Merge pull request #339 from PadmavathiCh/Issue319

Issue 319 - execute false

templates/RvsSAS_template.qmd

@@ -1,11 +1,7 @@
 ---
 title: "R vs SAS [Name of Method]"
-execute:
-  eval: false
 ---
 
-
-
 # R vs SAS \[Name of Method\]
 
 ## Introduction
@@ -14,8 +10,6 @@ Provide a brief overview of the analysis, its purpose, and its applications.
 
 **Example:** This section compares the implementation of Poisson Regression in R and SAS. Poisson regression is used to model count data and contingency tables. It's particularly useful for modeling the number of events occurring within a fixed period of time or space.
 
-
-
 ## General Comparison Table
 
 The following table provides an overview of the support and results comparability between R and SAS for the new analysis point.
@@ -30,8 +24,6 @@ The following table provides an overview of the support and results comparabilit
 | [Scenario 2: Advanced Feature]    | Example: [Yes](../R/example_link) | Example: [Yes](../SAS/example_link) | Example 3: Partial | Special considerations for data structure or assumptions |
 +-----------------------------------+-----------------------------------+-------------------------------------+--------------------+----------------------------------------------------------+
 
-
-
 ## Prerequisites: R Packages
 
 List the R packages required for this analysis. Include a brief description if necessary.
@@ -42,11 +34,9 @@ library() # Call what packages are needed
 library()
 ```
 
-
-
 ## Example Data
 
-Provide an example of the dataset being used. For simulated / dummy datasets please provide some context on how you construct the dataset and its relevance to the methodology. For real data please provide the code for how these data can be accessed and a reference that describes the data source. 
+Provide an example of the dataset being used. For simulated / dummy datasets please provide some context on how you construct the dataset and its relevance to the methodology. For real data please provide the code for how these data can be accessed and a reference that describes the data source.
 
 ```{r}
 # Simulate an example dataset for Poisson Regression
@@ -75,8 +65,6 @@ proc print data=example_data(obs=10);
 run;
 ```
 
-
-
 ## Analysis Scenarios
 
 ### Scenario 1: Basic Functionality
@@ -95,7 +83,6 @@ run;
 
 Describe key options utilized in the code, along with a screenshot showcasing the output.
 
-
 #### R Code Example
 
 ```{r}
@@ -118,12 +105,9 @@ Provide a detailed comparison of the results obtained from both SAS and R. Highl
 | Coefficient Estimate for Predictor | 0.1      | 0.1        | Yes   |       |
 | p-value                            | 0.05     | 0.05       | Yes   |       |
 
-
-
 ### Scenario 2: Advanced Feature
 
-Provide a detailed description of the scenario.
-**Example:** Address specific advanced features or configurations that may be necessary for more complex analyses.
+Provide a detailed description of the scenario. **Example:** Address specific advanced features or configurations that may be necessary for more complex analyses.
 
 #### SAS Code Example
 
@@ -159,22 +143,16 @@ Provide a detailed comparison of the results obtained from both SAS and R. Highl
 | Coefficient Estimate for Predictor | 0.1      | 0.1        | Yes   |       |
 | p-value                            | 0.05     | 0.05       | Yes   |       |
 
-
-
 ## Special Considerations
 
 Address any additional features or settings that need to be considered. This might include specific configuration settings, handling of special cases, or performance considerations.
 
 **Example:** For handling overdispersion in Poisson Regression, SAS provides the `scale` option in PROC GENMOD, while in R, one may have to switch to a quasi-Poisson family or use negative binomial regression.
 
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-
 ## Summary and Recommendation
 
 Summarize the key findings from the analysis. Highlight any major differences observed and offer recommendations on which software/packages to use. Provide guidance on how to interpret the differences between the results obtained from various software of packages.
 
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 ## Troubleshooting and Edge Cases
 
 List potential issues that users may encounter and propose solutions or troubleshooting steps.
@@ -185,8 +163,6 @@ List potential issues that users may encounter and propose solutions or troubles
 
 -   **Solution:** Check for multicollinearity among predictors, scale the predictors, or switch to a more appropriate model family.
 
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 ## Additional References
 
 Provide references and additional reading materials for both R and SAS documentation related to the analysis.
@@ -199,14 +175,10 @@ Provide references and additional reading materials for both R and SAS documenta
 
 -   `PROC GENMOD`: <https://documentation.sas.com/doc/en/statcdc/14.2/statug/statug_genmod_overview.htm>
 
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 ## Appendix (Optional)
 
 Include any additional information, extended examples, or advanced topics that may be helpful for expert users.
 
-
-
 This template is designed to be comprehensive and flexible, able to accommodate multiple scenarios and diverse datasets. It ensures that each section clearly communicates the necessary information and comparisons, making it easier for users to understand and apply the analyses appropriately across both R and SAS platforms.
 
 ::: {.callout-note collapse="true" title="Session Info"}