lag.plot: Time Series Lag Plots

lag.plot

R Documentation

Time Series Lag Plots

Description

Plot time series against lagged versions of themselves. Helps visualizing ‘auto-dependence’ even when auto-correlations vanish.

Usage

lag.plot(x, lags = 1, layout = NULL, set.lags = 1:lags,
         main = NULL, asp = 1,
         diag = TRUE, diag.col = "gray", type = "p", oma = NULL,
         ask = NULL, do.lines = (n <= 150), labels = do.lines,
         ...)

Arguments

`x`	time-series (univariate or multivariate)
`lags`	number of lag plots desired, see arg `set.lags`.
`layout`	the layout of multiple plots, basically the `mfrow` `par()` argument. The default uses about a square layout (see `n2mfrow`) such that all plots are on one page.
`set.lags`	vector of positive integers allowing specification of the set of lags used; defaults to `1:lags`.
`main`	character with a main header title to be done on the top of each page.
`asp`	Aspect ratio to be fixed, see `plot.default`.
`diag`	logical indicating if the x=y diagonal should be drawn.
`diag.col`	color to be used for the diagonal `if(diag)`.
`type`	plot type to be used, but see `plot.ts` about its restricted meaning.
`oma`	outer margins, see `par`.
`ask`	logical or `NULL`; if true, the user is asked to confirm before a new page is started.
`do.lines`	logical indicating if lines should be drawn.
`labels`	logical indicating if labels should be used.
`...`	Further arguments to `plot.ts`. Several graphical parameters are set in this function and so cannot be changed: these include `xlab`, `ylab`, `mgp`, `col.lab` and `font.lab`: this also applies to the arguments `xy.labels` and `xy.lines`.

Details

If just one plot is produced, this is a conventional plot. If more than one plot is to be produced, par(mfrow) and several other graphics parameters will be set, so it is not (easily) possible to mix such lag plots with other plots on the same page.

If ask = NULL, par(ask = TRUE) will be called if more than one page of plots is to be produced and the device is interactive.

Note

It is more flexible and has different default behaviour than the S version. We use main = instead of head = for internal consistency.

Author(s)

Martin Maechler

See Also

plot.ts which is the basic work horse.

Examples

require(graphics)

lag.plot(nhtemp, 8, diag.col = "forest green")
lag.plot(nhtemp, 5, main = "Average Temperatures in New Haven")
## ask defaults to TRUE when we have more than one page:
lag.plot(nhtemp, 6, layout = c(2,1), asp = NA,
         main = "New Haven Temperatures", col.main = "blue")

## Multivariate (but non-stationary! ...)
lag.plot(freeny.x, lags = 3)

## no lines for long series :
lag.plot(sqrt(sunspots), set.lags = c(1:4, 9:12), pch = ".", col = "gold")

acf: Auto- and Cross- Covariance and -Correlation Function... acf2AR: Compute an AR Process Exactly Fitting an ACF add1: Add or Drop All Possible Single Terms to a Model addmargins: Puts Arbitrary Margins on Multidimensional Tables or Arrays aggregate: Compute Summary Statistics of Data Subsets AIC: Akaike's An Information Criterion alias: Find Aliases (Dependencies) in a Model anova: Anova Tables anova.glm: Analysis of Deviance for Generalized Linear Model Fits anova.lm: ANOVA for Linear Model Fits anova.mlm: Comparisons between Multivariate Linear Models ansari.test: Ansari-Bradley Test aov: Fit an Analysis of Variance Model approxfun: Interpolation Functions ar: Fit Autoregressive Models to Time Series arima: ARIMA Modelling of Time Series arima0: ARIMA Modelling of Time Series - Preliminary Version arima.sim: Simulate from an ARIMA Model ARMAacf: Compute Theoretical ACF for an ARMA Process ARMAtoMA: Convert ARMA Process to Infinite MA Process ar.ols: Fit Autoregressive Models to Time Series by OLS as.hclust: Convert Objects to Class hclust asOneSidedFormula: Convert to One-Sided Formula ave: Group Averages Over Level Combinations of Factors bandwidth: Bandwidth Selectors for Kernel Density Estimation bartlett.test: Bartlett Test of Homogeneity of Variances Beta: The Beta Distribution Binomial: The Binomial Distribution binom.test: Exact Binomial Test biplot: Biplot of Multivariate Data biplot.princomp: Biplot for Principal Components birthday: Probability of coincidences box.test: Box-Pierce and Ljung-Box Tests cancor: Canonical Correlations case.names: Case and Variable Names of Fitted Models Cauchy: The Cauchy Distribution checkMFClasses: Functions to Check the Type of Variables passed to Model... chisq.test: Pearson's Chi-squared Test for Count Data Chisquare: The (non-central) Chi-Squared Distribution cmdscale: Classical (Metric) Multidimensional Scaling coef: Extract Model Coefficients complete.cases: Find Complete Cases confint: Confidence Intervals for Model Parameters constrOptim: Linearly Constrained Optimization contrast: (Possibly Sparse) Contrast Matrices contrasts: Get and Set Contrast Matrices convolve: Convolution of Sequences via FFT cophenetic: Cophenetic Distances for a Hierarchical Clustering cor: Correlation, Variance and Covariance (Matrices) cor.test: Test for Association/Correlation Between Paired Samples cov.wt: Weighted Covariance Matrices cpgram: Plot Cumulative Periodogram cutree: Cut a Tree into Groups of Data decompose: Classical Seasonal Decomposition by Moving Averages delete.response: Modify Terms Objects dendrapply: Apply a Function to All Nodes of a Dendrogram dendrogram: General Tree Structures density: Kernel Density Estimation deriv: Symbolic and Algorithmic Derivatives of Simple Expressions deviance: Model Deviance df.residual: Residual Degrees-of-Freedom diffinv: Discrete Integration: Inverse of Differencing dist: Distance Matrix Computation Distributions: Distributions in the stats package dummy.coef: Extract Coefficients in Original Coding ecdf: Empirical Cumulative Distribution Function eff.aovlist: Compute Efficiencies of Multistratum Analysis of Variance effects: Effects from Fitted Model embed: Embedding a Time Series expand.model.frame: Add new variables to a model frame Exponential: The Exponential Distribution extractAIC: Extract AIC from a Fitted Model factanal: Factor Analysis factor.scope: Compute Allowed Changes in Adding to or Dropping from a... family: Family Objects for Models Fdist: The F Distribution fft: Fast Discrete Fourier Transform (FFT) filter: Linear Filtering on a Time Series fisher.test: Fisher's Exact Test for Count Data fitted.values: Extract Model Fitted Values fivenum: Tukey Five-Number Summaries fligner.test: Fligner-Killeen Test of Homogeneity of Variances formula: Model Formulae formula.nls: Extract Model Formula from nls Object friedman.test: Friedman Rank Sum Test ftable: Flat Contingency Tables ftable.formula: Formula Notation for Flat Contingency Tables GammaDist: The Gamma Distribution Geometric: The Geometric Distribution getInitial: Get Initial Parameter Estimates glm: Fitting Generalized Linear Models glm.control: Auxiliary for Controlling GLM Fitting glm.summaries: Accessing Generalized Linear Model Fits hclust: Hierarchical Clustering heatmap: Draw a Heat Map HoltWinters: Holt-Winters Filtering Hypergeometric: The Hypergeometric Distribution identify.hclust: Identify Clusters in a Dendrogram influence.measures: Regression Deletion Diagnostics integrate: Integration of One-Dimensional Functions interaction.plot: Two-way Interaction Plot IQR: The Interquartile Range is.empty: Test if a Model's Formula is Empty isoreg: Isotonic / Monotone Regression KalmanLike: Kalman Filtering kernapply: Apply Smoothing Kernel kernel: Smoothing Kernel Objects kmeans: K-Means Clustering kruskal.test: Kruskal-Wallis Rank Sum Test ksmooth: Kernel Regression Smoother ks.test: Kolmogorov-Smirnov Tests lag: Lag a Time Series lag.plot: Time Series Lag Plots line: Robust Line Fitting listof: A Class for Lists of (Parts of) Model Fits lm: Fitting Linear Models lmfit: Fitter Functions for Linear Models lm.influence: Regression Diagnostics lm.summaries: Accessing Linear Model Fits loadings: Print Loadings in Factor Analysis loess: Local Polynomial Regression Fitting loess.control: Set Parameters for Loess Logistic: The Logistic Distribution logLik: Extract Log-Likelihood loglin: Fitting Log-Linear Models Lognormal: The Log Normal Distribution lowess: Scatter Plot Smoothing ls.diag: Compute Diagnostics for 'lsfit' Regression Results lsfit: Find the Least Squares Fit ls.print: Print 'lsfit' Regression Results mad: Median Absolute Deviation mahalanobis: Mahalanobis Distance make.link: Create a Link for GLM Families makepredictcall: Utility Function for Safe Prediction manova: Multivariate Analysis of Variance mantelhaen.test: Cochran-Mantel-Haenszel Chi-Squared Test for Count Data mauchly.test: Mauchly's Test of Sphericity mcnemar.test: McNemar's Chi-squared Test for Count Data median: Median Value medpolish: Median Polish (Robust Twoway Decomposition) of a Matrix model.extract: Extract Components from a Model Frame model.frame: Extracting the Model Frame from a Formula or Fit model.matrix: Construct Design Matrices model.tables: Compute Tables of Results from an Aov Model Fit monthplot: Plot a Seasonal or other Subseries from a Time Series mood.test: Mood Two-Sample Test of Scale Multinom: The Multinomial Distribution na.action: NA Action na.contiguous: Find Longest Contiguous Stretch of non-NAs na.fail: Handle Missing Values in Objects nafns: Adjust for Missing Values naprint: Adjust for Missing Values NegBinomial: The Negative Binomial Distribution nextn: Find Highly Composite Numbers nlm: Non-Linear Minimization nlminb: Optimization using PORT routines nls: Nonlinear Least Squares nls.control: Control the Iterations in nls NLSstAsymptotic: Fit the Asymptotic Regression Model NLSstClosestX: Inverse Interpolation NLSstLfAsymptote: Horizontal Asymptote on the Left Side NLSstRtAsymptote: Horizontal Asymptote on the Right Side nobs: Extract the Number of Observations from a Fit. Normal: The Normal Distribution numericDeriv: Evaluate Derivatives Numerically offset: Include an Offset in a Model Formula oneway.test: Test for Equal Means in a One-Way Layout optim: General-purpose Optimization optimize: One Dimensional Optimization order.dendrogram: Ordering or Labels of the Leaves in a Dendrogram p.adjust: Adjust P-values for Multiple Comparisons Pair: Construct paired-data object pairwise.prop.test: Pairwise comparisons for proportions pairwise.table: Tabulate p values for pairwise comparisons pairwise.t.test: Pairwise t tests pairwise.wilcox.test: Pairwise Wilcoxon Rank Sum Tests plot.acf: Plot Autocovariance and Autocorrelation Functions plot.density: Plot Method for Kernel Density Estimation plot.HoltWinters: Plot function for HoltWinters objects plot.isoreg: Plot Method for isoreg Objects plot.lm: Plot Diagnostics for an lm Object plot.ppr: Plot Ridge Functions for Projection Pursuit Regression Fit plot.profile.nls: Plot a profile.nls Object plot.spec: Plotting Spectral Densities plot.stepfun: Plot Step Functions plot.ts: Plotting Time-Series Objects Poisson: The Poisson Distribution poisson.test: Exact Poisson tests poly: Compute Orthogonal Polynomials power: Create a Power Link Object power.anova.test: Power Calculations for Balanced One-Way Analysis of Variance... power.prop.test: Power Calculations for Two-Sample Test for Proportions power.t.test: Power calculations for one and two sample t tests ppoints: Ordinates for Probability Plotting ppr: Projection Pursuit Regression pp.test: Phillips-Perron Test for Unit Roots prcomp: Principal Components Analysis predict: Model Predictions predict.arima: Forecast from ARIMA fits predict.glm: Predict Method for GLM Fits predict.HoltWinters: Prediction Function for Fitted Holt-Winters Models predict.lm: Predict method for Linear Model Fits predict.loess: Predict Loess Curve or Surface predict.nls: Predicting from Nonlinear Least Squares Fits predict.smooth.spline: Predict from Smoothing Spline Fit preplot: Pre-computations for a Plotting Object princomp: Principal Components Analysis printCoefmat: Print Coefficient Matrices print.power.htest: Print Methods for Hypothesis Tests and Power Calculation... print.ts: Printing and Formatting of Time-Series Objects profile: Generic Function for Profiling Models profile.nls: Method for Profiling nls Objects proj: Projections of Models prop.test: Test of Equal or Given Proportions prop.trend.test: Test for trend in proportions qqnorm: Quantile-Quantile Plots quade.test: Quade Test quantile: Sample Quantiles r2dtable: Random 2-way Tables with Given Marginals read.ftable: Manipulate Flat Contingency Tables rect.hclust: Draw Rectangles Around Hierarchical Clusters relevel: Reorder Levels of Factor reorder.dendrogram: Reorder a Dendrogram reorder.factor: Reorder Levels of a Factor replications: Number of Replications of Terms reshape: Reshape Grouped Data residuals: Extract Model Residuals runmed: Running Medians - Robust Scatter Plot Smoothing rWishart: Random Wishart Distributed Matrices scatter.smooth: Scatter Plot with Smooth Curve Fitted by Loess screeplot: Screeplots sd: Standard Deviation se.contrast: Standard Errors for Contrasts in Model Terms selfStart: Construct Self-starting Nonlinear Models setNames: Set the Names in an Object shapiro.test: Shapiro-Wilk Normality Test sigma: Extract Residual Standard Deviation 'Sigma' SignRank: Distribution of the Wilcoxon Signed Rank Statistic simulate: Simulate Responses smooth: Tukey's (Running Median) Smoothing smoothEnds: End Points Smoothing (for Running Medians) smooth.spline: Fit a Smoothing Spline sortedXyData: Create a 'sortedXyData' Object spec.ar: Estimate Spectral Density of a Time Series from AR Fit spec.pgram: Estimate Spectral Density of a Time Series by a Smoothed... spec.taper: Taper a Time Series by a Cosine Bell spectrum: Spectral Density Estimation splinefun: Interpolating Splines SSasymp: Self-Starting Nls Asymptotic Regression Model SSasympOff: Self-Starting Nls Asymptotic Regression Model with an Offset SSasympOrig: Self-Starting Nls Asymptotic Regression Model through the... SSbiexp: Self-Starting Nls Biexponential model SSD: SSD Matrix and Estimated Variance Matrix in Multivariate... SSfol: Self-Starting Nls First-order Compartment Model SSfpl: Self-Starting Nls Four-Parameter Logistic Model SSgompertz: Self-Starting Nls Gompertz Growth Model SSlogis: Self-Starting Nls Logistic Model SSmicmen: Self-Starting Nls Michaelis-Menten Model SSweibull: Self-Starting Nls Weibull Growth Curve Model start: Encode the Terminal Times of Time Series stat.anova: GLM Anova Statistics stats-defunct: Defunct Functions in Package 'stats' stats-deprecated: Deprecated Functions in Package 'stats' stats-package: The R Stats Package step: Choose a model by AIC in a Stepwise Algorithm stepfun: Step Functions - Creation and Class stl: Seasonal Decomposition of Time Series by Loess stlmethods: Methods for STL Objects StructTS: Fit Structural Time Series summary.aov: Summarize an Analysis of Variance Model summary.glm: Summarizing Generalized Linear Model Fits summary.lm: Summarizing Linear Model Fits summary.manova: Summary Method for Multivariate Analysis of Variance summary.nls: Summarizing Non-Linear Least-Squares Model Fits summary.princomp: Summary method for Principal Components Analysis supsmu: Friedman's SuperSmoother symnum: Symbolic Number Coding TDist: The Student t Distribution termplot: Plot Regression Terms terms: Model Terms terms.formula: Construct a terms Object from a Formula terms.object: Description of Terms Objects time: Sampling Times of Time Series toeplitz: Form Symmetric Toeplitz Matrix ts: Time-Series Objects tsdiag: Diagnostic Plots for Time-Series Fits ts-methods: Methods for Time Series Objects tsp: Tsp Attribute of Time-Series-like Objects ts.plot: Plot Multiple Time Series tsSmooth: Use Fixed-Interval Smoothing on Time Series ts.union: Bind Two or More Time Series t.test: Student's t-Test Tukey: The Studentized Range Distribution TukeyHSD: Compute Tukey Honest Significant Differences Uniform: The Uniform Distribution uniroot: One Dimensional Root (Zero) Finding update: Update and Re-fit a Model Call update.formula: Model Updating varimax: Rotation Methods for Factor Analysis var.test: F Test to Compare Two Variances vcov: Calculate Variance-Covariance Matrix for a Fitted Model... Weibull: The Weibull Distribution weighted.mean: Weighted Arithmetic Mean weighted.residuals: Compute Weighted Residuals weights: Extract Model Weights Wilcoxon: Distribution of the Wilcoxon Rank Sum Statistic wilcox.test: Wilcoxon Rank Sum and Signed Rank Tests window: Time (Series) Windows xtabs: Cross Tabulation zC: Sets Contrasts for a Factor

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