Publications by Ali
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LEEP<- read.csv("C:/Users/u6032404/OneDrive/backup 9.9.19/MJ/NDAA/LEEP.csv",header = T) LEEP <- LEEP[-c(2,1), ] #GH1 table(LEEP$Q2.4) ## ## Excellent Fair Good Poor Very good ## 26 139 212 19 115 levels(LEEP$Q2.4) ## NULL LEEP$Q2.4[LEEP$Q2.4=="Poor"] <- 5 LEEP$Q2.4[LEEP$Q2.4=="Fair"] <- 4 L...
61 sym R (51439 sym/56 pcs)
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library(readxl) library(mediation) #Mediation package ## Warning: package 'mediation' was built under R version 4.0.4 ## Loading required package: MASS ## Warning: package 'MASS' was built under R version 4.0.5 ## Loading required package: Matrix ## Warning: package 'Matrix' was built under R version 4.0.5 ## Loading required package: mvtnorm ##...
1023 sym R (142962 sym/710 pcs) 196 img
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Descriptive by three groups of Financial Status Having enough money(N=143) Having money to pay the bills(N=57) Having difficulty paying the bills(N=54) Overall(N=254) Fear_seizure_covid_fac Not Increased 101 (70.6%) 33 (57.9%) 27 (50.0%) 161 (63.4%) Increased 42 (29.4%) 24 (42.1%) 27 (50.0%) 93 (36.6%) Health_outcome_fac Not Increased 111 (...
672 sym 2 img 16 tbl
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Factor analysis of mixed data 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 conti...
14457 sym R (34959 sym/10 pcs) 14 img
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geom_point Row Scatter Chart with geom_point p <- ggplot(dat, aes(x=xvar, y=yvar)) + geom_point(shape=1) # Use hollow circles ggplotly(p) geom_smooth Linear Regression p <- ggplot(dat, aes(x=xvar, y=yvar)) + geom_point(shape=1) + # Use hollow circles geom_smooth(method=lm) # Add linear regressi...
370 sym R (2458 sym/10 pcs)
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The Main purpose of PCA: Identify hidden patterns in a data set. reduce the dimensionality by removing noise and redundancy in the data Identify correlated variables ## Access_mentalhealthcare Access_physicalhealthcare Fear_healthcare ## 1 No change Increased Not applicable ## 2 Not applicable ...
3790 sym R (23234 sym/18 pcs) 15 img
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Average time responce with outliers ## [1] 1.958262 Average time responce without outliers ## [1] 1.146887 Total questions answered by each participants ## ## 77579 77698 77899 77903 77909 77910 77911 77923 77946 78045 78049 78229 78246 ## 129 41 259 95 204 283 227 135 248 223 220 171 57 ...
137 sym R (198 sym/3 pcs) 2 img
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## Warning in compareGroups.fit(X = X, y = y, include.label = include.label, : ## Variables 'Q1.5', 'Q1.6_4', 'Q1.6_5', 'Q1.6_6', 'Q204_4', 'Q204_5', 'Q204_6', ## 'Q3.2_8_TEXT', 'Q10.2', 'Q14.18', 'Q14.20_1', 'Q14.21_1', 'Q14.22_1', 'Q14.25', ## 'Q14.27_1', 'Q14.28_1', 'Q14.29_1', 'Q14.30.1_4_TEXT', 'Q11.2', 'Q11.2_5_TEXT', ## 'Q11.4', 'Q13.1...
20 sym R (165488 sym/4 pcs)
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How I made TBI ## TBI crash fall fight blast ## 1 No TBI No No No No ## 2 TBI Yes Yes Yes Yes ## 3 TBI No Yes No No ## 4 No TBI No No No No ## 5 No TBI No No No No ## 6 No TBI No No No No ## 7 No TBI No No No No ## 8 TBI Yes Yes Yes Yes ...
428 sym R (15231 sym/19 pcs) 10 img 11 tbl
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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 ANCOVA for HIT speed dist 4 2 4 10 7 4 7 22 8 16 9 10 10 18 10 26 10 34 11 17 11 28 12 14 12 20 1...
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