residPlot {FSA} | R Documentation |

Constructs a residual plot for `lm`

or `nls`

objects. Different symbols for different groups can be added to the plot if an indicator variable regression is used.

residPlot(object, ...) ## S3 method for class 'lm' residPlot(object, ...) ## S3 method for class 'SLR' residPlot( object, xlab = "Fitted Values", ylab = "Residuals", main = "", pch = 16, col = "black", lty.ref = 3, lwd.ref = 1, col.ref = "black", resid.type = c("raw", "standardized", "studentized"), outlier.test = TRUE, alpha = 0.05, loess = FALSE, lty.loess = 2, lwd.loess = 1, col.loess = "black", trans.loess = 8, inclHist = TRUE, ... ) ## S3 method for class 'POLY' residPlot(object, ...) ## S3 method for class 'IVR' residPlot(object, legend = "topright", cex.leg = 1, box.lty.leg = 0, ...) ## S3 method for class 'ONEWAY' residPlot( object, xlab = "Fitted Values", ylab = "Residuals", main = "", pch = 16, col = "black", lty.ref = 3, lwd.ref = 1, col.ref = "black", resid.type = c("raw", "standardized", "studentized"), bp = TRUE, outlier.test = TRUE, alpha = 0.05, loess = FALSE, lty.loess = 2, lwd.loess = 1, col.loess = "black", trans.loess = 8, inclHist = TRUE, ... ) ## S3 method for class 'TWOWAY' residPlot( object, xlab = "Fitted Values", ylab = "Residuals", main = "", pch = 16, col = "black", lty.ref = 3, lwd.ref = 1, col.ref = "black", resid.type = c("raw", "standardized", "studentized"), bp = TRUE, outlier.test = TRUE, alpha = 0.05, loess = FALSE, lty.loess = 2, lwd.loess = 1, col.loess = "black", trans.loess = 8, inclHist = TRUE, ... ) ## S3 method for class 'nls' residPlot( object, xlab = "Fitted Values", ylab = "Residuals", main = "", pch = 16, col = "black", lty.ref = 3, lwd.ref = 1, col.ref = "black", resid.type = c("raw", "standardized", "studentized"), loess = FALSE, lty.loess = 2, lwd.loess = 1, col.loess = "black", trans.loess = 8, inclHist = TRUE, ... ) ## S3 method for class 'nlme' residPlot( object, xlab = "Fitted Values", ylab = "Residuals", main = "", pch = 16, col = "black", lty.ref = 3, lwd.ref = 1, col.ref = "black", resid.type = c("raw", "standardized", "studentized"), loess = FALSE, lty.loess = 2, lwd.loess = 1, col.loess = "black", trans.loess = 8, inclHist = TRUE, ... )

`object` |
An |

`...` |
Other arguments to the generic |

`xlab` |
A string for labeling the x-axis. |

`ylab` |
A string for labeling the y-axis. |

`main` |
A string for the main label to the plot. See details. |

`pch` |
A numeric that indicates the plotting character to be used or a vector of numerics that indicates what plotting character codes to use for the levels of the second factor. See |

`col` |
A vector of color names that indicates what color of points and lines to use for the levels of the first factor. See |

`lty.ref` |
A numeric that indicates the line type to use for the reference line at residual=0. See |

`lwd.ref` |
A numeric that indicates the line width to use for the reference line at residual=0. See |

`col.ref` |
A numeric or character that indicates the line color to use for the reference line at residual=0. See |

`resid.type` |
The type of residual to use. ‘Raw’ residuals are used by default. See details. |

`outlier.test` |
A logical that indicates if an |

`alpha` |
A numeric that indicates the alpha level to use for the outlier test (only used if |

`loess` |
A logical that indicates if a loess smoother line and approximate confidence interval band is fit to and shown on the residual plot ( |

`lty.loess` |
A numeric that indicates the line type to use for loess fit line. See |

`lwd.loess` |
A numeric that indicates the line width to use for loess fit line. See |

`col.loess` |
A numeric or character that indicates the line color to use for loess fit line. See |

`trans.loess` |
A single numeric that indicates how transparent the loess band should be (larger numbers are more transparent). |

`inclHist` |
A logical that indicates if a second pane that includes the histogram of residuals should be constructed. |

`legend` |
If |

`cex.leg` |
A single numeric values used to represent the character expansion value for the legend. Ignored if |

`box.lty.leg` |
A single numeric values used to indicate the type of line to use for the box around the legend. The default is to not plot a box. |

`bp` |
A logical that indicates if the plot for the one-way and two-way ANOVA will be a boxplot ( |

Three types of residuals are allowed for most model types. Raw residuals are simply the difference between the observed response variable and the predicted/fitted value. Standardized residuals are internally studentized residuals returned by `rstandard`

for linear models and are the raw residual divided by the standard deviation of the residuals for nonlinear models (as is done by `nlsResiduals`

from nlstools). Studentized residuals are the externally studentized residuals returned by `rstudent`

for linear models and are not available for nonlinear models.

Externally Studentized residuals are not supported for `nls`

or `nlme`

objects.

If `outlier.test=TRUE`

then significant outliers are detected with `outlierTest`

from the car package. See the help for this function for more details.

The user can include the model call as a title to the residual plot by using `main="MODEL"`

. This only works for models created with `lm()`

.

If the user chooses to add a legend without identifying coordinates for the upper-left corner of the legend (i.e., `legend=TRUE`

) then the R console is suspended until the user places the legend by clicking on the produced graphic at the point where the upper-left corner of the legend should appear. A legend will only be placed if the `mdl`

is an indicator variable regression, even if `legend=TRUE`

.

None. However, a residual plot is produced.

This function is meant to allow newbie students the ability to easily construct residual plots for one-way ANOVA, two-way ANOVA, simple linear regression, and indicator variable regressions. The plots can be constructed by submitting a saved linear model to this function which allows students to interact with and visualize moderately complex linear models in a fairly easy and efficient manner.

Derek H. Ogle, derek@derekogle.com

See `residualPlots`

in car and `nlsResiduals`

in nlstools) for similar functionality and `fitPlot`

and `outlierTest`

in car for related methods.

# create year factor variable & reduce number of years for visual simplicity Mirex$fyear <- factor(Mirex$year) Mirex2 <- filterD(Mirex,fyear %in% c(1977,1992)) ## Indicator variable regression with two factors lm1 <- lm(mirex~weight*fyear*species,data=Mirex2) # defaults residPlot(lm1) # remove the histogram residPlot(lm1,inclHist=FALSE) # add the loess line residPlot(lm1,loess=TRUE,inclHist=FALSE) # modify colors used residPlot(lm1,col="rainbow",inclHist=FALSE) # use only one point type -- notice that all points are of same type residPlot(lm1,pch=16,inclHist=FALSE) # use only one point and one color (might as well not use legend also) residPlot(lm1,pch=16,col="black",legend=FALSE,inclHist=FALSE) # can accomplish same thing just by removing the legend residPlot(lm1,legend=FALSE,inclHist=FALSE) # modify the reference line residPlot(lm1,col.ref="blue",lwd.ref=5,inclHist=FALSE) # include model in the title residPlot(lm1,main="MODEL") # use Studentized residuals residPlot(lm1,resid.type="studentized",inclHist=FALSE) # use Standardized residuals residPlot(lm1,resid.type="standardized",inclHist=FALSE) ## Indicator variable regression with same two factors but in different order ## (notice use of colors and symbols) lm1a <- lm(mirex~weight*species*fyear,data=Mirex2) residPlot(lm1a) ## Indicator variable regression with only one factor lm2 <- lm(mirex~weight*fyear,data=Mirex) residPlot(lm2) residPlot(lm2,inclHist=FALSE) residPlot(lm2,inclHist=FALSE,pch=19) residPlot(lm2,inclHist=FALSE,pch=19,col="black") residPlot(lm2,inclHist=FALSE,legend=FALSE) residPlot(lm2,inclHist=FALSE,pch=2,col="red",legend=FALSE) ## Indicator variable regression (assuming same slope) lm3 <- lm(mirex~weight+fyear,data=Mirex) residPlot(lm3) ## Simple linear regression lm4 <- lm(mirex~weight,data=Mirex) residPlot(lm4) ## One-way ANOVA lm5 <- lm(mirex~fyear,data=Mirex) # default (uses boxplots) residPlot(lm5) # use points rather than boxplot residPlot(lm5,bp=FALSE) ## Two-Way ANOVA lm6 <- lm(mirex~species*fyear,data=Mirex) # default (uses boxplots) residPlot(lm6) # No boxplots residPlot(lm6,bp=FALSE) ## Examples showing outlier detection x <- c(runif(100)) y <- c(7,runif(99)) lma <- lm(y~x) residPlot(lma) # with studentized residuals residPlot(lma,resid.type="studentized") # multiple outliers y <- c(7,runif(98),-5) lmb <- lm(y~x) residPlot(lmb) # check that NAs are handled properly ... label should be 100 y <- c(NA,NA,runif(97),7) lmc <- lm(y~x) residPlot(lmc) ## Nonlinear regression # from first example in nls() DNase1 <- subset(DNase,Run==1) fm1DNase1 <- nls(density~SSlogis(log(conc),Asym,xmid,scal),DNase1) residPlot(fm1DNase1) residPlot(fm1DNase1,resid.type="standardized")

[Package *FSA* version 0.8.26.9000 Index]