Nothing
plotResid.enetLTS <- function(object,vers=c("reweighted","raw"),...){
vers <- match.arg(vers)
family <- object$inputs$family
if (family=="multinomial"){
y.factor <- object$inputs$y_factor
}
x <- object$inputs$xx
y <- object$inputs$yy
coefficients <- object$coefficients
raw.coefficients <- object$raw.coefficients
if (family=="multinomial"){
if (vers=="reweighted"){
mainfit <- paste("Deviances vs Index for Multinomial Logistic Regression")
xlab <- "Index"
ylab <- "Deviances"
lab.val <- c("regular observation","outliers")
classification <- factor(as.numeric(!object$raw.wt==0))
ind <- factor(as.numeric(object$raw.wt==0))
yhat <- x %*% coefficients
probs <- exp(yhat)/apply(exp(yhat),1,sum)
residuals <- (-apply(y*log(probs),1,sum))
Index <- as.numeric(c(1:length(residuals)))
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
} else if (vers=="raw"){
mainfit <- paste("Raw Deviances vs Index for Multinomial Logistic Regression")
xlab <- "Index"
ylab <- "Raw deviances"
lab.val <- c("regular observation","outliers")
yhat <- x %*% raw.coefficients
probs <- exp(yhat)/apply(exp(yhat),1,sum)
residuals <- (-apply(y*log(probs),1,sum))
classification <- factor(as.numeric(c(1:length(residuals)) %in% object$best))
ind <- factor(as.numeric(!c(1:length(residuals)) %in% object$best))
Index <- as.numeric(c(1:length(residuals)))
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
}
plot <- ggplot(residuals, aes(x=Index,
y=residuals,
color=factor(y.factor),
shape=ind)) +
geom_point(size=2) +
geom_hline(yintercept=0, linetype="dashed",color="gray") +
labs(title = mainfit, x = xlab, y = ylab) +
theme(plot.title = element_text(size=rel(1.3),face="bold"),
axis.title = element_text(size = 13),
axis.text.y = element_text(size = 13),
axis.text.x = element_text(size = 13),
legend.text = element_text(size = 13),
legend.title = element_text(size = 13)) +
scale_colour_discrete(name= "Groups") +
# scale_shape_discrete(name = "Diagnostics", breaks=c("0", "1"), labels = lab.val)
scale_shape_manual(values=c(4, 17),name="Diagnostics",breaks=c("0", "1"), labels=lab.val)
print(plot)
} else if (family=="binomial"){
if (vers=="reweighted"){
mainfit <- paste("Deviances vs Index for Binary Logistic Regression")
xlab <- "Index"
ylab <- "Deviances"
lab.val <- c("regular observation","outliers")
classification <- factor(as.numeric(!object$raw.wt==0))
ind <- factor(as.numeric(object$raw.wt==0))
residuals <- -(y * x %*% coefficients) + log(1+exp(x %*% coefficients))
Index <- as.numeric(c(1:length(residuals)))
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
} else if (vers=="raw"){
mainfit <- paste("Raw Deviances vs Index for Binary Logistic Regression")
xlab <- "Index"
ylab <- "Raw deviances"
lab.val <- c("best subset","outliers")
residuals <- -(y * x %*% raw.coefficients) + log(1+exp(x %*% raw.coefficients))
classification <- factor(as.numeric(c(1:length(residuals)) %in% object$best))
ind <- factor(as.numeric(!c(1:length(residuals)) %in% object$best))
Index <- as.numeric(c(1:length(residuals)))
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
}
plot <- ggplot(residuals, aes(x=Index,
y=residuals,
color=classification,
shape=ind)) +
geom_point(size=2) +
geom_hline(yintercept=0, linetype="dashed",color="gray") +
labs(title = mainfit, x = xlab, y = ylab) +
theme(plot.title = element_text(size=rel(1.3),face="bold"),
axis.title = element_text(size = 13),
axis.text.y = element_text(size = 13),
axis.text.x = element_text(size = 13),
legend.text = element_text(size = 13),
legend.title = element_text(size = 13)) +
scale_colour_discrete(name="Diagnostics",breaks=c("1", "0"),labels=lab.val) +
# scale_shape_discrete(name="Diagnostics",breaks=c("0", "1"),labels=lab.val)
scale_shape_manual(values=c(4, 17),name="Diagnostics",breaks=c("0", "1"), labels=lab.val)
print(plot)
} else if (family=="gaussian"){
if (vers=="reweighted"){
mainfit <- paste("Standardized Residuals vs Index for Regression")
xlab <- "Index"
ylab <- "Residuals"
lab.val <- c("regular observation","outliers")
classification <- factor(as.numeric(!object$raw.wt==0))
ind <- factor(as.numeric(object$raw.wt==0))
residuals <- y - x %*% coefficients
residuals <- residuals/mad(residuals)
Index <- as.numeric(c(1:length(residuals)))
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
} else if (vers=="raw"){
mainfit <- paste("Standardized Raw Residuals vs Index for Regression")
xlab <- "Index"
ylab <- "Raw residuals"
lab.val <- c("best subset","outliers")
residuals <- y - x %*% raw.coefficients
residuals <- residuals/mad(residuals)
classification <- factor(as.numeric(c(1:length(residuals)) %in% object$best))
ind <- factor(as.numeric(!c(1:length(residuals)) %in% object$best))
Index <- as.numeric(c(1:length(residuals)))
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
}
plot <- ggplot(residuals, aes(x=Index,
y=residuals,
color=classification,
shape=ind)) +
geom_point(size=2) +
geom_hline(yintercept=0, linetype="dashed",color="gray") +
geom_hline(yintercept=-2.5,linetype="solid",color="gray") +
geom_hline(yintercept=2.5,linetype="solid",color="gray") +
labs(title = mainfit, x = xlab, y = ylab) +
theme(plot.title = element_text(size=rel(1.3),face="bold"),
axis.title = element_text(size = 13),
axis.text.y = element_text(size = 13),
axis.text.x = element_text(size = 13),
legend.text = element_text(size = 13),
legend.title = element_text(size = 13)) +
scale_colour_discrete(name="Diagnostics",breaks=c("1", "0"), labels=lab.val) +
# scale_shape_discrete(name="Diagnostics",breaks=c("0", "1"), labels=lab.val)
scale_shape_manual(values=c(4, 17),name="Diagnostics",breaks=c("0", "1"), labels=lab.val)
print(plot)
}
if (family=="binomial" | family=="gaussian"){
if (family=="binomial"){
if (vers=="reweighted"){
mainfit <- expression(paste("Deviances vs ", X*hat(beta), " for Binary Logistic Regression"))
xlab <- expression(X*hat(beta))
ylab <- "Deviances"
lab.val <- c("regular observation","outliers")
classification <- factor(as.numeric(!object$raw.wt==0))
ind <- factor(as.numeric(object$raw.wt==0))
residuals <- -(y * x %*% coefficients) + log(1+exp(x %*% coefficients))
fitted.values <- x %*% coefficients
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
} else if (vers=="raw"){
mainfit <- expression(paste("Raw Deviances vs ", X*hat(beta)[raw], " for Binary Logistic Regression"))
xlab <- expression(X*hat(beta)[raw])
ylab <- "Raw deviances"
lab.val <- c("best subset","outliers")
residuals <- -(y * x %*% raw.coefficients) + log(1+exp(x %*% raw.coefficients))
classification <- factor(as.numeric(c(1:length(residuals)) %in% object$best))
ind <- factor(as.numeric(!c(1:length(residuals)) %in% object$best))
fitted.values <- x %*% raw.coefficients
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
}
plot <- ggplot(residuals, aes(x=fitted.values,
y=residuals,
color=classification,
shape=ind)) +
geom_point(size=2) +
geom_hline(yintercept=0, linetype="dashed",color="gray") +
labs(title = mainfit, x = xlab, y = ylab) +
theme(plot.title = element_text(size=rel(1.3),face="bold"),
axis.title = element_text(size = 13),
axis.text.y = element_text(size = 13),
axis.text.x = element_text(size = 13),
legend.text = element_text(size = 13),
legend.title = element_text(size = 13)) +
scale_colour_discrete(name="Diagnostics",breaks=c("1", "0"),labels=lab.val) +
scale_shape_manual(values=c(4, 17),name="Diagnostics",breaks=c("0", "1"), labels=lab.val)
print(plot)
} else if (family=="gaussian"){
if (vers=="reweighted"){
mainfit <- expression(paste("Standardized Residuals vs ", X*hat(beta), " for Linear Regression"))
xlab <- expression(X*hat(beta))
ylab <- "Standarddized residuals"
lab.val <- c("regular observation","outliers")
classification <- factor(as.numeric(!object$raw.wt==0))
ind <- factor(as.numeric(object$raw.wt==0))
residuals <- y - x %*% coefficients
residuals <- residuals/mad(residuals)
fitted.values <- x %*% coefficients
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
} else if (vers=="raw"){
mainfit <- expression(paste("Standardized Raw Residuals vs ", X*hat(beta)[raw], " for Linear Regression"))
xlab <- expression(X*hat(beta)[raw])
ylab <- "Stamdardized raw residuals"
lab.val <- c("best subset","outliers")
residuals <- y - x %*% raw.coefficients
residuals <- residuals/mad(residuals)
classification <- factor(as.numeric(c(1:length(residuals)) %in% object$best))
ind <- factor(as.numeric(!c(1:length(residuals)) %in% object$best))
fitted.values <- x %*% raw.coefficients
names(residuals) <- 1:length(residuals)
residuals <- data.frame(residuals=residuals,nam=names(residuals),
llim=residuals,ulim=residuals)
}
plot <- ggplot(residuals, aes(x=fitted.values,
y=residuals,
color=classification,
shape=ind)) +
geom_point(size=2) +
geom_hline(yintercept=0, linetype="dashed",color="gray") +
geom_hline(yintercept=-2.5,linetype="solid",color="gray") +
geom_hline(yintercept=2.5,linetype="solid",color="gray") +
labs(title = mainfit, x = xlab, y = ylab) +
theme(plot.title = element_text(size=rel(1.3),face="bold"),
axis.title = element_text(size = 13),
axis.text.y = element_text(size = 13),
axis.text.x = element_text(size = 13),
legend.text = element_text(size = 13),
legend.title = element_text(size = 13)) +
scale_colour_discrete(name="Diagnostics",breaks=c("1", "0"),labels=lab.val) +
# scale_shape_discrete(name="Diagnostics",breaks=c("0", "1"),labels=lab.val)
scale_shape_manual(values=c(4, 17),name="Diagnostics",breaks=c("0", "1"), labels=lab.val)
print(plot)
}
}
}
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