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#corrected May 7, 2007
#modified October ,2011 to use apply for mean and sd
#modified April, 2012 to return 3 estimates, depending upon type
#partly based upon e1071 skewness and kurtosis
"skew" <-
function (x, na.rm = TRUE,type=3)
{
if (length(dim(x)) == 0) {
if (na.rm) {
x <- x[!is.na(x)]
}
sdx <- sd(x,na.rm=na.rm)
mx <- mean(x)
n <- length(x[!is.na(x)])
switch(type,
{skewer <- sqrt(n) *( sum((x - mx)^3, na.rm = na.rm)/( sum((x - mx)^2,na.rm = na.rm)^(3/2)))}, #case 1
{skewer <- n *sqrt(n-1) *( sum((x - mx)^3, na.rm = na.rm)/((n-2) * sum((x - mx)^2,na.rm = na.rm)^(3/2)))}, #case 2
{skewer <- sum((x - mx)^3)/(n * sd(x)^3) }) #case 3
} else {
skewer <- rep(NA,dim(x)[2])
if (is.matrix(x)) {mx <- colMeans(x,na.rm=na.rm)} else {mx <- apply(x,2,mean,na.rm=na.rm)}
sdx <- apply(x,2,sd,na.rm=na.rm)
for (i in 1:dim(x)[2]) {
n <- length(x[!is.na(x[,i]),i])
switch(type,
{skewer[i] <-sqrt(n) *( sum((x[,i] - mx[i])^3, na.rm = na.rm)/( sum((x[,i] - mx[i])^2,na.rm = na.rm)^(3/2)))}, #type 1
{skewer[i] <- n *sqrt(n-1) *( sum((x[,i] - mx[i])^3, na.rm = na.rm)/((n-2) * sum((x[,i] - mx[i])^2,na.rm = na.rm)^(3/2)))},#type 2
{skewer[i] <- sum((x[,i] - mx[i])^3, na.rm = na.rm)/(n * sdx[i]^3)} #type 3
) #end switch
} #end loop
}
return(skewer)
}
#modified November 24, 2010 to use an unbiased estimator of kurtosis as the default
#and again April 22, 2012 to include all three types
"kurtosi" <-
function (x, na.rm = TRUE,type=3)
{
if (length(dim(x)) == 0) {
if (na.rm) {
x <- x[!is.na(x)]
}
if (is.matrix(x) ) { mx <- colMeans(x,na.rm=na.rm)} else {mx <- mean(x,na.rm=na.rm)}
sdx <- sd(x,na.rm=na.rm)
n <- length(x[!is.na(x)])
switch(type,
{kurt <- sum((x - mx)^4, na.rm = na.rm)*n /(sum((x - mx)^2,na.rm = na.rm)^2) -3}, #type 1
{
kurt <- n*(n + 1)*sum((x - mx)^4, na.rm = na.rm)/( (n - 1)*(n - 2)*(n - 3)*(sum((x - mx)^2,na.rm = na.rm)/(n - 1))^2) -3 *(n- 1)^2 /((n - 2)*(n - 3)) }, # type 2
{kurt <- sum((x - mx)^4)/(n *sdx^4) -3} ) # type 3
} else {
kurt <- rep(NA,dim(x)[2])
# mx <- mean(x,na.rm=na.rm)
mx <-apply(x,2 ,mean,na.rm=na.rm)
if(type==3) sdx <- apply(x,2,sd,na.rm=na.rm)
for (i in 1:dim(x)[2]) {
n <- length(x[!is.na(x[,i]),i])
switch(type,
{ kurt[i] <- sum((x[,i] - mx[i])^4, na.rm = na.rm)*length(x[,i]) /(sum((x[,i] - mx[i])^2,na.rm = na.rm)^2) -3}, #type 1
{
xi <- x[,i]-mx[i]
kurt[i] <- n*(n + 1)*sum((x[,i] - mx[i])^4, na.rm = na.rm)/( (n - 1)*(n - 2)*(n - 3)*(sum((x[,i] - mx[i])^2,na.rm = na.rm)/(n - 1))^2) -3 *(n- 1)^2 /((n - 2)*(n - 3)) } #type 2
,
{
kurt[i] <- sum((x[,i] - mx[i])^4, na.rm = na.rm)/((length(x[,i]) - sum(is.na(x[,i]))) * sdx[i]^4) -3}, #type 3
{NULL})
names(kurt) <- colnames(x)
}}
return(kurt)
}
#added November 15, 2010
#adapted from the mult.norm function of the QuantPsyc package
"mardia" <-
function(x,na.rm=TRUE,plot=TRUE) {
cl <- match.call()
x <- as.matrix(x) #in case it was a dataframe
if(na.rm) x <- na.omit(x)
if(nrow(x) > 0) {
n <- dim(x)[1]
p <- dim(x)[2]
x <- scale(x,scale=FALSE) #zero center
S <- cov(x)
S.inv <- solve(S)
D <- x %*% S.inv %*% t(x)
b1p <- sum(D^3)/n^2
b2p <- tr(D^2)/n
chi.df <- p*(p+1)*(p+2)/6
k <- (p+1)*(n+1)*(n+3)/(n*((n+1)*(p+1) -6))
small.skew <- n*k*b1p/6
M.skew <- n*b1p/6
M.kurt <- (b2p - p * (p+2))*sqrt(n/(8*p*(p+2)))
#p.skew <- 1-pchisq(M.skew,chi.df)
p.skew <- -expm1(pchisq(M.skew,chi.df,log.p=TRUE))
#p.small <- 1 - pchisq(small.skew,chi.df)
p.small <- -expm1(pchisq(small.skew,chi.df,log.p=TRUE))
p.kurt <- 2*(1- pnorm(abs(M.kurt)))
d =sqrt(diag(D))
if(plot) {qqnorm(d)
qqline(d)}
results <- list(n.obs=n,n.var=p, b1p = b1p,b2p = b2p,skew=M.skew,small.skew=small.skew,p.skew=p.skew,p.small=p.small,kurtosis=M.kurt,p.kurt=p.kurt,d = d,Call=cl)
class(results) <- c("psych","mardia")
return(results) } else {warning("no cases with complete data, mardia quit.")}
}
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