mg object failed to be created when executing dsBase::glmerSLMADS2

[Adelca2018]

As I was carrying out some analysis using the command ds.glmerSLMA I received a DataSHIELD error saying that the object mg from the function dsBase::glmerSLMADS2 was not found.
When I checked the source code of the function dsBase::glmerSLMADS2 in GitHub, I then realized that this object is created within a try in line 392 (see code below) from this page: https://github.com/datashield/dsBase/blob/master/R/glmerSLMADS2.R

At line 392: iterations <- utils::capture.output(try(mg <- lme4::glmer(formula2use, offset=offset, weights=weights, data=dataDF,
  family = family, nAGQ=nAGQ,verbose = verbose, control=control.obj, start = start)))

It seems that for my case the function fails to create the mg object inside a try.

Datashield error Message I got:
Error while evaluating 'dsBase::glmerSLMADS2(flag_pavk~sex_cd+age+flag_nicotine+flag_aat+yyy1xxxpatient_numzzz, NULL, NULL, "D", "binomial", NULL, NULL, 1L, 0, NULL, NULL)' -> Error in summary(mg) : object 'mg' not found\n"

Can I have more details when or why the try can fail to create the mg object?

Thanks In advance for your explanations.

[StuartWheater]

@Adelca2018 Thank you for the information we will investigate

[tombisho]

Hi @Adelca2018 , are you able to provide the client side call that you made, please? And it would be very helpful if you could provide a set of dummy data that is representative of you real data, in order to recreate the issue. If the dummy data are not possible, please could you provide a statistical description of each variable? For example, flag_pakv is a binary variable, count0 = 200 and count1 = 340.

[Adelca2018]

Hi @tombisho .

The client side call I used was: ds.glmerSLMA(formula=flag_pavk ~ sex_cd + age + flag_nicotine + (1 | patient_num) ,dataName='D', datasources=connections, family="binomial")

Unfortunately I cannot provide a set of dummy data.
But for more information:

• flag_pavk is a dummy variable specifying whether or not a patient was diagnosed of PAVK during a visit. count0=22984 and count1=2093
• sex_cd is the gender as a factor count0=15649 and count1=9428
• age is the age as a coninuous variable
• flag_nicotine is a dummy variable specifying whether or not the patient is a smoker. count0=20799 and count1=4278
• patient_num is a unique numerical code attributed to each patient.

Best regards

[tombisho]

That's great, thanks. How many data points are there per patient approximately? Tom

…

On Tue, Feb 8, 2022 at 1:32 PM Adeline Makoudjou ***@***.***> wrote: Hi @tombisho <https://github.com/tombisho> . The client side call I used was: *ds.glmerSLMA(formula=flag_pavk ~ sex_cd + age + flag_nicotine + (1 | patient_num) ,dataName='D', datasources=connections, family="binomial")* Unfortunately I cannot provide a set of dummy data. But for more information: - *flag_pavk* is a dummy variable specifying whether or not a patient was diagnosed of PAVK during a visit. count0=22984 and count1=2093 - *sex_cd* is the gender as a factor count0=15649 and count1=9428 - *age* is the age as a coninuous variable - *flag_nicotine* is a dummy variable specifying whether or not the patient is a smoker. count0=20799 and count1=4278 - *patient_num* is a unique numerical code attributed to each patient. Best regards — Reply to this email directly, view it on GitHub <#267 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACBAPNSXRVRQVK6LOFZ4QXTU2ELOHANCNFSM5NR3LLGQ> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you were mentioned.Message ID: ***@***.***>

[davraam]

maybe the error is created because there are no quote marks around the formula?

[Adelca2018]

maybe the error is created because there are no quote marks around the formula?

No. The error is not the formula because I use it in a loop and the formula works well for other variables. So I am pretty sure the error does not come from the formula

[Adelca2018]

That's great, thanks. How many data points are there per patient approximately? Tom
…
On Tue, Feb 8, 2022 at 1:32 PM Adeline Makoudjou @.> wrote: Hi @tombisho https://github.com/tombisho . The client side call I used was: ds.glmerSLMA(formula=flag_pavk ~ sex_cd + age + flag_nicotine + (1 | patient_num) ,dataName='D', datasources=connections, family="binomial") Unfortunately I cannot provide a set of dummy data. But for more information: - flag_pavk is a dummy variable specifying whether or not a patient was diagnosed of PAVK during a visit. count0=22984 and count1=2093 - sex_cd is the gender as a factor count0=15649 and count1=9428 - age is the age as a coninuous variable - flag_nicotine is a dummy variable specifying whether or not the patient is a smoker. count0=20799 and count1=4278 - patient_num is a unique numerical code attributed to each patient. Best regards — Reply to this email directly, view it on GitHub <#267 (comment)>, or unsubscribe https://github.com/notifications/unsubscribe-auth/ACBAPNSXRVRQVK6LOFZ4QXTU2ELOHANCNFSM5NR3LLGQ . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub. You are receiving this because you were mentioned.Message ID: @.>

The average number of visits per patient is 2.35

[tombisho]

maybe the error is created because there are no quote marks around the formula?

No. The error is not the formula because I use it in a loop and the formula works well for other variables. So I am pretty sure the error does not come from the formula

I agree, I tested and ds.glmerSLMA does the string conversion for the user

[davraam]

OK thanks. that's good to know.

[tombisho]

Hi @Adelca2018 I think to recreate this I need to know more information about each of the input variables. Please could you provide the output from ds.summary for each of them?

Thanks

[Adelca2018]

Hi @tombisho .

I forgot to mention a variable in the formula in my previous message.

The right client side call I used was: ds.glmerSLMA(formula=flag_pavk ~ sex_cd + age + flag_nicotine + flag_aat + (1 | patient_num) ,dataName='D', datasources=connections, family="binomial")

Summaries:

flag_pavk is a dummy variable specifying whether or not a patient was diagnosed of PAVK during a visit. count0=22984 and count1=2093
sex_cd is the gender as a factor count0=15649 and count1=9428
age is the age as a coninuous variable
flag_nicotine is a dummy variable specifying whether or not the patient is a smoker. count0=20799 and count1=4278
flag_aat is also a dummy variable. count0=24832. count1=245
patient_num is a unique numerical code attributed to each patient.

you asked me to provide more details using ds. summary. Actually for categorical variables ds.summary gives exactly the counts I just provided you above. Since age is the only continuous variable, these are the summaries for age:

Min. 1st Qu. Median Mean 3rd Qu. Max.
0.00 61.00 69.00 67.96 77.00 101.00

Best regards

[tombisho]

Great - and is patient_num actually numeric or character? Ie with ds.class, does it return char or numeric?

…

On Fri, 11 Feb 2022, 07:52 Adeline Makoudjou, ***@***.***> wrote: Hi @tombisho <https://github.com/tombisho> . I forgot to mention a variable in the formula in my previous message. The *right client side call* I used was: ds.glmerSLMA(formula=flag_pavk ~ sex_cd + age + flag_nicotine *+ flag_aat* + (1 | patient_num) ,dataName='D', datasources=connections, family="binomial") *Summaries:* *flag_pavk* is a dummy variable specifying whether or not a patient was diagnosed of PAVK during a visit. count0=22984 and count1=2093 *sex_cd* is the gender as a factor count0=15649 and count1=9428 *age* is the age as a coninuous variable *flag_nicotine* is a dummy variable specifying whether or not the patient is a smoker. count0=20799 and count1=4278 *flag_aat* is also a dummy variable. count0=24832. count1=245 *patient_num* is a unique numerical code attributed to each patient. you asked me to provide more details using *ds. summary*. Actually for categorical variables ds.summary gives exactly the counts I just provided you above. Since *age* is the only continuous variable, these are the summaries for age: *Min. 1st Qu. Median Mean 3rd Qu. Max. 0.00 61.00 69.00 67.96 77.00 101.00* Best regards — Reply to this email directly, view it on GitHub <#267 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/ACBAPNRK5RQKRWKZQUY4SADU2S5Z5ANCNFSM5NR3LLGQ> . Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you were mentioned.Message ID: ***@***.***>