powertrip: Analysis of longevity data.

Description Usage Arguments Details Value Note Author(s) References See Also Examples

The main function of Survomatic, which takes the raw survival data and performs log-rank, quantile regression and mortality modeling analysis.

go(x, y, xynames = c(), save = T, path, prompt = 2, xlim = 1350, slwd = 4, scol = c("darkred", 1), spch = 24:25, spcex = 0, splwd = slwd, spbg = scol, sleg = T, sxcex = 1.5, sxlwd = 3, qstart = 1, demint = 30, qrse = "boot", dohz = 1)

`x`	Vector of ages at death (in days) for control group.
`y`	Vector of ages at death (in days) for experimental group.
`xynames`	Character vector with labels for the two groups.
`save`	Boolean value of whether to save the various output files or not.
`path`	Path for where to save the output files.
`prompt`	How aggressively to prompt you to make choices, versus deciding on your behalf.
`xlim`	The length of the x axis for the Kaplan-Meier curves.
`slwd`	Line width for the survival curve plots.
`scol`	Line colors for the survival surves, first the experimental group and second the control.
`spch`	Shapes to represent data points on the survival curves.
`spcex`	Size of the data points on the survival curves (set to 0 by default and therefore the survival curves are by default plotted as lines only).
`splwd`	Thickness of the lines used to plot the data points on the survival curves.
`spbg`	Fill color for the data points on the survival curves.
`sleg`	Whether or not to plot a legend on the survival curves.
`sxcex`	Font size for the axes in the survival curves.
`sxlwd`	Line thickness of the axes in the survival curves.
`qstart`	Which collection of quantiles to attempt to use first in quantile regression– an integer between 1 and 4 inclusive, and should not ordinarily be changed. This argument will probably removed in future version.
`demint`	By how many days to smooth the observed mortality rate. Should not be changed, and will probably be removed in future versions.
`qrse`	Algorithm for obtaining confidence intervals for quantile regression. See the help file for `summary.rq` for more information.
`dohz`	Boolean value controling whether or not to fit mortality models to the data.

This is intended to be a wrapper that automagically calls various other functions on your survival data. At the moment these are log-rank, quantile regression, quantile score test, and fitting mortality models to the data.

A list object with all potentially useful output about the comparison of the two populations. This list also contains a copy of go() which can be invoked as listname$go(), in which case it will print out and plot the previous output without recalculating it.

`smry`	Quick summary of the quantiles of the two groups being compared.
`zsc`	A table with all the output of the quantile score test.
`x`	The ages at death of the control group.
`y`	The ages at death of the experimental group.
`xy`	The ages at death of the two groups together (first x, then y).
`xynames`	The names of the two groups.
`path`	The path where to save files by default.
`group`	A vector of group assignments the same length as xy
`lr`	Output from the log-rank test on the two groups.
`tt`	Output from the Student's t-test on the logs of the two groups
`tests`	The names of all the tests performed and the names of the items that contain the results.
`sig.tests`	The subset of tests which returned significant results.
`qreg`	Output from quantile regression.
`qreg.tab`	Table with quantile regression results with standard errors, test statistics, and p-values added.
`qreg.sum`	Output from the summary.rq functon.
`demint`
`xd`	Demography table for x, including the observed hazard (ux).
`yd`	Demography table for x, including the observed hazard (ux).
`mod`	Output from mortality model fitting.
`report`	A table listing some summary statistics and the quantiles flagged by quantile regression and the score test as potentially significant.
`sigqreg`	Table with only the significantly different quantiles, values at that those quantiles, and p-values from qreg.tab.
`sigzsc`	Table with only the fraction surviving from each group at quantiles where a significant difference was observed and the p-value of that difference.
`xysurvfit`	Output from survfit of the two groups.
`slwd`	Line width for the survival curve plots.
`scol`	Line colors for the survival surves, first the experimental group and second the control.
`spch`	Shapes to represent data points on the survival curves.
`spcex`	Size of the data points on the survival curves (set to 0 by default and therefore the survival curves are by default plotted as lines only).
`splwd`	Thickness of the lines used to plot the data points on the survival curves.
`spbg`	Fill color for the data points on the survival curves.
`sleg`	Whether or not to plot a legend on the survival curves.
`sxcex`	Font size for the axes in the survival curves.
`sxlwd`	Line thickness of the axes in the survival curves.
`dohz`	Boolean value controling whether or not to fit mortality models to the data.
`go`	A copy of the function that originally generated the output, so the output can be used to plot/print itself.
`sys`	The exact phrasing of how this function was invoked in order to produce the current output.

Some older version of MacOS X do not load tcltk properly, and therefore will have problems running this package. Please make sure you are using the latest revision of Survomatic.

Alex F. Bokov

Pletcher, S. D. et al., (2000) Why Do Lifespans Differ? Partitioning Mean Longevity Differences in Terms of Age-Specific Mortality Parameters, _Journal of Gerontology: Biological Sciences_, *55A*, B381-B389

Wang, C. et al., (2004) Statistical Methods for Testing Effects on "Maximum Lifespan",_Mechanisms of Ageing and Development_, *125*, 629-632

rq,summary.rq,surv2.logrank

# Can be directly called with no arguments, and it will prompt you for everything.
# Can also be called with arguments.
# a<-simsurv(20);
# b<-simsurv(20,p=c(1e-6,0.003,0,0));
# You can capture the output within R.
# output<-go(a,b,xynames=c('a','b'));
# output$go();