tests/testthat/_snaps/nice_mod.md

In rempsyc: Convenience Functions for Psychology

nice_mod

Code
  nice_mod(data = mtcars, response = "mpg", predictor = "gear", moderator = "wt")
Output
    Dependent Variable Predictor df           B          t            p
  1                mpg      gear 28 -0.08718042 -0.7982999 4.314156e-01
  2                mpg        wt 28 -0.94959988 -8.6037724 2.383144e-09
  3                mpg   gear:wt 28 -0.23559962 -2.1551077 3.989970e-02
            sr2     CI_lower   CI_upper
  1 0.004805465 0.0000000000 0.02702141
  2 0.558188818 0.3142326391 0.80214500
  3 0.035022025 0.0003502202 0.09723370

Code
  nice_mod(data = mtcars, response = c("mpg", "disp", "hp"), predictor = "gear",
  moderator = "wt")
Output
    Model Number Dependent Variable Predictor df           B          t
  1            1                mpg      gear 28 -0.08718042 -0.7982999
  2            1                mpg        wt 28 -0.94959988 -8.6037724
  3            1                mpg   gear:wt 28 -0.23559962 -2.1551077
  4            2               disp      gear 28 -0.07488985 -0.6967831
  5            2               disp        wt 28  0.83273987  7.6662883
  6            2               disp   gear:wt 28 -0.08758665 -0.8140664
  7            3                 hp      gear 28  0.42308208  2.6537930
  8            3                 hp        wt 28  0.92572761  5.7454866
  9            3                 hp   gear:wt 28  0.15308972  0.9592587
               p         sr2     CI_lower   CI_upper
  1 4.314156e-01 0.004805465 0.0000000000 0.02702141
  2 2.383144e-09 0.558188818 0.3142326391 0.80214500
  3 3.989970e-02 0.035022025 0.0003502202 0.09723370
  4 4.916834e-01 0.003546038 0.0000000000 0.02230154
  5 2.373171e-08 0.429258143 0.1916386492 0.66687764
  6 4.224765e-01 0.004840251 0.0000000000 0.02679265
  7 1.297126e-02 0.113174016 0.0011317402 0.26928944
  8 3.637782e-06 0.530476631 0.2928664478 0.76808681
  9 3.456390e-01 0.014787139 0.0000000000 0.07139000

Code
  nice_mod(data = mtcars, response = "mpg", predictor = "gear", moderator = "wt",
    covariates = c("am", "vs"))
Output
    Dependent Variable Predictor df          B          t            p
  1                mpg      gear 26 -0.1106937 -0.8787865 3.875666e-01
  2                mpg        wt 26 -0.6977072 -5.0686874 2.803684e-05
  3                mpg        am 26  0.1306864  0.8569286 3.993141e-01
  4                mpg        vs 26  0.3192472  3.2441426 3.228614e-03
  5                mpg   gear:wt 26 -0.2511143 -2.5615471 1.656773e-02
            sr2     CI_lower   CI_upper
  1 0.004459982 0.0000000000 0.02257663
  2 0.148373834 0.0198201790 0.27692749
  3 0.004240876 0.0000000000 0.02189819
  4 0.060780767 0.0006078077 0.13504910
  5 0.037894048 0.0003789405 0.09436130

Code
  nice_mod(data = mtcars, response = "mpg", predictor = "gear", moderator = "wt",
    moderator2 = "am")
Output
    Dependent Variable  Predictor df          B         t           p         sr2
  1                mpg       gear 24  0.3013096  1.791291 0.085867454 0.015880099
  2                mpg         wt 24 -1.9824141 -3.296782 0.003035645 0.053790002
  3                mpg         am 24 -0.3381137 -1.431537 0.165170887 0.010142070
  4                mpg    gear:wt 24 -0.9906361 -1.567980 0.129977525 0.012167517
  5                mpg    gear:am 24 -0.2934245 -1.649909 0.111990122 0.013472278
  6                mpg      wt:am 24  0.2636573  0.499489 0.621983258 0.001234733
  7                mpg gear:wt:am 24  1.0037369  1.854357 0.076016767 0.017017980
     CI_lower    CI_upper
  1 0.0000000 0.047441438
  2 0.0005379 0.117448925
  3 0.0000000 0.034953502
  4 0.0000000 0.039504439
  5 0.0000000 0.042345492
  6 0.0000000 0.009659682
  7 0.0000000 0.049794162

rempsyc documentation built on July 3, 2024, 5:08 p.m.