tests/testthat/_snaps/ddfMLR.md
In drabinova/difNLR: DIF and DDF Detection by Non-Linear Regression Models
ddfMLR - examples at help page
Code
(fit1 <- ddfMLR(Data, group, focal.name = 1, key))
Output
Detection of both types of Differential Distractor
Functioning using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 76.8561 0.0000 ***
Item2 28.1954 0.0001 ***
Item3 6.1443 0.4072
Item4 6.8119 0.3386
Item5 4.2511 0.6427
Item6 3.6133 0.7288
Item7 8.6239 0.1959
Item8 10.0779 0.1214
Item9 14.5444 0.0241 *
Item10 3.9987 0.6768
Item11 6.2514 0.3956
Item12 6.9577 0.3248
Item13 3.6414 0.7251
Item14 5.9761 0.4259
Item15 2.2051 0.8999
Item16 2.2730 0.8930
Item17 3.9642 0.6815
Item18 8.0746 0.2327
Item19 7.4243 0.2834
Item20 5.0249 0.5406
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DDF items:
Item1
Item2
Item9
Code
AIC(fit1)
Output
[1] 4351.384 4328.702 3459.972 2861.326 2515.821 3593.464 3688.784 3845.248
[9] 4228.551 4126.332 3140.202 4006.963 3234.644 4434.031 3684.150 4421.824
[17] 3474.962 4456.081 4264.595 4967.780
Code
BIC(fit1)
Output
[1] 4418.594 4395.913 3493.577 2894.932 2549.426 3627.069 3722.389 3878.853
[9] 4295.762 4159.937 3173.808 4040.568 3268.249 4467.636 3717.755 4455.429
[17] 3508.568 4489.686 4298.201 5001.385
Code
logLik(fit1)
Output
[1] -2163.692 -2152.351 -1723.986 -1424.663 -1251.910 -1790.732 -1838.392
[8] -1916.624 -2102.276 -2057.166 -1564.101 -1997.482 -1611.322 -2211.015
[15] -1836.075 -2204.912 -1731.481 -2222.040 -2126.298 -2477.890
Code
AIC(fit1, item = 1)
Output
[1] 4351.384
Code
BIC(fit1, item = 1)
Output
[1] 4418.594
Code
logLik(fit1, item = 1)
Output
'log Lik.' -2163.692 (df=12)
Code
(fit2 <- ddfMLR(Data, group, focal.name = 1, key, p.adjust.method = "BH"))
Output
Detection of both types of Differential Distractor
Functioning using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
Multiple comparisons made with Benjamini-Hochberg adjustment of p-values
Chisq-value P-value Adj. P-value
Item1 76.8561 0.0000 0.0000 ***
Item2 28.1954 0.0001 0.0009 ***
Item3 6.1443 0.4072 0.7098
Item4 6.8119 0.3386 0.7098
Item5 4.2511 0.6427 0.8098
Item6 3.6133 0.7288 0.8098
Item7 8.6239 0.1959 0.7098
Item8 10.0779 0.1214 0.6070
Item9 14.5444 0.0241 0.1607
Item10 3.9987 0.6768 0.8098
Item11 6.2514 0.3956 0.7098
Item12 6.9577 0.3248 0.7098
Item13 3.6414 0.7251 0.8098
Item14 5.9761 0.4259 0.7098
Item15 2.2051 0.8999 0.8999
Item16 2.2730 0.8930 0.8999
Item17 3.9642 0.6815 0.8098
Item18 8.0746 0.2327 0.7098
Item19 7.4243 0.2834 0.7098
Item20 5.0249 0.5406 0.8098
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DDF items:
Item1
Item2
Code
(fit3 <- ddfMLR(Data, group, focal.name = 1, key, purify = TRUE))
Output
Detection of both types of Differential Distractor
Functioning using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was applied with 1 iteration
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 75.9559 0.0000 ***
Item2 27.1791 0.0001 ***
Item3 6.7634 0.3433
Item4 6.7449 0.3451
Item5 4.8937 0.5575
Item6 5.4903 0.4826
Item7 5.1100 0.5298
Item8 9.1139 0.1673
Item9 13.8481 0.0314 *
Item10 2.5269 0.8654
Item11 6.6844 0.3510
Item12 8.0727 0.2328
Item13 2.6738 0.8485
Item14 5.7490 0.4519
Item15 1.2392 0.9749
Item16 2.4622 0.8727
Item17 2.2130 0.8991
Item18 6.8336 0.3365
Item19 5.9072 0.4337
Item20 7.6451 0.2653
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DDF items:
Item1
Item2
Item9
Code
(fit4 <- ddfMLR(Data, group, key, focal.name = 1, type = "udif"))
Output
Detection of uniform Differential Distractor Functioning
using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 74.7785 0.0000 ***
Item2 15.0722 0.0018 **
Item3 0.6117 0.8937
Item4 3.1795 0.3648
Item5 1.3389 0.7199
Item6 0.2546 0.9683
Item7 7.4301 0.0594 .
Item8 2.7892 0.4253
Item9 8.6917 0.0337 *
Item10 1.6848 0.6403
Item11 1.6566 0.6466
Item12 5.8329 0.1200
Item13 1.5170 0.6783
Item14 2.6428 0.4500
Item15 1.2535 0.7402
Item16 0.5613 0.9052
Item17 3.8170 0.2819
Item18 7.9086 0.0479 *
Item19 1.7105 0.6346
Item20 3.6070 0.3072
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as uniform DDF items:
Item1
Item2
Item9
Item18
Code
(fit5 <- ddfMLR(Data, group, key, focal.name = 1, type = "udif"))
Output
Detection of uniform Differential Distractor Functioning
using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 74.7785 0.0000 ***
Item2 15.0722 0.0018 **
Item3 0.6117 0.8937
Item4 3.1795 0.3648
Item5 1.3389 0.7199
Item6 0.2546 0.9683
Item7 7.4301 0.0594 .
Item8 2.7892 0.4253
Item9 8.6917 0.0337 *
Item10 1.6848 0.6403
Item11 1.6566 0.6466
Item12 5.8329 0.1200
Item13 1.5170 0.6783
Item14 2.6428 0.4500
Item15 1.2535 0.7402
Item16 0.5613 0.9052
Item17 3.8170 0.2819
Item18 7.9086 0.0479 *
Item19 1.7105 0.6346
Item20 3.6070 0.3072
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as uniform DDF items:
Item1
Item2
Item9
Item18
Code
(fit6 <- ddfMLR(Data, group, key, focal.name = 1, match = "score"))
Output
Detection of both types of Differential Distractor
Functioning using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 76.8561 0.0000 ***
Item2 28.1954 0.0001 ***
Item3 6.1443 0.4072
Item4 6.8119 0.3386
Item5 4.2511 0.6427
Item6 3.6133 0.7288
Item7 8.6239 0.1959
Item8 10.0779 0.1214
Item9 14.5445 0.0241 *
Item10 3.9987 0.6768
Item11 6.2523 0.3955
Item12 6.9576 0.3248
Item13 3.6414 0.7251
Item14 5.9761 0.4259
Item15 2.2053 0.8999
Item16 2.2730 0.8930
Item17 3.9642 0.6815
Item18 8.0746 0.2327
Item19 7.4241 0.2834
Item20 5.0249 0.5406
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DDF items:
Item1
Item2
Item9
drabinova/difNLR documentation built on June 12, 2025, 4:47 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
ddfMLR - examples at help page
Code
(fit1 <- ddfMLR(Data, group, focal.name = 1, key))
Output
Detection of both types of Differential Distractor
Functioning using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 76.8561 0.0000 ***
Item2 28.1954 0.0001 ***
Item3 6.1443 0.4072
Item4 6.8119 0.3386
Item5 4.2511 0.6427
Item6 3.6133 0.7288
Item7 8.6239 0.1959
Item8 10.0779 0.1214
Item9 14.5444 0.0241 *
Item10 3.9987 0.6768
Item11 6.2514 0.3956
Item12 6.9577 0.3248
Item13 3.6414 0.7251
Item14 5.9761 0.4259
Item15 2.2051 0.8999
Item16 2.2730 0.8930
Item17 3.9642 0.6815
Item18 8.0746 0.2327
Item19 7.4243 0.2834
Item20 5.0249 0.5406
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DDF items:
Item1
Item2
Item9
Code
AIC(fit1)
Output
[1] 4351.384 4328.702 3459.972 2861.326 2515.821 3593.464 3688.784 3845.248
[9] 4228.551 4126.332 3140.202 4006.963 3234.644 4434.031 3684.150 4421.824
[17] 3474.962 4456.081 4264.595 4967.780
Code
BIC(fit1)
Output
[1] 4418.594 4395.913 3493.577 2894.932 2549.426 3627.069 3722.389 3878.853
[9] 4295.762 4159.937 3173.808 4040.568 3268.249 4467.636 3717.755 4455.429
[17] 3508.568 4489.686 4298.201 5001.385
Code
logLik(fit1)
Output
[1] -2163.692 -2152.351 -1723.986 -1424.663 -1251.910 -1790.732 -1838.392
[8] -1916.624 -2102.276 -2057.166 -1564.101 -1997.482 -1611.322 -2211.015
[15] -1836.075 -2204.912 -1731.481 -2222.040 -2126.298 -2477.890
Code
AIC(fit1, item = 1)
Output
[1] 4351.384
Code
BIC(fit1, item = 1)
Output
[1] 4418.594
Code
logLik(fit1, item = 1)
Output
'log Lik.' -2163.692 (df=12)
Code
(fit2 <- ddfMLR(Data, group, focal.name = 1, key, p.adjust.method = "BH"))
Output
Detection of both types of Differential Distractor
Functioning using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
Multiple comparisons made with Benjamini-Hochberg adjustment of p-values
Chisq-value P-value Adj. P-value
Item1 76.8561 0.0000 0.0000 ***
Item2 28.1954 0.0001 0.0009 ***
Item3 6.1443 0.4072 0.7098
Item4 6.8119 0.3386 0.7098
Item5 4.2511 0.6427 0.8098
Item6 3.6133 0.7288 0.8098
Item7 8.6239 0.1959 0.7098
Item8 10.0779 0.1214 0.6070
Item9 14.5444 0.0241 0.1607
Item10 3.9987 0.6768 0.8098
Item11 6.2514 0.3956 0.7098
Item12 6.9577 0.3248 0.7098
Item13 3.6414 0.7251 0.8098
Item14 5.9761 0.4259 0.7098
Item15 2.2051 0.8999 0.8999
Item16 2.2730 0.8930 0.8999
Item17 3.9642 0.6815 0.8098
Item18 8.0746 0.2327 0.7098
Item19 7.4243 0.2834 0.7098
Item20 5.0249 0.5406 0.8098
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DDF items:
Item1
Item2
Code
(fit3 <- ddfMLR(Data, group, focal.name = 1, key, purify = TRUE))
Output
Detection of both types of Differential Distractor
Functioning using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was applied with 1 iteration
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 75.9559 0.0000 ***
Item2 27.1791 0.0001 ***
Item3 6.7634 0.3433
Item4 6.7449 0.3451
Item5 4.8937 0.5575
Item6 5.4903 0.4826
Item7 5.1100 0.5298
Item8 9.1139 0.1673
Item9 13.8481 0.0314 *
Item10 2.5269 0.8654
Item11 6.6844 0.3510
Item12 8.0727 0.2328
Item13 2.6738 0.8485
Item14 5.7490 0.4519
Item15 1.2392 0.9749
Item16 2.4622 0.8727
Item17 2.2130 0.8991
Item18 6.8336 0.3365
Item19 5.9072 0.4337
Item20 7.6451 0.2653
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DDF items:
Item1
Item2
Item9
Code
(fit4 <- ddfMLR(Data, group, key, focal.name = 1, type = "udif"))
Output
Detection of uniform Differential Distractor Functioning
using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 74.7785 0.0000 ***
Item2 15.0722 0.0018 **
Item3 0.6117 0.8937
Item4 3.1795 0.3648
Item5 1.3389 0.7199
Item6 0.2546 0.9683
Item7 7.4301 0.0594 .
Item8 2.7892 0.4253
Item9 8.6917 0.0337 *
Item10 1.6848 0.6403
Item11 1.6566 0.6466
Item12 5.8329 0.1200
Item13 1.5170 0.6783
Item14 2.6428 0.4500
Item15 1.2535 0.7402
Item16 0.5613 0.9052
Item17 3.8170 0.2819
Item18 7.9086 0.0479 *
Item19 1.7105 0.6346
Item20 3.6070 0.3072
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as uniform DDF items:
Item1
Item2
Item9
Item18
Code
(fit5 <- ddfMLR(Data, group, key, focal.name = 1, type = "udif"))
Output
Detection of uniform Differential Distractor Functioning
using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 74.7785 0.0000 ***
Item2 15.0722 0.0018 **
Item3 0.6117 0.8937
Item4 3.1795 0.3648
Item5 1.3389 0.7199
Item6 0.2546 0.9683
Item7 7.4301 0.0594 .
Item8 2.7892 0.4253
Item9 8.6917 0.0337 *
Item10 1.6848 0.6403
Item11 1.6566 0.6466
Item12 5.8329 0.1200
Item13 1.5170 0.6783
Item14 2.6428 0.4500
Item15 1.2535 0.7402
Item16 0.5613 0.9052
Item17 3.8170 0.2819
Item18 7.9086 0.0479 *
Item19 1.7105 0.6346
Item20 3.6070 0.3072
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as uniform DDF items:
Item1
Item2
Item9
Item18
Code
(fit6 <- ddfMLR(Data, group, key, focal.name = 1, match = "score"))
Output
Detection of both types of Differential Distractor
Functioning using multinomial log-linear regression model
Likelihood-ratio chi-square statistics
Item purification was not applied
No p-value adjustment for multiple comparisons
Chisq-value P-value
Item1 76.8561 0.0000 ***
Item2 28.1954 0.0001 ***
Item3 6.1443 0.4072
Item4 6.8119 0.3386
Item5 4.2511 0.6427
Item6 3.6133 0.7288
Item7 8.6239 0.1959
Item8 10.0779 0.1214
Item9 14.5445 0.0241 *
Item10 3.9987 0.6768
Item11 6.2523 0.3955
Item12 6.9576 0.3248
Item13 3.6414 0.7251
Item14 5.9761 0.4259
Item15 2.2053 0.8999
Item16 2.2730 0.8930
Item17 3.9642 0.6815
Item18 8.0746 0.2327
Item19 7.4241 0.2834
Item20 5.0249 0.5406
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Items detected as DDF items:
Item1
Item2
Item9
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Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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