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Diffstat (limited to 'tests/massart97.Rout.save')
| -rw-r--r-- | tests/massart97.Rout.save | 128 |
1 files changed, 0 insertions, 128 deletions
diff --git a/tests/massart97.Rout.save b/tests/massart97.Rout.save deleted file mode 100644 index ce99c30..0000000 --- a/tests/massart97.Rout.save +++ /dev/null @@ -1,128 +0,0 @@ - -R version 3.1.0 (2014-04-10) -- "Spring Dance" -Copyright (C) 2014 The R Foundation for Statistical Computing -Platform: x86_64-pc-linux-gnu (64-bit) - -R is free software and comes with ABSOLUTELY NO WARRANTY. -You are welcome to redistribute it under certain conditions. -Type 'license()' or 'licence()' for distribution details. - -R is a collaborative project with many contributors. -Type 'contributors()' for more information and -'citation()' on how to cite R or R packages in publications. - -Type 'demo()' for some demos, 'help()' for on-line help, or -'help.start()' for an HTML browser interface to help. -Type 'q()' to quit R. - -> require(chemCal) -Loading required package: chemCal -> data(massart97ex1) -> m <- lm(y ~ x, data = massart97ex1) -> inverse.predict(m, 15) # 6.1 +- 4.9 -$Prediction -[1] 6.09381 - -$`Standard Error` -[1] 1.767278 - -$Confidence -[1] 4.906751 - -$`Confidence Limits` -[1] 1.187059 11.000561 - -> inverse.predict(m, 90) # 43.9 +- 4.9 -$Prediction -[1] 43.93983 - -$`Standard Error` -[1] 1.767747 - -$Confidence -[1] 4.908053 - -$`Confidence Limits` -[1] 39.03178 48.84788 - -> inverse.predict(m, rep(90,5)) # 43.9 +- 3.2 -$Prediction -[1] 43.93983 - -$`Standard Error` -[1] 1.141204 - -$Confidence -[1] 3.168489 - -$`Confidence Limits` -[1] 40.77134 47.10832 - -> -> data(massart97ex3) -> attach(massart97ex3) -> yx <- split(y, x) -> ybar <- sapply(yx, mean) -> s <- round(sapply(yx, sd), digits = 2) -> w <- round(1 / (s^2), digits = 3) -> weights <- w[factor(x)] -> m <- lm(y ~ x, w = weights) -> #calplot(m) -> -> inverse.predict(m, 15, ws = 1.67) # 5.9 +- 2.5 -$Prediction -[1] 5.865367 - -$`Standard Error` -[1] 0.8926109 - -$Confidence -[1] 2.478285 - -$`Confidence Limits` -[1] 3.387082 8.343652 - -> inverse.predict(m, 90, ws = 0.145) # 44.1 +- 7.9 -$Prediction -[1] 44.06025 - -$`Standard Error` -[1] 2.829162 - -$Confidence -[1] 7.855012 - -$`Confidence Limits` -[1] 36.20523 51.91526 - -> -> m0 <- lm(y ~ x) -> lod(m0) -$x -[1] 5.407085 - -$y - 1 -13.63911 - -> -> loq(m0) -$x -[1] 13.97764 - -$y - 1 -30.6235 - -> loq(m, w.loq = 1.67) -$x -[1] 7.346195 - -$y - 1 -17.90777 - -> -> proc.time() - user system elapsed - 0.529 0.327 0.443 |