Cumulative logit and probit models

Described in Chapter 6 "The Ordered 2xc Table"

Cumulative_models_for_2xc(n, linkfunction = "logit", alpha = 0.05)

Arguments

n: the observed table (a 2xc matrix) with at least 3 columns
linkfunction: either "logit" or "probit"
alpha: the nominal level, e.g. 0.05 for 95% CIs

Value

An object of the contingencytables_result class, basically a subclass of base::list(). Use the utils::str() function to see the specific elements returned.

Examples

Cumulative_models_for_2xc(fontanella_2008)
#> 
#> Testing the fit of a proportional odds model
#>   Pearson goodness of fit:     P =  0.02368, X2 =  7.487 (df=2)
#>   Likelihodd ratio (deviance): P =  0.02157, D  =  7.673 (df=2)
#> 
#> Testing the effect in a proportional odds model
#>   Wald (Z-statistic):          P =  0.00000, Z =  6.738
#>   Likelihood ratio:            P =  0.00000, T = 47.158 (df=1)
#>   Score (WMW):                 P =  0.00000, Z =  6.783
#> 
#> Estimation of the effect parameter beta with 95% CIs
#> in the proportional odds model
#> ----------------------------------------------------
#> Interval         Estimate     Conf. int       Width
#> ----------------------------------------------------
#>   Wald            1.145     0.812 to  1.478   0.6662
#>   Wald (OR)       0.318     0.228 to  0.444
#> ----------------------------------------------------
Cumulative_models_for_2xc(lydersen_2012a)
#> 
#> Testing the fit of a proportional odds model
#>   Pearson goodness of fit:     P =  0.43208, X2 =  1.678 (df=2)
#>   Likelihodd ratio (deviance): P =  0.42391, D  =  1.716 (df=2)
#> 
#> Testing the effect in a proportional odds model
#>   Wald (Z-statistic):          P =  0.14097, Z = -1.472
#>   Likelihood ratio:            P =  0.13823, T =  2.198 (df=1)
#>   Score (WMW):                 P =  0.14421, Z = -1.460
#> 
#> Estimation of the effect parameter beta with 95% CIs
#> in the proportional odds model
#> ----------------------------------------------------
#> Interval         Estimate     Conf. int       Width
#> ----------------------------------------------------
#>   Wald           -0.716    -1.668 to  0.237   1.9053
#>   Wald (OR)       2.045     0.789 to  5.303
#> ----------------------------------------------------