Difference between revisions of "Regression Exercise"
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load("dbp.R") | load("dbp.R") | ||
| − | ls() | + | ls() |
| − | # | + | dbp[1:5,] |
| + | # | ||
| + | result.snp12 = glm (affection ~ rs1112, family=binomial("logit"), data=dbp) | ||
| + | print (result.snp12) | ||
| + | print ( class (result.snp12) ) | ||
| + | print ( summary(result.snp12) ) | ||
| + | # | ||
| + | dev.geno = anova (result.snp12, test="Chi") | ||
| + | lrt.pvalue = pchisq(dev.geno[dim(dev.geno)[1],"Deviance"], df=2, ncp=0, FALSE) | ||
| + | print ( lrt.pvalue ) | ||
| + | # | ||
| + | print ( summary(result.snp12)$coefficients ) | ||
| + | snp.beta = summary(result.snp12)$coefficients[2:3,1] | ||
| + | print ( snp.beta ) | ||
| + | print ( exp(snp.beta) ) | ||
| + | ci = confint (result.snp12) | ||
| + | print (ci) | ||
| + | print ( exp(ci) ) | ||
| + | # | ||
| + | snp.data = dbp[,c("affection", "rs1112")] | ||
| + | summary(snp.data) | ||
| + | snp.data[,"rs1112"] <- as.numeric(snp.data[,"rs1112"]) - 1<br />summary(snp.data) | ||
| + | # | ||
| + | result.all = glm (affection ~ rs1112, family=binomial("logit"), data=snp.data) | ||
| + | dev.all = anova (result.all, test="Chi") | ||
| + | summary(result.all) | ||
| + | print(dev.all) | ||
| + | # | ||
| + | snp.data = dbp[,c("affection", "trait","sex", "age", "rs1112", "rs1117")] | ||
| + | summary(snp.data) | ||
| + | snp.data[,"rs1112"] <- as.numeric(snp.data[,"rs1112"]) - 1<br />snp.data[,"rs1117"] <- as.numeric(snp.data[,"rs1117"]) - 1 | ||
| + | # | ||
| + | result.adj = glm (affection ~ sex + rs1112 , family=binomial("logit"), data=snp.data) | ||
| + | summary(result.adj) | ||
| + | # | ||
| + | result.adj = glm (affection ~ age + rs1112 , family=binomial("logit"), data=snp.data) | ||
| + | summary(result.adj) | ||
| + | # | ||
| + | result.adj = glm (affection ~ sex + age + rs1112, family=binomial("logit"), data=snp.data) | ||
| + | summary(result.adj) | ||
| + | # | ||
| + | result.adj = glm (affection ~ rs1117 + rs1112, family=binomial("logit"), data=snp.data) | ||
| + | summary(result.adj) | ||
| + | anova (result.adj, test="Chi") | ||
| + | result.adj = glm (affection ~ rs1112 + rs1117, family=binomial("logit"), data=snp.data) | ||
| + | summary(result.adj) | ||
| + | anova (result.adj, test="Chi") | ||
| + | # | ||
| + | result.adj = lm (trait ~ rs1112, data=snp.data) | ||
| + | summary(result.adj) | ||
| + | result.adj = lm (trait ~ sex + rs1112, data=snp.data) | ||
| + | summary(result.adj) | ||
| + | # | ||
| + | result.inter = glm (affection ~ sex * rs1112, family=binomial("logit"), data=snp.data) | ||
| + | summary(result.inter) | ||
| + | result.inter = glm (affection ~ age * rs1112, family=binomial("logit"), data=snp.data) | ||
| + | summary(result.inter) | ||
| + | # | ||
| + | result.inter = glm (affection ~ rs1112 * rs1117, family=binomial("logit"), data=snp.data) | ||
| + | summary(result.inter) | ||
| + | # | ||
| + | q() | ||
Latest revision as of 03:30, 8 March 2017
Regression exercise
In R:
load("dbp.R")
ls()
dbp[1:5,]
#
result.snp12 = glm (affection ~ rs1112, family=binomial("logit"), data=dbp)
print (result.snp12)
print ( class (result.snp12) )
print ( summary(result.snp12) )
#
dev.geno = anova (result.snp12, test="Chi")
lrt.pvalue = pchisq(dev.geno[dim(dev.geno)[1],"Deviance"], df=2, ncp=0, FALSE)
print ( lrt.pvalue )
#
print ( summary(result.snp12)$coefficients )
snp.beta = summary(result.snp12)$coefficients[2:3,1]
print ( snp.beta )
print ( exp(snp.beta) )
ci = confint (result.snp12)
print (ci)
print ( exp(ci) )
#
snp.data = dbp[,c("affection", "rs1112")]
summary(snp.data)
snp.data[,"rs1112"] <- as.numeric(snp.data[,"rs1112"]) - 1
summary(snp.data)
#
result.all = glm (affection ~ rs1112, family=binomial("logit"), data=snp.data)
dev.all = anova (result.all, test="Chi")
summary(result.all)
print(dev.all)
#
snp.data = dbp[,c("affection", "trait","sex", "age", "rs1112", "rs1117")]
summary(snp.data)
snp.data[,"rs1112"] <- as.numeric(snp.data[,"rs1112"]) - 1
snp.data[,"rs1117"] <- as.numeric(snp.data[,"rs1117"]) - 1
#
result.adj = glm (affection ~ sex + rs1112 , family=binomial("logit"), data=snp.data)
summary(result.adj)
#
result.adj = glm (affection ~ age + rs1112 , family=binomial("logit"), data=snp.data)
summary(result.adj)
#
result.adj = glm (affection ~ sex + age + rs1112, family=binomial("logit"), data=snp.data)
summary(result.adj)
#
result.adj = glm (affection ~ rs1117 + rs1112, family=binomial("logit"), data=snp.data)
summary(result.adj)
anova (result.adj, test="Chi")
result.adj = glm (affection ~ rs1112 + rs1117, family=binomial("logit"), data=snp.data)
summary(result.adj)
anova (result.adj, test="Chi")
#
result.adj = lm (trait ~ rs1112, data=snp.data)
summary(result.adj)
result.adj = lm (trait ~ sex + rs1112, data=snp.data)
summary(result.adj)
#
result.inter = glm (affection ~ sex * rs1112, family=binomial("logit"), data=snp.data)
summary(result.inter)
result.inter = glm (affection ~ age * rs1112, family=binomial("logit"), data=snp.data)
summary(result.inter)
#
result.inter = glm (affection ~ rs1112 * rs1117, family=binomial("logit"), data=snp.data)
summary(result.inter)
#
q()
