Customizing a Workflow in SAS Solution for IFRS 17
Recent Library Articles
Recently in the SAS Community Library: SAS' @sunilbhardwaj details steps to add a customized Excel based workflow template to the SAS solution for IFRS 17. These steps enable you to try out and test several customized workflows based on your requirements.
I am running a regression having a binary response variable and need to estimate fixed effects for hundreds of thousands of classes (eleven_digit_account_id). Each code below produces hundreds of thousands of coefficients, one for each class, overwhelming system resources. Is there a way to suppress class-level coefficients in any of these procs or is there another proc that can handle a binary response variable with hundreds of thousands of classes? proc glimmix data=summary_statistics NOCLPRINT; class eleven_digit_account_id; model bet_win = net_stake last_round_profit miles_fan_bet_team eleven_digit_account_id/noint solution dist=bin link=logit; run; quit; proc genmod data=summary_statistics descending; class eleven_digit_account_id; model bet_win = net_stake last_round_profit miles_fan_bet_team eleven_digit_account_id/noint dist=bin link=logit; run; quit; proc logistic data=summary_statistics; class eleven_digit_account_id; model bet_win (event='1') = net_stake last_round_profit miles_fan_bet_team eleven_digit_account_id/noint; run; quit;
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Hi all, I have a test data with missing values and I want to fill the table by taking previous month's value. Test data ID response date 1 CR 02Jan2020 2 PR . 3 NE 04Jan2020 4 SD . 5 PR 05Jan2020 6 CR . data I want to have ID response date 1 CR 02Jan2020 2 PR 02Jan2020 3 NE 04Jan2020 4 SD 02Jan2020 5 PR 05Jan2020 6 CR 05Jan2020 So, if the response NE, then I don't want to retain the NE date but the date before NE. But if the response is not NE, then I wanna retain the previous date. I tried multiple ways, but couldn't figure it out. Please help.
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Hello, everyone
It is not uncommon that we have to control more than one time-dependent variable in a Cox regression model. A SAS support PDF document teaches two methods to code for a time-dependent Cox model, but, with only one time-dependent variable adjusted (link: https://support.sas.com/resources/papers/proceedings12/168-2012.pdf).
The first method involves constructing a special data set for the time-dependent variable data and the example is for only one time-dependent variable. It does not teach what to do if there is more than one such variable.
The second method is more advanced, termed "programming statment method". It has only one record for each individual, compared with the first method which has multiple records for each individual.
I wonder how to code for the Cox model if there are 2 or more time-dependent variables, by both method 1 and 2. Thank you very much.
Tom
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Hello, since a few days ago, I have a problem using the virtual lab for the Programming 2 course. Whenever I click to start it the citrix site opens with the autofilled password and username. But when I press login, it says "Invalid Login info. Please try again". Also when I press the refresh data or reset password option I get an error. When I try to launch the Programming 1 virtual lab, everything works as it should. Can someone tell me why this happens?
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Hello, I am running a simulation study using PROC FMM for a two-component mixture model. When I compare the parameter estimates to the underlying simulated values, I want to ensure that what is called "Component 1" in one iteration of the simulation is consistent with "Component 1" across other iterations of the simulation (e.g., Component 1 is always the mixture with a larger intercept), so that components are comparable across iterations. I have reviewed the documentation for the RESTRICT statement, and see that it says it can be used "to impose order conditions on the parameters in a model" but I can't quite figure out how to implement this. The current code to implement my mixture model using PROC FMM is shown below. I appreciate any suggestions for ordering the components. Thank you. proc fmm data=sim55.scenario55_sim_data_events;
where iteration = 1;
model log_outc_yrs = age_dx_centered / cl dist=normal k = 2 equate=scale;
probmodel pdl1_perc_norm / noint cl;
weight iptw_ipcw_trim97;
run;
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