item.response.table: Create an Item Response Table or Data Frame
In MRCV: Methods for Analyzing Multiple Response Categorical Variables (MRCVs)
Description
Usage
Arguments
Value
See Also
Examples
Description
The item.response.table function is used to summarize data arising from one, two, or three MRCVs. For the one and two MRCV cases, a cross-tabulation of the positive and negative responses for each (Wi, Yj) pair is presented as a table or data frame (where Wi = W for the one MRCV case). For the three MRCV case, a cross-tabulation of the positive and negative responses for each (Wi, Yj) pair is presented conditional on the response for each Zk.
Usage
1
item.response.table(data, I, J, K = NULL, create.dataframe = FALSE)
Arguments
data
A data frame containing the raw data where rows correspond to the individual item response vectors, and columns correspond to the items W1, ..., WI, Y1, ..., YJ, and Z1, ..., ZK (in this order).
I
The number of items corresponding to row variable W. I = 1 for the one MRCV case.
J
The number of items corresponding to column variable Y.
K
The number of items corresponding to strata variable Z.
create.dataframe
A logical value indicating whether the results should be presented as a data frame instead of a table.
Value
For create.dataframe = FALSE, item.response.table uses the tabular function (package tables) to produce tables of marginal counts.
For create.dataframe = TRUE, item.response.table returns the same information as above but presents it as a data frame. For the one MRCV case, the data frame contains rx2J rows and 4 columns generically named W, Y, yj, and count. For the two MRCV case, the data frame contains 2Ix2J rows and 5 columns named W, Y, wi, yj, and count. For the three MRCV case, the data frame contains 2Ix2Jx2K rows and 7 columns named W, Y, Z, wi, yj, zk, and count.
See Also
The marginal.table function for creating a marginal table that summarizes only the positive responses for each pair.
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
# Create an item response table for 1 SRCV and 1 MRCV
farmer.irtable.one <- item.response.table(data = farmer1, I = 1, J = 5)
farmer.irtable.one
# Create an item response data frame for 1 SRCV and 1 MRCV
farmer.irdataframe.one <- item.response.table(data = farmer1, I = 1, J = 5,
create.dataframe = TRUE)
farmer.irdataframe.one
# Create an item response table for 2 MRCVs
farmer.irtable.two <- item.response.table(data = farmer2, I = 3, J = 4)
farmer.irtable.two
# Create an item response table for 3 MRCVs
farmer.irtable.three <- item.response.table(data = farmer3, I = 3, J = 4, K = 5)
farmer.irtable.three
Example output
Y1 Y2 Y3 Y4 Y5
0 1 0 1 0 1 0 1 0 1
1 69 19 50 38 59 29 41 47 48 40
2 14 2 10 6 8 8 8 8 12 4
3 30 1 18 13 21 10 14 17 17 14
4 94 19 84 29 73 40 60 53 84 29
5 11 3 10 4 6 8 8 6 8 6
W Y yj count
1 1 Y1 0 69
2 2 Y1 0 14
3 3 Y1 0 30
4 4 Y1 0 94
5 5 Y1 0 11
6 1 Y1 1 19
7 2 Y1 1 2
8 3 Y1 1 1
9 4 Y1 1 19
10 5 Y1 1 3
11 1 Y2 0 50
12 2 Y2 0 10
13 3 Y2 0 18
14 4 Y2 0 84
15 5 Y2 0 10
16 1 Y2 1 38
17 2 Y2 1 6
18 3 Y2 1 13
19 4 Y2 1 29
20 5 Y2 1 4
21 1 Y3 0 59
22 2 Y3 0 8
23 3 Y3 0 21
24 4 Y3 0 73
25 5 Y3 0 6
26 1 Y3 1 29
27 2 Y3 1 8
28 3 Y3 1 10
29 4 Y3 1 40
30 5 Y3 1 8
31 1 Y4 0 41
32 2 Y4 0 8
33 3 Y4 0 14
34 4 Y4 0 60
35 5 Y4 0 8
36 1 Y4 1 47
37 2 Y4 1 8
38 3 Y4 1 17
39 4 Y4 1 53
40 5 Y4 1 6
41 1 Y5 0 48
42 2 Y5 0 12
43 3 Y5 0 17
44 4 Y5 0 84
45 5 Y5 0 8
46 1 Y5 1 40
47 2 Y5 1 4
48 3 Y5 1 14
49 4 Y5 1 29
50 5 Y5 1 6
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 123 116 175 64 156 83 228 11
1 13 27 24 16 38 2 38 2
w2 0 128 121 181 68 165 84 237 12
1 8 22 18 12 29 1 29 1
w3 0 134 124 184 74 174 84 245 13
1 2 19 15 6 20 1 21 0
$`z1 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 115 88 152 51 126 77 193 10
1 11 21 20 12 30 2 30 2
w2 0 119 93 158 54 134 78 201 11
1 7 16 14 9 22 1 22 1
w3 0 125 95 161 59 142 78 208 12
1 1 14 11 4 14 1 15 0
$`z1 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 8 28 23 13 30 6 35 1
1 2 6 4 4 8 0 8 0
w2 0 9 28 23 14 31 6 36 1
1 1 6 4 3 7 0 7 0
w3 0 9 29 23 15 32 6 37 1
1 1 5 4 2 6 0 6 0
$`z2 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 93 72 126 39 104 61 157 8
1 7 17 16 8 23 1 23 1
w2 0 96 75 130 41 109 62 162 9
1 4 14 12 6 18 0 18 0
w3 0 99 76 131 44 113 62 166 9
1 1 13 11 3 14 0 14 0
$`z2 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 30 44 49 25 52 22 71 3
1 6 10 8 8 15 1 15 1
w2 0 32 46 51 27 56 22 75 3
1 4 8 6 6 11 1 11 1
w3 0 35 48 53 30 61 22 79 4
1 1 6 4 3 6 1 7 0
$`z3 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 82 74 121 35 101 55 149 7
1 9 19 17 11 28 0 26 2
w2 0 85 77 124 38 107 55 154 8
1 6 16 14 8 22 0 21 1
w3 0 90 80 127 43 115 55 161 9
1 1 13 11 3 14 0 14 0
$`z3 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 41 42 54 29 55 28 79 4
1 4 8 7 5 10 2 12 0
w2 0 43 44 57 30 58 29 83 4
1 2 6 4 4 7 1 8 0
w3 0 44 44 57 31 59 29 84 4
1 1 6 4 3 6 1 7 0
$`z4 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 61 63 95 29 89 35 118 6
1 7 17 16 8 23 1 23 1
w2 0 64 66 99 31 94 36 124 6
1 4 14 12 6 18 0 17 1
w3 0 67 68 101 34 99 36 128 7
1 1 12 10 3 13 0 13 0
$`z4 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 62 53 80 35 67 48 110 5
1 6 10 8 8 15 1 15 1
w2 0 64 55 82 37 71 48 113 6
1 4 8 6 6 11 1 12 0
w3 0 67 56 83 40 75 48 117 6
1 1 7 5 3 7 1 8 0
$`z5 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 76 81 123 34 107 50 148 9
1 8 21 20 9 28 1 27 2
w2 0 79 86 129 36 114 51 155 10
1 5 16 14 7 21 0 20 1
w3 0 83 87 130 40 119 51 159 11
1 1 15 13 3 16 0 16 0
$`z5 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 47 35 52 30 49 33 80 2
1 5 6 4 7 10 1 11 0
w2 0 49 35 52 32 51 33 82 2
1 3 6 4 5 8 1 9 0
w3 0 51 37 54 34 55 33 86 2
1 1 4 2 3 4 1 5 0
MRCV documentation built on May 2, 2019, 3:46 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value See Also Examples
Description
The item.response.table function is used to summarize data arising from one, two, or three MRCVs. For the one and two MRCV cases, a cross-tabulation of the positive and negative responses for each (Wi, Yj) pair is presented as a table or data frame (where Wi = W for the one MRCV case). For the three MRCV case, a cross-tabulation of the positive and negative responses for each (Wi, Yj) pair is presented conditional on the response for each Zk.
Usage
1 | item.response.table(data, I, J, K = NULL, create.dataframe = FALSE)
|
Arguments
data |
A data frame containing the raw data where rows correspond to the individual item response vectors, and columns correspond to the items W1, ..., WI, Y1, ..., YJ, and Z1, ..., ZK (in this order). |
I |
The number of items corresponding to row variable W. I = 1 for the one MRCV case. |
J |
The number of items corresponding to column variable Y. |
K |
The number of items corresponding to strata variable Z. |
create.dataframe |
A logical value indicating whether the results should be presented as a data frame instead of a table. |
Value
For create.dataframe = FALSE, item.response.table uses the tabular function (package tables) to produce tables of marginal counts.
For create.dataframe = TRUE, item.response.table returns the same information as above but presents it as a data frame. For the one MRCV case, the data frame contains rx2J rows and 4 columns generically named W, Y, yj, and count. For the two MRCV case, the data frame contains 2Ix2J rows and 5 columns named W, Y, wi, yj, and count. For the three MRCV case, the data frame contains 2Ix2Jx2K rows and 7 columns named W, Y, Z, wi, yj, zk, and count.
See Also
The marginal.table function for creating a marginal table that summarizes only the positive responses for each pair.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # Create an item response table for 1 SRCV and 1 MRCV
farmer.irtable.one <- item.response.table(data = farmer1, I = 1, J = 5)
farmer.irtable.one
# Create an item response data frame for 1 SRCV and 1 MRCV
farmer.irdataframe.one <- item.response.table(data = farmer1, I = 1, J = 5,
create.dataframe = TRUE)
farmer.irdataframe.one
# Create an item response table for 2 MRCVs
farmer.irtable.two <- item.response.table(data = farmer2, I = 3, J = 4)
farmer.irtable.two
# Create an item response table for 3 MRCVs
farmer.irtable.three <- item.response.table(data = farmer3, I = 3, J = 4, K = 5)
farmer.irtable.three
|
Example output
Y1 Y2 Y3 Y4 Y5
0 1 0 1 0 1 0 1 0 1
1 69 19 50 38 59 29 41 47 48 40
2 14 2 10 6 8 8 8 8 12 4
3 30 1 18 13 21 10 14 17 17 14
4 94 19 84 29 73 40 60 53 84 29
5 11 3 10 4 6 8 8 6 8 6
W Y yj count
1 1 Y1 0 69
2 2 Y1 0 14
3 3 Y1 0 30
4 4 Y1 0 94
5 5 Y1 0 11
6 1 Y1 1 19
7 2 Y1 1 2
8 3 Y1 1 1
9 4 Y1 1 19
10 5 Y1 1 3
11 1 Y2 0 50
12 2 Y2 0 10
13 3 Y2 0 18
14 4 Y2 0 84
15 5 Y2 0 10
16 1 Y2 1 38
17 2 Y2 1 6
18 3 Y2 1 13
19 4 Y2 1 29
20 5 Y2 1 4
21 1 Y3 0 59
22 2 Y3 0 8
23 3 Y3 0 21
24 4 Y3 0 73
25 5 Y3 0 6
26 1 Y3 1 29
27 2 Y3 1 8
28 3 Y3 1 10
29 4 Y3 1 40
30 5 Y3 1 8
31 1 Y4 0 41
32 2 Y4 0 8
33 3 Y4 0 14
34 4 Y4 0 60
35 5 Y4 0 8
36 1 Y4 1 47
37 2 Y4 1 8
38 3 Y4 1 17
39 4 Y4 1 53
40 5 Y4 1 6
41 1 Y5 0 48
42 2 Y5 0 12
43 3 Y5 0 17
44 4 Y5 0 84
45 5 Y5 0 8
46 1 Y5 1 40
47 2 Y5 1 4
48 3 Y5 1 14
49 4 Y5 1 29
50 5 Y5 1 6
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 123 116 175 64 156 83 228 11
1 13 27 24 16 38 2 38 2
w2 0 128 121 181 68 165 84 237 12
1 8 22 18 12 29 1 29 1
w3 0 134 124 184 74 174 84 245 13
1 2 19 15 6 20 1 21 0
$`z1 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 115 88 152 51 126 77 193 10
1 11 21 20 12 30 2 30 2
w2 0 119 93 158 54 134 78 201 11
1 7 16 14 9 22 1 22 1
w3 0 125 95 161 59 142 78 208 12
1 1 14 11 4 14 1 15 0
$`z1 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 8 28 23 13 30 6 35 1
1 2 6 4 4 8 0 8 0
w2 0 9 28 23 14 31 6 36 1
1 1 6 4 3 7 0 7 0
w3 0 9 29 23 15 32 6 37 1
1 1 5 4 2 6 0 6 0
$`z2 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 93 72 126 39 104 61 157 8
1 7 17 16 8 23 1 23 1
w2 0 96 75 130 41 109 62 162 9
1 4 14 12 6 18 0 18 0
w3 0 99 76 131 44 113 62 166 9
1 1 13 11 3 14 0 14 0
$`z2 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 30 44 49 25 52 22 71 3
1 6 10 8 8 15 1 15 1
w2 0 32 46 51 27 56 22 75 3
1 4 8 6 6 11 1 11 1
w3 0 35 48 53 30 61 22 79 4
1 1 6 4 3 6 1 7 0
$`z3 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 82 74 121 35 101 55 149 7
1 9 19 17 11 28 0 26 2
w2 0 85 77 124 38 107 55 154 8
1 6 16 14 8 22 0 21 1
w3 0 90 80 127 43 115 55 161 9
1 1 13 11 3 14 0 14 0
$`z3 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 41 42 54 29 55 28 79 4
1 4 8 7 5 10 2 12 0
w2 0 43 44 57 30 58 29 83 4
1 2 6 4 4 7 1 8 0
w3 0 44 44 57 31 59 29 84 4
1 1 6 4 3 6 1 7 0
$`z4 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 61 63 95 29 89 35 118 6
1 7 17 16 8 23 1 23 1
w2 0 64 66 99 31 94 36 124 6
1 4 14 12 6 18 0 17 1
w3 0 67 68 101 34 99 36 128 7
1 1 12 10 3 13 0 13 0
$`z4 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 62 53 80 35 67 48 110 5
1 6 10 8 8 15 1 15 1
w2 0 64 55 82 37 71 48 113 6
1 4 8 6 6 11 1 12 0
w3 0 67 56 83 40 75 48 117 6
1 1 7 5 3 7 1 8 0
$`z5 = 0`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 76 81 123 34 107 50 148 9
1 8 21 20 9 28 1 27 2
w2 0 79 86 129 36 114 51 155 10
1 5 16 14 7 21 0 20 1
w3 0 83 87 130 40 119 51 159 11
1 1 15 13 3 16 0 16 0
$`z5 = 1`
y1 y2 y3 y4
0 1 0 1 0 1 0 1
w1 0 47 35 52 30 49 33 80 2
1 5 6 4 7 10 1 11 0
w2 0 49 35 52 32 51 33 82 2
1 3 6 4 5 8 1 9 0
w3 0 51 37 54 34 55 33 86 2
1 1 4 2 3 4 1 5 0
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.