psychotools: Interfaces for psychotools package for data science...
In intubate: Interface to Popular R Functions for Data Science Pipelines
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Interfaces to psychotools functions that can be used
in a pipeline implemented by magrittr.
Usage
1
2
Arguments
data
data frame, tibble, list, ...
...
Other arguments passed to the corresponding interfaced function.
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
1
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## Not run:
library(intubate)
library(magrittr)
library(psychotools)
## ntbt_anchor: Anchor Methods for the Detection of Uniform DIF the Rasch Model
data("VerbalAggression", package = "psychotools")
## Original function to interface
anchor(resp2[, 1:12] ~ gender, data = VerbalAggression,
class = "forward", select = "MTT", range = c(0.05, 1))
## The interface puts data as first parameter
ntbt_anchor(VerbalAggression, resp2[, 1:12] ~ gender,
class = "forward", select = "MTT", range = c(0.05, 1))
## so it can be used easily in a pipeline.
VerbalAggression %>%
ntbt_anchor(resp2[, 1:12] ~ gender,
class = "forward", select = "MTT", range = c(0.05, 1))
## ntbt_anchortest: Anchor methods for the detection of uniform DIF the Rasch model
## Original function to interface
anchortest(resp2[, 1:12] ~ gender, data = VerbalAggression,
class = "forward", select = "MTT", range = c(0.05, 1))
## The interface puts data as first parameter
ntbt_anchortest(VerbalAggression, resp2[, 1:12] ~ gender,
class = "forward", select = "MTT", range = c(0.05, 1))
## so it can be used easily in a pipeline.
VerbalAggression %>%
ntbt_anchortest(resp2[, 1:12] ~ gender,
class = "forward", select = "MTT", range = c(0.05, 1))
## End(Not run)
Example output
Loading required namespace: multcomp
Anchor items:
[1] 3 9 5 1 6 4 10
Ranking order:
[1] 7 3 9 5 1 6 4 10 12 2 8 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Anchor items:
[1] 9 5 1 3 6 4 2
Ranking order:
[1] 7 9 5 1 3 6 4 2 8 10 12 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Anchor items:
[1] 1 5 9 6 3 4 2
Ranking order:
[1] 7 1 5 9 6 3 4 2 8 12 10 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Anchor items:
[1] 5 3 1 6 9 4 10
Anchored item parameters:
`resp2[, 1:12]`S1WantCurse_1 `resp2[, 1:12]`S1DoCurse_1
-1.09813262 -0.93463721
`resp2[, 1:12]`S1WantScold_1 `resp2[, 1:12]`S1DoScold_1
-0.39548331 0.04885082
`resp2[, 1:12]`S1DoShout_1 `resp2[, 1:12]`S2WantCurse_1
1.26467752 -1.70260511
`resp2[, 1:12]`S2DoCurse_1 `resp2[, 1:12]`S2WantScold_1
-0.42040437 -0.57109738
`resp2[, 1:12]`S2DoScold_1 `resp2[, 1:12]`S2WantShout_1
0.62824492 0.04885082
`resp2[, 1:12]`S2DoShout_1 `resp2[, 1:12]`S1WantCurse_2
2.16336265 -0.69043430
`resp2[, 1:12]`S1DoCurse_2 `resp2[, 1:12]`S1WantScold_2
-1.24162833 -0.03772488
`resp2[, 1:12]`S1DoScold_2 `resp2[, 1:12]`S1DoShout_2
-0.69043427 1.45966568
`resp2[, 1:12]`S2WantCurse_2 `resp2[, 1:12]`S2DoCurse_2
-1.04784139 -1.45047608
`resp2[, 1:12]`S2WantScold_2 `resp2[, 1:12]`S2DoScold_2
-0.11699075 -0.43928734
`resp2[, 1:12]`S2WantShout_2 `resp2[, 1:12]`S2DoShout_2
1.09471060 1.87448368
Ranking order:
[1] 7 5 3 1 6 9 4 10 12 8 2 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Final DIF tests:
Simultaneous Tests for General Linear Hypotheses
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
`resp2[, 1:12]`S1WantCurse == 0 -0.4077 0.3132 -1.302 0.193053
`resp2[, 1:12]`S1DoCurse == 0 0.3070 0.3812 0.805 0.420688
`resp2[, 1:12]`S1WantScold == 0 -0.3578 0.3003 -1.191 0.233477
`resp2[, 1:12]`S1DoScold == 0 0.7393 0.3088 2.394 0.016646 *
`resp2[, 1:12]`S1DoShout == 0 -0.1950 0.3323 -0.587 0.557376
`resp2[, 1:12]`S2WantCurse == 0 -0.6548 0.3823 -1.713 0.086795 .
`resp2[, 1:12]`S2DoCurse == 0 1.0301 0.3907 2.637 0.008371 **
`resp2[, 1:12]`S2WantScold == 0 -0.4541 0.3013 -1.507 0.131804
`resp2[, 1:12]`S2DoScold == 0 1.0675 0.3058 3.491 0.000481 ***
`resp2[, 1:12]`S2WantShout == 0 -1.0459 0.3600 -2.905 0.003674 **
`resp2[, 1:12]`S2DoShout == 0 0.2889 0.4239 0.681 0.495595
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Univariate p values reported)
Anchor items:
[1] 1 9 5 6 3 4 2
Anchored item parameters:
`resp2[, 1:12]`S1DoCurse_1 `resp2[, 1:12]`S1WantScold_1
-0.711368331 -0.172214434
`resp2[, 1:12]`S1DoScold_1 `resp2[, 1:12]`S1WantShout_1
0.272119692 0.346208928
`resp2[, 1:12]`S1DoShout_1 `resp2[, 1:12]`S2WantCurse_1
1.487946396 -1.479336230
`resp2[, 1:12]`S2DoCurse_1 `resp2[, 1:12]`S2WantScold_1
-0.197135499 -0.347828506
`resp2[, 1:12]`S2DoScold_1 `resp2[, 1:12]`S2WantShout_1
0.851513801 0.272119692
`resp2[, 1:12]`S2DoShout_1 `resp2[, 1:12]`S1DoCurse_2
2.386631529 -1.127008193
`resp2[, 1:12]`S1WantScold_2 `resp2[, 1:12]`S1DoScold_2
0.076895265 -0.575814129
`resp2[, 1:12]`S1WantShout_2 `resp2[, 1:12]`S1DoShout_2
0.629825996 1.574285825
`resp2[, 1:12]`S2WantCurse_2 `resp2[, 1:12]`S2DoCurse_2
-0.933221244 -1.335855934
`resp2[, 1:12]`S2WantScold_2 `resp2[, 1:12]`S2DoScold_2
-0.002370609 -0.324667202
`resp2[, 1:12]`S2WantShout_2 `resp2[, 1:12]`S2DoShout_2
1.209330739 1.989103817
Ranking order:
[1] 7 1 9 5 6 3 4 2 12 8 10 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Final DIF tests:
Simultaneous Tests for General Linear Hypotheses
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
`resp2[, 1:12]`S1DoCurse == 0 0.41564 0.32998 1.260 0.207820
`resp2[, 1:12]`S1WantScold == 0 -0.24911 0.30085 -0.828 0.407661
`resp2[, 1:12]`S1DoScold == 0 0.84793 0.30912 2.743 0.006087 **
`resp2[, 1:12]`S1WantShout == 0 -0.28362 0.30264 -0.937 0.348677
`resp2[, 1:12]`S1DoShout == 0 -0.08634 0.33397 -0.259 0.796001
`resp2[, 1:12]`S2WantCurse == 0 -0.54611 0.38200 -1.430 0.152828
`resp2[, 1:12]`S2DoCurse == 0 1.13872 0.39053 2.916 0.003547 **
`resp2[, 1:12]`S2WantScold == 0 -0.34546 0.30179 -1.145 0.252342
`resp2[, 1:12]`S2DoScold == 0 1.17618 0.35331 3.329 0.000871 ***
`resp2[, 1:12]`S2WantShout == 0 -0.93721 0.36109 -2.595 0.009446 **
`resp2[, 1:12]`S2DoShout == 0 0.39753 0.42546 0.934 0.350123
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Univariate p values reported)
Anchor items:
[1] 5 6 3 9 1 4 8
Anchored item parameters:
`resp2[, 1:12]`S1WantCurse_1 `resp2[, 1:12]`S1DoCurse_1
-0.94832558 -0.78483016
`resp2[, 1:12]`S1WantScold_1 `resp2[, 1:12]`S1DoScold_1
-0.24567627 0.19865786
`resp2[, 1:12]`S1DoShout_1 `resp2[, 1:12]`S2WantCurse_1
1.41448456 -1.55279806
`resp2[, 1:12]`S2DoCurse_1 `resp2[, 1:12]`S2WantScold_1
-0.27059733 -0.42129034
`resp2[, 1:12]`S2DoScold_1 `resp2[, 1:12]`S2WantShout_1
0.77805197 0.19865786
`resp2[, 1:12]`S2DoShout_1 `resp2[, 1:12]`S1WantCurse_2
2.31316970 -0.54597876
`resp2[, 1:12]`S1DoCurse_2 `resp2[, 1:12]`S1WantScold_2
-1.09717280 0.10673066
`resp2[, 1:12]`S1DoScold_2 `resp2[, 1:12]`S1DoShout_2
-0.54597874 1.60412122
`resp2[, 1:12]`S2WantCurse_2 `resp2[, 1:12]`S2DoCurse_2
-0.90338585 -1.30602054
`resp2[, 1:12]`S2WantScold_2 `resp2[, 1:12]`S2DoScold_2
0.02746478 -0.29483181
`resp2[, 1:12]`S2WantShout_2 `resp2[, 1:12]`S2DoShout_2
1.23916613 2.01893921
Ranking order:
[1] 7 5 6 3 9 1 4 8 12 2 10 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Final DIF tests:
Simultaneous Tests for General Linear Hypotheses
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
`resp2[, 1:12]`S1WantCurse == 0 -0.4023 0.3135 -1.284 0.19931
`resp2[, 1:12]`S1DoCurse == 0 0.3123 0.3812 0.819 0.41258
`resp2[, 1:12]`S1WantScold == 0 -0.3524 0.3011 -1.170 0.24188
`resp2[, 1:12]`S1DoScold == 0 0.7446 0.3093 2.408 0.01605 *
`resp2[, 1:12]`S1DoShout == 0 -0.1896 0.3342 -0.567 0.57038
`resp2[, 1:12]`S2WantCurse == 0 -0.6494 0.3823 -1.699 0.08935 .
`resp2[, 1:12]`S2DoCurse == 0 1.0354 0.3381 3.063 0.00219 **
`resp2[, 1:12]`S2WantScold == 0 -0.4488 0.3021 -1.486 0.13740
`resp2[, 1:12]`S2DoScold == 0 1.0729 0.3534 3.036 0.00240 **
`resp2[, 1:12]`S2WantShout == 0 -1.0405 0.3614 -2.879 0.00398 **
`resp2[, 1:12]`S2DoShout == 0 0.2942 0.4256 0.691 0.48935
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Univariate p values reported)
intubate documentation built on May 2, 2019, 2:46 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Interfaces to psychotools functions that can be used
in a pipeline implemented by magrittr.
Usage
1 2 |
Arguments
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | ## Not run:
library(intubate)
library(magrittr)
library(psychotools)
## ntbt_anchor: Anchor Methods for the Detection of Uniform DIF the Rasch Model
data("VerbalAggression", package = "psychotools")
## Original function to interface
anchor(resp2[, 1:12] ~ gender, data = VerbalAggression,
class = "forward", select = "MTT", range = c(0.05, 1))
## The interface puts data as first parameter
ntbt_anchor(VerbalAggression, resp2[, 1:12] ~ gender,
class = "forward", select = "MTT", range = c(0.05, 1))
## so it can be used easily in a pipeline.
VerbalAggression %>%
ntbt_anchor(resp2[, 1:12] ~ gender,
class = "forward", select = "MTT", range = c(0.05, 1))
## ntbt_anchortest: Anchor methods for the detection of uniform DIF the Rasch model
## Original function to interface
anchortest(resp2[, 1:12] ~ gender, data = VerbalAggression,
class = "forward", select = "MTT", range = c(0.05, 1))
## The interface puts data as first parameter
ntbt_anchortest(VerbalAggression, resp2[, 1:12] ~ gender,
class = "forward", select = "MTT", range = c(0.05, 1))
## so it can be used easily in a pipeline.
VerbalAggression %>%
ntbt_anchortest(resp2[, 1:12] ~ gender,
class = "forward", select = "MTT", range = c(0.05, 1))
## End(Not run)
|
Example output
Loading required namespace: multcomp
Anchor items:
[1] 3 9 5 1 6 4 10
Ranking order:
[1] 7 3 9 5 1 6 4 10 12 2 8 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Anchor items:
[1] 9 5 1 3 6 4 2
Ranking order:
[1] 7 9 5 1 3 6 4 2 8 10 12 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Anchor items:
[1] 1 5 9 6 3 4 2
Ranking order:
[1] 7 1 5 9 6 3 4 2 8 12 10 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Anchor items:
[1] 5 3 1 6 9 4 10
Anchored item parameters:
`resp2[, 1:12]`S1WantCurse_1 `resp2[, 1:12]`S1DoCurse_1
-1.09813262 -0.93463721
`resp2[, 1:12]`S1WantScold_1 `resp2[, 1:12]`S1DoScold_1
-0.39548331 0.04885082
`resp2[, 1:12]`S1DoShout_1 `resp2[, 1:12]`S2WantCurse_1
1.26467752 -1.70260511
`resp2[, 1:12]`S2DoCurse_1 `resp2[, 1:12]`S2WantScold_1
-0.42040437 -0.57109738
`resp2[, 1:12]`S2DoScold_1 `resp2[, 1:12]`S2WantShout_1
0.62824492 0.04885082
`resp2[, 1:12]`S2DoShout_1 `resp2[, 1:12]`S1WantCurse_2
2.16336265 -0.69043430
`resp2[, 1:12]`S1DoCurse_2 `resp2[, 1:12]`S1WantScold_2
-1.24162833 -0.03772488
`resp2[, 1:12]`S1DoScold_2 `resp2[, 1:12]`S1DoShout_2
-0.69043427 1.45966568
`resp2[, 1:12]`S2WantCurse_2 `resp2[, 1:12]`S2DoCurse_2
-1.04784139 -1.45047608
`resp2[, 1:12]`S2WantScold_2 `resp2[, 1:12]`S2DoScold_2
-0.11699075 -0.43928734
`resp2[, 1:12]`S2WantShout_2 `resp2[, 1:12]`S2DoShout_2
1.09471060 1.87448368
Ranking order:
[1] 7 5 3 1 6 9 4 10 12 8 2 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Final DIF tests:
Simultaneous Tests for General Linear Hypotheses
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
`resp2[, 1:12]`S1WantCurse == 0 -0.4077 0.3132 -1.302 0.193053
`resp2[, 1:12]`S1DoCurse == 0 0.3070 0.3812 0.805 0.420688
`resp2[, 1:12]`S1WantScold == 0 -0.3578 0.3003 -1.191 0.233477
`resp2[, 1:12]`S1DoScold == 0 0.7393 0.3088 2.394 0.016646 *
`resp2[, 1:12]`S1DoShout == 0 -0.1950 0.3323 -0.587 0.557376
`resp2[, 1:12]`S2WantCurse == 0 -0.6548 0.3823 -1.713 0.086795 .
`resp2[, 1:12]`S2DoCurse == 0 1.0301 0.3907 2.637 0.008371 **
`resp2[, 1:12]`S2WantScold == 0 -0.4541 0.3013 -1.507 0.131804
`resp2[, 1:12]`S2DoScold == 0 1.0675 0.3058 3.491 0.000481 ***
`resp2[, 1:12]`S2WantShout == 0 -1.0459 0.3600 -2.905 0.003674 **
`resp2[, 1:12]`S2DoShout == 0 0.2889 0.4239 0.681 0.495595
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Univariate p values reported)
Anchor items:
[1] 1 9 5 6 3 4 2
Anchored item parameters:
`resp2[, 1:12]`S1DoCurse_1 `resp2[, 1:12]`S1WantScold_1
-0.711368331 -0.172214434
`resp2[, 1:12]`S1DoScold_1 `resp2[, 1:12]`S1WantShout_1
0.272119692 0.346208928
`resp2[, 1:12]`S1DoShout_1 `resp2[, 1:12]`S2WantCurse_1
1.487946396 -1.479336230
`resp2[, 1:12]`S2DoCurse_1 `resp2[, 1:12]`S2WantScold_1
-0.197135499 -0.347828506
`resp2[, 1:12]`S2DoScold_1 `resp2[, 1:12]`S2WantShout_1
0.851513801 0.272119692
`resp2[, 1:12]`S2DoShout_1 `resp2[, 1:12]`S1DoCurse_2
2.386631529 -1.127008193
`resp2[, 1:12]`S1WantScold_2 `resp2[, 1:12]`S1DoScold_2
0.076895265 -0.575814129
`resp2[, 1:12]`S1WantShout_2 `resp2[, 1:12]`S1DoShout_2
0.629825996 1.574285825
`resp2[, 1:12]`S2WantCurse_2 `resp2[, 1:12]`S2DoCurse_2
-0.933221244 -1.335855934
`resp2[, 1:12]`S2WantScold_2 `resp2[, 1:12]`S2DoScold_2
-0.002370609 -0.324667202
`resp2[, 1:12]`S2WantShout_2 `resp2[, 1:12]`S2DoShout_2
1.209330739 1.989103817
Ranking order:
[1] 7 1 9 5 6 3 4 2 12 8 10 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Final DIF tests:
Simultaneous Tests for General Linear Hypotheses
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
`resp2[, 1:12]`S1DoCurse == 0 0.41564 0.32998 1.260 0.207820
`resp2[, 1:12]`S1WantScold == 0 -0.24911 0.30085 -0.828 0.407661
`resp2[, 1:12]`S1DoScold == 0 0.84793 0.30912 2.743 0.006087 **
`resp2[, 1:12]`S1WantShout == 0 -0.28362 0.30264 -0.937 0.348677
`resp2[, 1:12]`S1DoShout == 0 -0.08634 0.33397 -0.259 0.796001
`resp2[, 1:12]`S2WantCurse == 0 -0.54611 0.38200 -1.430 0.152828
`resp2[, 1:12]`S2DoCurse == 0 1.13872 0.39053 2.916 0.003547 **
`resp2[, 1:12]`S2WantScold == 0 -0.34546 0.30179 -1.145 0.252342
`resp2[, 1:12]`S2DoScold == 0 1.17618 0.35331 3.329 0.000871 ***
`resp2[, 1:12]`S2WantShout == 0 -0.93721 0.36109 -2.595 0.009446 **
`resp2[, 1:12]`S2DoShout == 0 0.39753 0.42546 0.934 0.350123
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Univariate p values reported)
Anchor items:
[1] 5 6 3 9 1 4 8
Anchored item parameters:
`resp2[, 1:12]`S1WantCurse_1 `resp2[, 1:12]`S1DoCurse_1
-0.94832558 -0.78483016
`resp2[, 1:12]`S1WantScold_1 `resp2[, 1:12]`S1DoScold_1
-0.24567627 0.19865786
`resp2[, 1:12]`S1DoShout_1 `resp2[, 1:12]`S2WantCurse_1
1.41448456 -1.55279806
`resp2[, 1:12]`S2DoCurse_1 `resp2[, 1:12]`S2WantScold_1
-0.27059733 -0.42129034
`resp2[, 1:12]`S2DoScold_1 `resp2[, 1:12]`S2WantShout_1
0.77805197 0.19865786
`resp2[, 1:12]`S2DoShout_1 `resp2[, 1:12]`S1WantCurse_2
2.31316970 -0.54597876
`resp2[, 1:12]`S1DoCurse_2 `resp2[, 1:12]`S1WantScold_2
-1.09717280 0.10673066
`resp2[, 1:12]`S1DoScold_2 `resp2[, 1:12]`S1DoShout_2
-0.54597874 1.60412122
`resp2[, 1:12]`S2WantCurse_2 `resp2[, 1:12]`S2DoCurse_2
-0.90338585 -1.30602054
`resp2[, 1:12]`S2WantScold_2 `resp2[, 1:12]`S2DoScold_2
0.02746478 -0.29483181
`resp2[, 1:12]`S2WantShout_2 `resp2[, 1:12]`S2DoShout_2
1.23916613 2.01893921
Ranking order:
[1] 7 5 6 3 9 1 4 8 12 2 10 11
Criterion values (not sorted):
[1] 6 9 6 7 6 6 5 9 6 9 10 9
Final DIF tests:
Simultaneous Tests for General Linear Hypotheses
Linear Hypotheses:
Estimate Std. Error z value Pr(>|z|)
`resp2[, 1:12]`S1WantCurse == 0 -0.4023 0.3135 -1.284 0.19931
`resp2[, 1:12]`S1DoCurse == 0 0.3123 0.3812 0.819 0.41258
`resp2[, 1:12]`S1WantScold == 0 -0.3524 0.3011 -1.170 0.24188
`resp2[, 1:12]`S1DoScold == 0 0.7446 0.3093 2.408 0.01605 *
`resp2[, 1:12]`S1DoShout == 0 -0.1896 0.3342 -0.567 0.57038
`resp2[, 1:12]`S2WantCurse == 0 -0.6494 0.3823 -1.699 0.08935 .
`resp2[, 1:12]`S2DoCurse == 0 1.0354 0.3381 3.063 0.00219 **
`resp2[, 1:12]`S2WantScold == 0 -0.4488 0.3021 -1.486 0.13740
`resp2[, 1:12]`S2DoScold == 0 1.0729 0.3534 3.036 0.00240 **
`resp2[, 1:12]`S2WantShout == 0 -1.0405 0.3614 -2.879 0.00398 **
`resp2[, 1:12]`S2DoShout == 0 0.2942 0.4256 0.691 0.48935
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Univariate p values reported)
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