#
#
############ Fun with ogives, binomials, & logistic regression.

n <- 1000
x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = 1)
plot(x,y, col = "lightblue")

x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = .75)
plot(x,y, col = "red")
summary(x)
summary(y)

x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = .5)
plot(x,y, col = "lightgreen")
summary(x)
summary(y)

##############################################

for (i in 1:6){
x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = 1.5)
plot(x,y, col = "yellow")
Sys.sleep(.5)
x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = 1.25)
plot(x,y, col = "orange")
Sys.sleep(.5)
x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = 1)
plot(x,y, col = "red")
Sys.sleep(.5)
x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = .75)
plot(x,y, col = "darkgreen")
Sys.sleep(.5)
x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = .5)
plot(x,y, col = "blue")
Sys.sleep(.5)
x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = .25)
plot(x,y, col = "purple")
Sys.sleep(.5)
}

#############################################################

x <- seq(-5, 5, length.out = n)
y0 <- plogis(x, location = 0, scale = 1.5)
y1 <- plogis(x, location = 0, scale = 1.25)
y2 <- plogis(x, location = 0, scale = 1)
y3 <- plogis(x, location = 0, scale = .75)
y4 <- plogis(x, location = 0, scale = .5)
y5 <- plogis(x, location = 0, scale = .25)

plot(x,y0, col = "yellow", xlim = c(-5,5), ylim = c(0,1))
par(new=T)
plot(x,y1, col = "orange", xlim = c(-5,5), ylim = c(0,1))
par(new=T)
plot(x,y2, col = "red", xlim = c(-5,5), ylim = c(0,1))
par(new=T)
plot(x,y3, col = "darkgreen", xlim = c(-5,5), ylim = c(0,1))
par(new=T)
plot(x,y4, col = "blue", xlim = c(-5,5), ylim = c(0,1))
par(new=T)
plot(x,y5, col = "purple", xlim = c(-5,5), ylim = c(0,1))
lines(x = c(-5,5), y = c(0,1.0), lwd = 2) 

##################################################################

n <- 200
z <- 5.0
my.colors <- c("darkgreen", "darkblue")
v <- 1
x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = z)
plot(x,y, col = my.colors[v], xlim = c(-5,5), ylim = c(0,1), xlab = "X", ylab = "Y", pch = ".")

for (i in 1:24){
z <- z - 0.2
v <- sample(my.colors, 1)
x <- seq(-5, 5, length.out = n)
y <- plogis(x, location = 0, scale = z)
par(new=T)
plot(x,y, col = v, xlim = c(-5,5), ylim = c(0,1), xlab = "X", ylab = "Y", pch = ".")
Sys.sleep(.5)
}


#######################################################

library(car)
library(QuantPsyc)

n <- 200
x <- seq(-5, 5, length.out = n)
y1 <- plogis(x, location = 0, scale = 2.0)
y2 <- plogis(x, location = 0, scale = 1.5)
y3 <- plogis(x, location = 0, scale = 1.0)
y4 <- plogis(x, location = 0, scale = 0.5)

oldpar <- par(oma=c(0,0,3,0), mfrow=c(2,2))
plot(y1)
plot(y2)
plot(y3)
plot(y4)
par(oldpar)

###

x1.1 <- x
x1.2 <- y1*.6 + rnorm(200, 0, 1.0)
x1.3 <- y1*.3 + rnorm(200, 0, 1.0)
x1.4 <- y1*.8 + rnorm(200, 0, 0.2)

x2.2 <- y2*.6 + rnorm(200, 0, 1.0)
x2.3 <- y2*.3 + rnorm(200, 0, 1.0)
x2.4 <- y2*.8 + rnorm(200, 0, 0.2)

x3.2 <- y3*.6 + rnorm(200, 0, 1.0)
x3.3 <- y3*.3 + rnorm(200, 0, 1.0)
x3.4 <- y3*.8 + rnorm(200, 0, 0.2)

x4.2 <- y4*.6 + rnorm(200, 0, 1.0)
x4.3 <- y4*.3 + rnorm(200, 0, 1.0)
x4.4 <- y4*.8 + rnorm(200, 0, 0.2)

df <- data.frame(y1, x1.1, x1.2, x1.3, x1.4, y2, x2.2, x2.3, x2.4, y3, x3.2, x3.3, x3.4,
   y4, x4.2, x4.3, x4.4)

subset.1 <- data.frame(y1, x1.1, x1.2, x1.3, x1.4)
subset.2 <- data.frame(y2, x1.1, x2.2, x2.3, x2.4)
subset.3 <- data.frame(y3, x1.1, x3.2, x3.3, x3.4)
subset.4 <- data.frame(y4, x1.1, x4.2, x4.3, x4.4)

rm(n, x, y1, x1.1, x1.2, x1.3, x1.4, y2, x2.2, x2.3, x2.4, y3, x3.2, x3.3, x3.4,
   y4, x4.2, x4.3, x4.4)

summary(df)
ls()

###

cor(subset.1)

lm.1 <- lm(y2 ~ x1.1 + x1.2 + x1.3 + x1.4, df)
summary(lm.1)
lm.beta(lm.1)
cor(subset.2)

lm.2 <- lm(y2 ~ x1.1 + x2.2 + x2.3 + x2.4, df)
summary(lm.2)
lm.beta(lm.2)
cor(subset.3)

lm.3 <- lm(y3 ~ x1.1 + x3.2 + x3.3 + x3.4, df)
summary(lm.3)
lm.beta(lm.3)
cor(subset.4)

lm.4 <- lm(y4 ~ x1.1 + x4.2 + x4.3 + x4.4, df)
summary(lm.4)
lm.beta(lm.4)

###

oldpar <- par(oma=c(0,0,3,0), mfrow=c(2,2))
plot(lm.1)

plot(lm.2)

plot(lm.3)

plot(lm.4)
par(oldpar)

###

glm.1 <- glm(y1 ~ x1.1 + x1.2 + x1.3 + x1.4, df, family = binomial(logit))
summary(glm.1)

glm.2 <- glm(y2 ~ x1.1 + x2.2 + x2.3 + x2.4, df, family = binomial(logit))
summary(glm.2)

glm.3 <- glm(y3 ~ x1.1 + x3.2 + x3.3 + x3.4, df, family = binomial(logit))
summary(glm.3)

glm.4 <- glm(y4 ~ x1.1 + x4.2 + x4.3 + x4.4, df, family = binomial(logit))
summary(glm.4)

###

oldpar <- par(oma=c(0,0,3,0), mfrow=c(2,2))
plot(df$y1, col = "darkblue", pch = 20)
plot(df$y2, col = "lightblue1", pch = 20)
plot(df$y3, col = "darkgreen", pch = 20)
plot(df$y4, col = "lightgreen", pch = 20)
par(oldpar)

ls()
rm(lm.1, lm.2, lm.3, lm.4, glm.1, glm.2, glm.3, glm.4)
ls()

###

scatterplotMatrix(subset.1)
cor(subset.1)

scatterplotMatrix(subset.2)
cor(subset.2)

scatterplotMatrix(subset.3)
cor(subset.3)

scatterplotMatrix(subset.4)
cor(subset.4)

rm(df, oldpar, subset.1, subset.2, subset.3, subset.4)




# End; last updated: Apr. 18, 2011.