Closed peclayson closed 1 month ago
Hi Peter,
it really depends on exactly what information you have at hand. If you can compute p-values for each data point directly, then there is nothing stopping you from using zcurve(p = p)
. If you have some censored p-values, then using zcurve(data = df_zcurve_data)
might be advantageous (extracting the precise p-values from the zcurve_data
object directly does not make that much sense to me though---maybe there is something that I'm missing?) .
Cheers, Frantisek
I'm sure you're not missing anything. I likely just confused both of us.
I have 100+ observations of test statistics and test values (no p values though), formatted as follows:
c("F(2,38)=0.789", "F(2,46)=7.55", "F(1,61)=8.33", "F(1.83,49.4)=3.32", "F(1,52)=4.83", "chi(1)=28.72")
etc.
Since I do not have z scores or p values, I am using the zcurve_data
function to calculate the p values and then plug those into zcurve
for analysis.
zcurve_prepped <- zcurve_data(zcrv_stats, stat_precise = 1)
fit <- zcurve(data = zcurve_prepped)
That is the intended application data = zcurve_prepped
, correct? Not zcurve(p = zcurve_prepped$precise$p)
?
I apologize. I think I confused myself thinking I need to operate directly on the p values, rather than using data = zcurve_prepped
.
Peter
Hi Peter,
No worries; this is indeed the function's intended use!
I will close it as solved. Please, reopen it if you have any further questions. Cheers, Frantisek
Hi @FBartos,
I have extracted test test statistics, degrees of freedom, and test values in preparation for a zcurve analysis. I'm trying to understand what the "best" approach is to prepare the statistics for the z curve. The data have been formatted to be put into
zcurve_data
.The function provides the precise p values. Then I can use
zcurve(p = df_zcurve_data$precise$p)
to run a z-curve analysis. Does this make sense?I couldn't find an example of using
zcurve_data
and then the approach for passing this tozcurve
, so I wanted to double check.Thanks for your help! Peter