Computes correlation coefficients for all combinations of the specified variables. If no variables are specified, all numeric (integer or double) variables are used.
correlate(data, ..., method = "pearson", partial = NULL, with = NULL)Variables to compute correlations for (column names). Leave empty to compute for all numeric variables in data.
a character string indicating which correlation coefficient is to be computed. One of "pearson" (default), "kendall", or "spearman"
Specifies a variable to be used as a control in a partial correlation.
By default, this parameter is set to NULL, indicating that no control variable
is used in the correlation. If used, with must be set to NULL (default).
Specifies a focus variable to correlate all other variables with.
By default, this parameter is set to NULL, indicating that no focus variable
is used in the correlation. If used, partial must be set to NULL (default).
a tdcmm model
WoJ %>% correlate(ethics_1, ethics_2, ethics_3)
#> # A tibble: 3 × 6
#> x y r df p n
#> * <chr> <chr> <dbl> <int> <dbl> <int>
#> 1 ethics_1 ethics_2 0.172 1198 2.04e- 9 1200
#> 2 ethics_1 ethics_3 0.165 1198 8.44e- 9 1200
#> 3 ethics_2 ethics_3 0.409 1198 1.05e-49 1200
WoJ %>% correlate()
#> # A tibble: 55 × 6
#> x y r df p n
#> * <chr> <chr> <dbl> <int> <dbl> <int>
#> 1 autonomy_selection autonomy_emphasis 0.644 1192 4.83e-141 1194
#> 2 autonomy_selection ethics_1 -0.0766 1195 7.98e- 3 1197
#> 3 autonomy_selection ethics_2 -0.0274 1195 3.43e- 1 1197
#> 4 autonomy_selection ethics_3 -0.0257 1195 3.73e- 1 1197
#> 5 autonomy_selection ethics_4 -0.0781 1195 6.89e- 3 1197
#> 6 autonomy_selection work_experience 0.161 1182 2.71e- 8 1184
#> 7 autonomy_selection trust_parliament -0.00840 1195 7.72e- 1 1197
#> 8 autonomy_selection trust_government 0.0414 1195 1.53e- 1 1197
#> 9 autonomy_selection trust_parties 0.0269 1195 3.52e- 1 1197
#> 10 autonomy_selection trust_politicians 0.0109 1195 7.07e- 1 1197
#> # ℹ 45 more rows
WoJ %>% correlate(ethics_1, ethics_2, ethics_3, with = work_experience)
#> # A tibble: 3 × 6
#> x y r df p n
#> * <chr> <chr> <dbl> <int> <dbl> <int>
#> 1 work_experience ethics_1 -0.103 1185 0.000387 1187
#> 2 work_experience ethics_2 -0.168 1185 0.00000000619 1187
#> 3 work_experience ethics_3 -0.0442 1185 0.128 1187
WoJ %>% correlate(autonomy_selection, autonomy_emphasis, partial = work_experience)
#> # A tibble: 1 × 7
#> x y z r df p n
#> * <chr> <chr> <chr> <dbl> <dbl> <dbl> <int>
#> 1 autonomy_selection autonomy_emphasis work_experie… 0.637 1178 3.07e-135 1181
WoJ %>% correlate(with = work_experience)
#> Warning: At least one of work_experience and country is not numeric, skipping computation.
#> Warning: At least one of work_experience and reach is not numeric, skipping computation.
#> Warning: At least one of work_experience and employment is not numeric, skipping computation.
#> Warning: At least one of work_experience and temp_contract is not numeric, skipping computation.
#> # A tibble: 10 × 6
#> x y r df p n
#> * <chr> <chr> <dbl> <int> <dbl> <int>
#> 1 work_experience autonomy_selection 0.161 1182 0.0000000271 1184
#> 2 work_experience autonomy_emphasis 0.155 1180 0.0000000887 1182
#> 3 work_experience ethics_1 -0.103 1185 0.000387 1187
#> 4 work_experience ethics_2 -0.168 1185 0.00000000619 1187
#> 5 work_experience ethics_3 -0.0442 1185 0.128 1187
#> 6 work_experience ethics_4 -0.116 1185 0.0000602 1187
#> 7 work_experience trust_parliament -0.00941 1185 0.746 1187
#> 8 work_experience trust_government -0.0708 1185 0.0146 1187
#> 9 work_experience trust_parties -0.0454 1185 0.118 1187
#> 10 work_experience trust_politicians -0.00976 1185 0.737 1187