plot.emdi: Plots for an emdi object
In akreutzmann/fayherriot:

Description Usage Arguments Value See Also Examples

Diagnostic plots of the underlying model in the EBP (see also ebp) or Fay-Herriot approach (see also fh) are obtained. These include Q-Q plots and density plots of residuals and random effects from the nested error linera regression model/ the Fay-Herriot model, a Cook's distance plot for detecting outliers and the log-likelihood of the estimation of the optimal parameter in Box-Cox transformations (the latter two only for ebp). The return depends on the transformation such that a plot for the optimal parameter is only returned in case a Box-Cox transformation is chosen. The range of the x-axis is optional but necessary to change if there are convergence problems. All plots are obtained by ggplot.

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## S3 method for class 'emdi'
plot(x, label = "orig", color = c("blue", "lightblue3"),
  gg_theme = NULL, cooks = TRUE, range = NULL, ...)

`x`	an object of type "emdi", "model", representing point and, if chosen, MSE estimates obtained by the EBP or Fay-Herriot approach (see also `ebp` and `fh`).
`label`	argument that enables to customize title and axis labels. There are three instant options to label the diagnostic plot: (i) original labels ("orig"), (ii) axis lables but no title ("no_title"), (iii) neither axis labels nor title ("blank"). (iv) individual labels by a list that needs to have below structure. Six elements can be defined called `qq_res, qq_ran, d_res, d_ran, cooks` and `box_cox` for the six different plots and these list elements need to have three elements each called `title, y_lab and x_lab`. Only the labels for the plots that should be different to the original need to be specified. list( qq_res = c(title="Error term", y_lab="Quantiles of pearson residuals", x_lab="Theoretical quantiles"), qq_ran = c(title="Random effect", y_lab="Quantiles of random effects", x_lab="Theoretical quantiles"), d_res = c(title="Density - Pearson residuals", y_lab="Density", x_lab="Pearson residuals"), d_ran = c(title="Density - Standardized random effects", y_lab="Density", x_lab="Standardized random effects"), cooks = c(title="Cook's Distance Plot", y_lab="Cook's Distance", x_lab="Index"), box_cox = c(title="Box-Cox - REML", y_lab="Log-Likelihood", x_lab="expression(lambda)"))
`color`	a character vector with two elements. The first element defines the color for the line in the QQ-plots, for the Cook's Distance plot and for the Box-Cox plot. The second element defines the color for the densities.
`gg_theme`	`theme` list from package ggplot2. For using this argument, package ggplot2 must be loaded via `library(ggplot2)`. See also Example 4.
`cooks`	if `TRUE`, a Cook's distance plot is returned when the ebp function is used. The used method `mdffits.default` from the package HLMdiag struggles when data sets get large. In these cases, `cooks` should be set to `FALSE`. It defaults to `TRUE`.
`range`	optional sequence determining the range of the x-axis for plots of the optimal transformation parameter that defaults to `NULL`. In that case a range of the optimal parameter +2/-1 is used for the plots of the optimal parameter. This leads in some cases to convergence problems such that it should be changed to e.g. the selected `interval`. This means for the default interval `seq(-1, 2, by = 0.05)`.
`...`	optional arguments passed to generic function.

Two Q-Q plots in one grid, two density plots, a Cook' distance plot and a likelihood plot for the optimal parameter of the Box-Cox transformation obtained by ggplot. The latter two plots are only provided for ebp object.

emdiObject, ebp, fh

## Not run: 
# Loading data - population and sample data
data("eusilcA_pop")
data("eusilcA_smp")

# With default setting but na.rm = TRUE; with Box-Cox transformation
emdi_model <- ebp(fixed = eqIncome ~ gender + eqsize + cash + self_empl +
unempl_ben + age_ben + surv_ben + sick_ben + dis_ben + rent + fam_allow +
house_allow + cap_inv + tax_adj, pop_data = eusilcA_pop,
pop_domains = "district", smp_data = eusilcA_smp, smp_domains = "district",
na.rm = TRUE)

# Example 1: Creation of default diagnostic plots
plot(emdi_model)

# Example 2: Creation of diagnostic plots without labels and titles, different colors
# and without Cook's distance plot.
plot(emdi_model, label = "no_title", color = c("red", "yellow"), cooks = FALSE)

# Example 3: Creation of diagnostic plots where labels and title differs for
# residual plot
plot(emdi_model, label = list(qq_res = c(title = "Pearson resid.",
y_lab = "Quant.", x_lab = "Theo. Quant.")), color = c("red", "yellow"),
cooks = FALSE)

# Example 4: Usage of theme from ggplot2 within plot.emdi
library(ggplot2)
plot(emdi_model, gg_theme = theme(panel.background = element_rect(fill = "white",
colour = "white"), plot.title = element_text(face = "bold"),
title = element_text(color = "navy")))

# Load aggregated data ---------------------------------------------------------
data("eusilcA_popAgg")
data("eusilcA_smpAgg")

# Combine sample and population data -------------------------------------------
combined_data <- combine_data(pop_data = eusilcA_popAgg, pop_domains = "Domain",
                             smp_data = eusilcA_smpAgg, smp_domains = "Domain")
# Estimation of EBLUP means without transformation -----------------------------

# REML
fh_reml <- fh(fixed = Mean ~ eqsize + cash + self_empl, vardir = "Var_Mean",
             combined_data = combined_data, domains = "Domain",
             method = "reml", interval = c(0, 100000000))

# Example 5: Creation of default diagnostic plots for Fay-Herriot model
plot(fh_reml)


## End(Not run)