Yes, this was written a bit bone-headed (as I am allowed to say...)
If you look at the code, you will see inside:
a <- anova(lm(freq ~ score, data = list(freq = x/n, score = as.vector(score)),
weights = w))
and the lm() inside should give you the direction via the sign of the regression coefficient on "score".
So, at least for now, you could just doctor a copy of the code for your own purposes, as in
fit <- lm(freq ~ score, data = list(freq = x/n, score = as.vector(score)),
weights = w)
a <- anova(fit)
and arrange to return coef(fit)["score"] at the end. Something like structure(... estimate=c(lpm.slope=coef(fit)["score"]) ....)
(I expect that you might also extract the t-statistic from coef(summary(fit)) and find that it is the signed square root of the Chi-square, but I won't have time to test that just now.)
-pd
> On 8 Sep 2023, at 07:22 , Thomas Subia via R-help <r-help using r-project.org> wrote:
>
> 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]]
>>
>> ______________________________________________
>> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
> 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
>
> ______________________________________________
> R-help using r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
--
Peter Dalgaard, Professor,
Center for Statistics, Copenhagen Business School
Solbjerg Plads 3, 2000 Frederiksberg, Denmark
Phone: (+45)38153501
Office: A 4.23
Email: pd.mes using cbs.dk Priv: PDalgd using gmail.com