View source: R/input_manual_nested.R
input_manual_nested | R Documentation |
Generates manual data input for a nested model with several tests.
input_manual_nested( construct_name, test_names, items_per_test, item_names, construct_loadings, test_loadings, correlation_matrix )
construct_name |
character; the name of the overall construct. |
test_names |
character; the names of the tests in correct order. |
items_per_test |
integer; number of items per test in correct order (determined by test_names), if all tests have the same number of items a single number can be used, e.g. 10 instead of c(10, 10, 10). |
item_names |
character or integer; the names of the items in correct order (determined by test_names). |
construct_loadings |
integer; vector of the factor loadings from the single factor model of the construct in correct order (determined by item_names). |
test_loadings |
integer; vector of the factor loadings on the test factors from the group factor model in correct order (determined by item_names). |
correlation_matrix |
matrix containing the latent correlations between tests, pay attention to the order of rows and columns, which is determined by test_names. |
Pay attention to the order of tests and items, it has to be coherent throughout the whole data. test_names and items_per_test determine which test is listed first and how many items are listed for that test. item_names, construct_loadings and test_loadings have to match that order. The correlation matrix uses the order in test_names for rows and columns.
This function only lists the name of the tests in output$tests. For each of
those tests, the data on the facets needs to be added using
input_manual_simple
. Every test for which you do not provide
this data will be treated as having no facets.
Visually inspect the returned object before continuing with
input_manual_process
!
list containing "raw" data. The data on the facets of the tests needs
to be added using input_manual_simple
. Afterwards, the whole
data needs to be pre-processed using input_manual_process
.
input_manual_simple
input_manual_process
# these data can also be seen in self_confidence, the example data of # this package mydata <- input_manual_nested( construct_name = "Self-Confidence", test_names = c("DSSEI", "SMTQ", "RSES"), items_per_test = c(20, 14, 10), item_names = c( 1, 5, 9, 13, 17, # DSSEI 3, 7, 11, 15, 19, # DSSEI 16, 4, 12, 8, 20, # DSSEI 2, 6, 10, 14, 18, # DSSEI 11, 13, 14, 1, 5, 6, # SMTQ 3, 10, 12, 8, # SMTQ 7, 2, 4, 9, # SMTQ 1, 3, 4, 7, 10, # RSES 2, 5, 6, 8, 9), # RSES construct_loadings = c( .5189, .6055, .618, .4074, .4442, .5203, .2479, .529, .554, .5144, .3958, .5671, .5559, .4591, .4927, .3713, .5941, .4903, .5998, .6616, .4182, .2504, .4094, .3977, .5177, .4603, .3271, .261, .3614, .4226, .2076, .3375, .5509, .3495, .5482, .4627, .4185, .4185, .5319, .4548, .4773, .4604, .4657, .4986), test_loadings = c( .5694, .6794, .6615, .4142, .4584, # DSSEI .5554, .2165, .5675, .5649, .4752, # DSSEI .443 , .6517, .6421, .545 , .5266, # DSSEI .302 , .6067, .5178, .5878, .6572, # DSSEI .4486, .3282, .4738, .4567, .5986, .5416, # SMTQ .3602, .2955, .3648, .4814, # SMTQ .2593, .4053, .61 , .4121, # SMTQ .6005, .4932, .4476, .5033, .6431, # RSES .5806, .5907, .6179, .5899, .6559), # RSES correlation_matrix = matrix(data = c( 1, .73, .62, .73, 1, .75, .62, .75, 1), nrow = 3, ncol = 3)) mydata
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