linREGSUM <- function(x=runif(10), y=runif(10)){
linMOD <- lm(y~x)
myOUT <- summary(linMOD)
return(myOUT)
}
linREGSUM()
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.39368 -0.15105 -0.04351 0.19356 0.37839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.7087 0.1798 3.943 0.00428 **
## x -0.5642 0.3382 -1.668 0.13383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2895 on 8 degrees of freedom
## Multiple R-squared: 0.2581, Adjusted R-squared: 0.1653
## F-statistic: 2.783 on 1 and 8 DF, p-value: 0.1338
##With dummy data
linREGSUM(x=rep(1,10), y=runif(10))
##
## Call:
## lm(formula = y ~ x)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.36216 -0.19064 0.05141 0.14800 0.35632
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.51035 0.08159 6.255 0.000149 ***
## x NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.258 on 9 degrees of freedom
linREGPLT <- function(x=runif(10), y=runif(10)){
linMOD <- lm(y~x)
plot(y=y,x=x,pch=21,bg="lightblue",cex=2, main="Linear Regression", xlab="Generations of Exposure", ylab="Fecundity")
abline(linMOD, add=TRUE)
}
linREGPLT()
## Warning in int_abline(a = a, b = b, h = h, v = v, untf = untf, ...): "add"
## is not a graphical parameter
##With dummy data
linREGPLT(x=runif(10), y=runif(10))
## Warning in int_abline(a = a, b = b, h = h, v = v, untf = untf, ...): "add"
## is not a graphical parameter
aovSUM <- function(x=as.factor(rep(c("Control","Toxic"),each=5)), y=c(rgamma(5,shape=5,scale=5),rgamma(5,shape=5,scale=10))){
aovMOD <- aov(y~x)
myOUT <- summary(aovMOD)
return(myOUT)
}
aovSUM()
## Df Sum Sq Mean Sq F value Pr(>F)
## x 1 2730 2730.1 17.73 0.00295 **
## Residuals 8 1232 153.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##With dummy data
aovSUM(x=as.factor(rep(c("Maine","New Jersey"),each=5)), y=c(rgamma(8,shape=5,scale=5),rgamma(2,shape=5,scale=10)))
## Df Sum Sq Mean Sq F value Pr(>F)
## x 1 453.8 453.8 1.878 0.208
## Residuals 8 1932.5 241.6
aovPLT <- function(x=as.factor(rep(c("Control","Toxic"),each=5)), y=c(rgamma(5,shape=5,scale=5),rgamma(5,shape=5,scale=10))){
aovMOD <- aov(y~x)
boxplot(y~x,col=c("darkolivegreen1","darkgreen"), ylab="Fecundity")
}
aovPLT()
##With dummy data
aovPLT(x=as.factor(rep(c("Maine","New Jersey"),each=5)), y=c(rgamma(8,shape=5,scale=5),rgamma(2,shape=5,scale=10)))
ContingencySUM <- function(dataMATRIX= rbind(c(90,98,89), c(70,60,20))){
rownames(dataMATRIX) <- c("Control","Toxic")
colnames(dataMATRIX) <-c("LowTOX",
"MedTOX",
"HighTOX")
myOUT <- print(chisq.test(dataMATRIX))
return(myOUT)
}
ContingencySUM()
##
## Pearson's Chi-squared test
##
## data: dataMATRIX
## X-squared = 19.248, df = 2, p-value = 6.612e-05
##
## Pearson's Chi-squared test
##
## data: dataMATRIX
## X-squared = 19.248, df = 2, p-value = 6.612e-05
##With dummy data
ContingencySUM(dataMATRIX = rbind(c(87,66, 77), c(66, 44, 23)))
##
## Pearson's Chi-squared test
##
## data: dataMATRIX
## X-squared = 11.331, df = 2, p-value = 0.003463
##
## Pearson's Chi-squared test
##
## data: dataMATRIX
## X-squared = 11.331, df = 2, p-value = 0.003463
ContingencyPLT <- function(dataMATRIX= rbind(c(90,98,89), c(70,60,20))){
rownames(dataMATRIX) <- c("Control","Toxic")
colnames(dataMATRIX) <-c("LowTOX",
"MedTOX",
"HighTOX")
barplot(height=dataMATRIX,
beside=TRUE,
col=c("darkolivegreen1","darkgreen"), ylim=c(0,max(colSums(dataMATRIX))), legend = rownames(dataMATRIX),args.legend=list(
x=ncol(dataMATRIX) + 3,
y=max(colSums(dataMATRIX)),
bty = "n"
))
}
ContingencyPLT()
##With dummy data
ContingencySUM(dataMATRIX = rbind(c(87,66, 77), c(66, 44, 23)))
##
## Pearson's Chi-squared test
##
## data: dataMATRIX
## X-squared = 11.331, df = 2, p-value = 0.003463
##
## Pearson's Chi-squared test
##
## data: dataMATRIX
## X-squared = 11.331, df = 2, p-value = 0.003463
LogisticSUM <- function(xVar, yVar){
xVar <- rgamma(n=20,shape=5,scale=5)
yVar <- rbinom(n=20,size=1,p=0.5)
dataFrame <- data.frame(xVar,yVar)
logRegMod <- glm(yVar ~ xVar,
data=dataFrame,
family=binomial(link="logit"))
summary(logRegMod)
}
LogisticSUM()
##
## Call:
## glm(formula = yVar ~ xVar, family = binomial(link = "logit"),
## data = dataFrame)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3455 -1.0861 -0.9348 1.2015 1.4415
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.80843 1.12595 -0.718 0.473
## xVar 0.02618 0.04419 0.592 0.554
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 27.526 on 19 degrees of freedom
## Residual deviance: 27.168 on 18 degrees of freedom
## AIC: 31.168
##
## Number of Fisher Scoring iterations: 4
## With dummy data
LogisticSUM(c(rgamma(n=100,shape=7, scale=1)), c(rbinom(n=100,size=1,p=0.2)))
##
## Call:
## glm(formula = yVar ~ xVar, family = binomial(link = "logit"),
## data = dataFrame)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.6448 -1.0186 0.4447 1.0142 1.4953
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.63367 1.81963 -1.447 0.1478
## xVar 0.12817 0.07739 1.656 0.0977 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 26.92 on 19 degrees of freedom
## Residual deviance: 23.00 on 18 degrees of freedom
## AIC: 27
##
## Number of Fisher Scoring iterations: 4
LogisticPLT <- function(xVar, yVar){
xVar <- rgamma(n=20,shape=5,scale=5)
yVar <- rbinom(n=20,size=1,p=0.5)
dataFrame <- data.frame(xVar,yVar)
logRegMod <- glm(yVar ~ xVar,
data=dataFrame,
family=binomial(link="logit"))
plot(x=dataFrame$xVar, y=dataFrame$yVar,pch=21,bg="gray31",cex=2, xlab = "Treatment Days", ylab = "Survival", main = "Toxic Algal Survival")
curve(predict(logRegMod,data.frame(xVar=x),type="response"),add=TRUE,lwd=2)
}
LogisticPLT()
## With dummy data
LogisticPLT(c(rgamma(n=100,shape=7, scale=1)), c(rbinom(n=100,size=1,p=0.8)))