[R] subtract multiple columns from single column for Nash Sutcliffe efficiency

[iembry]

Hi everyone, I am having trouble using my own data in the Nash-Sutcliffe
efficiency (NSE) function.

In R, this is what I have done:
Vobsr <- read.csv("Observed_Flow.csv", header = TRUE, sep =",") # see data
below
Vsimr <- read.csv("1000Samples_Vsim.csv", header = TRUE, sep =",") # see
data below

Vobsr <- as.matrix(Vobsr[,-1]) # remove column 1 from analysis thus Vobsr is
101x1 double matrix (column 1 is date information)
Vsimr <- as.matrix(Vsimr[,-1]) # remove column 1 from analysis thus Vsimr is
101x1000 double matrix (column 1 is date information)

How can I subtract each row in "Vsimr" from each row in "Vobsr" (Vobsr -
Vsimr)? That is the problem that I am having that keeps me from using the
following code below for NSE (Nash-Sutcliffe efficiency):

NSE(Vobsr,Vsimr)
NSE = 1 - (sum((obs - sim)^2)/(sum(obs - mean(obs))^2)) # obs is Vobsr and
sim is Vsimr


I would like to thank each of you in advance for your assistance.


I am including some of the data from the files that I am operating on:

1 column of Observed_Flow.csv
81.071
73.187
66.991
62.482
59.662
58.529
59.085
61.328
65.259
70.878
78.184
87.179
97.862
110.23
124.29
140.08
157.57
176.76
197.63
220.18
244.4
270.31
297.88
327.14
358.09
390.71
425.03
461.03
498.72
538.09
579.16
621.91
666.35
712.48
760.29
809.8
860.99
913.87
968.44
1024.7
1082.6
1142.3
1203.6
1266.6
1331.3
1397.7
1465.7
1535.5
1606.9
1680.1
1754.9
1831.4
1907.1
1981.9
2055.9
2129
2201.3
2272.7
2343.3
2413.1
2482
2550.1
2617.3
2683.7
2749.2
2813.9
2877.8
2940.8
3003
3064.3
3124.8
3184.4
3243.2
3301.1
3358.2
3414.5
3469.9
3524.4
3578.2
3631
3683.1
3734.3
3784.6
3834.1
3882.8
3930.6
3977.6
4023.7
4069
4113.4
4157
4199.8
4241.7
4282.7
4323
4362.3
4400.9
4438.6
4475.4
4511.4
4546.6


2 columns of 1000 columns of 1000Samples_Vsim.csv
81.07	81.07
73.19	73.19
65.81	67.16
58.93	63
52.55	60.7
46.68	60.25
41.31	61.67
36.44	64.95
32.08	70.08
28.22	77.08
24.86	85.94
22.01	96.65
19.65	109.23
17.8	123.67
16.46	139.96
15.61	158.12
15.27	178.14
15.43	200.02
16.1	223.75
17.27	249.35
18.94	276.81
21.11	306.13
23.79	337.31
26.97	370.34
30.65	405.24
34.84	442
39.52	480.62
44.71	521.1
50.41	563.44
56.61	607.64
63.31	653.7
70.51	701.62
78.21	751.4
86.42	803.04
95.13	856.53
104.35	911.89
114.06	969.11
124.28	1028.2
135.01	1089.1
146.23	1151.9
157.96	1216.6
170.19	1283.1
182.93	1351.5
196.16	1421.7
209.9	1493.8
224.15	1567.8
238.89	1643.6
254.14	1721.3
269.89	1800.8
286.15	1882.2
302.91	1965.5
320.17	2050.6
337.18	2134.8
353.93	2218.1
370.44	2300.4
386.69	2381.8
402.7	2462.3
418.45	2541.8
433.95	2620.4
449.2	2698.1
464.2	2774.9
478.94	2850.7
493.44	2925.6
507.68	2999.5
521.67	3072.6
535.41	3144.7
548.9	3215.8
562.14	3286.1
575.12	3355.4
587.86	3423.8
600.34	3491.2
612.57	3557.7
624.55	3623.3
636.28	3688
647.76	3751.7
658.98	3814.5
669.96	3876.4
680.68	3937.3
691.15	3997.3
701.37	4056.4
711.34	4114.6
721.06	4171.8
730.52	4228.1
739.74	4283.4
748.7	4337.9
757.41	4391.4
765.87	4443.9
774.08	4495.6
782.04	4546.3
789.74	4596.1
797.2	4644.9
804.4	4692.8
811.35	4739.8
818.05	4785.9
824.5	4831
830.7	4875.2
836.64	4918.5
842.33	4960.8
847.78	5002.2
852.97	5042.7
857.91	5082.3




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