mwheymans / psfmi Goto Github PK

I would like to thank you for the wonderful work you have done.

There is a problem installing the package.

I am currently using Mac OS X version 12.3.

The following steps were followed.

1)devtools::install_github("mwheymans/psfmi")

2)Enter one or more numbers, or an empty line to skip updates:1(All)

3)Do you want to install from sources the packages which need compilation? Yes

I saw the error message.

installing to /Users/jaeman/Library/R/4.0/library/00LOCK-minqa/00new/minqa/libs

** R

** byte-compile and prepare package for lazy loading

** help

*** installing help indices

** building package indices

** testing if installed package can be loaded from temporary location

Error: package or namespace load failed for ‘minqa’ in dyn.load(file, DLLpath = DLLpath, ...):

unable to load shared object '/Users/jaeman/Library/R/4.0/library/00LOCK-minqa/00new/minqa/libs/minqa.so':

dlopen(/Users/jaeman/Library/R/4.0/library/00LOCK-minqa/00new/minqa/libs/minqa.so, 0x0006): symbol not found in flat namespace 'bobyqa'

Error: loading failed

Execution halted

ERROR: loading failed

• removing ‘/Users/jaeman/Library/R/4.0/library/minqa’

• restoring previous ‘/Users/jaeman/Library/R/4.0/library/minqa’

Error: Failed to install 'psfmi' from GitHub:

(converted from warning) installation of package ‘minqa’ had non-zero exit status

Hi all,
thank you for providing this amazing package! I have tried to used psfmi to run a stratified cox model, but as it seems this is not implemented. Interactions and splines are covered, yet is there any plan of including this feature in the future? Thank you!

Hello,

Is it possible to parallelise the psfmi_validate function?

This would provide very useful speed improvements in large datasets.

Thank you

Ok, so i performed the pooled logistic regression model in order to pick out my predictor variables using the forward approach. The ORs seem correct to me based off of other analyses but I find the p-values very strange because according to the 95% CI, many of the variables are not significantly associated with my outcome, but the p-values tell another story, and i think there is a problem with my p-values, but I am not sure what I did or what needs to be changed in order to get the correct p-values. here is my code and my results:

Code :

pool_lr <- psfmi_lr(data=dataset_mom, nimp=25, impvar="x_imputation", Outcome="M2_M_PPD", predictors=c("M0_M_nation", "M0_P_nation", "M2_P_PPD", "M00M2_PEREACC", "mother_medicine", "fchild", "number_household", "relative_poverty"), p.crit = 0.25, cat.predictors = c("M02M_CONGPAT", "Fwanted_child", "Mwanted_child", "M0_P_age", "M2_conflict","M0_siblingbis", "mother_diploma", "dad_profession", "mom_profession","M0_zone", "conab","condp","conbp"), method="D2", keep.predictors = "M02M_CONGPAT", direction = "FW")

and here are the results (found in the link):

any ideas why the p-values would be like this or what i can fix?
thank you so much for your help!

The package 'psfmi' is very useful to calculate the pool C-statistic (AUC), with the use of 'pool_performance'. I wondering if there is a way to plot the pooled ROC curve (like below)? Thank you very much!

I have an imputated data with several nonmissing and not-imputated variables. However, I realised when I use mivalext_lr() to obtain pooled AUC and 95% CI of my logistic regression, the pooled 95% CI is bigger then I get from individual imputated data, (which are the same as the variable used was not imputated), and actually exactly the square root of the CI upper and lower limit obtained from each imputated data set. I understand I should pool AUC after logit transformation with Rubin's rule, integrating the variance of each AUC itself and the variance between imputation. So I tried to look into the source code, and added some print out command, the variate between each imputation (b.roc.logit) truly print out as 0, but I felt the p.se.roc.logit had an extra sqrt.... Can someone help me to take a look? unless it supposed to have bigger SE even for not imputated variables after pooling..... I tried psfmi_perform() in the same package, got the same big CI.

Hello!
I found a few broken links in the online documentation:
https://mwheymans.github.io/psfmi/reference/pool_D2.html
https://mwheymans.github.io/psfmi/tutorials/Variable_Selection.html
https://mwheymans.github.io/psfmi/articles/Pooling_AUC_values.html

I did not check the other links, but there may be more broken ones.