Changes for iNZightRegression

iNZightRegression 1.3.0

  • refactor plots using ggplot creating new method for inzplot (from iNZightPlots)
  • removes some functions that aren't used by iNZightRegression, and are available in iNZightMR instead

iNZightRegression 1.2.8

  • add column for exponentiated estimates where appropriate
  • add exponentiate.cis argument to iNZightSummary which replaces the CIs with exponentiated versions if appropriate (FALSE by default)
  • factorComp() for survey GLMs includes Wald test for term effects (regTermTest())
  • add summary method for Cox PH models

iNZightRegression 1.2.7

Release date: 21 April 2020

  • specify stringsAsFactors = TRUE for upcoming R 4.0.0
  • fix bug in processing summary for models with exluded variables

iNZightRegression 1.2.6

Release date: 11 November 2019

  • fix bug in regression summary causing interactions to fail

iNZightRegression 1.2.5

Release date: 2 September 2019

  • add parameter confidence limits to regression summary output
  • add model comparison function (AIC, BIC)
  • add factorComp() function to obtain adjusted pairwise comparisons of factor levels in a model

iNZightRegression 1.2.4

Release date: 15 July 2019

  • disable smoother for intercept-only models (y ~ 1)
  • fix some issues with bootstrapping

iNZightRegression 1.2.3

Release date: 30 April 2019

  • display link function used in binomial regression fits
  • suppress warnings from loess() calls

iNZightRegression 1.2.2

Release date: 01 February 2019

  • fix bug where residual plot for null model was painfully slow to draw

iNZightRegression 1.2.1

Release date: 15 November 2018

  • display baseline level for binary GLMs

iNZightRegression 1.2.0-2

Release date: 02 October 2017

  • fix bootstrapping algorithm
  • summary of model with only confounding variables (i.e., "adjusted intercept")

iNZightRegression 1.2.0-1

Release date: 25 August 2017

  • remove NAs from qq-plot abline

iNZightRegression 1.2

Release date: 23 August 2017

This isn't a hugely updated version, however fixing up a bunch of bugs to make the Model Fitting module better (over on iNZightModules).

Minor changes

  • export a Poly() function, which is just a poly() function that supports NAs

Bug fixes:

  • catch errors in model bootstrapping so the rest of the plot still works
  • use the subset argument to lm (via update()) to perform bootstraps, rather than the long-winded data-bootstrapping call-modifying version that was buggy
  • fix up bootstrapping algorithms for QQ-plot and histogram arrays

iNZightRegression 1.1.7

Release date: 18 August 2017

  • various fixes, package maintenance

iNZightRegression 1.1.6

Release date: 23 March 2017

  • fix bootstrapping method for linear models

iNZightRegression 1.1.5

Release date: 9 January 2017

  • fix a bug preventing plots from drawing when provided a glm object

iNZightRegression 1.1.4

Release date: 20 July 2015

Minor Changes

  • Adds an extra argument use.inzightplots to the plotting functions that allows users to enable/disable the use of them as desired. Currently, the default is FALSE as the latest version of iNZightPlots is incompatible with iNZightRegression.

Bug Fixes

  • added missed function and method exports

iNZightRegression 1.1.3

Release date: 17 September 2014

Bug Fixes

  • on some graphics devices (e.g., RStudio) grid.rect() is not transparent; now enforces these to be transparent

iNZightRegression 1.1.2

Release date: 4 April 2014

Minor Changes

  • Continuing on from previous update, the partial residual plots are now modified to make use of the iNZightPlots library if it is installed.

iNZightRegression 1.1.1

Release date: 27 March 2014

New Features

  • Residual summary plots from plotlm6 can now make use of the iNZightPlots graphics rather than the defaults. It requires the user to have iNZightPlots installed to work, but reverts to the old plots if it is not.

  • In the new grid based plots, quantile smoothers are used rather than loess smoothers. This greatly increases efficiency when large data sets are analysed.

Minor Changes

  • Maximum sample size for drawing bootstraps implemented (currently at 4000), as over this they don't provide much information (this can be overridden by showBootstraps = TRUE).

  • P-values for normality test are printed as "P < 0.001" rather than "P = 0".

Bug Fixes

  • Shapiro-Wilk test not used if sample size > 5000 (resulted in an error).

iNZightRegression 1.1

Release date: 18 January 2014

New Features

  • Support for generalized linear models (GLMs) and svyglm objects from the survey package.

  • Changes to the iNZightSummary output include:

    • Output now hides output of confounding variables through the exclude argument, and lists these at the top of the output.

    • Displays the type of fit (e.g., Survey / Generalized Linear / Model).
  • The QQ-plot array has been replaced by a single plot with the parametric bootstrap data QQ-plots all on a single plot, overlaid by the QQ-plot of the true data (this was suggested by Thomas Lumley).


  • The Histogram Array has been rewritten using grid, and minimizes margin whitespace and draws simulated histograms in a different colour.

Bug Fixes

  • The bootstrap models functions have been re-written to account for the design option in survey GLMs, as well as the case when the GLM binomial response is SUCCESS / N.TRIALS. This caused errors in the fit$model that was previously being used.

  • The bootstrap lines from plotlm6 have been fixed so that they now work for (svy)glm objects. There is also an optional cut-off if the sample size becomes too small (which can be overridden by the showBootstraps = TRUE|FALSE argument.

  • factorMeans and adjustedMeans have been enhanced to work with GLMs.

iNZightRegression 1.0.2-20130913

  • Initial support for generalized linear models (GLMs), including the survey package's svyglm.

iNZightRegression 1.0.2-20130122

New Features

  • First release of new package. Contains model fitting subset used for the iNZight package.

  • Added histogramArray and qqplotArray plots to show how residuals from a model compare to the residuals generated from that model.

  • New margin of error calculation functions. Initially written by Danny Chang. Used for comparison between levels of a factor. moecalc has a few standard methods that can be used: print, plot, and summary. In addition, a multicomp method has been added which is a useful tabulation of multiple comparison output. Note however with multicomp that the p-values are currently unadjusted.

  • New summary output, iNZightSummary. Includes several Changes compared to the R-base model summary output. These include the following:

    • Now showing the factor itself in the output, not just rows for coefficients for levels of the factor.

    • When a factor is included in a model, the summary output will show the name of the factor and show the p-value for the factor (based on Type-III sums of squares). This p-value is not affected by further use of the factor (i.e. in an interaction). Sometimes this p-value cannot be calculated (i.e. when there are unobserved factor level combinations) and the p-value will be omitted.

    • The baseline level of a factor is now shown, with an estimate of 0.

    • All p-value output for levels of a factor is indented to the right by two characters to distinguish it from being a level.

    • The output for each factor level is now just the level name and not the name of the variable concatenated with the level name. The level name is also indented by two characters, again to distinguish it from the variable itself.

    • Removing F-statistic and associated p-value as it's mostly useless. It only shows us whether nothing is correlated with the response variable, i.e. whether we're completely wasting our time.

    • Removed model call output and residual output. Replaced with "Model for:" (plus response name).
  • Added a new plot.lm function. The main difference being that it includes bootstrapped smoothers in its output as well as the regular trend lines. Also includes plots based on the s20x package's normcheck function.

  • Added partial residual plots. Most useful for determining whether the inclusion of a transformation of a variable is necessary. For example, adding a logged or polynomial explanatory variable to the model.

  • Adding bootstrapped estimates to iNZightSummary. Accessed by calling the function with method="bootstrap".

Bug Fixes

  • Using loess for smoothers instead of lowess (newer and more robust).