Summary of Descriptive Statistics

summary(SARsData2)
##        X             ID       SARsCOVID            Delta          
##  Min.   :  1   Min.   :  1   Length:577         Length:577        
##  1st Qu.:145   1st Qu.:145   Class :character   Class :character  
##  Median :289   Median :289   Mode  :character   Mode  :character  
##  Mean   :289   Mean   :289                                        
##  3rd Qu.:433   3rd Qu.:433                                        
##  Max.   :577   Max.   :577                                        
##    Omicron               Age           Sex           
##  Length:577         Min.   : 1.0   Length:577        
##  Class :character   1st Qu.:27.0   Class :character  
##  Mode  :character   Median :37.0   Mode  :character  
##                     Mean   :37.7                     
##                     3rd Qu.:46.0                     
##                     Max.   :89.0

Inferential Statistic

table(SARsData2$SARsCOVID, SARsData2$Delta)
##           
##            negative positive
##   negative      291        7
##   positive      113      166
str(SARsData2)
## 'data.frame':    577 obs. of  7 variables:
##  $ X        : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ ID       : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ SARsCOVID: chr  "positive" "positive" "positive" "positive" ...
##  $ Delta    : chr  "positive" "positive" "negative" "positive" ...
##  $ Omicron  : chr  "positive" "positive" "positive" "positive" ...
##  $ Age      : int  50 44 36 70 52 39 67 60 45 25 ...
##  $ Sex      : chr  "female" "female" "male" "female" ...
names(SARsData2)
## [1] "X"         "ID"        "SARsCOVID" "Delta"     "Omicron"   "Age"      
## [7] "Sex"
mytable<-table(SARsData2$SARsCOVID, SARsData2$Delta)
mytable
##           
##            negative positive
##   negative      291        7
##   positive      113      166
addmargins(mytable, margin=c(1,2))
##           
##            negative positive Sum
##   negative      291        7 298
##   positive      113      166 279
##   Sum           404      173 577
prop.table(mytable)
##           
##              negative   positive
##   negative 0.50433276 0.01213172
##   positive 0.19584055 0.28769497
chisq.test(SARsData2$SARsCOVID, SARsData2$Delta)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  SARsData2$SARsCOVID and SARsData2$Delta
## X-squared = 221.46, df = 1, p-value < 2.2e-16
chisq.test(SARsData2$SARsCOVID, SARsData2$Sex)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  SARsData2$SARsCOVID and SARsData2$Sex
## X-squared = 5.0656, df = 1, p-value = 0.02441
chisq.test(SARsData2$SARsCOVID, SARsData2$Omicron)
## 
##  Pearson's Chi-squared test with Yates' continuity correction
## 
## data:  SARsData2$SARsCOVID and SARsData2$Omicron
## X-squared = 109.97, df = 1, p-value < 2.2e-16
#ANOVA
anova <- aov(Age ~ SARsCOVID, data = SARsData2)
anova
## Call:
##    aov(formula = Age ~ SARsCOVID, data = SARsData2)
## 
## Terms:
##                 SARsCOVID Residuals
## Sum of Squares      14.46 120328.67
## Deg. of Freedom         1       575
## 
## Residual standard error: 14.46607
## Estimated effects may be unbalanced
summary(anova)
##              Df Sum Sq Mean Sq F value Pr(>F)
## SARsCOVID     1     14   14.46   0.069  0.793
## Residuals   575 120329  209.27
anova1 <- aov(Age ~ Delta, data = SARsData2)
summary(anova1)
##              Df Sum Sq Mean Sq F value Pr(>F)
## Delta         1     66   65.58   0.314  0.576
## Residuals   575 120278  209.18
anova2 <- aov(Age ~ Sex, data = SARsData2)
summary(anova2)
##              Df Sum Sq Mean Sq F value Pr(>F)
## Sex           1     15    14.9   0.071   0.79
## Residuals   575 120328   209.3

Prediction Model Estimation

SARsData2$SARsCOVID<-as.factor(SARsData2$SARsCOVID)
SARsData2$Sex<-as.factor(SARsData2$Sex)
SARsData2$Delta<-as.factor(SARsData2$Delta)
SARsData2$Omicron<-as.factor(SARsData2$Omicron)
model<-glm(SARsCOVID~Delta+Omicron+Sex+Age, family = binomial, data=SARsData2)
summary(model)
## 
## Call:
## glm(formula = SARsCOVID ~ Delta + Omicron + Sex + Age, family = binomial, 
##     data = SARsData2)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -2.2694  -0.7413  -0.7349   0.3672   1.7012  
## 
## Coefficients:
##                   Estimate Std. Error z value Pr(>|z|)    
## (Intercept)     -9.898e-01  3.405e-01  -2.907  0.00365 ** 
## Deltapositive    3.646e+00  4.096e-01   8.900  < 2e-16 ***
## Omicronpositive  1.768e+01  5.923e+02   0.030  0.97619    
## Sexmale         -1.930e-01  2.321e-01  -0.832  0.40563    
## Age              6.878e-04  7.384e-03   0.093  0.92578    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 799.27  on 576  degrees of freedom
## Residual deviance: 489.02  on 572  degrees of freedom
## AIC: 499.02
## 
## Number of Fisher Scoring iterations: 17

Visualizations

plot(SARsData2$SARsCOVID~SARsData2$Sex)

str(SARsData2)
## 'data.frame':    577 obs. of  7 variables:
##  $ X        : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ ID       : int  1 2 3 4 5 6 7 8 9 10 ...
##  $ SARsCOVID: Factor w/ 2 levels "negative","positive": 2 2 2 2 2 2 2 2 2 2 ...
##  $ Delta    : Factor w/ 2 levels "negative","positive": 2 2 1 2 1 1 1 1 1 2 ...
##  $ Omicron  : Factor w/ 2 levels "negative","positive": 2 2 2 2 1 2 2 2 2 2 ...
##  $ Age      : int  50 44 36 70 52 39 67 60 45 25 ...
##  $ Sex      : Factor w/ 2 levels "female","male": 1 1 2 1 2 2 1 1 2 2 ...
plot(SARsData2$Sex)

hist(SARsData2$Age)

boxplot(SARsData2$Age~SARsData2$Sex)

stem(SARsData2$Age)
## 
##   The decimal point is 1 digit(s) to the right of the |
## 
##   0 | 1234
##   0 | 6788899
##   1 | 0000111112344
##   1 | 5667778888889999
##   2 | 000000000111111111222222233333333333333333334444444444444
##   2 | 55555555555555566666666666666677777777777777777778888888888888888888+1
##   3 | 00000000000001111111111111122222222222222222233333333333334444444444
##   3 | 55555555555555555666666666666666667777777777777777777777788888888888+4
##   4 | 00000000000000011111111111122222222222222222233333333333333444444444
##   4 | 55555555555555566666666666677777777777788888888889999999999
##   5 | 000000000001111222222222223333333444444
##   5 | 55566666666777788899
##   6 | 000000011111111334
##   6 | 556677778899
##   7 | 000001124
##   7 | 78
##   8 | 001
##   8 | 779
boxplot(SARsData2$Age~SARsData2$SARsCOVID)

boxplot(SARsData2$Age~SARsData2$Omicron)

boxplot(SARsData2$Age~SARsData2$Delta)

END