# lmThresh: Finds and analyzes significance reversal regions for each... In reverseR: Linear Regression Stability to Significance Reversal

## Description

This function finds (by iterating through a grid of values for each response) the approximate response value range(s) in which the regression is significant (when inside) or not (when outside), as defined by alpha. Here, two scenarios can be tested: i) if newobs = FALSE (default), the model's significance is tested by shifting y_i along the search grid. If newobs = TRUE, y_i is kept fixed and a new observation y_{2i} is added and shifted along the search grid. Hence, this function tests the regression for the sensitivity of being reversed in its significance through minor shifting of the original or added response values, as opposed to the effect of point removal (lmInfl).

## Usage

 1 2 3 lmThresh(model, factor = 5, alpha = 0.05, method = c("pearson", "spearman"), steps = 10000, newobs = FALSE, ...) 

## Arguments

 model the linear model of class lm. factor a factor for the initial search grid. See 'Details'. alpha the α-level to use as the threshold border. method select either parametric ("pearson") or rank-based ("spearman") statistics. steps the number of steps within the search range. See 'Details'. newobs logical. Should the significance region for each y_i be calculated from shifting y_i or from keeping y_i fixed and adding a new observation y2_i? ... other arguments to future methods.

## Details

In a first step, a grid is created with a range from y_i \pm \mathrm{factor} \cdot \mathrm{range}(y_{1...n}) with steps cuts. For each cut, the p-value is calculated for the model when y_i is shifted to that value (newobs = TRUE) or a second observation y_{2i} is added to the fixed y_i (newobs = TRUE). When the original model y = β_0 + β_1x + \varepsilon is significant (p < alpha), there are two boundaries that result in insignificance: one decreases the slope β_1 and the other inflates the standard error \mathrm{s.e.}(β_1) in a way that P_t(\frac{β_1}{\mathrm{s.e.}(β_1)}, n-2) > α. If the original model was insignificant, also two boundaries exists that either increase β_1 or reduce \mathrm{s.e.}(β_1). Often, no boundaries are found and increasing the factor grid range may alleviate this problem.

This function is quite fast (~ 300ms/10 response values), as the slope's p-value is calculated from the corr.test function of the 'psych' package, which utilizes matrix multiplication and vectorized pt calculation. The vector of correlation coefficients r_i from the cor function is transformed to t-values by

t_i = \frac{r_i√{n-2}}{√{1-r_i^2}}

which is equivalent to that employed in the linear regression's slope test.

## Value

A list with the following items:

 x the predictor values. y the response values. pmat the p-value matrix, with length(x) columns and steps rows. alpha the selected α-level. ySeq the grid sequence for which the algorithm calculates p-values when y_i is shifted within. model the original lm model. data the original model.frame. eosr the y-values of the ends of the significance region. diff the Δ value between y_i and the nearest border of significance reversal. closest the (approx.) value of the nearest border of significance reversal. newobs should a new observation be added?

## Author(s)

Andrej-Nikolai Spiess

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 ## Significant model, no new observation. set.seed(125) a <- 1:20 b <- 5 + 0.08 * a + rnorm(length(a), 0, 1) LM1 <- lm(b ~ a) res1 <- lmThresh(LM1) threshPlot(res1) stability(res1) ## Insignificant model, no new observation. set.seed(125) a <- 1:20 b <- 5 + 0.08 * a + rnorm(length(a), 0, 2) LM2 <- lm(b ~ a) res2 <- lmThresh(LM2) threshPlot(res2) stability(res2) ## Significant model, new observation. ## Some significance reversal regions ## are within the prediction interval, ## e.g. 1 to 6 and 14 to 20. set.seed(125) a <- 1:20 b <- 5 + 0.08 * a + rnorm(length(a), 0, 1) LM3 <- lm(b ~ a) res3 <- lmThresh(LM3, newobs = TRUE) threshPlot(res3) stability(res3) ## More detailed example to the above: ## a (putative) new observation within the ## prediction interval may reverse significance. set.seed(125) a <- 1:20 b <- 5 + 0.08 * a + rnorm(length(a), 0, 1) LM1 <- lm(b ~ a) summary(LM1) # => p-value = 0.02688 res1 <- lmThresh(LM1, newobs = TRUE) threshPlot(res1) st <- stability(res1, pval = TRUE) st\$stats # => upper prediction boundary = 7.48 # and eosr = 6.49 stabPlot(st, 1) ## reverse significance if we add a new response y_1 = 7 a <- c(1, a) b <- c(7, b) LM2 <- lm(b ~ a) summary(LM2) # => p-value = 0.0767 

reverseR documentation built on May 2, 2019, 10:59 a.m.