marginal.table: Create a Marginal Table
In MRCV: Methods for Analyzing Multiple Response Categorical Variables (MRCVs)
Description
Usage
Arguments
Value
See Also
Examples
Description
The marginal.table function is used to summarize only the positive responses (joint positive responses for multiple MRCVs) for data arising from one, two, or three MRCVs. This function essentially provides a subset of counts from the item.response.table function.
Usage
1
marginal.table(data, I, J, K = NULL)
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.
Value
The marginal.table function uses the tabular function (package tables) to produce a table containing positive counts.
See Also
The item.response.table function for creating an item response table or data frame that summarizes both the positive and negative responses for each (Wi, Yj) pair (conditional on the response for each Zk in the case of three MRCVs).
Examples
1
2
3
4
5
6
7
8
9
10
11
# Create a marginal table for 1 MRCV
farmer.mtable.one <- marginal.table(data = farmer1, I = 1, J = 5)
farmer.mtable.one
# Create a marginal table for 2 MRCVs
farmer.mtable.two <- marginal.table(data = farmer2, I = 3, J = 4)
farmer.mtable.two
# Create a marginal table for 3 MRCVs
farmer.mtable.three <- marginal.table(data = farmer3, I = 3, J = 4, K = 5)
farmer.mtable.three
Example output
Y1 Y2 Y3 Y4 Y5
n count % count % count % count % count
1 88 19 21.59 38 43.18 29 32.95 47 53.41 40
2 16 2 12.50 6 37.50 8 50.00 8 50.00 4
3 31 1 3.23 13 41.94 10 32.26 17 54.84 14
4 113 19 16.81 29 25.66 40 35.40 53 46.90 29
5 14 3 21.43 4 28.57 8 57.14 6 42.86 6
%
45.45
25.00
45.16
25.66
42.86
y1 y2 y3 y4
count % count % count % count %
w1 27 9.68 16 5.73 2 0.72 2 0.72
w2 22 7.89 12 4.30 1 0.36 1 0.36
w3 19 6.81 6 2.15 1 0.36 0 0.00
$z1
y1 y2 y3 y4
count % count % count % count %
w1 6 2.15 4 1.43 0 0.00 0 0.00
w2 6 2.15 3 1.08 0 0.00 0 0.00
w3 5 1.79 2 0.72 0 0.00 0 0.00
$z2
y1 y2 y3 y4
count % count % count % count %
w1 10 3.58 8 2.87 1 0.36 1 0.36
w2 8 2.87 6 2.15 1 0.36 1 0.36
w3 6 2.15 3 1.08 1 0.36 0 0.00
$z3
y1 y2 y3 y4
count % count % count % count %
w1 8 2.87 5 1.79 2 0.72 0 0.00
w2 6 2.15 4 1.43 1 0.36 0 0.00
w3 6 2.15 3 1.08 1 0.36 0 0.00
$z4
y1 y2 y3 y4
count % count % count % count %
w1 10 3.58 8 2.87 1 0.36 1 0.36
w2 8 2.87 6 2.15 1 0.36 0 0.00
w3 7 2.51 3 1.08 1 0.36 0 0.00
$z5
y1 y2 y3 y4
count % count % count % count %
w1 6 2.15 7 2.51 1 0.36 0 0.00
w2 6 2.15 5 1.79 1 0.36 0 0.00
w3 4 1.43 3 1.08 1 0.36 0 0.00
MRCV documentation built on May 2, 2019, 3:46 a.m.
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Description Usage Arguments Value See Also Examples
Description
The marginal.table function is used to summarize only the positive responses (joint positive responses for multiple MRCVs) for data arising from one, two, or three MRCVs. This function essentially provides a subset of counts from the item.response.table function.
Usage
1 | marginal.table(data, I, J, K = NULL)
|
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. |
Value
The marginal.table function uses the tabular function (package tables) to produce a table containing positive counts.
See Also
The item.response.table function for creating an item response table or data frame that summarizes both the positive and negative responses for each (Wi, Yj) pair (conditional on the response for each Zk in the case of three MRCVs).
Examples
1 2 3 4 5 6 7 8 9 10 11 | # Create a marginal table for 1 MRCV
farmer.mtable.one <- marginal.table(data = farmer1, I = 1, J = 5)
farmer.mtable.one
# Create a marginal table for 2 MRCVs
farmer.mtable.two <- marginal.table(data = farmer2, I = 3, J = 4)
farmer.mtable.two
# Create a marginal table for 3 MRCVs
farmer.mtable.three <- marginal.table(data = farmer3, I = 3, J = 4, K = 5)
farmer.mtable.three
|
Example output
Y1 Y2 Y3 Y4 Y5
n count % count % count % count % count
1 88 19 21.59 38 43.18 29 32.95 47 53.41 40
2 16 2 12.50 6 37.50 8 50.00 8 50.00 4
3 31 1 3.23 13 41.94 10 32.26 17 54.84 14
4 113 19 16.81 29 25.66 40 35.40 53 46.90 29
5 14 3 21.43 4 28.57 8 57.14 6 42.86 6
%
45.45
25.00
45.16
25.66
42.86
y1 y2 y3 y4
count % count % count % count %
w1 27 9.68 16 5.73 2 0.72 2 0.72
w2 22 7.89 12 4.30 1 0.36 1 0.36
w3 19 6.81 6 2.15 1 0.36 0 0.00
$z1
y1 y2 y3 y4
count % count % count % count %
w1 6 2.15 4 1.43 0 0.00 0 0.00
w2 6 2.15 3 1.08 0 0.00 0 0.00
w3 5 1.79 2 0.72 0 0.00 0 0.00
$z2
y1 y2 y3 y4
count % count % count % count %
w1 10 3.58 8 2.87 1 0.36 1 0.36
w2 8 2.87 6 2.15 1 0.36 1 0.36
w3 6 2.15 3 1.08 1 0.36 0 0.00
$z3
y1 y2 y3 y4
count % count % count % count %
w1 8 2.87 5 1.79 2 0.72 0 0.00
w2 6 2.15 4 1.43 1 0.36 0 0.00
w3 6 2.15 3 1.08 1 0.36 0 0.00
$z4
y1 y2 y3 y4
count % count % count % count %
w1 10 3.58 8 2.87 1 0.36 1 0.36
w2 8 2.87 6 2.15 1 0.36 0 0.00
w3 7 2.51 3 1.08 1 0.36 0 0.00
$z5
y1 y2 y3 y4
count % count % count % count %
w1 6 2.15 7 2.51 1 0.36 0 0.00
w2 6 2.15 5 1.79 1 0.36 0 0.00
w3 4 1.43 3 1.08 1 0.36 0 0.00
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