deseasonalized_trend {bandit} | R Documentation |

## deseasonalized_trend

### Description

A convenience function to analyze a timeseries and return an estimate (via gam, using day of week factors and smoothed timestamp) of whether, after accounting for day-of-week, there is a significant time-based influence and what that influence is.

### Usage

```
deseasonalized_trend(df, w=NULL)
```

### Arguments

`df` |
a data frame containing timestamp and value entries |

`w` |
number of attempts (n for binomial data) |

### Value

a list with the following items:

`pval` |
pval given by anova on gam, to indicate whether s(timestamp) is significant |

`smoothed_prediction` |
a smoothed prediction over time (on Wednesdays), to give a human-understandable idea of what the change over time has been |

### Author(s)

Thomas Lotze <thomaslotze@thomaslotze.com>

### Examples

```
timestamps = as.numeric(as.POSIXct(seq(as.Date("2012-01-01"),as.Date("2012-05-03"),by=1)))
df=data.frame(timestamp = timestamps, value = rnorm(length(timestamps)))
dt = deseasonalized_trend(df)
if (dt$pval < 0.01) {
print("Significant time-based factor")
plot(df$timestamp, dt$smoothed_prediction)
} else {
print("No significant time-based factor")
}
df=data.frame(timestamp = timestamps,
value = sapply(timestamps, function(t) {rpois(1, lambda=t-min(timestamps))}))
dt = deseasonalized_trend(df)
if (dt$pval < 0.01) {
print("Significant time-based factor")
plot(df$timestamp, dt$smoothed_prediction)
} else {
print("No significant time-based factor")
}
```

[Package

*bandit*version 0.5.1 Index]