R/drawDiagPlots.R
In gajdosandrej/fdslrm: R package for modeling and prediction of time series using linear mixed models
Defines functions
drawDiagPlots
Documented in
drawDiagPlots
#' @title Draw diagnostic plots for FDSLRM
#'
#' @description
#' \code{drawDiagPlots(plots_names, fit_diag_fdslrm_output)} draws diagnostics plots of fitted FDSLRM.
#' Plot with original time series, fitted values and fitted mean values is included.
#'
#' @param plots_names vector of type character containing names of available diagnostic plots.
#' Default is \code{plots_names = "all"}.
#' See details section of documentation and example for more information.
#' @param fit_diag_fdslrm_output list - an output of function \code{fitDiagFDSLRM}.
#'
#' @details
#' You can find names of all available diagnostic plots in the list, which is an ouput of function \code{fitDiagFDSLRM}.
#' These names are under the item "diagnostic_plots_names".
#'
#' @return Diagnostic plots of particular fitted FDSLRM.
#'
#' @note Ver.: 12-Jan-2019 17:18:53.
#'
#' @example R/Examples/example_fitDiagFDSLRM.R
#'
#' @export
#'
drawDiagPlots <- function(plots_names = "all", fit_diag_fdslrm_output) {
output <- fit_diag_fdslrm_output
if(!is.null(output$lm_fit)) {
#lm
if(plots_names == "all") {
# windows()
par(mfrow = c(3, 4))
acf(output$raw_residuals, main = "ACF of raw residuals")
pacf(output$raw_residuals, main = "PACF of raw residuals")
hist(output$raw_residuals, main = "Distribution of raw residuals", xlab = "Raw residuals")
acf(output$stud_residuals, main = "ACF of studentized residuals")
pacf(output$stud_residuals, main = "PACF of studentized residuals")
hist(output$stud_residuals, main = "Distribution of studentized residuals", xlab = "Studentized residuals")
plot(output$stud_residuals, type = "o", xlab = "Time", ylab = "Studentized residuals", main = "")
grid()
plot(fitted(output$lm_fit), resid(output$lm_fit), xlab = "Fitted values", ylab = "Raw residuals", main = "")
grid()
plot(fitted(output$lm_fit), output$stud_residuals, xlab = "Fitted values", ylab = "Studentized residuals", main = "")
grid()
plot(output$raw_residuals, type = "o", xlab = "Time", ylab = "Raw residuals", main = "")
grid()
qqnorm(resid(output$lm_fit), ylab = "Raw residuals")
qqline(resid(output$lm_fit))
grid()
qqnorm(output$stud_residuals, ylab = "Studentized residuals")
qqline(output$stud_residuals)
grid()
par(mfrow = c(1, 1))
# car::spreadLevelPlot(output$lm_fit)
# grid()
} else {
if("FittedTimeSeries" %in% plots_names) {
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$fitted_values)
ts.plot(x_ts, fitted_values_ts, gpars = list(col = c("black", "blue"), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values"), col = c("black", "blue"),
lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
par(mfrow = c(1, 1))
}
if("RawResid" %in% plots_names) {
plot(output$raw_residuals, type = "o", xlab = "Time", ylab = "Raw residuals", main = "")
grid()
par(mfrow = c(1, 1))
}
if("StdResid" %in% plots_names) {
plot(output$stud_residuals, type = "o", xlab = "Time", ylab = "Studentized residuals", main = "")
grid()
par(mfrow = c(1, 1))
}
if("FittedValuesVsRawResid" %in% plots_names) {
plot(fitted(output$lm_fit), resid(output$lm_fit), xlab = "Fitted values", ylab = "Raw residuals", main = "")
grid()
par(mfrow = c(1, 1))
}
if("FittedValuesVsStdResid" %in% plots_names) {
plot(fitted(output$lm_fit), output$stud_residuals, xlab = "Fitted values", ylab = "Studentized residuals", main = "")
grid()
par(mfrow = c(1, 1))
}
if("SpreadLevelPlot" %in% plots_names) {
car::spreadLevelPlot(output$lm_fit)
grid()
}
if("QQPlotRawResid" %in% plots_names) {
qqnorm(resid(output$lm_fit), ylab = "Raw residuals")
qqline(resid(output$lm_fit))
grid()
par(mfrow = c(1, 1))
}
if("QQPlotStdResid" %in% plots_names) {
qqnorm(output$stud_residuals, ylab = "Studentized residuals")
qqline(output$stud_residuals)
grid()
par(mfrow = c(1, 1))
}
if("ACFRawResid" %in% plots_names) {
acf(output$raw_residuals, main = "ACF of raw residuals")
par(mfrow = c(1, 1))
}
if("PACFRawResid" %in% plots_names) {
pacf(output$raw_residuals, main = "PACF of raw residuals")
par(mfrow = c(1, 1))
}
if("HistogramRawResid" %in% plots_names) {
hist(output$raw_residuals, main = "Distribution of raw residuals", xlab = "Raw residuals")
par(mfrow = c(1, 1))
}
if("ACFStdResid" %in% plots_names) {
acf(output$stud_residuals, main = "ACF of studentized residuals")
par(mfrow = c(1, 1))
}
if("PACFStdResid" %in% plots_names) {
pacf(output$stud_residuals, main = "PACF of studentized residuals")
par(mfrow = c(1, 1))
}
if("HistogramStdResid" %in% plots_names) {
hist(output$stud_residuals, main = "Distribution of studentized residuals", xlab = "Studentized residuals")
par(mfrow = c(1, 1))
}
if("CumulatPeriodogStudResid" %in% plots_names) {
cpgram(output$stud_residuals, main = "Cumulative periodogram of studentized residuals", ci.col = "red")
par(mfrow = c(1, 1))
}
}
} else if (!is.null(output$lme_fit)) {
#lme
if(plots_names == "all") {
# windows()
par(mfrow = c(3, 3))
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(2), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V))
plot(output$diag_resid$std.marginal.residuals[,2], type = "o", ylab = "Standardized marginal residuals")
abline(0,0)
grid()
acf(output$cond_residuals, main = "ACF of conditional residuals")
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(5), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V))
plot(output$diag_resid$std.conditional.residuals[,2], type = "o", ylab = "Standardized conditional residuals")
abline(0,0)
grid()
pacf(output$cond_residuals, main = "PACF of conditional residuals")
hist(output$norm_cond_resid, main = "Distribution of norm. cond. residuals", xlab = "Norm. cond. residuals")
hist(output$diag_resid$least.confounded.residuals, main = "Distribution of stand. least conf. residuals", xlab = "Stand. least conf. residuals")
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(6), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V))
par(mfrow = c(1, 1))
} else {
if("StdMarginalResidVsFittedValues" %in% plots_names) {
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(2), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V), histogram = TRUE)
par(mfrow=c(1,1))
}
if("StdConditionalResidVsFittedValues" %in% plots_names) {
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(5), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V), histogram = TRUE)
par(mfrow=c(1,1))
}
if("NormalQQPlotStdLeastConfCondResid" %in% plots_names) {
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(6), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V), histogram = TRUE)
par(mfrow=c(1,1))
}
if("FittedTimeSeries" %in% plots_names) {
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
par(mfrow = c(1, 1))
}
if("StdMarginalResid" %in% plots_names) {
plot(output$diag_resid$std.marginal.residuals[,2], type = "o", ylab = "Standardized marginal residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("StdConditionalResid" %in% plots_names) {
plot(output$diag_resid$std.conditional.residuals[,2], type = "o", ylab = "Standardized conditional residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ACFCondtResid" %in% plots_names) {
acf(output$cond_residuals, main = "ACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("PACFCondResid" %in% plots_names) {
pacf(output$cond_residuals, main = "PACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("HistNormCondResid" %in% plots_names) {
hist(output$norm_cond_resid, main = "Distribution of norm. cond. residuals", xlab = "Norm. cond. residuals")
par(mfrow = c(1, 1))
}
if("HistLeastConfCondResid" %in% plots_names) {
hist(output$diag_resid$least.confounded.residuals, main = "Distribution of stand. least conf. residuals", xlab = "Stand. least conf. residuals")
par(mfrow = c(1, 1))
}
if("CumulatPeriodogCondResid" %in% plots_names) {
cpgram(output$cond_residuals, main = "Cumulative periodogram of conditional residuals", ci.col = "red")
par(mfrow = c(1, 1))
}
}
} else if(!is.null(output$mixed_fit)){
# mixed
if(plots_names == "all") {
# windows()
par(mfrow = c(3, 3))
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
plot(output$marg_residuals, type = "o", ylab = "Marginal residuals")
abline(0,0)
grid()
plot(output$marg_fitted_values, output$marg_residuals, ylab = "Marginal residuals", xlab = "Marginal fitted values")
abline(0,0)
grid()
plot(output$cond_residuals, type = "o", ylab = "Conditional residuals")
abline(0,0)
grid()
plot(output$cond_fitted_values, output$cond_residuals, ylab = "Conditional residuals", xlab = "Conditional fitted values")
abline(0,0)
grid()
acf(output$cond_residuals, main = "ACF of conditional residuals")
pacf(output$cond_residuals, main = "PACF of conditional residuals")
hist(output$marg_residuals, main = "Distribution of marginal residuals", xlab = "Marginal residuals")
hist(output$cond_residuals, main = "Distribution of conditional residuals", xlab = "Conditional residuals")
par(mfrow = c(1, 1))
} else {
if("MarginalResidVsFittedValues" %in% plots_names) {
plot(output$marg_fitted_values, output$marg_residuals, ylab = "Marginal residuals", xlab = "Marginal fitted values")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ConditionalResidVsFittedValues" %in% plots_names) {
plot(output$cond_fitted_values, output$cond_residuals, ylab = "Conditional residuals", xlab = "Conditional fitted values")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("FittedTimeSeries" %in% plots_names) {
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
par(mfrow = c(1, 1))
}
if("MarginalResid" %in% plots_names) {
plot(output$marg_residuals, type = "o", ylab = "Marginal residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ConditionalResid" %in% plots_names) {
plot(output$cond_residuals, type = "o", ylab = "Conditional residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ACFCondtResid" %in% plots_names) {
acf(output$cond_residuals, main = "ACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("PACFCondResid" %in% plots_names) {
pacf(output$cond_residuals, main = "PACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("HistCondResid" %in% plots_names) {
hist(output$cond_residuals, main = "Distribution of conditional residuals", xlab = "Conditional residuals")
par(mfrow = c(1, 1))
}
if("HistMargResid" %in% plots_names) {
hist(output$marg_residuals, main = "Distribution of marginal residuals", xlab = "Marginal residuals")
par(mfrow = c(1, 1))
}
if("CumulatPeriodogCondResid" %in% plots_names) {
cpgram(output$cond_residuals, main = "Cumulative periodogram of conditional residuals", ci.col = "red")
par(mfrow = c(1, 1))
}
}
} else {
# mmer
if(plots_names == "all") {
# windows()
par(mfrow = c(3, 3))
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
plot(output$marg_residuals, type = "o", ylab = "Marginal residuals")
abline(0,0)
grid()
plot(output$marg_fitted_values, output$marg_residuals, ylab = "Marginal residuals", xlab = "Marginal fitted values")
abline(0,0)
grid()
plot(output$cond_residuals, type = "o", ylab = "Conditional residuals")
abline(0,0)
grid()
plot(output$cond_fitted_values, output$cond_residuals, ylab = "Conditional residuals", xlab = "Conditional fitted values")
abline(0,0)
grid()
acf(output$cond_residuals, main = "ACF of conditional residuals")
pacf(output$cond_residuals, main = "PACF of conditional residuals")
hist(output$marg_residuals, main = "Distribution of marginal residuals", xlab = "Marginal residuals")
hist(output$cond_residuals, main = "Distribution of conditional residuals", xlab = "Conditional residuals")
par(mfrow = c(1, 1))
} else {
if("MarginalResidVsFittedValues" %in% plots_names) {
plot(output$marg_fitted_values, output$marg_residuals, ylab = "Marginal residuals", xlab = "Marginal fitted values")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ConditionalResidVsFittedValues" %in% plots_names) {
plot(output$cond_fitted_values, output$cond_residuals, ylab = "Conditional residuals", xlab = "Conditional fitted values")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("FittedTimeSeries" %in% plots_names) {
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
par(mfrow = c(1, 1))
}
if("MarginalResid" %in% plots_names) {
plot(output$marg_residuals, type = "o", ylab = "Marginal residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ConditionalResid" %in% plots_names) {
plot(output$cond_residuals, type = "o", ylab = "Conditional residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ACFCondtResid" %in% plots_names) {
acf(output$cond_residuals, main = "ACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("PACFCondResid" %in% plots_names) {
pacf(output$cond_residuals, main = "PACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("HistCondResid" %in% plots_names) {
hist(output$cond_residuals, main = "Distribution of conditional residuals", xlab = "Conditional residuals")
par(mfrow = c(1, 1))
}
if("HistMargResid" %in% plots_names) {
hist(output$marg_residuals, main = "Distribution of marginal residuals", xlab = "Marginal residuals")
par(mfrow = c(1, 1))
}
if("CumulatPeriodogCondResid" %in% plots_names) {
cpgram(output$cond_residuals, main = "Cumulative periodogram of conditional residuals", ci.col = "red")
par(mfrow = c(1, 1))
}
}
}
}
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Defines functions drawDiagPlots
Documented in drawDiagPlots
#' @title Draw diagnostic plots for FDSLRM
#'
#' @description
#' \code{drawDiagPlots(plots_names, fit_diag_fdslrm_output)} draws diagnostics plots of fitted FDSLRM.
#' Plot with original time series, fitted values and fitted mean values is included.
#'
#' @param plots_names vector of type character containing names of available diagnostic plots.
#' Default is \code{plots_names = "all"}.
#' See details section of documentation and example for more information.
#' @param fit_diag_fdslrm_output list - an output of function \code{fitDiagFDSLRM}.
#'
#' @details
#' You can find names of all available diagnostic plots in the list, which is an ouput of function \code{fitDiagFDSLRM}.
#' These names are under the item "diagnostic_plots_names".
#'
#' @return Diagnostic plots of particular fitted FDSLRM.
#'
#' @note Ver.: 12-Jan-2019 17:18:53.
#'
#' @example R/Examples/example_fitDiagFDSLRM.R
#'
#' @export
#'
drawDiagPlots <- function(plots_names = "all", fit_diag_fdslrm_output) {
output <- fit_diag_fdslrm_output
if(!is.null(output$lm_fit)) {
#lm
if(plots_names == "all") {
# windows()
par(mfrow = c(3, 4))
acf(output$raw_residuals, main = "ACF of raw residuals")
pacf(output$raw_residuals, main = "PACF of raw residuals")
hist(output$raw_residuals, main = "Distribution of raw residuals", xlab = "Raw residuals")
acf(output$stud_residuals, main = "ACF of studentized residuals")
pacf(output$stud_residuals, main = "PACF of studentized residuals")
hist(output$stud_residuals, main = "Distribution of studentized residuals", xlab = "Studentized residuals")
plot(output$stud_residuals, type = "o", xlab = "Time", ylab = "Studentized residuals", main = "")
grid()
plot(fitted(output$lm_fit), resid(output$lm_fit), xlab = "Fitted values", ylab = "Raw residuals", main = "")
grid()
plot(fitted(output$lm_fit), output$stud_residuals, xlab = "Fitted values", ylab = "Studentized residuals", main = "")
grid()
plot(output$raw_residuals, type = "o", xlab = "Time", ylab = "Raw residuals", main = "")
grid()
qqnorm(resid(output$lm_fit), ylab = "Raw residuals")
qqline(resid(output$lm_fit))
grid()
qqnorm(output$stud_residuals, ylab = "Studentized residuals")
qqline(output$stud_residuals)
grid()
par(mfrow = c(1, 1))
# car::spreadLevelPlot(output$lm_fit)
# grid()
} else {
if("FittedTimeSeries" %in% plots_names) {
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$fitted_values)
ts.plot(x_ts, fitted_values_ts, gpars = list(col = c("black", "blue"), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values"), col = c("black", "blue"),
lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
par(mfrow = c(1, 1))
}
if("RawResid" %in% plots_names) {
plot(output$raw_residuals, type = "o", xlab = "Time", ylab = "Raw residuals", main = "")
grid()
par(mfrow = c(1, 1))
}
if("StdResid" %in% plots_names) {
plot(output$stud_residuals, type = "o", xlab = "Time", ylab = "Studentized residuals", main = "")
grid()
par(mfrow = c(1, 1))
}
if("FittedValuesVsRawResid" %in% plots_names) {
plot(fitted(output$lm_fit), resid(output$lm_fit), xlab = "Fitted values", ylab = "Raw residuals", main = "")
grid()
par(mfrow = c(1, 1))
}
if("FittedValuesVsStdResid" %in% plots_names) {
plot(fitted(output$lm_fit), output$stud_residuals, xlab = "Fitted values", ylab = "Studentized residuals", main = "")
grid()
par(mfrow = c(1, 1))
}
if("SpreadLevelPlot" %in% plots_names) {
car::spreadLevelPlot(output$lm_fit)
grid()
}
if("QQPlotRawResid" %in% plots_names) {
qqnorm(resid(output$lm_fit), ylab = "Raw residuals")
qqline(resid(output$lm_fit))
grid()
par(mfrow = c(1, 1))
}
if("QQPlotStdResid" %in% plots_names) {
qqnorm(output$stud_residuals, ylab = "Studentized residuals")
qqline(output$stud_residuals)
grid()
par(mfrow = c(1, 1))
}
if("ACFRawResid" %in% plots_names) {
acf(output$raw_residuals, main = "ACF of raw residuals")
par(mfrow = c(1, 1))
}
if("PACFRawResid" %in% plots_names) {
pacf(output$raw_residuals, main = "PACF of raw residuals")
par(mfrow = c(1, 1))
}
if("HistogramRawResid" %in% plots_names) {
hist(output$raw_residuals, main = "Distribution of raw residuals", xlab = "Raw residuals")
par(mfrow = c(1, 1))
}
if("ACFStdResid" %in% plots_names) {
acf(output$stud_residuals, main = "ACF of studentized residuals")
par(mfrow = c(1, 1))
}
if("PACFStdResid" %in% plots_names) {
pacf(output$stud_residuals, main = "PACF of studentized residuals")
par(mfrow = c(1, 1))
}
if("HistogramStdResid" %in% plots_names) {
hist(output$stud_residuals, main = "Distribution of studentized residuals", xlab = "Studentized residuals")
par(mfrow = c(1, 1))
}
if("CumulatPeriodogStudResid" %in% plots_names) {
cpgram(output$stud_residuals, main = "Cumulative periodogram of studentized residuals", ci.col = "red")
par(mfrow = c(1, 1))
}
}
} else if (!is.null(output$lme_fit)) {
#lme
if(plots_names == "all") {
# windows()
par(mfrow = c(3, 3))
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(2), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V))
plot(output$diag_resid$std.marginal.residuals[,2], type = "o", ylab = "Standardized marginal residuals")
abline(0,0)
grid()
acf(output$cond_residuals, main = "ACF of conditional residuals")
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(5), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V))
plot(output$diag_resid$std.conditional.residuals[,2], type = "o", ylab = "Standardized conditional residuals")
abline(0,0)
grid()
pacf(output$cond_residuals, main = "PACF of conditional residuals")
hist(output$norm_cond_resid, main = "Distribution of norm. cond. residuals", xlab = "Norm. cond. residuals")
hist(output$diag_resid$least.confounded.residuals, main = "Distribution of stand. least conf. residuals", xlab = "Stand. least conf. residuals")
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(6), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V))
par(mfrow = c(1, 1))
} else {
if("StdMarginalResidVsFittedValues" %in% plots_names) {
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(2), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V), histogram = TRUE)
par(mfrow=c(1,1))
}
if("StdConditionalResidVsFittedValues" %in% plots_names) {
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(5), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V), histogram = TRUE)
par(mfrow=c(1,1))
}
if("NormalQQPlotStdLeastConfCondResid" %in% plots_names) {
SingerEtAl_justPlots256(output$lme_fit, limit = 2, plotid = c(6), d = output$data_frame, kk = ncol(output$matrix_F), ll = ncol(output$matrix_V), histogram = TRUE)
par(mfrow=c(1,1))
}
if("FittedTimeSeries" %in% plots_names) {
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
par(mfrow = c(1, 1))
}
if("StdMarginalResid" %in% plots_names) {
plot(output$diag_resid$std.marginal.residuals[,2], type = "o", ylab = "Standardized marginal residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("StdConditionalResid" %in% plots_names) {
plot(output$diag_resid$std.conditional.residuals[,2], type = "o", ylab = "Standardized conditional residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ACFCondtResid" %in% plots_names) {
acf(output$cond_residuals, main = "ACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("PACFCondResid" %in% plots_names) {
pacf(output$cond_residuals, main = "PACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("HistNormCondResid" %in% plots_names) {
hist(output$norm_cond_resid, main = "Distribution of norm. cond. residuals", xlab = "Norm. cond. residuals")
par(mfrow = c(1, 1))
}
if("HistLeastConfCondResid" %in% plots_names) {
hist(output$diag_resid$least.confounded.residuals, main = "Distribution of stand. least conf. residuals", xlab = "Stand. least conf. residuals")
par(mfrow = c(1, 1))
}
if("CumulatPeriodogCondResid" %in% plots_names) {
cpgram(output$cond_residuals, main = "Cumulative periodogram of conditional residuals", ci.col = "red")
par(mfrow = c(1, 1))
}
}
} else if(!is.null(output$mixed_fit)){
# mixed
if(plots_names == "all") {
# windows()
par(mfrow = c(3, 3))
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
plot(output$marg_residuals, type = "o", ylab = "Marginal residuals")
abline(0,0)
grid()
plot(output$marg_fitted_values, output$marg_residuals, ylab = "Marginal residuals", xlab = "Marginal fitted values")
abline(0,0)
grid()
plot(output$cond_residuals, type = "o", ylab = "Conditional residuals")
abline(0,0)
grid()
plot(output$cond_fitted_values, output$cond_residuals, ylab = "Conditional residuals", xlab = "Conditional fitted values")
abline(0,0)
grid()
acf(output$cond_residuals, main = "ACF of conditional residuals")
pacf(output$cond_residuals, main = "PACF of conditional residuals")
hist(output$marg_residuals, main = "Distribution of marginal residuals", xlab = "Marginal residuals")
hist(output$cond_residuals, main = "Distribution of conditional residuals", xlab = "Conditional residuals")
par(mfrow = c(1, 1))
} else {
if("MarginalResidVsFittedValues" %in% plots_names) {
plot(output$marg_fitted_values, output$marg_residuals, ylab = "Marginal residuals", xlab = "Marginal fitted values")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ConditionalResidVsFittedValues" %in% plots_names) {
plot(output$cond_fitted_values, output$cond_residuals, ylab = "Conditional residuals", xlab = "Conditional fitted values")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("FittedTimeSeries" %in% plots_names) {
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
par(mfrow = c(1, 1))
}
if("MarginalResid" %in% plots_names) {
plot(output$marg_residuals, type = "o", ylab = "Marginal residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ConditionalResid" %in% plots_names) {
plot(output$cond_residuals, type = "o", ylab = "Conditional residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ACFCondtResid" %in% plots_names) {
acf(output$cond_residuals, main = "ACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("PACFCondResid" %in% plots_names) {
pacf(output$cond_residuals, main = "PACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("HistCondResid" %in% plots_names) {
hist(output$cond_residuals, main = "Distribution of conditional residuals", xlab = "Conditional residuals")
par(mfrow = c(1, 1))
}
if("HistMargResid" %in% plots_names) {
hist(output$marg_residuals, main = "Distribution of marginal residuals", xlab = "Marginal residuals")
par(mfrow = c(1, 1))
}
if("CumulatPeriodogCondResid" %in% plots_names) {
cpgram(output$cond_residuals, main = "Cumulative periodogram of conditional residuals", ci.col = "red")
par(mfrow = c(1, 1))
}
}
} else {
# mmer
if(plots_names == "all") {
# windows()
par(mfrow = c(3, 3))
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
plot(output$marg_residuals, type = "o", ylab = "Marginal residuals")
abline(0,0)
grid()
plot(output$marg_fitted_values, output$marg_residuals, ylab = "Marginal residuals", xlab = "Marginal fitted values")
abline(0,0)
grid()
plot(output$cond_residuals, type = "o", ylab = "Conditional residuals")
abline(0,0)
grid()
plot(output$cond_fitted_values, output$cond_residuals, ylab = "Conditional residuals", xlab = "Conditional fitted values")
abline(0,0)
grid()
acf(output$cond_residuals, main = "ACF of conditional residuals")
pacf(output$cond_residuals, main = "PACF of conditional residuals")
hist(output$marg_residuals, main = "Distribution of marginal residuals", xlab = "Marginal residuals")
hist(output$cond_residuals, main = "Distribution of conditional residuals", xlab = "Conditional residuals")
par(mfrow = c(1, 1))
} else {
if("MarginalResidVsFittedValues" %in% plots_names) {
plot(output$marg_fitted_values, output$marg_residuals, ylab = "Marginal residuals", xlab = "Marginal fitted values")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ConditionalResidVsFittedValues" %in% plots_names) {
plot(output$cond_fitted_values, output$cond_residuals, ylab = "Conditional residuals", xlab = "Conditional fitted values")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("FittedTimeSeries" %in% plots_names) {
x_ts <- ts(output$time_series)
fitted_values_ts <- ts(output$cond_fitted_values)
trend_ts <- ts(as.vector(output$matrix_F%*%output$fixed_effects))
ts.plot(x_ts, fitted_values_ts, trend_ts, gpars = list(col = c("black", "blue", "red"), lty = c(1, 1, 2), lwd = 2,
xlab="Time", ylab="",main="Original time series vs fitted values"))
legend("topleft", legend = c("time series", "fitted values", "estimated trend"), col = c("black", "blue", "red"),
lty = c(1, 1, 2), lwd = 2, cex = 0.5, bty = "n", bg="transparent")
grid()
par(mfrow = c(1, 1))
}
if("MarginalResid" %in% plots_names) {
plot(output$marg_residuals, type = "o", ylab = "Marginal residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ConditionalResid" %in% plots_names) {
plot(output$cond_residuals, type = "o", ylab = "Conditional residuals")
abline(0,0)
grid()
par(mfrow = c(1, 1))
}
if("ACFCondtResid" %in% plots_names) {
acf(output$cond_residuals, main = "ACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("PACFCondResid" %in% plots_names) {
pacf(output$cond_residuals, main = "PACF of conditional residuals")
par(mfrow = c(1, 1))
}
if("HistCondResid" %in% plots_names) {
hist(output$cond_residuals, main = "Distribution of conditional residuals", xlab = "Conditional residuals")
par(mfrow = c(1, 1))
}
if("HistMargResid" %in% plots_names) {
hist(output$marg_residuals, main = "Distribution of marginal residuals", xlab = "Marginal residuals")
par(mfrow = c(1, 1))
}
if("CumulatPeriodogCondResid" %in% plots_names) {
cpgram(output$cond_residuals, main = "Cumulative periodogram of conditional residuals", ci.col = "red")
par(mfrow = c(1, 1))
}
}
}
}
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