I have a matrix that looks like the following. For rows 1:23, I would like to calculate the weighted mean, where the data in rows 1:23 are the weights and row 24 is the data.
1 107 33 41 22 12 4 122 44 297 123 51 16 7 9 1 1 0
10 5 2 2 1 0 3 4 6 12 3 3 0 1 1 0 0 0
11 1 3 1 0 0 0 4 2 8 3 4 0 0 0 0 0 0
12 2 1 1 0 0 0 2 1 5 6 3 1 0 0 0 0 0
13 1 0 1 0 0 0 3 1 3 5 2 2 0 1 0 0 0
14 3 0 0 0 0 0 3 1 2 3 0 1 0 0 0 0 0
15 0 0 0 0 0 0 2 0 0 1 0 1 0 0 0 0 0
16 0 0 0 0 1 0 0 0 2 0 0 0 0 0 0 0 0
17 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
18 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
19 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
2 80 27 37 5 6 4 97 48 242 125 44 27 7 8 8 0 2
20 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
21 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
22 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
23 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
3 47 12 33 12 6 1 63 42 200 96 45 19 6 6 9 2 0
4 45 14 21 9 4 2 54 26 130 71 36 17 8 5 1 0 2
5 42 10 14 6 3 2 45 19 89 45 26 7 4 8 2 1 0
6 17 3 12 5 2 0 18 21 51 41 19 15 5 1 1 0 0
7 16 2 6 0 0 1 14 9 37 23 17 7 3 0 3 0 0
8 9 4 4 2 1 0 7 9 30 15 8 3 3 1 1 0 1
9 12 2 3 1 1 1 6 5 14 12 5 1 2 0 0 1 0
24 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
As an example using the top two rows, there would have an additional column at the end indicated the weighted mean.
1 107 33 41 22 12 4 122 44 297 123 51 16 7 9 1 1 0 6.391011
10 5 2 2 1 0 3 4 6 12 3 3 0 1 1 0 0 0 6.232558
I'm a little new to coding so I wasn't too sure how to do it - any advice would be appreciated!
You can do:
apply(df[-nrow(df), ], 1, function(row) weighted.mean(df[nrow(df), ], row))
I'm assuming your first columns is some kind of index and not used for the weighted mean (and the data is stored in matr_dat):
apply(matr_dat[-nrow(matr_dat), -1], 1,
function(row) weighted.mean(matr_dat[nrow(matr_dat), -1], row))
Using apply and setting the margin to 1, the function defined in the third argument of apply to each row of the data; to calculate the weighted mean, you can use weighted.mean and set the weights to the values of the row.
Related
I have dummy coded the data in R using the package named "dummies". This gave me the output in various levels and create the dummies for each level. I want to consolidate all those levels on the basis of attributes. Please help me out! Thanks in advance. The following is the code I used:
#To read the data from the working directory
bankfull<-read.csv("bank.csv")
#Calling the library named dummies
library(dummies)
#Redefining the bankfull data with the dummy codes
bankfull<-dummy.data.frame(bankfull,sep=",")
#Viewing the data after dummy coding
print(bankfull)
The output is as follows:
#To read the data from the working directory
> bankfull<-read.csv("bank.csv")
> #Calling the library named dummies
> library(dummies)
dummies-1.5.6 provided by Decision Patterns
> #Redefining the bankfull data with the dummy codes
> bankfull<-dummy.data.frame(bankfull,sep=",")
> #Viewing the data after dummy coding
> View(bankfull)
> library(carData)
> #Viewing the data after dummy coding
> print(bankfull)
CHK_ACCT DURATION HISTORY NEW_CAR USED_CAR FURNITURE RADIO_TV EDUCATION RETRAINING AMOUNT SAV_ACCT EMPLOYMENT INSTALL_RATE MALE_DIV
1 0 6 4 0 0 0 1 0 0 1169 4 4 4 0
2 1 48 2 0 0 0 1 0 0 5951 0 2 2 0
3 3 12 4 0 0 0 0 1 0 2096 0 3 2 0
4 0 42 2 0 0 1 0 0 0 7882 0 3 2 0
5 0 24 3 1 0 0 0 0 0 4870 0 2 3 0
6 3 36 2 0 0 0 0 1 0 9055 4 2 2 0
7 3 24 2 0 0 1 0 0 0 2835 2 4 3 0
8 1 36 2 0 1 0 0 0 0 6948 0 2 2 0
9 3 12 2 0 0 0 1 0 0 3059 3 3 2 1
10 1 30 4 1 0 0 0 0 0 5234 0 0 4 0
11 1 12 2 1 0 0 0 0 0 1295 0 1 3 0
12 0 48 2 0 0 0 0 0 1 4308 0 1 3 0
13 1 12 2 0 0 0 1 0 0 1567 0 2 1 0
14 0 24 4 1 0 0 0 0 0 1199 0 4 4 0
15 0 15 2 1 0 0 0 0 0 1403 0 2 2 0
16 0 24 2 0 0 0 1 0 0 1282 1 2 4 0
17 3 24 4 0 0 0 1 0 0 2424 4 4 4 0
18 0 30 0 0 0 0 0 0 1 8072 4 1 2 0
19 1 24 2 0 1 0 0 0 0 12579 0 4 4 0
20 3 24 2 0 0 0 1 0 0 3430 2 4 3 0
21 3 9 4 1 0 0 0 0 0 2134 0 2 4 0
22 0 6 2 0 0 0 1 0 0 2647 2 2 2 0
23 0 10 4 1 0 0 0 0 0 2241 0 1 1 0
24 1 12 4 0 1 0 0 0 0 1804 1 1 3 0
25 3 10 4 0 0 1 0 0 0 2069 4 2 2 0
26 0 6 2 0 0 1 0 0 0 1374 0 2 1 0
27 3 6 0 0 0 0 1 0 0 426 0 4 4 0
28 2 12 1 0 0 0 1 0 0 409 3 2 3 0
29 1 7 2 0 0 0 1 0 0 2415 0 2 3 0
30 0 60 3 0 0 0 0 0 1 6836 0 4 3 0
31 1 18 2 0 0 0 0 0 1 1913 3 1 3 0
32 0 24 2 0 0 1 0 0 0 4020 0 2 2 0
MALE_SINGLE MALE_MAR_or_WID CO_APPLICANT GUARANTOR PRESENT_RESIDENT REAL_ESTATE PROP_UNKN_NONE AGE OTHER_INSTALL RENT OWN_RES NUM_CREDITS
1 1 0 0 0 4 1 0 67 0 0 1 2
2 0 0 0 0 2 1 0 22 0 0 1 1
3 1 0 0 0 3 1 0 49 0 0 1 1
4 1 0 0 1 4 0 0 45 0 0 0 1
5 1 0 0 0 4 0 1 53 0 0 0 2
6 1 0 0 0 4 0 1 35 0 0 0 1
7 1 0 0 0 4 0 0 53 0 0 1 1
8 1 0 0 0 2 0 0 35 0 1 0 1
9 0 0 0 0 4 1 0 61 0 0 1 1
10 0 1 0 0 2 0 0 28 0 0 1 2
11 0 0 0 0 1 0 0 25 0 1 0 1
12 0 0 0 0 4 0 0 24 0 1 0 1
13 0 0 0 0 1 0 0 22 0 0 1 1
14 1 0 0 0 4 0 0 60 0 0 1 2
15 0 0 0 0 4 0 0 28 0 1 0 1
16 0 0 0 0 2 0 0 32 0 0 1 1
17 1 0 0 0 4 0 0 53 0 0 1 2
18 1 0 0 0 3 0 0 25 1 0 1 3
19 0 0 0 0 2 0 1 44 0 0 0 1
20 1 0 0 0 2 0 0 31 0 0 1 1
21 1 0 0 0 4 0 0 48 0 0 1 3
22 1 0 0 0 3 1 0 44 0 1 0 1
23 1 0 0 0 3 1 0 48 0 1 0 2
24 1 0 0 0 4 0 0 44 0 0 1 1
25 0 1 0 0 1 0 0 26 0 0 1 2
26 1 0 0 0 2 1 0 36 1 0 1 1
27 0 1 0 0 4 0 0 39 0 0 1 1
28 0 0 0 0 3 1 0 42 0 1 0 2
29 1 0 0 1 2 1 0 34 0 0 1 1
30 1 0 0 0 4 0 1 63 0 0 1 2
31 0 1 0 0 3 1 0 36 1 0 1 1
32 1 0 0 0 2 0 0 27 1 0 1 1
JOB NUM_DEPENDENTS TELEPHONE FOREIGN RESPONSE
1 2 1 1 0 1
2 2 1 0 0 0
3 1 2 0 0 1
4 2 2 0 0 1
5 2 2 0 0 0
6 1 2 1 0 1
7 2 1 0 0 1
8 3 1 1 0 1
9 1 1 0 0 1
10 3 1 0 0 0
11 2 1 0 0 0
12 2 1 0 0 0
13 2 1 1 0 1
14 1 1 0 0 0
15 2 1 0 0 1
16 1 1 0 0 0
17 2 1 0 0 1
18 2 1 0 0 1
19 3 1 1 0 0
20 2 2 1 0 1
21 2 1 1 0 1
22 2 2 0 0 1
23 1 2 0 1 1
24 2 1 0 0 1
25 2 1 0 1 1
26 1 1 1 0 1
27 1 1 0 0 1
28 2 1 0 0 1
29 2 1 0 0 1
30 2 1 1 0 0
31 2 1 1 0 1
32 2 1 0 0 1
[ reached getOption("max.print") -- omitted 968 rows ]
In the output the dummies are given according to the levels. I need the output according to the attributes. Can I merge all of them without using any third party packages?
I hava a text file that i want to use for survival data analysis:
1 0 0 0 15 0 0 1 1 0 0 2 12 0 12 0 12 0
2 0 0 1 20 0 0 1 0 0 0 4 9 0 9 0 9 0
3 0 0 1 15 0 0 0 1 1 0 2 13 0 13 0 7 1
4 0 0 0 20 1 0 1 0 0 0 2 11 1 29 0 29 0
5 0 0 1 70 1 1 1 1 0 0 2 28 1 31 0 4 1
6 0 0 1 20 1 0 1 0 0 0 4 11 0 11 0 8 1
7 0 0 1 5 0 0 0 0 0 1 4 12 0 12 0 11 1
8 0 0 1 30 1 0 1 1 0 0 4 8 1 34 0 4 1
9 0 0 1 25 0 1 0 1 1 0 4 10 1 53 0 4 1
10 0 0 1 20 0 1 0 1 0 0 4 7 0 1 1 7 0
11 0 0 1 30 1 0 1 0 0 1 4 7 1 21 1 44 1
12 0 0 0 20 0 0 1 0 0 1 4 20 0 1 1 20 0
13 0 0 1 25 0 0 1 1 1 0 4 12 1 32 0 32 0
14 0 0 1 70 0 0 0 0 0 1 4 16 0 16 0 16 0
15 0 0 1 20 1 0 1 0 0 0 4 39 0 39 0 39 0
16 0 0 0 10 1 0 1 0 0 1 4 23 1 34 0 34 0
17 0 0 1 10 1 0 0 0 0 0 4 8 0 8 0 8 0
18 0 0 1 15 0 0 0 0 0 0 4 15 0 15 0 6 1
19 0 0 1 10 0 0 0 0 0 1 4 8 0 8 0 8 0
20 0 0 1 15 0 0 0 0 1 0 4 24 1 32 0 32 0
21 0 0 1 16 0 0 1 0 0 0 4 25 1 22 1 43 0
22 0 1 1 55 1 0 1 1 0 0 4 14 1 3 1 56 0
23 0 0 1 20 1 0 1 1 0 0 4 24 1 47 0 11 1
24 0 0 0 30 0 0 0 1 1 0 4 6 1 43 0 43 0
25 0 0 1 40 0 1 0 1 1 0 1 25 0 3 1 25 0
26 0 0 1 15 1 0 1 1 0 0 4 12 0 12 0 12 0
27 0 1 1 50 0 0 1 0 0 1 4 15 1 53 0 32 1
28 0 0 1 40 1 0 1 1 0 0 4 18 1 52 0 51 1
29 0 1 1 45 0 1 1 1 1 0 4 13 1 11 1 21 0
30 0 1 0 40 0 1 1 1 1 0 2 29 0 2 1 29 0
31 0 0 1 28 0 0 1 0 0 0 2 7 0 7 0 3 1
32 0 0 1 19 1 0 1 0 0 0 3 16 0 16 0 16 0
33 0 0 1 15 0 0 1 0 0 0 2 10 0 10 0 3 1
34 0 0 1 5 0 0 1 0 1 0 3 6 0 6 0 4 1
35 0 1 1 35 0 0 1 0 0 0 4 8 1 43 0 7 1
36 0 0 1 2 1 0 1 0 0 0 1 1 1 27 0 27 0
37 0 1 1 5 0 0 1 0 0 0 2 18 0 18 0 18 0
38 0 0 1 55 1 0 1 0 0 1 4 6 1 5 1 47 1
39 0 0 0 10 0 0 0 1 0 0 2 19 1 29 0 29 0
40 0 0 1 15 0 0 1 0 0 0 4 5 0 5 0 5 0
41 0 1 1 20 1 0 1 0 0 1 4 1 1 4 1 97 0
42 0 1 0 30 1 0 1 1 0 1 4 15 1 28 0 28 0
43 0 0 1 25 1 1 1 1 0 1 4 14 1 4 1 7 1
44 0 0 1 95 1 1 1 1 1 1 4 9 0 9 0 3 1
45 0 1 1 30 0 0 0 0 1 0 4 1 1 39 0 39 0
46 0 0 1 15 1 0 1 0 0 0 4 10 0 10 0 10 0
47 0 0 1 20 0 1 1 1 0 0 4 6 1 5 1 46 0
48 0 1 1 6 0 0 1 0 0 0 2 13 1 28 0 28 0
49 0 0 1 15 0 0 1 0 0 1 4 11 1 21 0 21 0
50 0 0 1 7 0 0 1 1 0 0 1 8 1 17 1 38 0
51 0 0 1 13 0 0 1 1 1 0 4 10 0 10 0 10 0
52 0 0 1 25 1 0 1 0 0 1 4 6 1 40 0 5 1
53 0 0 1 25 1 0 1 0 1 1 4 18 1 22 0 9 1
54 0 1 1 20 1 0 1 0 0 1 4 16 1 16 1 21 1
55 0 1 1 25 0 0 1 1 0 0 4 7 1 26 0 26 0
56 0 0 1 95 1 0 1 1 1 1 4 14 0 14 0 14 0
57 0 0 1 17 1 0 1 0 0 0 4 16 0 16 0 16 0
58 0 0 1 3 0 0 1 0 1 0 3 4 0 4 0 1 1
59 0 0 1 15 1 0 1 0 0 0 4 19 0 6 1 19 0
60 0 0 1 65 1 1 1 1 1 1 4 21 1 8 1 10 1
61 0 1 1 15 1 0 1 1 1 1 4 18 0 18 0 18 0
62 0 0 1 40 1 0 1 0 0 0 3 31 0 31 0 13 1
63 0 0 1 45 1 0 1 1 0 1 4 11 1 24 1 40 0
64 0 1 0 35 0 0 1 1 0 0 4 4 1 5 1 47 0
65 0 0 1 85 1 1 1 1 0 1 4 12 1 8 1 9 1
66 0 1 1 15 0 1 0 1 0 1 4 11 1 35 0 19 1
67 0 0 1 70 0 1 1 1 1 0 2 23 1 8 1 60 0
68 0 0 1 6 1 0 0 0 0 1 4 7 0 7 0 7 0
69 0 0 1 20 0 0 1 0 0 0 4 19 1 26 0 6 1
70 0 1 1 36 1 0 1 0 1 1 4 16 1 20 1 23 1
71 1 1 1 50 1 1 1 0 1 0 4 15 0 1 1 15 0
72 1 0 1 21 1 0 1 0 0 0 4 6 1 13 1 23 0
73 1 0 1 16 1 0 1 0 0 0 4 2 1 9 0 9 0
74 1 1 1 3 0 0 1 0 0 0 4 6 1 14 0 14 0
75 1 0 1 5 1 0 1 0 0 0 3 8 0 8 0 2 1
76 1 0 1 32 0 1 1 1 0 1 4 18 1 51 0 18 1
77 1 0 1 38 0 1 1 1 0 0 4 12 1 22 0 22 0
78 1 0 1 16 1 0 1 0 0 0 4 7 1 16 0 16 0
79 1 1 1 9 0 1 0 1 0 0 4 6 1 2 1 2 1
80 1 0 1 17 0 1 1 0 0 0 2 10 1 10 1 22 0
81 1 0 1 22 1 0 1 0 0 0 4 12 1 20 0 5 1
82 1 0 1 10 0 0 1 0 0 0 4 5 1 5 1 14 0
83 1 0 1 12 1 0 1 0 0 0 4 12 0 12 0 12 0
84 1 0 1 80 1 1 1 1 1 1 4 6 1 4 1 41 0
85 1 1 1 15 0 0 1 1 0 0 4 9 1 9 1 21 0
86 1 0 1 50 1 0 1 0 0 1 4 18 1 7 1 56 0
87 1 0 1 50 1 1 1 1 1 1 4 7 1 42 1 67 0
88 1 0 1 15 1 0 1 0 0 0 3 11 0 11 0 11 0
89 1 0 1 8 1 0 1 0 0 0 4 9 1 17 0 17 0
90 1 1 1 45 1 1 1 1 0 0 1 11 1 11 1 18 1
91 1 0 1 20 0 1 1 1 0 1 4 6 1 6 1 14 1
92 1 0 1 5 0 0 1 0 1 0 3 4 1 8 0 5 1
93 1 0 1 25 0 0 1 0 0 0 2 5 1 10 0 5 1
94 1 0 1 40 0 1 1 1 0 0 4 11 1 8 1 31 0
95 1 0 1 4 0 0 1 0 1 0 3 9 1 7 1 23 0
96 1 0 1 25 0 0 1 1 0 1 4 4 1 14 1 46 0
97 1 1 1 20 0 0 1 0 1 0 4 5 1 1 1 38 0
98 1 1 1 26 0 0 1 0 0 1 4 8 1 3 1 35 0
99 1 0 1 10 0 1 1 1 0 0 4 13 1 21 0 21 0
100 1 1 1 85 1 1 1 1 0 1 4 11 0 3 1 11 0
101 1 0 1 75 1 0 1 1 1 0 4 29 1 49 0 16 1
102 1 0 0 5 0 0 1 0 1 0 1 13 0 13 0 13 0
103 1 0 1 20 1 0 1 0 0 0 4 1 1 12 0 12 0
104 1 1 1 8 0 1 0 1 1 0 4 6 1 6 1 13 0
105 1 1 1 10 0 0 1 0 0 1 4 6 1 23 0 23 0
106 1 0 1 10 0 0 0 0 1 1 4 3 1 31 0 31 0
107 1 1 0 2 0 0 1 0 0 0 1 2 1 2 1 10 0
108 1 0 0 5 0 0 0 0 1 0 2 4 1 4 1 17 0
109 1 0 1 10 1 0 0 0 1 0 4 5 1 18 0 18 0
110 1 0 1 18 0 0 1 1 1 0 4 6 1 5 1 33 0
111 1 0 1 20 1 0 1 1 0 0 4 9 1 8 1 17 0
112 1 0 1 80 1 1 1 1 1 1 4 4 1 11 1 13 0
113 1 0 0 17 1 0 1 1 1 1 4 5 1 4 1 35 0
114 1 0 0 35 1 0 1 0 0 0 4 7 1 7 1 71 0
115 1 0 1 50 1 0 1 0 1 1 4 11 0 11 0 3 1
116 1 0 0 20 0 0 1 0 0 0 4 6 1 31 1 42 1
117 1 0 1 25 0 1 1 1 0 0 3 8 0 8 0 5 1
118 1 0 1 20 0 0 0 1 0 1 1 3 1 2 1 30 0
119 1 0 1 20 0 0 1 1 0 0 4 6 1 38 0 38 0
120 1 0 1 10 1 0 1 0 0 0 4 16 0 16 0 16 0
121 1 0 0 15 1 0 1 0 0 0 2 20 0 20 0 20 0
122 1 0 1 15 0 0 1 0 1 0 4 30 0 2 1 30 0
123 1 0 1 15 0 0 1 0 0 0 4 2 1 7 0 7 0
124 1 0 1 20 0 0 1 1 0 0 2 8 1 6 1 22 0
125 1 0 1 13 1 0 1 0 0 0 4 13 0 4 1 5 1
126 1 0 1 25 1 0 1 0 0 1 4 13 1 1 1 31 0
127 1 0 1 25 0 0 1 1 0 1 4 17 0 17 0 10 1
128 1 0 1 8 1 0 1 0 0 0 4 14 0 14 0 14 0
129 1 1 1 30 1 0 1 0 0 1 4 13 0 5 1 13 0
130 1 0 1 40 0 1 1 1 1 0 4 24 0 7 1 17 1
131 1 1 1 12 0 1 1 1 1 0 1 14 1 21 0 21 0
132 1 0 1 15 0 0 1 0 0 0 4 8 1 19 1 25 0
133 1 0 1 25 1 0 1 0 0 0 4 23 0 23 0 8 1
134 1 0 1 15 0 0 1 0 0 0 4 17 1 17 0 11 1
135 1 0 0 20 0 0 1 1 1 0 4 19 1 31 0 31 0
136 1 0 1 22 0 1 1 0 0 0 4 14 1 20 0 20 0
137 1 0 1 15 1 0 1 0 1 0 4 15 1 22 0 22 0
138 1 0 1 7 1 0 1 0 0 0 3 13 0 3 1 13 0
139 1 0 1 30 0 1 1 1 1 0 2 49 0 49 0 4 1
140 1 0 1 20 1 0 1 0 0 1 4 14 0 10 1 14 0
141 1 1 1 35 1 0 1 0 0 1 4 6 1 5 1 49 0
142 1 0 0 10 0 0 1 0 0 0 4 12 0 12 0 12 0
143 1 0 1 8 0 0 1 0 1 0 3 14 0 1 1 14 0
144 1 0 1 13 0 0 0 0 1 0 4 32 1 38 0 38 0
145 1 1 0 10 0 1 1 1 0 0 2 12 1 13 1 41 0
146 1 0 1 8 0 0 0 1 1 0 4 10 1 18 0 18 0
147 1 0 1 7 1 0 1 0 0 0 4 8 0 8 0 8 0
148 1 0 1 52 1 0 1 1 1 1 4 15 1 39 1 76 0
149 1 1 1 14 0 1 1 1 1 0 4 8 1 62 0 62 0
150 1 1 1 7 0 0 1 0 0 0 1 5 1 17 0 17 0
151 1 1 1 20 1 0 1 0 0 0 4 7 1 6 1 17 1
152 1 0 1 15 0 0 0 1 1 1 4 19 1 3 1 42 0
153 1 0 1 10 0 0 1 0 0 0 4 10 0 10 0 2 1
154 1 0 1 35 1 1 1 0 0 0 4 10 1 27 0 27 0
I have used the Import Dataset tool within R, but I cannot seem to find the right setting to import the dataset. The columns are either merged together, or there are additional columns (with many) NAs.
I have looked around for similar questions, however I cannot find a solution that suits my problem.
How can I import this dataset?
Ensure it is saved as a text file (for example text.txt) then apply the following: read.table("text.txt").
I would like to estimate Maximum Likelihood parameters of the Weibull distribution by applying to the following data with a given censoring vector in R:
data= 9 2 11 49 7 5 3 36 30 6 62 5 3 29 29 1 13 1 24 11 9 4 7 15 11 15 1 1 1 1 1 2 6 12 12 28 14 14 57 17 4 2 3 6 21 6 16 19 28 18 19 9 59 12 3 27 8 26 19 47 68 17 15 25 25 6 54 1 2 11 4 1 36 2 5 5 3 38 3 1 10 69 1 8 3 17 21 19 11 1 6 1 1 18 2 51 6 12 11 13 3 19 16 18 28 10 26 32 6 25 1 44
cens= 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
I would be very thankful if anyone could help me.
Use the Abrem package:
install.packages("abrem", repos="http://R-Forge.R-project.org")
You may need to manually install an older version of RccpArmadillo if you have issues like I did:
install.packages("https://cran.r-project.org/src/contrib/Archive/RcppArmadillo/RcppArmadillo_0.6.100.0.0.tar.gz", repos=NULL, type="source")
Then have at it:
library(abrem)
a = Abrem(fail = c(2, 11, 49, ...), susp = c(9, 44))
a = abrem.fit(a, dist = 'weibull', method.fit = 'mle')
a = abrem.conf(a) # add 90% confidence bands
plot.abrem(a) # plot the points and fit distribution
print.abrem(a) # print the results, which includes the fitted parameters
I may have confused your failures vs. suspensions data, but hopefully the example makes it clear where each goes.
I've got the following code to create a classification table in R:
> table(class = class1, truth = valid[,1])
1 2 3 4 5 6 7 8 9 10 11 12
1 357 73 0 0 47 0 5 32 20 0 4 7
2 25 71 0 0 23 4 1 0 2 1 8 3
3 1 2 120 1 5 0 1 0 0 0 0 0
4 0 0 0 77 0 0 0 0 1 0 0 0
5 15 27 0 0 67 6 7 0 4 1 5 7
6 1 2 0 0 2 44 0 0 0 7 7 0
7 1 1 0 0 10 0 66 0 1 0 1 7
9 1 0 0 0 3 0 0 2 8 0 0 2
10 1 1 0 0 1 6 0 0 0 17 0 0
11 0 7 0 0 3 1 0 0 0 4 10 2
12 0 1 0 0 1 0 0 0 0 0 0 1
However, I need this table to be a square (line 8 is missing in this example), i.e. the number of rows should equal the number of columns, and I need the rownames and colnames to be preserved. The missing line should be filled with zeros. Any way of doing this?
The problem most probably comes from a difference in levels.
Try copying the levels from valid to class1:
class1 <- factor(class1, levels=levels(valid[,1])
table(class = class1, truth = valid[,1])
I have a pass traffic data which shows the pass traffic between Members, here's the sample dataset
It shows the Interactions between Members in consecutive rows. I want to count that interactions, and obtain a new dataset which shows how many interactions occured between Members for Each Member, the direction doesn't matters
For example:
between 26 and 11 = X
between 26 and 27 = Y
I just can't figure it out which function I can use and how can I write a code for this calculation. Thanks
You could use the rollaply function from the zoo package to find all interactions. The frequency of these interactions could be calculated using table. (I assume your object is called dat.)
library(zoo)
table(as.data.frame(rollapply(dat[[1]], 2, sort)))
The result:
V2
V1 4 8 10 11 13 17 19 25 26 27 53
4 2 13 17 1 2 5 6 3 1 9 4
8 0 2 14 11 10 4 5 0 13 13 11
10 0 0 3 9 7 2 4 2 8 11 8
11 0 0 0 1 6 5 4 4 5 4 25
13 0 0 0 0 0 1 3 5 7 9 8
17 0 0 0 0 0 0 1 1 1 5 5
19 0 0 0 0 0 0 1 1 1 5 4
25 0 0 0 0 0 0 0 0 5 8 5
26 0 0 0 0 0 0 0 0 1 5 3
27 0 0 0 0 0 0 0 0 0 0 1
53 0 0 0 0 0 0 0 0 0 0 1
The lower triangular part of the matrix contains zeros only since the direction does not matter.
If you are not interested in interactions between the same values, use the following command:
table(as.data.frame(rollapply(rle(dat[[1]])$values, 2, sort)))
V2
V1 8 10 11 13 17 19 25 26 27 53
4 13 17 1 2 5 6 3 1 9 4
8 0 14 11 10 4 5 0 13 13 11
10 0 0 9 7 2 4 2 8 11 8
11 0 0 0 6 5 4 4 5 4 25
13 0 0 0 0 1 3 5 7 9 8
17 0 0 0 0 0 1 1 1 5 5
19 0 0 0 0 0 0 1 1 5 4
25 0 0 0 0 0 0 0 5 8 5
26 0 0 0 0 0 0 0 0 5 3
27 0 0 0 0 0 0 0 0 0 1