Publications by Ali
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sample presentation First Slide For more details on authoring R presentations please visit https://support.rstudio.com/hc/en-us/articles/200486468. Bullet 1 Bullet 2 Bullet 3 Slide With Code speed dist Min. : 4.0 Min. : 2.00 1st Qu.:12.0 1st Qu.: 26.00 Median :15.0 Median : 36.00 Mean :15.4 Mean...
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Workflow Inputs Dimensionality reduction using principal component methods is a very handy tool for identifying relationships amongst variables and hidden patterns in a dataset. Principal component analysis (PCA) is arguably the most commonly known, but it is limited by its use for datasets containing only continuous variables. As real-world dat...
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factor analysis of mixed data (LEEP) Workflow Inputs Dimensionality reduction using principal component methods is a very handy tool for identifying relationships amongst variables and hidden patterns in a dataset. Principal component analysis (PCA) is arguably the most commonly known, but it is limited by its use for datasets containing onl...
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Introduction In the following section, we will show the process of detecting specious cases in this survey based on the respondents’ age, military status,and their claimed serve date. Serve Date Overall(N=1020) September 2001 or later No 623 (61.1%) Yes 397 (38.9%) August 1990 to August 2001 No 836 (82.0%) Yes 184 (18.0%) Currently in ...
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Military Status Overall(N=1268) Separated.from.active.duty.at.end.of.commitment No 731 (57.6%) Yes 537 (42.4%) Traditional.retirement No 952 (75.1%) Yes 316 (24.9%) National.Guard.or.Reserve.currently.serving No 1118 (88.2%) Yes 150 (11.8%) Active.duty.military No 1237 (97.6%) Yes 31 (2.4%) National.Guard.or.Reserve.separated.at.end...
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Military Status Female(N=478) Male(N=758) Non-binary(N=6) Prefer not to say(N=3) Transgender female(N=3) Transgender male(N=1) Overall(N=1249) Separated.from.active.duty.at.end.of.commitment No 357 (74.7%) 353 (46.6%) 4 (66.7%) 2 (66.7%) 1 (33.3%) 1 (100%) 718 (57.5%) Yes 121 (25.3%) 405 (53.4%) 2 (33.3%) 1 (33.3%) 2 (66.7%) 0 (0%) 531 (42.5%...
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NDAA_gender<-NDAA_v_final %>% select(ResponseId, gender) serve_date = merge(x=serve_date,y=NDAA_gender,by="ResponseId",all=TRUE) serve_date<-serve_date %>% select(-ResponseId,-X) table1(~.|gender, data= serve_date) Female(N=478) Male(N=758) Non-binary(N=6) Prefer not to say(N=3) Transgender female(N=3) Transgender male(N=1) Overall...
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Disability Q2.6 (v) Female(N=402) Male(N=683) Other(N=10) Overall(N=1095) Q2.6 I have submitted a claim and am waiting on a VA decision 41 (10.2%) 48 (7.0%) 2 (20.0%) 91 (8.3%) No 201 (50.0%) 362 (53.0%) 3 (30.0%) 566 (51.7%) No, but I would like to submit a claim 35 (8.7%) 64 (9.4%) 2 (20.0%) 101 (9.2%) Yes 125 (31.1%) 209 (30.6%) 3 (30.0...
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In the following sections, I am going to describe the differences between OSU and UTAH screeners in the PTH data. The main advantage of the UTAH screener over OSU is asking about being”ever dazed or had a gap in memory”. The differnces between the screeners Overall(N=1004) status Mild TBI without LOC.Mild TBI without LOC 276 (27.5%) Mild...
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## ## --------Summary descriptives table by 'ZaritBurdenClinicalSignificance'--------- ## ## ______________________________________________________________________ ## 0 1 p.overall ## N=107 N=149 ## ¯¯¯¯¯¯¯¯¯¯¯...
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