There is a note to make about using Tweedie models for zero-inflated data for friends in this Community who may not have the time I do to read the note cited by @StatDave thoroughly. This note informs me that a Tweedie model "never produces a predicted mean exactly equal to zero". That potentially makes it a bad tool for prediction because in reality, there are of course zero-valued observations. After all, we are modeling zero-inflated data! Yet things are different if you are trying to explore the association of the dependent variable and the independent variables. I think if that is the objective, Tweedie models can be selected. ... View more

也许您可以试试把RTF先另存为一份.doc或.docx文档，然后在另存为的文档上更改字体。 Maybe you can try saving your RTF as a .doc or .docx document and change fonts in that document. 本论坛上有几位网友是ODS的高手，也许您可以咨询他们：@Rick_SAS、 @Ksharp、 @PaigeMiller。 There are several users of this Community who are very skilled at ODS. Maybe you can ask them: @Rick_SAS, @Ksharp, @PaigeMiller. 另外，您也可以到微信上的SAS论坛去咨询。您在微信里添加“数科赛仕”微信公众号后可以在公众号中进入中文版SAS论坛。那里的中文用户更多，相信也有人遇到了和您类似的问题。 In addition, you can also raise your questions in the SAS Forum on Wechat. You can enter the Chinese edition of SAS forum via the account named "数科赛仕" after adding this account as your friend. There are more Chinese users there and I believe that someone has encountered similar problems. 最后，我推荐几位SAS畅销书的作者。我经常阅读他们写的书，包括他们写的关于ODS的内容，受益匪浅。您也可以问问他们。不过我不确定他们在这个论坛上有没有账号，您还得自行寻找他们的联系方式。他们是冯国双、胡良平、谷鸿秋和Art Carpenter。 Finally, I would like to recommend several authors of SAS bestsellers, whose books, including those concerning ODS, are frequently consulted by me. I benefit I lot from their contributions. You can also seek for their help. But I am not sure if they have accounts on this Community. You have to look for their contact information themselves. They are Guoshuang Feng (冯国双), Liangping Hu (胡良平), Hongqiu Gu (谷鸿秋) and Art Carpenter. 祝您好运！也烦请您在知晓如何用ODS解决您这个问题后在这个论坛上回帖，和大家说一下解决方案。我本人对如何处理这个问题也很感兴趣。 Wish you good luck! Please post the ODS solutions to this problem here on this Community so that others can get informed. I personally am interested in how to tackle this problem. ... View more

很抱歉，如果是这样的话，那我也确实不知道怎么在SAS中解决这个问题了。不过我觉得可以绕过SAS解决它。您可以通过先将PROC REPORT生成的报告导出，然后在Microsoft Office或WPS等办公软件内全选您的报告后修改字体。例如，在Microsoft Word中，您全选报告后点击鼠标右键，选择“字体”，在弹出的窗口中分别修改“中文字体”和“英文字体”。如果您的目标是生成一份PDF，可以先把报告导出成一份RTF文件，在RTF文件内修改字体后再通过另存为等方式转换为PDF。这样就能达成批量转换中英文字体的目标。 Sorry, if that is the case, then I really do not know how to tackle this problem in SAS. But I think we can bypass SAS in the course of addressing this problem. You can first output your report generated by PROC REPORT, and then change fonts in softwares like Microsoft Office and WPS. For instance, in Microsoft Word, you select the entire report, right-click the mouse and choose "Fonts". In the popped up window, you can change "Chinese Font" and "English font" respectively. If your objective is to generate a PDF, you can first output your report into a RTF document, change fonts in the RTF document and then transform it into a PDF by means like "Saved as...". In this way, you achieve the goal of transforming Chinese and English fonts in batches. ... View more

My reply to @Clemence is on this page. You can simply scroll up to see it. In case you did not find it, I have copied it and am pasting it right here: You mentioned a very important limitation of PROC MIANALYZE that seems to have been ignored by the majority of literature introducing this module. To the best of my knowledge, currently existent multiple comparison procedures adjust P-values on a single dataset. I have personally searched for guidelines on how multiple comparisons should be done when it is complicated by multiple imputation, which generates multiple imputed datasets. Unfortunately, I have not retrieved any guideline on that. Still, a heuristic approach to this issue may be as follows: (1) Perform multiple comparisons on each imputed dataset and therefore obtain adjusted P-values; (2) Attempt to pool (i.e., combine) the adjusted P-values generated in Step (1). Step (1) can be done by using options offered in the LSMEANS statement in the statistical procedures (i.e., PROCs). For instance, the LSMEANS statement of PROC SURVEYPHREG offers the ADJUST= option, which you can utilize for adjusting P-values in multiple comparisons. For more about multiple comparisons, I suggest reading the book by Westfall et al. (Amazon.com: Multiple Comparisons and Multiple Tests Using SAS, Second Edition: 9781607647836: Westfall PhD, Peter H., Tobias PhD, Randall D., Wolfinger PhD, Russell D.: Books). Step (2) entails combining P-values in multiple imputation, a topic that has been, to the best of my knowledge, hardly discussed. However, there are some research papers on this issue. One of the commonly cited papers in a paper published in 1991 (SIGNIFICANCE LEVELS FROM REPEATED p-VALUES WITH MULTIPLY-IMPUTED DATA on JSTOR). Later on, there is a dissertation on this topic (New methods for generating significance levels from multiply-imputed data). However, I have read the entirety of this dissertation and found that it concerned combining P-values from Wald's test and other tests but did not address the issue of pooling adjusted P-values in multiple comparison procedures. So I think you can try out the method mentioned in the 1991 paper. The last thing to note is that PROC MIANALYZE does not offer the solution of pooling P-values. It is incorrect to simply use the built-in methods (those that are called for when executing the MODELEFFECTS statement) in PROC MIANALYZE when the variable to be pooled is a P-value, as the built-in methods utilize Rubin's rule for pooling the results. Rubin's rule, on the other hand, requires that the variables to be pooled follow asymptotic normality, which is what the P-values infringe. ... View more

Thank you! I am halfway through the reading process. I will consult you again should I have questions. ... View more

Well, I have detailed my viewpoint on pooling P-values after performing multiple comparisons on each imputed dataset in my reply to @Clemence. You can refer to my reply there. By the way, the issue of multiple imputation+multiple comparison is in fact a field hardly investigated. For the past several months, I personally have retrieved on the web and in several monographs on missing data and multiple comparisons, but has hardly found any literature addressing this issue and have not found any guideline on tackling their coincidence. You can conduct search for information by yourself, but at the end of the day, you might have to accept the fact that there is no ‘gold standard’ for the time being, at least to the best of my knowledge. ... View more

您好，您尝试了行内格式化吗？您可以通过ods escapechar语句搭配{}号修改一段文字中部分（而不是全部）文字的字体。 Hello, have you ever tried inline formatting? You can use ods escapechar statement along with {} symbols to modify part of (instead of the entirety of) the fonts of a line of words. 示例代码如下： Example code are as follows: proc report data=xxx style=a /*你在PROC TEMPLATE设置好的模板名（name of the template you have set up in PROC TEMPLATE）*/ style(header)=[fontfamily="Times New Roman"] style(column)=[fontfamily="Times New Roman"]; col a b; ods escapechar '^'; define a/display "^{style[fontfamily="Simsun"] 中文}bianliangming"; define b/display "English variable name"; run; 在刚才这段代码中，我默认把所有标题和表格内容的字体设置成Times New Roman，再用ods escapechar语句搭配在define语句的{}号中将行标题的中文部分改为Simsun。 In the preceding code, I set the fonts of the entirety of the headers and table contents as Times New Roman and changed the Chinese part of the column header into Simsun by the duo of ods escapechar statement and the {} symbols in the define statement. ... View more

There are a long-standing questions regarding Tweedie models, as the explanation regarding Tweedie distribution in SAS Help is too hard for me to understand. Moreover, I have searched extensively on the Internet and have retrieved little information on this distribution. My questions are: are Tweedie models suitable for modeling all zero-inflated continuous variables? Moreover, are there are goodness-of-fit statistics regarding Tweedie models? Thanks! ... View more

Thank you so much! The topic of this note perfectly matches my need. I will go into its details to see if this encompasses the exact modeling method I wish to apply. ... View more

Thank you, Koen, for your detailed reply! @sbxkoenk wrote: "Zero-inflated" response and "limited" response are not the same. Are you dealing with count data or continuous data (0 and above)? The nomenclature "limited dependent variable", albeit strange and not that intuitive to me, comes from the econometric literature. For instance, I have retrieved two books (Limited-Dependent and Qualitative Variables in Econometrics (cambridge.org) and Analysis of Panels and Limited Dependent Variable Models (cambridge.org)), skimmed through their contents and found that the so-called "limited dependent variables" are in fact sample-selected variables. On many occassions, they are zero-inflated ones as well. I averted using the term "zero-inflated" in the title to avoid people who are attracted by this pharse, come straight into this post and tell me to use PROC GENMOD to tackle this problem because PROC GENMOD can model zero-inflated count data, without noticing the fact that the variable I wish to model is continuous rather than discrete, an issue whose solution has been given in SAS Help as well as many other literature. As I have said in the post and the paragraph above, the variable I wish to model is continuous. @sbxkoenk wrote: Are you talking about zero-inflated models (gamma, lognormal, Poisson, negative binomial) or are you talking about (Gaussian) mixture models? I am not sure the exact definition of a "lognormal zero-inflated model" as I have not yet seen this phrase in the literature on zero-inflated models that I have come across. If this model refers to a model that is capable of modeling a variable that is zero-inflated in nature and whose non-zero part follows a lognormal distribution, then that is the model I wish to build. By the way, I am not sure about the definition of (Gaussian) mixture models. Do you mean finite mixture models whose dependent variables follow a finite mixture of (normal) distributions that can be built by PROC FMM? To the best of my knowledge, these models are not typically classified as zero-inflated models. Can they model zero-inflated data as well? @sbxkoenk wrote: I think your 2-parts are : a logistic regression to P(y=0) and a gamma (or log-normal) error regression with log link to E(y | y>0)) I do not think that if the link function is the natural log of the expectation of the dependent variable given that it has exceeded zero (lnE(y|y>0)), the errors would still be log-normal. But that is a trivial issue. Aside from that, what you have outlined is exactly what I want. @sbxkoenk wrote: With PROC NLMIXED you can maximize the (log-)likelihood jointly. However, it could be (will be) the likelihood separates anyway, so you don't get improved parameter estimates as a result (only advantage then is that you do it with one function call and you estimate the combined function E(y) which includes the zeroes). Thank you for your reminder! I have been reading a monograph (Regression Models: Censored, Sample Selected, or Truncated Data (Quantitative Applications in the Social Sciences): Breen, Richard: 9780803957107: Amazon.com: Books) on zero-inflated continuous data, which has also been termed as "sample selected data". When elaborating the way to model a zero-inflated variable whose non-zero part follows a normal distribution, the author demonstrated the inappropriateness of not maximizing the joint likelihood in that ordinary least squares estimators of the regression coefficients conducted on the non-zero portion, on the entire sample are all biased and (or) inconsistent, except in rare conditions that is, in my opinion, hard to verify with neither the data at hand nor professional knowledge. On the contrary, the estimator of the regression coefficients obtained by maximizing the joint likelihood is guaranteed to be unbiased and consistent. So I think it a safer choice. @sbxkoenk wrote: Note that you can use PROC NLMIXED in the absence of random effects (only fixed effects is fine here).   There are other ways in SAS to maximize (any tailored) likelihood beyond PROC NLMIXED.   Koen Thank you for pointing out the fact that PROC NLMIXED can be of help! Could you please provide some details on the other ways of maximizing tailored likelihood functions that you mentioned? Thanks! ... View more

I don't think you have phrased the question clear enough for others to understand what you are trying to ask. @mmagnuson wrote: Would it be fair to say that the model would be more appropriate because it takes into account 13 other variables which is more "real life" since when making conclusions one should look at many variables?  Looking at just one variable is limiting? First of all, logistic regression is not primarily about making decisions. Instead, prediction is one of the jobs logistic regression can accomplish. Second, I guess you were trying to compare the goodness-of-fit of the 13-variable model against that of the one-variable model. If that is the objective, then both the c-statistic and the Hosmer-Lemeshow test (which is, in essence, a Chi-squared test) can be of use. ... View more

@dwhitney wrote: I am running PROC MI for multiple imputation for a 5-level categorical variable, "gmfcs_final", which is the only variable in the dataset with missing values. The imputation phase works great (code under "STEP 1").   I then do the 2nd analysis phase (code under "STEP 2"). This analysis is focused purely on estimate the c-statistic with 95% CI for several models based on various covariate sets. This runs well and I output the c-stat and CI in the "auc_2" output file.    For the 3rd step, I am unable to figure out what code to run to pool the c-stats and CI appropriately (Rubin's rules?). There seems to be code to get parameter estimates in the PROC MIANALYZE, but that is not the interest of this study. Any idea on the code using the PROC MIANALYZE  or other code to get the appropriately pooled c-stats and CI? Yes, you can use PROC MIANALYZE (i.e., Rubin's rule) to pool the c-statistics and get the corresponding 95% CI of the pooled c-statistic. The theoretical foundation of this practice is that asymtotic normality of c-statistic holds. As for Bootstrap, unfortunately, Bootstrap can be of some use in the imputation process, but is not used for pooling the results generated in each imputed dataset. ... View more

可以截个图，告诉我们你想要做出的结果大致长什么样吗？ 另外，冯国双老师的《小白学SAS》(冯国双 编著)【简介_书评_在线阅读】 - 当当图书 (dangdang.com)里面有关于PROC REPORT的内容，还算丰富，可以参考。 ... View more