Running examples in 'CausalImpact-Ex.R' failed
The error most likely occurred in:

> ### Name: CausalImpact
> ### Title: Inferring causal impact using structural time-series models
> ### Aliases: CausalImpact
> 
> ### ** Examples
> 
> # Example 1
> #
> # Example analysis on a simple artificial dataset
> # consisting of a response variable y and a
> # single covariate x1.
> set.seed(1)
> x1 <- 100 + arima.sim(model = list(ar = 0.999), n = 52)
> y <- 1.2 * x1 + rnorm(52)
> y[41:52] <- y[41:52] + 10
> data <- cbind(y, x1)
> pre.period <- c(1, 40)
> post.period <- c(41, 52)
> impact <- CausalImpact(data, pre.period, post.period)
> 
> # Print and plot results
> summary(impact)
Posterior inference {CausalImpact}

                         Average       Cumulative  
Actual                   112           1340        
Prediction (s.d.)        102 (0.41)    1223 (4.86) 
95% CI                   [101, 103]    [1214, 1233]
                                                   
Absolute effect (s.d.)   9.8 (0.41)    117.1 (4.86)
95% CI                   [9, 11]       [108, 127]  
                                                   
Relative effect (s.d.)   9.6% (0.4%)   9.6% (0.4%) 
95% CI                   [8.8%, 10%]   [8.8%, 10%] 

Posterior tail-area probability p:   0.00104
Posterior prob. of a causal effect:  99.89551%

For more details, type: summary(impact, "report")

> summary(impact, "report")
Analysis report {CausalImpact}


During the post-intervention period, the response variable had an average value of approx. 111.70. By contrast, in the absence of an intervention, we would have expected an average response of 101.93. The 95% interval of this counterfactual prediction is [101.14, 102.73]. Subtracting this prediction from the observed response yields an estimate of the causal effect the intervention had on the response variable. This effect is 9.76 with a 95% interval of [8.97, 10.56]. For a discussion of the significance of this effect, see below.

Summing up the individual data points during the post-intervention period (which can only sometimes be meaningfully interpreted), the response variable had an overall value of 1.34K. By contrast, had the intervention not taken place, we would have expected a sum of 1.22K. The 95% interval of this prediction is [1.21K, 1.23K].

The above results are given in terms of absolute numbers. In relative terms, the response variable showed an increase of +10%. The 95% interval of this percentage is [+9%, +10%].

This means that the positive effect observed during the intervention period is statistically significant and unlikely to be due to random fluctuations. It should be noted, however, that the question of whether this increase also bears substantive significance can only be answered by comparing the absolute effect (9.76) to the original goal of the underlying intervention.

The probability of obtaining this effect by chance is very small (Bayesian one-sided tail-area probability p = 0.001). This means the causal effect can be considered statistically significant. 
> plot(impact)
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