[1] '2.1.5'cluster_gen(n, N = 1, cluster_labels = NULL, resp_labels = NULL, cat_prop = NULL, n_X = NULL,
n_W = NULL, c_mean = NULL, sigma = NULL, cor_matrix = NULL, separate_questionnaires = TRUE,
collapse = "none", sum_pop = sapply(N, sum), calc_weights = TRUE, sampling_method = "mixed",
rho = NULL, theta = FALSE, verbose = TRUE, print_pop_structure = verbose)As its single mandatory argument, cluster_gen requires a numeric list
or vector containing the hierarchical structure of the data. As a
general rule, as far as this first argument (n) as well as
the second argument (N, representing the population
structure) are concerned, vectors can be used to represent symmetric
structures and lists can be used for asymmetric structures. What follows
are some examples.
The function cluster_gen generates clustered samples
which resembles the composition of international large-scale assessments
participants. The required argument is n and the other
optional arguments include
n: a numeric vector with the number of sampled
observations (clusters or subjects) on each level.N: a list of numeric vector(s) with the population size
of each sampled cluster element on each level.cluster_labels: a character vector with the names of
each cluster level.resp_labels: a character vector with the names of the
questionnaire respondents on each level.cat_prop: a list of vectors where each vector contains
the cumulative proportions for each category of a given item. If theta =
TRUE, the first element of cat_prop must be a scalar 1, which
corresponds to the theta.n_X: the number of continuous (X)
variables per cluster level.n_W: the number of ordinal (W) variables
per cluster level.cor_matrix: a correlation matrix between all variables
(except weights).c_mean: the vector of means for the continuous
variables or list of vectors for the continuous variables for each
level.sigma: the vector of of standard deviations for the
continuous variables or list of vectors for the continuous variables for
each level.separate_questionnaires: if the logical argument
separate_questionnaires ‘TRUE’, each level will have its
own questionnaire. Otherwise, it will be labeled ‘q1’.theta: if the logical argument theta is
TRUE then the latent trait will be generated as the first
continuous variable and labeled ‘theta’.collapse: if the logical argument collapse
is ‘TRUE’, then function output contains only one data frame with all
answers.sum_pop: is the specification of the total population
at each level (sampled or not)calc_weights: if the logical argument
calc_weights is ‘TRUE’, then sampling weights are
calculated.sampling_method: can be “SRS” for Simple Random
Sampling or “PPS” for Probabilities Proportional to Size.rho: specifies the estimated intraclass
correlation.verbose: if the logical argument verbose
is ‘TRUE’, then messages are printed in the output.print_pop_structure: if
print_pop_structure is ‘TRUE’, then the population
hierarchical structure is printed out (as long as it differs from the
sample structure)....: additional parameters to be passed to
questionnaire_gen().We can specify a simple structure of 3 schools with 5 students in
each school. That is, n = 3 and N = 5.
── Hierarchical structure ────────────────────────────────────────────────────────────────n1 (5 Ns)
n2 (5 Ns)
n3 (5 Ns)
── Information on sampling weights ─────────────────────────────────────────────────────── subject q1 q2 q3 q4 q5 q6 q7 q8 n.weight within.n.weight
1 1 -0.7985768 0.55776842 0.9278102 1 1 4 2 1 1 1
2 2 -1.0486078 2.28259560 -0.2269337 3 1 1 2 3 1 1
3 3 -0.1680413 -0.02049366 -0.7900484 3 1 3 2 1 1 1
4 4 1.4115562 -1.12757547 1.6993672 2 4 3 2 4 1 1
5 5 0.6689374 -1.51117001 -0.1845164 3 4 4 2 2 1 1
final.N.weight uniqueID
1 1 N1_n1
2 1 N2_n1
3 1 N3_n1
4 1 N4_n1
5 1 N5_n1 subject q1 q2 q3 q4 q5 q6 q7 q8 n.weight within.n.weight
1 1 0.83893595 0.4238664 -0.7212927 2 1 2 1 1 1 1
2 2 0.07260641 -0.3279862 -0.3841153 2 3 4 2 1 1 1
3 3 0.61013495 -1.1129113 -0.6149362 2 2 4 2 1 1 1
4 4 -2.53529525 0.2130771 2.3516784 3 5 1 1 2 1 1
5 5 0.46853204 -0.1806199 -0.4231896 2 3 1 1 4 1 1
final.N.weight uniqueID
1 1 N1_n2
2 1 N2_n2
3 1 N3_n2
4 1 N4_n2
5 1 N5_n2 subject q1 q2 q3 q4 q5 q6 q7 q8 n.weight within.n.weight
1 1 -1.9544485 -0.7390583 -2.1217731 1 1 1 1 1 1 1
2 2 1.1813684 -0.4654562 0.7780397 3 4 4 1 4 1 1
3 3 -0.4846220 0.5223044 -0.4788684 1 1 1 2 2 1 1
4 4 1.4972177 -0.5354472 -0.4295909 3 1 1 1 2 1 1
5 5 0.0806642 -1.6934998 0.8183471 1 1 4 1 4 1 1
final.N.weight uniqueID
1 1 N1_n3
2 1 N2_n3
3 1 N3_n3
4 1 N4_n3
5 1 N5_n3We can specify a more complex structure of 2 schools with different numbers of students, sampling weights, and custom numbers of questions.
set.seed(4388)
n <- list(3, c(20, 15, 25))
N <- list(5, c(200, 500, 400, 100, 100))
cg <- cluster_gen(n, N, n_X = 5, n_W = 2)── Hierarchical structure ────────────────────────────────────────────────────────────────school1 (200 students)
school2 (500 students)
school3 (400 students)
school4 (100 students)
school5 (100 students)school1 (20 students)
school2 (15 students)
school3 (25 students)
── Information on sampling weights ───────────────────────────────────────────────────────'data.frame': 20 obs. of 12 variables:
$ subject : int 1 2 3 4 5 6 7 8 9 10 ...
$ q1 : num -1.351 -0.249 0.241 1.178 -0.104 ...
$ q2 : num -0.672 -0.849 1.678 -0.22 1.848 ...
$ q3 : num 0.175 -0.901 0.961 0.364 1.401 ...
$ q4 : num 0.0527 0.4653 -0.8303 -0.7196 0.1548 ...
$ q5 : num -0.2185 -0.0847 1.2169 -1.363 -1.1152 ...
$ q6 : Factor w/ 2 levels "1","2": 2 1 2 1 2 2 1 1 1 2 ...
$ q7 : Factor w/ 5 levels "1","2","3","4",..: 1 2 4 5 5 3 1 4 5 5 ...
$ school.weight : num 2.17 2.17 2.17 2.17 2.17 ...
$ within.school.weight: num 10 10 10 10 10 10 10 10 10 10 ...
$ final.student.weight: num 21.7 21.7 21.7 21.7 21.7 ...
$ uniqueID : chr "student1_school1" "student2_school1" "student3_school1" "student4_school1" ...'data.frame': 15 obs. of 12 variables:
$ subject : int 1 2 3 4 5 6 7 8 9 10 ...
$ q1 : num 0.548 -0.51 0.373 0.527 0.163 ...
$ q2 : num 0.0978 -1.6416 0.2355 0.4376 0.0315 ...
$ q3 : num 1.574 0.512 0.49 1.264 -0.279 ...
$ q4 : num -0.646 -1.127 -0.39 0.119 -1.174 ...
$ q5 : num 0.27 0.466 -0.134 -0.326 -0.153 ...
$ q6 : Factor w/ 2 levels "1","2": 2 1 2 1 1 2 2 1 2 2 ...
$ q7 : Factor w/ 3 levels "1","2","4": 1 2 2 2 2 3 1 1 3 2 ...
$ school.weight : num 0.867 0.867 0.867 0.867 0.867 ...
$ within.school.weight: num 33.3 33.3 33.3 33.3 33.3 ...
$ final.student.weight: num 28.9 28.9 28.9 28.9 28.9 ...
$ uniqueID : chr "student1_school2" "student2_school2" "student3_school2" "student4_school2" ...'data.frame': 25 obs. of 12 variables:
$ subject : int 1 2 3 4 5 6 7 8 9 10 ...
$ q1 : num 1.405 0.273 -0.911 0.237 -0.35 ...
$ q2 : num -1.4873 0.5872 0.8679 0.5469 -0.0578 ...
$ q3 : num -0.987 -0.034 -2.169 0.486 -0.273 ...
$ q4 : num -0.95 -0.143 0.692 -0.853 0.761 ...
$ q5 : num 0.965 -0.707 0.578 0.197 -0.944 ...
$ q6 : Factor w/ 2 levels "1","2": 1 1 2 1 2 1 1 1 1 1 ...
$ q7 : Factor w/ 4 levels "1","2","3","4": 1 4 4 4 2 4 4 4 4 1 ...
$ school.weight : num 1.08 1.08 1.08 1.08 1.08 ...
$ within.school.weight: num 16 16 16 16 16 16 16 16 16 16 ...
$ final.student.weight: num 17.3 17.3 17.3 17.3 17.3 ...
$ uniqueID : chr "student1_school3" "student2_school3" "student3_school3" "student4_school3" ...We can also control the intra-class correlations and the grand mean.
── Hierarchical structure ────────────────────────────────────────────────────────────────school1 (1000 students)
school2 (1000 students)
school3 (1000 students)
school4 (1000 students)
school5 (1000 students)
── Information on sampling weights ───────────────────────────────────────────────────────[1] 4.929322 4.037097 10.849103 6.516552 6.440307[1] 6.554476List of 1
$ school:List of 5
..$ :'data.frame': 1000 obs. of 7 variables:
.. ..$ subject : int [1:1000] 1 2 3 4 5 6 7 8 9 10 ...
.. ..$ q1 : num [1:1000] 4.79 4.68 3.7 6.23 4.27 ...
.. ..$ q2 : num [1:1000] 9.74 9.76 10.94 8.27 10.37 ...
.. ..$ school.weight : num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ within.school.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ final.student.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ uniqueID : chr [1:1000] "student1_school1" "student2_school1" "student3_school1" "student4_school1" ...
..$ :'data.frame': 1000 obs. of 7 variables:
.. ..$ subject : int [1:1000] 1 2 3 4 5 6 7 8 9 10 ...
.. ..$ q1 : num [1:1000] 4.99 2.95 5.69 4.06 5.98 ...
.. ..$ q2 : num [1:1000] 4.77 6.1 8.14 5.35 9.23 ...
.. ..$ school.weight : num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ within.school.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ final.student.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ uniqueID : chr [1:1000] "student1_school2" "student2_school2" "student3_school2" "student4_school2" ...
..$ :'data.frame': 1000 obs. of 7 variables:
.. ..$ subject : int [1:1000] 1 2 3 4 5 6 7 8 9 10 ...
.. ..$ q1 : num [1:1000] 10.1 10.3 10.7 13.2 10.4 ...
.. ..$ q2 : num [1:1000] 14.5 14.1 14.9 19.3 15.3 ...
.. ..$ school.weight : num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ within.school.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ final.student.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ uniqueID : chr [1:1000] "student1_school3" "student2_school3" "student3_school3" "student4_school3" ...
..$ :'data.frame': 1000 obs. of 7 variables:
.. ..$ subject : int [1:1000] 1 2 3 4 5 6 7 8 9 10 ...
.. ..$ q1 : num [1:1000] 6.59 7.78 5.26 7.74 6.19 ...
.. ..$ q2 : num [1:1000] 11.7 10.4 13.6 11.4 12 ...
.. ..$ school.weight : num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ within.school.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ final.student.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ uniqueID : chr [1:1000] "student1_school4" "student2_school4" "student3_school4" "student4_school4" ...
..$ :'data.frame': 1000 obs. of 7 variables:
.. ..$ subject : int [1:1000] 1 2 3 4 5 6 7 8 9 10 ...
.. ..$ q1 : num [1:1000] 4.77 6.24 6.04 8.05 6 ...
.. ..$ q2 : num [1:1000] 17.6 16.2 18.1 15.4 12 ...
.. ..$ school.weight : num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ within.school.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ final.student.weight: num [1:1000] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ uniqueID : chr [1:1000] "student1_school5" "student2_school5" "student3_school5" "student4_school5" ...
- attr(*, "class")= chr [1:2] "lsasimcluster" "list"We can make the intraclass variance explode by forcing “incompatible” rho and c_mean.
── Hierarchical structure ────────────────────────────────────────────────────────────────school1 (1000 students)
school2 (1000 students)
school3 (1000 students)
school4 (1000 students)
school5 (1000 students)
── Information on sampling weights ───────────────────────────────────────────────────────ANOVA estimators
Source Sample.statistic Population.estimate
1 Within-group variance 2504.179 2504.179
2 Between-group variance 4853.708 4851.203
3 Total variance 6385.918 NA
Intraclass correlation
Estimated Standard.error
q1 0.6595447 0.1589391
Testing for group differences
F-statistic: 1938.243 on 4 and 4995 DF. p-value: 0 ANOVA estimators
Source Sample.statistic Population.estimate
1 Within-group variance 2419.402 2419.402
2 Between-group variance 1117.501 1115.081
3 Total variance 3311.645 NA
Intraclass correlation
Estimated Standard.error
q2 0.3154864 0.153111
Testing for group differences
F-statistic: 461.8914 on 4 and 4995 DF. p-value: 0 cluster_gen.The named vector below represents a sampling structure of 1 country, 2 schools, 5 students per school. The naming of the vector is optional.
── Hierarchical structure ────────────────────────────────────────────────────────────────cnt1
├─cnt1_sch1 (5 stus)
└─cnt1_sch2 (5 stus)
── Information on sampling weights ───────────────────────────────────────────────────────$cnt
$cnt[[1]]
subject q1 q2 q3 q4 q5 q6 q7 q8 q9 cnt.weight
1 1 -0.1857563 0.5894552 -0.004878196 4 1 4 3 3 1 1
2 2 -1.3145390 -0.6319421 0.682163912 4 2 3 3 3 3 1
within.cnt.weight final.sch.weight uniqueID
1 1 1 sch1_cnt1
2 1 1 sch2_cnt1
$sch
$sch[[1]]
subject q1 q2 q3 q4 q5 sch.weight within.sch.weight final.stu.weight
1 1 1.50583778 3 1 1 2 1 1 1
2 2 0.06801399 2 2 4 3 1 1 1
3 3 -1.50350211 2 3 1 4 1 1 1
4 4 -0.31483916 2 2 1 3 1 1 1
5 5 0.92196178 2 2 1 4 1 1 1
uniqueID
1 stu1_sch1_cnt1
2 stu2_sch1_cnt1
3 stu3_sch1_cnt1
4 stu4_sch1_cnt1
5 stu5_sch1_cnt1
$sch[[2]]
subject q1 q2 q3 q4 q5 sch.weight within.sch.weight final.stu.weight
1 1 3.6056537 3 3 1 4 1 1 1
2 2 0.9469375 3 3 2 4 1 1 1
3 3 -0.2483334 3 3 4 4 1 1 1
4 4 0.4508385 2 3 1 4 1 1 1
5 5 -1.2610979 1 2 2 3 1 1 1
uniqueID
1 stu1_sch2_cnt1
2 stu2_sch2_cnt1
3 stu3_sch2_cnt1
4 stu4_sch2_cnt1
5 stu5_sch2_cnt1
attr(,"class")
[1] "lsasimcluster" "list" The named vector below represents a sampling structure of 1 country,
2 schools, 5 students per school. In the example, the number of
continuous variables have been specified as n_X = 10. Only
5 means have been expressed to correspond to the 10 continuous
variables. That is, c_mean = c(0.3, 0.4, 0.5, 0.6, 0.7).
The function will still run by recycling the means over the other, five,
variables. In this case, a warning message that reads
Warning: c_mean recycled to fit all continuous variables
will be reported.
set.seed(4388)
n <- c(cnt = 1, sch = 2, stu = 5)
cg <- cluster_gen(n = n, n_X = 10, c_mean = c(0.3, 0.4, 0.5, 0.6, 0.7))── Hierarchical structure ────────────────────────────────────────────────────────────────cnt1
├─cnt1_sch1 (5 stus)
└─cnt1_sch2 (5 stus)
── Information on sampling weights ───────────────────────────────────────────────────────Warning: c_mean recycled to fit all continuous variables
Warning: c_mean recycled to fit all continuous variables
Warning: c_mean recycled to fit all continuous variables$cnt
$cnt[[1]]
subject q1 q2 q3 q4 q5 q6 q7
1 1 0.1493738 -0.2941853 -0.1356861 1.0942713 0.0007041074 -0.403876 -1.8308171
2 2 1.4250256 0.4499239 0.3912278 -0.5754092 1.6994017416 1.321272 0.7349227
q8 q9 q10 q11 q12 cnt.weight within.cnt.weight final.sch.weight
1 1.7664319 -0.5383132 1.3683602 1 1 1 1 1
2 0.2770982 0.9291194 0.7627068 1 2 1 1 1
uniqueID
1 sch1_cnt1
2 sch2_cnt1
$sch
$sch[[1]]
subject q1 q2 q3 q4 q5 q6 q7
1 1 0.7107490 -0.068956415 1.6418185 -0.2682343 2.23323556 0.1442393 0.8616713
2 2 -0.5855985 -0.585710878 -0.5414412 0.2357256 0.07303423 -1.3597101 -0.9964523
3 3 -1.0835479 1.880217061 -0.1643727 0.1607227 -0.78261691 1.1395126 -0.2356088
4 4 0.5342037 0.007799633 1.0866804 1.1594587 1.03261534 0.2807431 -0.2725278
5 5 -0.2916682 0.884475474 2.3551113 0.6455583 1.48256259 0.6658749 0.7246589
q8 q9 q10 q11 q12 q13 q14 sch.weight within.sch.weight
1 1.6934789 1.0571774 2.438611779 3 5 4 2 1 1
2 -0.3013998 0.1720372 0.001787841 5 5 2 3 1 1
3 0.8925784 0.6388118 -0.015964416 5 3 1 2 1 1
4 1.8254067 -0.7224324 0.934325677 4 5 2 2 1 1
5 0.2864523 -0.6233521 1.339058111 5 5 2 1 1 1
final.stu.weight uniqueID
1 1 stu1_sch1_cnt1
2 1 stu2_sch1_cnt1
3 1 stu3_sch1_cnt1
4 1 stu4_sch1_cnt1
5 1 stu5_sch1_cnt1
$sch[[2]]
subject q1 q2 q3 q4 q5 q6 q7
1 1 0.8857965 0.6805171 1.70146414 0.8126441 1.21032818 2.24556963 0.5436328
2 2 0.4251834 -0.9716282 -0.14179050 1.6298518 0.69780010 -0.81250703 0.9940499
3 3 -2.6623576 -0.9154311 1.00495412 -2.4114249 -0.50579666 -2.79170273 0.4186921
4 4 -0.4276990 1.1312695 -0.03547911 2.1496012 0.08720867 -0.05207824 0.6496843
5 5 1.0640377 0.5922904 -0.15503371 0.2651547 2.00796094 1.47916002 0.8115288
q8 q9 q10 q11 q12 q13 q14 q15 q16 q17 sch.weight
1 0.739390885 0.4754976 -0.006058309 5 1 4 4 2 2 3 1
2 -0.110809246 0.8357513 1.108207060 5 4 1 1 3 1 3 1
3 -0.274077470 2.3694774 2.700262947 5 5 5 2 3 1 3 1
4 0.007779354 0.6725442 0.339168074 5 1 2 2 1 1 2 1
5 2.031369897 0.1953666 1.566951833 5 4 5 1 2 1 3 1
within.sch.weight final.stu.weight uniqueID
1 1 1 stu1_sch2_cnt1
2 1 1 stu2_sch2_cnt1
3 1 1 stu3_sch2_cnt1
4 1 1 stu4_sch2_cnt1
5 1 1 stu5_sch2_cnt1
attr(,"class")
[1] "lsasimcluster" "list" The named vector below represents a sampling structure of 3 schools,
2 classes, and 5 students per class. Again, the naming of the vector is
optional. However, n_X and sigma can be
expressed as lists that coincide with the different levels (i.e.,
schools and classes). For example, n_X = c(1, 2) and
sigma = list(.1, c(1, 2) can be represented to represent
the school and classroom levels. Note that, sigma =
list(.1, c(1, 2) means that at cluster 1 (class), the standard
deviations are .1, where as the standard deviations for level 2 (class)
are 1 and 2.
set.seed(4388)
n <- c(school = 3, class = 2, student = 5)
cg <- cluster_gen(n, n_X = c(1, 2), sigma = list(0.1, c(1, 2)))── Hierarchical structure ────────────────────────────────────────────────────────────────school1
├─school1_class1 (5 students)
└─school1_class2 (5 students)
school2
├─school2_class1 (5 students)
└─school2_class2 (5 students)
school3
├─school3_class1 (5 students)
└─school3_class2 (5 students)
── Information on sampling weights ─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────[[1]]
q1 q2
Min. :-0.099338 1:5
Mean : 0.007348 2:1
Max. : 0.102284
Prop.
Stddev.: 0.0795 1:0.8333
2:0.1667
q1 q2
q1 1.0000000 0.7913169
q2 0.7913169 1.0000000[[1]]
q1 q2 q3 q4 q5 q6
Min. :-2.3736 Min. :-3.609443 2: 5 1:19 1:8 1: 9
Mean :-0.1235 Mean :-0.268586 3: 4 2:11 2:7 2:10
Max. : 2.0970 Max. : 3.514859 4: 4 4:9 4: 5
5:13 Prop. 3:6 3: 6
Stddev.: 1.1163 Stddev.: 2.0337 1: 4 1:0.6333
2:0.3667 Prop. Prop.
Prop. 1:0.2667 1:0.3
2:0.1667 2:0.2333 2:0.3333
3:0.1333 4:0.3 4:0.1667
4:0.1333 3:0.2 3:0.2
5:0.4333
1:0.1333
q1 q2 q3 q4 q5 q6
q1 1.00000000 -0.08248772 0.49946142 0.2345035 -0.01698463 -0.2893691
q2 -0.08248772 1.00000000 -0.09477988 0.4691688 0.20661211 0.1793382
q3 0.49946142 -0.09477988 1.00000000 0.3187024 -0.13300839 0.1824814
q4 0.23450350 0.46916880 0.31870236 1.0000000 -0.21498773 0.2768035
q5 -0.01698463 0.20661211 -0.13300839 -0.2149877 1.00000000 -0.1618769
q6 -0.28936909 0.17933817 0.18248136 0.2768035 -0.16187694 1.0000000The named vector below represents a sampling structure of 3 schools,
2 classes, and 5 students per class. Again, the naming of the vector is
optional. However, c_mean can also be expressed as a list
that coincide with the different levels (i.e., schools and classes). For
example, c_mean = list(.1, c(0.55, 0.32) can be represented
to represent the school and classroom levels. Note that,
c_mean = list(.1, c(0.55, 0.32)) means that at cluster 1
(class), the means for the continuous variables are .1, where as the
means for level 2 (class) are 0.55 and 0.32.
set.seed(4388)
n <- c(school = 3, class = 2, student = 5)
cg <- cluster_gen(n, n_X = c(1, 2), n_W = c(0, 1), c_mean = list(0.1, c(0.55, 0.32)))── Hierarchical structure ────────────────────────────────────────────────────────────────school1
├─school1_class1 (5 students)
└─school1_class2 (5 students)
school2
├─school2_class1 (5 students)
└─school2_class2 (5 students)
school3
├─school3_class1 (5 students)
└─school3_class2 (5 students)
── Information on sampling weights ───────────────────────────────────────────────────────$school
$school[[1]]
subject q1 school.weight within.school.weight final.class.weight uniqueID
1 1 -0.2247919 1 1 1 class1_school1
2 2 -2.0902316 1 1 1 class2_school1
$school[[2]]
subject q1 school.weight within.school.weight final.class.weight uniqueID
1 1 1.9632649 1 1 1 class1_school2
2 2 0.2743303 1 1 1 class2_school2
$school[[3]]
subject q1 school.weight within.school.weight final.class.weight uniqueID
1 1 1.339111 1 1 1 class1_school3
2 2 -1.577978 1 1 1 class2_school3
$class
$class[[1]]
subject q1 q2 q3 class.weight within.class.weight final.student.weight
1 1 0.6939464 1.429263 3 1 1 1
2 2 0.6444333 -1.340612 2 1 1 1
3 3 0.7724895 1.191870 2 1 1 1
4 4 2.5861532 1.496156 3 1 1 1
5 5 0.9868058 1.428098 2 1 1 1
uniqueID
1 student1_class1_school1
2 student2_class1_school1
3 student3_class1_school1
4 student4_class1_school1
5 student5_class1_school1
$class[[2]]
subject q1 q2 q3 class.weight within.class.weight final.student.weight
1 1 0.04328771 0.9667577 1 1 1 1
2 2 0.85682751 -0.5343172 3 1 1 1
3 3 0.95461020 0.9208733 3 1 1 1
4 4 0.03384666 0.6000454 2 1 1 1
5 5 -0.01723126 2.6036897 1 1 1 1
uniqueID
1 student1_class2_school1
2 student2_class2_school1
3 student3_class2_school1
4 student4_class2_school1
5 student5_class2_school1
$class[[3]]
subject q1 q2 q3 class.weight within.class.weight
1 1 0.2135740 -0.230983361 3 1 1
2 2 0.7499582 1.431284170 1 1 1
3 3 -1.3288479 0.761635852 2 1 1
4 4 1.7718949 -0.501747308 5 1 1
5 5 0.6906895 0.004222373 3 1 1
final.student.weight uniqueID
1 1 student1_class1_school2
2 1 student2_class1_school2
3 1 student3_class1_school2
4 1 student4_class1_school2
5 1 student5_class1_school2
$class[[4]]
subject q1 q2 q3 class.weight within.class.weight
1 1 2.563471234 -0.05330786 3 1 1
2 2 -1.059654042 0.32407999 1 1 1
3 3 -0.336184584 -0.72432619 1 1 1
4 4 -0.004385578 0.58409392 4 1 1
5 5 0.296826906 0.10677686 1 1 1
final.student.weight uniqueID
1 1 student1_class2_school2
2 1 student2_class2_school2
3 1 student3_class2_school2
4 1 student4_class2_school2
5 1 student5_class2_school2
$class[[5]]
subject q1 q2 q3 class.weight within.class.weight final.student.weight
1 1 0.33690460 -1.19476433 1 1 1 1
2 2 1.56177909 -0.26130250 1 1 1 1
3 3 2.76962281 -1.28985141 1 1 1 1
4 4 1.29857289 -0.02952429 1 1 1 1
5 5 0.07510387 1.01647674 3 1 1 1
uniqueID
1 student1_class1_school3
2 student2_class1_school3
3 student3_class1_school3
4 student4_class1_school3
5 student5_class1_school3
$class[[6]]
subject q1 q2 q3 class.weight within.class.weight final.student.weight
1 1 0.1379774 0.99118261 2 1 1 1
2 2 2.1143383 -0.08084345 3 1 1 1
3 3 1.0878675 0.38597927 3 1 1 1
4 4 0.4066554 0.48029670 1 1 1 1
5 5 -0.4821055 -0.04994921 1 1 1 1
uniqueID
1 student1_class2_school3
2 student2_class2_school3
3 student3_class2_school3
4 student4_class2_school3
5 student5_class2_school3
attr(,"class")
[1] "lsasimcluster" "list"