Note: As a priority, this analyses were run using only those with a diagnosis of congenital hypotonia.
vars <- c("edad_cronologica_meses","semanas_prematurez","edad_corregida_meses",
"asq3_meses","comunicacion_total","motora_gruesa_total","motora_fina_total",
"resolucion_problemas_total","socio_individual_total")
first_evaluation <- dataset[n_evaluacion == 1 & diagnostico %like% "hipotonia", .SD, .SDcols = vars]As numerical variables do not follow a normal distribution, we will use Spearman’s \(\rho\) test as our non-parametric correlation test, as it is not tied to the assumption of normality. For more information on the function used here for the calculation of the correlation, you can read the corresponding documentation.
cors <- correlation(first_evaluation, method = "spearman", p_adjust = "none")
ind <- order(abs(cors$rho), decreasing = TRUE)
print(cors[ind, -7L])
#> Parameter1 | Parameter2 | rho | 95% CI | p
#> -----------------------------------------------------------------------------------------------
#> edad_cronologica_meses | edad_corregida_meses | 1.00 | [ 1.00, 1.00] | < .001***
#> edad_cronologica_meses | asq3_meses | 0.99 | [ 0.99, 0.99] | < .001***
#> edad_corregida_meses | asq3_meses | 0.99 | [ 0.99, 0.99] | < .001***
#> resolucion_problemas_total | socio_individual_total | 0.52 | [ 0.41, 0.61] | < .001***
#> comunicacion_total | socio_individual_total | 0.48 | [ 0.37, 0.58] | < .001***
#> motora_fina_total | resolucion_problemas_total | 0.47 | [ 0.35, 0.56] | < .001***
#> asq3_meses | comunicacion_total | -0.43 | [-0.53, -0.32] | < .001***
#> comunicacion_total | resolucion_problemas_total | 0.43 | [ 0.32, 0.53] | < .001***
#> edad_cronologica_meses | comunicacion_total | -0.42 | [-0.52, -0.30] | < .001***
#> edad_corregida_meses | comunicacion_total | -0.41 | [-0.52, -0.30] | < .001***
#> motora_gruesa_total | socio_individual_total | 0.36 | [ 0.24, 0.47] | < .001***
#> motora_fina_total | socio_individual_total | 0.33 | [ 0.20, 0.44] | < .001***
#> motora_gruesa_total | motora_fina_total | 0.25 | [ 0.12, 0.37] | < .001***
#> comunicacion_total | motora_fina_total | 0.19 | [ 0.06, 0.32] | 0.003**
#> motora_gruesa_total | resolucion_problemas_total | 0.18 | [ 0.05, 0.31] | 0.005**
#> comunicacion_total | motora_gruesa_total | 0.12 | [-0.02, 0.24] | 0.075
#> asq3_meses | socio_individual_total | -0.11 | [-0.24, 0.02] | 0.092
#> semanas_prematurez | resolucion_problemas_total | 0.11 | [-0.03, 0.23] | 0.106
#> semanas_prematurez | edad_corregida_meses | -0.10 | [-0.23, 0.03] | 0.127
#> edad_cronologica_meses | motora_gruesa_total | 0.09 | [-0.04, 0.22] | 0.180
#> semanas_prematurez | motora_gruesa_total | 0.09 | [-0.04, 0.22] | 0.180
#> edad_corregida_meses | motora_gruesa_total | 0.08 | [-0.05, 0.21] | 0.211
#> edad_corregida_meses | socio_individual_total | -0.08 | [-0.21, 0.05] | 0.214
#> edad_cronologica_meses | socio_individual_total | -0.08 | [-0.21, 0.06] | 0.245
#> semanas_prematurez | socio_individual_total | 0.08 | [-0.06, 0.21] | 0.245
#> asq3_meses | motora_gruesa_total | 0.07 | [-0.06, 0.20] | 0.254
#> semanas_prematurez | asq3_meses | -0.07 | [-0.20, 0.06] | 0.296
#> edad_cronologica_meses | semanas_prematurez | -0.04 | [-0.17, 0.09] | 0.567
#> edad_corregida_meses | motora_fina_total | 0.02 | [-0.11, 0.15] | 0.735
#> asq3_meses | resolucion_problemas_total | -0.02 | [-0.15, 0.11] | 0.767
#> edad_cronologica_meses | motora_fina_total | 0.02 | [-0.11, 0.15] | 0.787
#> semanas_prematurez | comunicacion_total | -0.01 | [-0.14, 0.12] | 0.853
#> semanas_prematurez | motora_fina_total | 7.31e-03 | [-0.12, 0.14] | 0.911
#> edad_cronologica_meses | resolucion_problemas_total | 3.97e-03 | [-0.13, 0.14] | 0.952
#> asq3_meses | motora_fina_total | 2.91e-03 | [-0.13, 0.13] | 0.965
#> edad_corregida_meses | resolucion_problemas_total | 2.09e-03 | [-0.13, 0.13] | 0.975
#>
#> Observations: 234