Example Datasets for miceadds Package
Example datasets for miceadds package.
data(data.ma01) data(data.ma02) data(data.ma03) data(data.ma04) data(data.ma05) data(data.ma06) data(data.ma07) data(data.ma08)
Dataset data.ma01:
Dataset with students nested within school and
student weights (studwgt). The format is
'data.frame': 4073 obs. of 11 variables: $ idstud : num 1e+07 1e+07 1e+07 1e+07 1e+07 ... $ idschool: num 1001 1001 1001 1001 1001 ... $ studwgt : num 6.05 6.05 5.27 5.27 6.05 ... $ math : int 594 605 616 524 685 387 536 594 387 562 ... $ read : int 647 651 539 551 689 502 503 597 580 576 ... $ migrant : int 0 0 0 1 0 0 1 0 0 0 ... $ books : int 6 6 5 2 6 3 4 6 6 5 ... $ hisei : int NA 77 69 45 66 53 43 NA 64 50 ... $ paredu : int 3 7 7 2 7 3 4 NA 7 3 ... $ female : int 1 1 0 0 1 1 0 0 1 1 ... $ urban : num 1 1 1 1 1 1 1 1 1 1 ...
Dataset data.ma02:
10 multiply imputed datasets of incomplete data data.ma01.
The format is
List of 10 $ :'data.frame': 4073 obs. of 11 variables: $ :'data.frame': 4073 obs. of 11 variables: $ :'data.frame': 4073 obs. of 11 variables: $ :'data.frame': 4073 obs. of 11 variables: $ :'data.frame': 4073 obs. of 11 variables: $ :'data.frame': 4073 obs. of 11 variables: $ :'data.frame': 4073 obs. of 11 variables: $ :'data.frame': 4073 obs. of 11 variables: $ :'data.frame': 4073 obs. of 11 variables: $ :'data.frame': 4073 obs. of 11 variables:
Dataset data.ma03:
This dataset contains one variable
math_EAP for which a conditional posterior distribution with EAP
and its associated standard deviation is available.
'data.frame': 120 obs. of 8 variables: $ idstud : int 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 ... $ female : int 0 1 1 1 1 0 1 1 1 1 ... $ migrant : int 1 1 0 1 1 0 0 0 1 0 ... $ hisei : int 44 NA 26 NA 32 60 31 NA 34 26 ... $ educ : int NA 2 NA 1 4 NA 2 NA 2 NA ... $ read_wle : num 74.8 78.1 103.2 81.2 119.2 ... $ math_EAP : num 337 342 264 285 420 ... $ math_SEEAP: num 28 29.5 28.6 28.5 27.5 ...
Dataset data.ma04:
This dataset contains two hypothetical
scales A and B and single variables V5, V6 and
V7.
'data.frame': 281 obs. of 13 variables: $ group: int 1 1 1 1 1 1 1 1 1 1 ... $ A1 : int 2 2 2 1 1 3 3 NA 2 1 ... $ A2 : int 2 2 2 3 1 2 4 4 4 4 ... $ A3 : int 2 3 3 4 1 3 2 2 2 4 ... $ A4 : int 3 4 6 4 7 5 3 5 5 1 ... $ V5 : int 2 2 5 5 4 3 4 1 3 4 ... $ V6 : int 2 5 5 1 1 3 2 2 2 4 ... $ V7 : int 6 NA 4 5 6 2 5 5 6 7 ... $ B1 : int 7 NA 6 4 5 2 5 7 3 7 ... $ B2 : int 6 NA NA 6 3 3 4 6 6 7 ... $ B3 : int 7 NA 7 4 3 4 3 7 5 NA ... $ B4 : int 4 5 6 5 4 3 4 5 2 1 ... $ B5 : int 7 NA 7 4 4 3 5 7 5 4 ...
Dataset data.ma05:
This is a two-level dataset with students nested within classes. Variables
at the student level are Dscore, Mscore, denote,
manote, misei and migrant. Variables at the class
level are sprengel and groesse.
'data.frame': 1673 obs. of 10 variables: $ idstud : int 100110001 100110002 100110003 100110004 100110005 ... $ idclass : int 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ... $ Dscore : int NA 558 643 611 518 552 NA 534 409 543 ... $ Mscore : int 404 563 569 621 653 651 510 NA 517 566 ... $ denote : int NA 1 1 1 3 2 3 2 3 2 ... $ manote : int NA 1 1 1 1 1 2 2 2 1 ... $ misei : int NA 51 NA 38 NA 50 53 53 38 NA ... $ migrant : int NA 0 0 NA 0 0 0 0 0 NA ... $ sprengel: int 0 0 0 0 0 0 0 0 0 0 ... $ groesse : int 25 25 25 25 25 25 25 25 25 25 ...
Dataset data.ma06:
This is a dataset in which the variable FC is only available
with grouped values (coarse data or interval data).
'data.frame': 198 obs. of 7 variables: $ id : num 1001 1002 1003 1004 1005 ... $ A1 : int 14 7 10 15 0 5 9 6 8 0 ... $ A2 : int 5 6 4 8 2 5 4 0 7 0 ... $ Edu : int 4 3 1 5 5 1 NA 1 5 3 ... $ FC : int 3 2 2 2 2 NA NA 2 2 NA ... $ FC_low: num 10 5 5 5 5 0 0 5 5 0 ... $ FC_upp: num 15 10 10 10 10 100 100 10 10 100 ...
Dataset data.ma07:
This is a three-level dataset in which the variable FC is only available
with grouped values (coarse data or interval data).
'data.frame': 1600 obs. of 9 variables: $ id3: num 1001 1001 1001 1001 1001 ... $ id2: num 101 101 101 101 101 101 101 101 101 101 ... $ id1: int 1 2 3 4 5 6 7 8 9 10 ... $ x1 : num 0.91 1.88 NA 1.52 0.93 0.51 2.11 0.99 2.42 NA ... $ x2 : num -0.58 1.12 0.87 -0.01 -0.14 0.48 1.85 -0.9 0.93 0.63 ... $ y1 : num 1.66 1.66 1.66 1.66 1.66 1.66 1.66 1.66 1.66 1.66 ... $ y2 : num 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 ... $ z1 : num -0.53 -0.53 -0.53 -0.53 -0.53 -0.53 -0.53 -0.53 -0.53 -0.53 ... $ z2 : num 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 ...
Dataset data.ma08:
List with several vector of strings containing descriptive data from
published articles. See string_to_matrix for converting
these strings into matrices.
List of 4 $ mat1: chr [1:6] "1. T1_mental_health" ... $ mat2: chr [1:16] "1. Exp voc-T1 -" ... $ mat3: chr [1:12] "1. TOWRE age 7\t-\t\t\t\t\t\t" ... $ mat4: chr [1:18] "1. Vocab. age 7\t-\t\t\t\t\t" ...
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.