Computes three parametric correlation coefficients for one X variable and the corresponding R squared for multiple X variables, and a regression equation for censored data.

cencorreg(y.var, cen.var, x.vars, LOG = TRUE, verbose = 2)

y.var | The column of y (response variable) values plus detection limits. |
---|---|

cen.var | The column of indicators, where 1 (or |

x.vars | One or more uncensored explanatory variable(s). For multiple variables it must be a data frame of numeric, character and factor variables. See Details |

LOG | Indicator of whether to compute the regression in the original y units, or on their logarithms. The default is to use the logarithms ( |

verbose | default |

When `x.vars`

is one variable, likelihood, rescaled likelihood and McFaddens correlation coefficient (`R`

) are printed.
When `x.vars`

is a `data.frame`

of more than one variable, likelihood, rescaled likelihood and McFaddens coefficent of determination (`R2`

) are printed.

Model coefficients (intercept and slopes), Chi-Squared statistic and p-value for the test that all slope coefficients equal zero (overall test), and model AIC and BIC are provided.

A Q-Q plot of model residuals with corresponding Shapiro-Francia W and p-value are plotted for evaluation of model distributional assumptions when `verbose=2`

(the default).

`x.vars`

: If one x variable only, enter its name. If multiple x variables, enter the name of a data frame of columns of the x variables. Only columns used as `X`

variables in the regression are allowed. Create this by `x.frame <- data.frame (Temp, Flow, Time)`

for 3 variables (temperature, flow and time) used as the `X`

variables in the regression.

AIC and BIC are printed to help evaluate the ‘best’ regression model.

The default is that the Y variable will be log transformed.

`verbose`

option. Default is 2 which provides full output in the console and qqplots in a graphics window. A value of 1 only provides partial results in the console and no qqplots. A value of 0 provides no output; the returning computations will be stored in the specified object.

Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

Helsel, D.R., 2005. Nondetects and Data Analysis: Statistics for Censored Environmental Data, 1st ed. John Wiley and Sons, USA, N.J.

#> Likelihood R = 0.2765 AIC = 366.2191 #> Rescaled Likelihood R = 0.2855 BIC = 373.9126 #> McFaddens R = 0.1691 #>#> Call: #> survreg(formula = "log(Brumbaugh$Hg)", data = "Brumbaugh$SedMeHg", #> dist = "gaussian") #> #> Coefficients: #> (Intercept) `Brumbaugh$SedMeHg` #> -1.7594846 0.1238447 #> #> Scale= 0.960393 #> #> Loglik(model)= -179.6 Loglik(intercept only)= -184.9 #> Chisq= 10.58 on 1 degrees of freedom, p= 0.00114 #> n= 133# More than one variable for demonstration purposes cencorreg(Brumbaugh$Hg,Brumbaugh$HgCen,Brumbaugh[,c("SedMeHg","PctWetland")])#> Likelihood R2 = 0.1371 AIC = 359.1937 #> Rescaled Likelihood R2 = 0.1461 BIC = 369.7851 #> McFaddens R2 = 0.05301555 #>#> Call: #> survreg(formula = "log(Brumbaugh$Hg)", data = "SedMeHg+PctWetland", #> dist = "gaussian") #> #> Coefficients: #> (Intercept) SedMeHg PctWetland #> -1.79140776 0.06634913 0.01402344 #> #> Scale= 0.9264126 #> #> Loglik(model)= -175.1 Loglik(intercept only)= -184.9 #> Chisq= 19.61 on 2 degrees of freedom, p= 5.53e-05 #> n= 133