| Type: | Package |
| Title: | Calculates Transition Matrices and Mobility Indices |
| Version: | 0.2.1 |
| Author: | Brett Mullins and Trevor Harkreader |
| Maintainer: | Brett Mullins <brettcmullins@gmail.com> |
| Description: | Measures mobility in a population through transition matrices and mobility indices. Relative, mixed, and absolute transition matrices are supported. The Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices are supported. Example income and grade data are included. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.1.1 |
| Depends: | R (≥ 2.10) |
| Imports: | stats |
| Suggests: | testthat (≥ 2.1.0), covr, knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2021-01-16 22:14:14 UTC; brettm |
| Repository: | CRAN |
| Date/Publication: | 2021-01-20 11:50:09 UTC |
Hypothesis Test for Two Mobility Datasets
Description
Calculates hypothesis tests of mobility indices from two datasets. Specifically, for datasets A and B, this function performs one-sided nonparametric hypothesis tests that the index value for A is greater than the corresponding index value for B. Supports Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices and relative, mixed, and absolute types of rankings in the calculation these indices.
Usage
getHypothesisTest(
dat_A,
dat_B,
cols_A,
cols_B,
type,
indices = "all",
num_ranks,
exclude_value,
bounds,
rerank_exclude_value = FALSE,
strict = TRUE,
bootstrap_iter = 100
)
Arguments
dat_A |
a dataframe with an "id" column |
dat_B |
a dataframe with an "id" column |
cols_A |
a list of character strings denoting the first and second column to be used in the index calculations for dat_A |
cols_B |
a list of character strings denoting the first and second column to be used in the index calculations for dat_B |
type |
a character string indicating the type of ranking; accepts 'relative', 'mixed', and 'absolute' |
indices |
a vector of character strings indicating which mobility indices are desired; currently support 'prais_bibby', 'average_movement', 'wgm', and 'origin_specific'. The default value is 'all'. |
num_ranks |
an integer specifying the number of ranks for a relative or mixed ranking |
exclude_value |
a single numeric value that is excluded in calculating the transition matrix; see the rerank_exclude_value parameter to specify how the exclude value is handled |
bounds |
a sequence of numeric bounds for defining absolute ranks |
rerank_exclude_value |
a character string indicating how the exclude value is handled when present; accepts 'as_new_rank', 'as_existing_rank', and 'exclude' |
strict |
logical. If TRUE, indices are calculated from the given values. If FALSE, indices are calculated by jittering the values to ensure uniqueness of bounds of ranks. Only used with relative and mixed types. The default value is TRUE. |
bootstrap_iter |
the number of bootstrap iterations used to estimate hypothesis tests. The default value is 100. |
Value
Returns a named vector containing the estimated probabilities that index value for dataset A is greater than the corresponding index value for dataset B
Examples
getHypothesisTest(dat_A = incomeMobility,
dat_B = incomeMobility,
cols_A = c("t0", "t3"),
cols_B = c("t5", "t8"),
type = "relative",
num_ranks = 5)
Calculates Mobility Indices at Two Points in Time
Description
Calculates mobility indices from two columns in dataset. Supports Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices and relative, mixed, and absolute types of rankings in the calculation these indices.
Usage
getMobilityIndices(
dat,
col_x,
col_y,
type,
indices = "all",
num_ranks,
exclude_value,
bounds,
rerank_exclude_value,
strict = TRUE,
intervals = FALSE,
interval_pct = 0.95,
bootstrap_iter = 100
)
Arguments
dat |
a dataframe with an "id" column |
col_x |
a character string denoting the first column to be used in the index calculation |
col_y |
a character string denoting the second column to be used in the index calculation |
type |
a character string indicating the type of ranking; accepts 'relative', 'mixed', and 'absolute' |
indices |
a vector of character strings indicating which mobility indices are desired; currently support 'prais_bibby', 'average_movement', 'wgm', and 'origin_specific'. The default value is 'all'. |
num_ranks |
an integer specifying the number of ranks for a relative or mixed ranking |
exclude_value |
a single numeric value that is excluded in calculating the transition matrix; see the rerank_exclude_value parameter to specify how the exclude value is handled |
bounds |
a sequence of numeric bounds for defining absolute ranks |
rerank_exclude_value |
a character string indicating how the exclude value is handled when present; accepts 'as_new_rank', 'as_existing_rank', and 'exclude' |
strict |
logical. If TRUE, indices are calculated from the given values. If FALSE, indices are calculated by jittering the values to ensure uniqueness of bounds of ranks. Only used with relative and mixed types. The default value is TRUE. |
intervals |
logical. If TRUE, will calculate bootstrapped confidence intervals using the percentile method. The default value is FALSE. |
interval_pct |
a number between zero and one indicating the size of the bootstrapped intervals. The default value is 0.95. |
bootstrap_iter |
the number of bootstrap iterations used to estimate intervals. The default value is 100. |
Value
Returns a named vector containing the desired index values
Examples
data(incomeMobility)
getMobilityIndices(dat = incomeMobility,
col_x = 't0',
col_y = 't2',
type = 'relative',
num_ranks = 5)
Calculates Transition Matrix
Description
Returns transition matrix from two columns in dataset. Supports relative, mixed, and absolute transition matrices as well as handling an excluded value.
Usage
getTMatrix(
dat,
col_x,
col_y,
type,
probs = TRUE,
num_ranks,
exclude_value,
bounds,
rerank_exclude_value,
strict = TRUE
)
Arguments
dat |
a dataframe with an "id" column |
col_x |
a character string denoting the first column from dat to be used in the transition matrix |
col_y |
a character string denoting the second column from dat to be used in the transition matrix |
type |
a character string indicating the type of transition matrix; accepts 'relative', 'mixed', and 'absolute' |
probs |
logical. If TRUE, values in transition matrix are probabilities; if FALSE, values in transition matrix are counts |
num_ranks |
an integer specifying the number of ranks for a relative or mixed transition matrix |
exclude_value |
a single numeric value that is excluded in calculating the transition matrix; see the rerank_exclude_value parameter to specify how the exclude value is handled |
bounds |
a sequence of numeric bounds for defining absolute transition matrix ranks |
rerank_exclude_value |
a character string indicating how the exclude value is handled when present; accepts 'as_new_rank', 'as_existing_rank', and 'exclude' |
strict |
logical. If TRUE, transition matrix is calculated from the given values. If FALSE, transition matrix is calculated by jittering the values to ensure uniqueness of bounds. Only used with relative and mixed types. |
Value
Returns a list with a transition matrix as a Matrix and vectors of the the x and y bounds corresponding to the ranks in the matrix
Examples
data(incomeMobility)
getTMatrix(dat = incomeMobility,
col_x = 't0',
col_y = 't9',
type = 'relative',
num_ranks = 5)
Example Grades Dataset
Description
This is an example grade dataset for measuring grade mobility.
Usage
gradeMobility
Format
A data.frame with 13 columns:
- id
A unique integer id for each row representing a student
- cohort
Class of student
- t0
Grade at time 0
- t1
Grade at time 1
- t2
Grade at time 2
- t3
Grade at time 3
- t4
Grade at time 4
- t5
Grade at time 5
- t6
Grade at time 6
- t7
Grade at time 7
- t8
Grade at time 8
- t9
Grade at time 9
- t10
Grade at time 10
Example Income Dataset
Description
This is an example income dataset to measure income mobility.
Usage
incomeMobility
Format
A data.frame with 13 columns:
- id
A unique integer id for each row representing an individual
- cohort
Year when first income is observed
- t0
Income at time 0
- t1
Income at time 1
- t2
Income at time 2
- t3
Income at time 3
- t4
Income at time 4
- t5
Income at time 5
- t6
Income at time 6
- t7
Income at time 7
- t8
Income at time 8
- t9
Income at time 9
- t10
Income at time 10
Example Zero-Inflated Income Dataset
Description
These is an example income dataset to measure income mobility with an inflated number of zeros. Zero-inflated data is often observed in practice and will cause difficulties for some transition matrix and mobility index approaches.
Usage
incomeZeroInfMobility
Format
A data.frame with 13 columns:
- id
A unique integer id for each row representing an individual
- cohort
Year when first income is observed
- t0
Income at time 0
- t1
Income at time 1
- t2
Income at time 2
- t3
Income at time 3
- t4
Income at time 4
- t5
Income at time 5
- t6
Income at time 6
- t7
Income at time 7
- t8
Income at time 8
- t9
Income at time 9
- t10
Income at time 10
mobilityIndexR: A package for calculating transition matrices and mobility indices
Description
This package measures mobility in a population through transition matrices and mobility indices. Relative, mixed, and absolute transition matrices are supported for various use cases. With respect to indices, the Prais-Bibby, Absolute Movement, Origin Specific, and Shorrocks indices are supported. Example income and grade data are included for demos.
mobilityIndexR functions
getTMatrix getMobilityIndices getHypothesisTest