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#' Plots the estimated intervention variances.
#'
#' @param estIntVars A (Gxp)-dimensional matrix with the estimated intervention
#' variances returned by \code{backShift} (as \code{varianceEnv}).
#' \code{G} is the number of unique environments, \code{p} is the number of variables.
#' @param trueIntVars A (Gxp)-dimensional matrix with the true intervention variances
#' if these are known (for simulations). By default this paramter is set to NULL.
#' @param scales_facet scales argument passed to ggplot's facet_wrap
#'
plotInterventionVars <- function(estIntVars,
trueIntVars = NULL,
scales_facet = "free"){
gg.df <- melt(as.data.frame(estIntVars))
gg.df <- cbind(gg.df, rep(1:nrow(estIntVars), times = ncol(estIntVars)))
if(!is.null(trueIntVars)){
true.vars <- melt(trueIntVars)[,3]
gg.df <- cbind(gg.df, true.vars)
gg.df <- as.data.frame(gg.df)
colnames(gg.df) <- c("variable", "est.variance", "env", "true.variance")
gg.df <- melt(gg.df, c("variable","env"), variable.name = "Variance")
p <- ggplot(gg.df, aes_string(x = 'env', y = 'value', aes_string(color = 'Variance')))
p <- p + theme_classic(base_size = 8)
p <- p + geom_line(aes_string(type = 'Variance', color = 'Variance'), size = 0.5)
p <- p + scale_color_discrete(labels = c("Estimated int. variance", "True int. variance"))
}else{
gg.df <- as.data.frame(gg.df)
colnames(gg.df) <- c("variable", "est.variance", "env")
p <- ggplot(gg.df, aes_string(x = 'env', y = 'est.variance', aes_string(color = 'variable')))
p <- p + theme_classic(base_size = 8)
p <- p + geom_line(aes_string(group = 'variable', color = 'variable'), size = 0.5)
p <- p + scale_color_discrete(name = "Variable")
}
p <- p + facet_wrap(~variable, scales = scales_facet)
p <- p + scale_x_discrete(labels = 1:nrow(estIntVars))
p <- p + theme(axis.text.x=element_text(angle=90, hjust=1))
p <- p + xlab("Environment") + ylab("Intervention variance")
p <- p + ggtitle("Estimated intervention variances (up to an offset)")
p
}
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