#ISR X CPS
#These numbers are from the table labeled ISR X CPS
ISR_CPS <- matrix(
c(175470, 17270, 84, 42), 2, 2, # <- Update Numbers inside c()
byrow = TRUE,
dimnames = list(c("No ISR", "ISR"), c("No CPS", "CPS"))
)
chisq.test(ISR_CPS, correct = FALSE) ##
## Pearson's Chi-squared test
##
## data: ISR_CPS
## X-squared = 91.551, df = 1, p-value < 2.2e-16
#ISR X Race X CPS
#These numbers are from the table labeled ISR X CPS X RACE
ISR_CPS_RACE <- array(c(
# Enter Numbers in order of:
# No ISR - No CPS, ISR - No CPS, No ISR - CPS, ISR - CPS,
66439, 26, 4998, 9, # White
13098, 22, 5479, 18, # Black
52216, 12, 3551, 2, # Hispanic
26486, 3, 725, 0, # Asian/PI
17231, 21, 2517, 13), # Other/Missing"
dim = c(2, 2, 5),
dimnames = list(
ISR = c("No", "Yes"),
CPS = c("None", "History"),
Race = c("White", "Black", "Hispanic", "Asian/PI", "Other/Missing")
)
)
cmh <- mantelhaen.test(ISR_CPS_RACE)
cmh ##
## Mantel-Haenszel chi-squared test with continuity correction
##
## data: ISR_CPS_RACE
## Mantel-Haenszel X-squared = 31.43, df = 1, p-value = 2.067e-08
## alternative hypothesis: true common odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.961175 4.283476
## sample estimates:
## common odds ratio
## 2.898387
##
## Breslow-Day test on Homogeneity of Odds Ratios
##
## data: ISR_CPS_RACE
## X-squared = 4.4537, df = 4, p-value = 0.3481
## odds ratios for ISR and CPS by Race
##
## White Black Hispanic Asian/PI Other/Missing
## 4.765016 1.965485 2.940532 5.215418 4.297827
## 2.5 % 97.5 %
## White 2.2695447 10.004377
## Black 1.0616319 3.638863
## Hispanic 0.7559821 11.437744
## Asian/PI 0.2691502 101.060973
## Other/Missing 2.1732410 8.499433
##
## z test of coefficients:
##
## Estimate Std. Error z value Pr(>|z|)
## White 4.76502 1.80326 2.6425 0.008231 **
## Black 1.96549 0.61767 3.1821 0.001462 **
## Hispanic 2.94053 2.03790 1.4429 0.149042
## Asian/PI 5.21542 7.88741 0.6612 0.508463
## Other/Missing 4.29783 1.49525 2.8743 0.004049 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1