[R] prop.trend.test

[Thomas Subia]

Colleagues,

Thanks all for the responses. 

I am monitoring the daily total number of defects per sample unit. 
I need to know whether this daily defect proportion is trending upward (a bad thing for a manufacturing process).

My first thought was to use either a u or a u' control chart for this.
As far as I know, u or u' charts are poor to detect drifts. 

This is why I chose to use prop.trend.test to detect trends in proportions.

While prop.trend.test can confirm the existence of a trend, as far as I know, it is left to the user
to determine what direction that trend is. 

One way to illustrate trending is of course to plot the data and use geom_smooth and method lm
For the non-statisticians in my group, I've found that using this method along with the p-value of prop.trend.test, makes it easier for the users to determine the existence of trending and its direction.

If there are any other ways to do this, please let me know.

Thomas Subia












On Thursday, September 7, 2023 at 10:31:27 AM PDT, Rui Barradas <ruipbarradas using sapo.pt> wrote: 





Às 14:23 de 07/09/2023, Thomas Subia via R-help escreveu:
> 
> Colleagues
> 
>   Consider
> smokers  <- c( 83, 90, 129, 70 )
> patients <- c( 86, 93, 136, 82 )
> 
>   prop.trend.test(smokers, patients)
> 
>   Output:
> 
>       Chi-squared Test for Trend inProportions
> 
>   data:  smokers out of patients ,
> 
> using scores: 1 2 3 4
> 
> X-squared = 8.2249, df = 1, p-value = 0.004132
> 
>   # trend test for proportions indicates proportions aretrending.
> 
>   How does one identify the direction of trending?
>   # prop.test indicates that the proportions are unequal but doeslittle to indicate trend direction.
> All the best,
> Thomas Subia
> 
> 
>     [[alternative HTML version deleted]]
> 
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Hello,

By visual inspection it seems that there is a decreasing trend.
Note that the sample estimates of prop.test and smokers/patients are equal.


smokers  <- c( 83, 90, 129, 70 )
patients <- c( 86, 93, 136, 82 )

prop.test(smokers, patients)$estimate
#>    prop 1    prop 2    prop 3    prop 4
#> 0.9651163 0.9677419 0.9485294 0.8536585

smokers/patients

#> [1] 0.9651163 0.9677419 0.9485294 0.8536585

plot(smokers/patients, type = "b")



Hope this helps,

Rui Barradas