$NT), main="r_data", ylab="Total", beside=TRUE)
The above command showed the barplot of ACT column per year but didn't show the Bar plot of NT column.
You have to create the matrix in a different way:
barplot(as.matrix(r_data[c("ACT", "NT")]),
main="r_data", ylab="Total", beside=TRUE)
You can also use cbind instead of as.matrix and keep the rest of your original approach:
barplot(cbind(r_data$ACT, r_data$NT),
main="r_data", ylab="Total", beside=TRUE)
So, I have these data given below, and my goal is to aggregate column v3 in terms of columns v1 and v2 and add the v3 values for each bin of v1 and v2. For example, the first line correspond to interval v1=21, v2=16, so the value of v3 will be aggregated over its (v1,v2) interval. And repeat this for the rest of rows. I want to use the mean as the aggregation function!
> df
v1 v2 v3
1 21.359 16.234 24.283
2 47.340 9.184 21.328
3 35.363 -13.258 14.556
4 -29.888 14.154 17.718
5 -10.109 -16.994 20.200
6 -32.387 1.722 15.735
7 49.240 -5.266 17.601
8 -38.933 2.558 16.377
9 41.213 5.937 21.654
10 -33.287 -4.028 19.525
11 -10.223 11.961 16.756
12 -48.652 16.558 20.800
13 44.778 27.741 17.793
14 -38.546 29.708 13.948
15 -45.622 4.729 17.793
16 -36.290 12.383 18.014
17 -19.626 19.767 18.182
18 -32.248 29.480 15.108
19 -41.859 35.502 8.490
20 -36.058 21.191 16.714
21 -23.588 0.524 21.471
22 -24.423 39.963 18.257
23 -0.042 -45.899 17.654
24 -35.479 32.049 9.294
25 -24.632 20.603 17.757
26 -26.591 25.882 18.968
27 -34.364 43.959 13.905
28 -19.334 29.728 20.102
29 12.304 -39.997 17.002
30 0.958 37.162 20.779
31 -35.475 -40.611 14.719
32 -39.268 44.382 11.247
33 -10.154 39.053 19.458
34 -12.612 32.056 17.759
35 2.730 -1.473 20.228
36 -45.326 -52.299 9.305
37 -1.996 -15.551 13.295
38 -26.655 -37.319 19.148
39 -18.509 -30.047 18.889
40 -22.705 -25.577 19.007
41 -15.705 -15.397 19.112
42 -2.637 9.790 10.548
43 -14.107 -3.145 19.654
44 -29.272 -19.906 18.503
45 -9.569 -4.632 11.334
46 2.114 18.048 14.744
47 -4.241 16.073 15.420
48 31.869 -3.394 21.559
49 20.425 35.205 22.250
50 -18.605 -8.866 20.082
51 -26.677 -7.690 21.850
52 -5.240 4.805 11.399
53 -6.766 2.538 6.292
54 4.567 22.554 19.682
55 -20.701 6.430 20.996
56 -23.972 16.141 17.976
57 -6.651 24.048 18.082
58 -32.243 -6.100 19.517
59 2.236 29.736 19.667
60 18.830 15.586 15.969
61 -9.598 28.414 17.806
62 -30.825 12.194 22.346
63 -17.415 15.795 18.135
64 -14.823 5.931 17.915
65 -14.234 12.882 13.001
66 9.937 18.368 20.421
67 -38.766 9.590 21.648
68 -30.896 27.047 16.453
69 -4.432 -10.562 10.061
70 -4.290 33.170 22.942
71 7.285 41.416 23.906
72 24.411 40.531 23.584
73 45.409 -32.420 20.831
74 49.341 -34.047 15.269
75 -7.730 -47.724 21.692
76 -10.563 -29.082 17.984
77 4.412 -41.182 16.845
78 31.822 -37.297 19.665
79 -43.355 31.093 17.688
80 -44.353 -44.723 13.832
81 -16.961 38.438 20.715
82 -21.225 -39.244 18.156
83 -42.022 -8.686 20.362
84 -42.904 -25.498 18.394
85 43.822 -25.990 21.287
86 43.013 -9.071 19.285
87 -36.901 -24.185 21.938
88 -28.251 -36.583 19.330
89 -19.830 -22.412 21.677
90 -3.789 -15.663 17.439
91 40.453 -21.796 17.432
92 -40.778 -31.188 18.762
93 -27.072 -48.609 18.913
94 -18.035 -1.791 19.909
95 -20.781 -7.912 22.563
96 47.307 -15.432 19.101
97 30.700 5.097 22.801
98 46.453 0.171 17.810
99 -27.439 -5.860 22.626
100 -30.526 -18.007 23.219
101 -18.280 -15.187 25.302
102 -18.367 6.044 18.864
103 41.265 -1.686 22.743
104 29.227 -14.814 19.196
105 -36.080 -32.715 18.930
106 7.475 7.061 25.002
107 -18.586 -45.207 21.864
108 35.227 11.148 21.388
109 -7.581 38.773 22.048
110 -43.685 14.083 22.037
111 -29.533 39.735 17.613
112 8.760 -39.400 22.421
113 -14.962 24.624 12.030
114 18.627 -32.888 23.036
115 -31.300 33.612 15.608
116 -38.024 45.839 16.567
117 -15.104 36.893 18.162
118 -12.809 -23.029 21.589
119 -21.614 36.264 16.680
120 42.917 -36.838 18.738
121 6.104 -14.961 14.468
122 44.032 -41.556 17.618
123 -24.493 21.886 17.366
124 -24.361 29.941 14.374
125 -25.060 43.383 16.437
126 -6.017 -24.640 19.207
127 -32.617 -40.549 18.059
128 -43.285 -43.364 18.827
129 -29.856 -46.089 16.881
130 -16.547 -43.619 22.547
131 -16.257 42.814 18.932
132 -9.236 -11.694 14.455
133 13.488 -35.422 24.436
134 -47.456 -32.714 18.123
135 39.476 -28.008 16.087
136 -21.933 -43.522 15.390
137 -17.347 -38.250 16.738
138 -4.948 -39.747 21.598
139 -31.018 -28.912 21.332
140 -36.364 30.461 17.542
141 -39.639 18.272 23.663
142 -24.162 -13.582 19.136
143 -8.935 -32.699 22.108
144 0.001 -19.219 17.888
145 -6.912 -24.885 20.683
146 7.785 -31.229 15.972
147 22.176 -7.478 21.335
148 8.755 -13.323 20.831
149 44.081 41.160 11.938
150 -8.451 -37.721 17.465
151 18.671 -2.776 23.374
152 12.668 -26.749 18.071
153 1.582 -21.252 20.750
154 20.832 -27.718 16.190
155 44.220 -45.690 12.598
156 -0.226 -37.737 17.634
157 -25.130 -19.197 23.170
158 2.086 -31.271 18.180
159 -20.445 -33.083 19.984
160 23.801 1.116 24.230
161 18.283 -17.922 20.256
162 -38.985 -13.770 20.702
163 -26.264 -27.413 20.276
164 10.396 -19.375 20.415
165 -16.343 -22.847 16.516
166 29.992 -8.215 21.661
167 35.052 -19.475 16.953
168 3.052 -6.800 22.509
169 -10.350 -5.413 19.222
170 14.371 -10.383 23.471
171 11.896 -4.191 21.773
172 18.152 8.741 23.669
173 25.748 -47.786 18.578
174 31.613 -0.735 23.898
175 12.660 25.645 23.549
176 2.933 29.345 25.170
177 9.369 18.791 26.817
178 15.805 4.798 27.866
179 27.556 -25.571 14.796
180 -5.112 -7.835 21.201
181 -30.571 3.471 20.496
182 19.816 -22.114 21.210
183 2.826 47.437 22.911
184 25.488 -33.064 21.442
185 44.826 42.162 22.994
186 25.208 -48.487 25.325
187 14.635 -17.430 17.083
188 -1.901 -33.370 22.163
189 12.306 -47.265 20.052
190 42.552 35.750 23.213
191 37.318 -46.069 22.599
192 4.725 -22.289 21.600
193 -40.815 -37.793 17.371
194 11.890 -12.862 14.286
195 35.251 -31.746 17.816
196 27.121 -27.638 19.677
197 36.024 -39.105 20.202
198 -47.119 41.940 17.526
199 0.837 -40.694 23.063
200 23.797 -39.795 20.198
201 -42.859 -21.372 23.554
202 39.407 -20.211 21.246
203 25.782 -18.892 20.423
204 34.529 -9.576 20.411
205 44.397 -13.247 23.180
206 5.534 6.856 14.248
207 31.598 -18.085 22.350
208 7.250 -0.481 15.453
209 -43.458 -15.204 23.193
210 -38.296 -31.524 21.776
211 4.276 -3.483 12.145
212 25.757 -11.708 22.360
213 15.634 37.478 24.624
214 -43.669 -3.197 20.742
215 45.381 6.365 21.351
216 -38.755 -6.877 20.879
217 -6.925 3.994 21.120
218 8.059 12.831 26.032
219 3.572 22.105 26.920
220 16.042 30.267 21.039
221 26.629 13.042 23.633
222 -12.126 -0.151 21.261
223 -11.981 24.600 19.236
224 29.480 28.362 21.838
225 -2.500 22.858 23.177
226 -41.163 19.863 20.059
227 35.953 27.401 19.101
228 -16.641 13.248 17.984
229 -3.778 14.090 18.943
230 11.643 34.817 21.621
231 34.921 38.666 17.359
232 25.621 22.451 22.866
233 34.936 17.384 19.836
234 40.017 37.599 13.987
235 19.547 33.838 22.575
236 11.197 39.977 19.347
237 16.972 -33.927 14.205
238 22.938 38.064 20.351
239 40.234 18.672 23.030
240 -0.846 42.320 18.383
241 -11.437 18.284 16.502
242 19.552 43.222 21.370
243 13.925 -46.486 18.917
244 41.709 -39.559 16.143
245 19.014 -44.563 17.796
246 32.260 33.114 18.402
247 -4.693 29.228 18.622
248 21.765 -38.452 15.147
249 39.157 -31.135 19.800
250 32.638 46.241 18.943
251 2.797 10.089 21.330
252 8.256 46.910 18.834
253 38.634 -2.429 20.413
254 28.642 2.763 19.580
255 0.456 1.422 7.452
256 3.050 11.792 14.196
257 24.736 14.532 17.886
258 16.787 -10.155 18.607
259 12.676 11.651 18.656
260 13.184 1.081 15.385
261 27.365 26.576 25.486
262 -7.878 -18.191 14.547
263 -42.112 32.576 20.865
264 15.069 21.684 17.986
265 33.045 27.166 25.252
266 21.810 -0.186 19.477
267 18.227 26.690 20.415
268 33.759 18.366 21.255
269 39.491 13.272 23.036
270 30.662 9.368 20.192
271 5.470 35.303 22.685
272 21.663 -44.343 20.999
273 31.261 33.178 24.335
274 21.854 22.665 20.876
275 21.853 7.932 18.588
276 -40.168 3.682 19.642
277 -42.292 23.997 22.199
278 10.233 28.731 21.263
279 17.745 41.831 19.536
280 38.406 25.165 26.534
281 -49.329 -0.465 20.887
282 40.398 -8.120 21.362
283 -2.531 46.118 22.933
284 7.959 -30.856 20.497
285 -34.467 -23.724 22.206
286 30.541 44.284 25.878
287 45.682 29.897 21.964
288 -22.251 -0.089 20.756
289 21.484 16.532 23.513
290 46.912 10.195 21.908
291 35.320 -13.352 16.102
292 -30.431 14.048 17.362
293 -8.976 -17.325 21.645
294 -32.661 2.301 16.805
295 49.317 -5.509 17.711
296 -37.756 4.459 16.054
297 41.445 6.158 21.442
298 -33.148 -3.499 19.543
299 -10.065 12.238 16.649
300 -48.323 17.153 20.974
301 45.010 28.147 17.838
302 -39.630 29.183 13.254
303 -45.191 5.065 18.214
304 -35.936 11.953 16.540
305 -19.816 19.624 18.279
306 -32.055 29.757 15.358
307 -41.533 36.169 10.005
308 -35.448 20.960 16.720
309 -23.384 0.511 20.005
310 -25.101 40.569 18.180
311 -0.547 -45.779 17.603
312 -35.291 32.643 9.548
313 -25.109 20.826 17.494
314 -26.202 27.012 18.678
315 -34.805 43.850 14.006
316 -18.819 30.611 20.309
317 13.019 -40.248 16.874
318 -0.655 37.112 20.924
319 -34.142 -41.553 15.237
320 -39.509 43.886 12.464
321 -9.491 38.639 18.839
322 -12.164 31.977 17.598
323 3.437 -1.596 20.318
324 -45.713 -52.599 9.918
325 -2.062 -15.946 12.847
326 -27.435 -37.600 18.257
327 -18.094 -29.624 18.791
328 -22.647 -26.123 18.746
329 -16.775 -15.505 19.204
330 -2.628 9.599 11.219
331 -15.718 -1.797 19.491
332 -29.476 -20.107 17.485
333 -10.618 -4.938 12.227
334 1.423 17.458 14.706
335 -4.503 16.630 14.718
336 32.450 -2.029 21.591
337 20.529 35.464 21.630
338 -19.348 -7.844 19.464
339 -26.760 -6.856 21.422
340 -4.539 4.393 11.819
341 -5.741 1.934 7.121
342 4.781 21.919 18.908
343 -19.797 6.928 20.928
344 -24.555 16.834 19.796
345 -5.664 24.465 18.432
346 -32.891 -6.571 18.691
347 2.354 28.462 19.825
348 18.058 16.251 16.335
349 -9.603 28.582 17.743
350 -31.282 11.454 22.342
351 -17.580 16.428 18.401
352 -13.884 6.206 17.270
353 -13.631 13.767 11.761
354 9.712 18.008 18.896
355 -37.987 9.024 21.309
356 -29.969 27.506 16.964
357 -4.248 -10.813 9.284
358 -5.755 32.673 22.541
359 6.675 41.952 24.227
360 24.564 41.173 23.241
361 45.314 -32.299 20.778
362 -45.890 -33.510 16.314
363 -8.277 -47.943 21.573
364 -11.044 -29.464 17.708
365 3.972 -41.396 17.411
366 31.776 -36.643 19.998
367 -43.072 31.311 17.828
368 -45.805 -43.071 14.477
369 -15.628 39.837 19.709
370 -21.129 -39.101 18.814
371 -41.628 -8.980 19.850
372 -42.244 -23.659 18.856
373 44.149 -25.710 21.099
374 42.623 -9.185 20.147
375 -35.949 -23.979 22.255
376 -28.512 -36.367 19.378
377 -19.827 -21.781 21.621
378 -3.429 -15.706 18.677
379 39.741 -20.721 18.670
380 -41.663 -29.499 19.260
381 -26.931 -48.467 18.185
382 -17.571 -1.467 19.770
383 -20.039 -7.591 22.737
384 46.370 -14.790 19.922
385 30.710 4.167 22.987
386 46.755 0.417 18.088
387 -27.293 -4.398 22.168
388 -30.364 -17.573 23.869
389 -16.870 -14.893 25.817
390 -18.152 6.546 18.392
391 40.134 0.160 23.661
392 28.179 -14.323 19.301
393 -35.907 -32.647 19.306
394 8.486 7.101 24.551
395 -17.155 -45.435 22.745
396 34.226 10.748 19.773
397 -7.760 38.754 22.211
398 -42.899 13.804 22.628
399 -29.972 40.435 17.784
400 8.764 -39.195 22.070
401 -15.624 25.585 12.291
402 18.620 -33.314 23.282
403 -30.436 34.219 15.102
404 -37.665 44.955 15.257
405 -15.861 37.488 18.956
406 -13.375 -22.408 20.312
407 -20.972 36.906 17.387
408 43.162 -35.948 19.695
409 6.639 -15.783 14.608
410 44.186 -41.037 17.398
411 -23.917 22.236 18.702
412 -23.957 30.033 14.725
413 -25.056 43.824 15.489
414 -6.795 -24.375 18.537
415 -33.485 -40.651 17.538
416 -43.186 -43.071 17.481
417 -30.325 -46.122 16.440
418 -17.489 -43.551 22.006
419 -16.376 43.928 18.992
420 -9.076 -10.921 14.131
421 13.704 -36.352 23.812
422 -47.302 -31.918 18.719
423 39.459 -27.814 15.558
424 -22.509 -42.660 14.366
425 -17.920 -37.614 16.572
426 -5.780 -39.212 21.667
427 -30.519 -28.942 21.931
428 -35.937 31.435 17.106
429 -38.680 18.435 23.342
430 -24.796 -13.279 18.543
431 -9.283 -32.388 21.895
432 0.493 -19.505 17.276
433 -7.046 -25.243 20.741
434 7.884 -32.006 16.727
435 22.451 -7.834 21.082
436 8.379 -13.690 22.002
437 43.730 41.697 11.894
438 -9.040 -38.086 17.500
439 18.831 -2.759 23.252
440 12.732 -27.410 18.948
441 0.739 -21.091 21.354
442 20.339 -27.959 16.514
443 44.688 -46.449 12.356
444 -0.402 -36.951 17.891
445 -24.790 -18.139 23.337
446 2.173 -30.577 18.023
447 -18.995 -33.799 20.730
448 23.372 0.223 24.855
449 17.835 -17.372 19.878
450 -38.915 -13.815 20.923
451 -26.241 -27.800 19.877
452 11.074 -18.156 19.249
453 -16.478 -22.928 16.386
454 29.646 -8.349 21.115
455 33.910 -20.809 16.629
456 3.306 -6.830 22.059
457 -10.512 -5.322 19.876
458 14.024 -10.406 23.456
459 12.365 -3.699 21.818
460 18.186 8.532 23.951
461 25.140 -47.653 18.592
462 32.288 -2.117 23.423
463 10.836 24.937 23.310
464 4.531 28.913 25.238
465 9.944 18.397 26.661
466 16.274 4.852 27.837
467 27.316 -26.007 15.934
468 -4.508 -8.010 20.906
469 -29.858 2.412 19.958
470 20.376 -21.957 21.306
471 2.077 47.431 23.248
472 25.777 -33.367 21.695
473 44.854 42.801 22.904
474 25.356 -48.833 25.402
475 15.322 -16.926 17.318
476 -2.656 -33.400 20.365
477 11.950 -47.390 20.328
478 42.961 36.955 22.919
479 35.726 -45.402 24.272
480 4.675 -21.758 21.780
481 -40.568 -36.931 16.934
482 11.758 -12.859 14.206
483 35.483 -31.760 16.975
484 27.336 -27.577 19.429
485 36.689 -39.218 19.668
486 -46.357 41.618 17.456
487 0.002 -40.589 22.558
488 23.525 -39.918 21.247
489 -43.269 -21.304 22.699
490 40.191 -20.594 21.145
491 25.728 -18.024 20.298
492 34.964 -10.441 20.189
493 43.627 -13.279 23.038
494 5.766 6.876 14.077
495 32.432 -18.172 21.848
496 7.087 -1.122 15.098
497 -44.110 -14.034 23.080
498 -39.474 -31.289 22.312
499 4.118 -4.077 11.067
500 26.597 -11.667 22.641
so, using these commands I can find the intervals, as below
x.bin <- seq(floor(min(d[,1])), ceiling(max(df[,1])), by=2)
y.bin <- seq(floor(min(d[,2])), ceiling(max(df[,2])), by=2)
> x.bin
[1] -50 -48 -46 -44 -42 -40 -38 -36 -34 -32 -30 -28 -26 -24 -22 -20 -18 -16 -14
[20] -12 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24
[39] 26 28 30 32 34 36 38 40 42 44 46 48 50
> y.bin
[1] -53 -51 -49 -47 -45 -43 -41 -39 -37 -35 -33 -31 -29 -27 -25 -23 -21 -19 -17
[20] -15 -13 -11 -9 -7 -5 -3 -1 1 3 5 7 9 11 13 15 17 19 21
[39] 23 25 27 29 31 33 35 37 39 41 43 45 47
But, then I don't know how to assign each row of the raw data (df) to each x.bin and y.bin and calculate the aggregate (sum) of each bin.
library(plyr)
#I am using cut function with 50 breaks for both v1 and v2 and ddply from plyr package for computing the mean
newdata<-ddply(df,.(cut(v1,50),cut(v2,50)),summarise,mean.v3=mean(v3))
> head(newdata)
cut(v1, 50) cut(v2, 50) mean.v3
1 (-49.4,-47.5] (-34.7,-32.7] 18.123
2 (-49.4,-47.5] (-0.576,1.43] 20.887
3 (-49.4,-47.5] (15.5,17.5] 20.887
4 (-47.5,-45.5] (-52.7,-50.7] 9.918
5 (-47.5,-45.5] (-44.7,-42.7] 14.477
6 (-47.5,-45.5] (-34.7,-32.7] 16.314
Updated as per the comments: If you want the lower, middle and mid-points, you can use the following function or use with details as follow(you need to use the sub function to deal with ( and ]):
df$newv1<-with(df,cut(v1,50))
df$newv2<-with(df,cut(v2,50))
df$lowerv1<-with(df,as.numeric( sub("\\((.+),.*", "\\1", newv1))) #lower value
df$upperv1<-with(df,as.numeric( sub("[^,]*,([^]]*)\\]", "\\1", newv1))) # upper value
df$midv1<-with(df,(lowerv1+upperv1)/2) #mid value
df$lowerv2<-with(df,as.numeric( sub("\\((.+),.*", "\\1",newv2))) #lower value
df$upperv2<-with(df,as.numeric( sub("[^,]*,([^]]*)\\]", "\\1", newv2))) # upper value
df$midv2<-with(df,(lowerv2+upperv2)/2)#mid value
newdata<-ddply(df,.(newv1,newv2),transform,mean.v3=mean(v3))
> head(newdata)
v1 v2 v3 newv1 newv2 lowerv1 upperv1 midv1 lowerv2 upperv2 midv2 mean.v3
1 -47.456 -32.714 18.123 (-49.4,-47.5] (-34.7,-32.7] -49.4 -47.5 -48.45 -34.700 -32.70 -33.700 18.123
2 -49.329 -0.465 20.887 (-49.4,-47.5] (-0.576,1.43] -49.4 -47.5 -48.45 -0.576 1.43 0.427 20.887
3 -48.652 16.558 20.800 (-49.4,-47.5] (15.5,17.5] -49.4 -47.5 -48.45 15.500 17.50 16.500 20.887
4 -48.323 17.153 20.974 (-49.4,-47.5] (15.5,17.5] -49.4 -47.5 -48.45 15.500 17.50 16.500 20.887
5 -45.713 -52.599 9.918 (-47.5,-45.5] (-52.7,-50.7] -47.5 -45.5 -46.50 -52.700 -50.70 -51.700 9.918
6 -45.805 -43.071 14.477 (-47.5,-45.5] (-44.7,-42.7] -47.5 -45.5 -46.50 -44.700 -42.70 -43.700 14.477