Nothing
asterix <- function(x){
y <- rep("",length(x))
y[x<0.05] <- "*"
y[x<0.01] <- "**"
y[x<0.001] <- "***"
y
}
## fixedEffects:
fixedEffects <- function(object,digits=5){
# global dummies:
. <- NULL
effect <- NULL
if (is(object,"mlVAR_MW")){
return(object$fixedEffects)
}
if (!is(object,"mlVAR0")){
stop("Only works for mlVAR0 objects.")
}
warning("Function is deprecated and will be removed soon.")
# Nodes:
Nodes <- unique(object$fixedEffects$dep)
# Predictors:
Predictor = names(object$fixedEffects)[-1]
coef <- as.matrix(object$fixedEffects[,-1])
if (!is.null(object$se.fixedEffects)){
s.coef <- as.matrix(object$se.fixedEffects[,-1])
} else {
s.coef <- NA
}
if (!is.null(object$pval)){
pvals <- as.matrix(object$pvals[,-1])
} else {
pvals <- NA
}
# Data frame of fixed effects:
df <- data.frame(
Response = object$fixedEffects$dep,
Predictor = Predictor[col(coef)],
effect = round(c(coef),digits),
se = round(c(s.coef),digits),
p = round(c(pvals),digits),
sig = asterix(c(pvals)),
stringsAsFactors=FALSE
)
if (any(duplicated(df[c("Response","Predictor")]))){
message("Duplicate effects found (possibly due to moving window approach), averaging effect, se and p-value.")
suppressWarnings(df <- df %>% group_by(.data[["Response"]],.data[["Predictor"]]) %>% summarise_each_(funs(mean(., na.rm=TRUE)), vars = c("effect","se","p")))
}
# Remove NA rows:
df <- df %>% filter(!is.na(effect))
return(df)
}
# Random effects:
randomEffects<- function(object, digits=5){
if (is(object,"mlVAR_MW")) stop("Cannot estimate random effects with moving window approach")
# global dummies:
. <- NULL
variance <- NULL
if (!is(object,"mlVAR0")){
stop("Only works for mlVAR0 objects.")
}
warning("Function is deprecated and will be removed soon.")
# Predictors:
Predictor = colnames(object$randomEffectsVariance)[-1]
# Data frame of fixed effects:
df <- data.frame(
Response = object$randomEffectsVariance$dep,
Predictor = Predictor[col(object$randomEffectsVariance[,-1])],
variance = round(unlist(object$randomEffectsVariance[,-1]),digits)
)
rownames(df) <- NULL
if (any(duplicated(df[c("Response","Predictor")]))){
message("Duplicate effects found (possibly due to moving window approach), averaging variance.")
suppressWarnings(df <- df %>% group_by(.data[["Response"]],.data[["Predictor"]]) %>% summarise_each_(funs(mean(., na.rm=TRUE)), vars = c("variance")))
}
# Remove NA rows:
df <- df %>% filter(!is.na(variance))
return(df)
return(df)
}
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