I'd like to include values generated for previous rows as inputs to a mutate calculation. Some data:

```
mydiamonds <- diamonds %>%
mutate(Ideal = ifelse(cut == 'Ideal', 1, 0)) %>%
group_by(Ideal) %>%
mutate(rn = row_number()) %>%
arrange(Ideal, rn) %>%
mutate(CumPrice = cumsum(price)) %>%
mutate(InitialPrice = min(price)) %>%
select(Ideal, rn, CumPrice, InitialPrice)
```

Looks like this:

```
mydiamonds %>% head
# A tibble: 6 x 4
# Groups: Ideal [1]
Ideal rn CumPrice InitialPrice
<dbl> <int> <int> <int>
1 0 1 326 326
2 0 2 653 326
3 0 3 987 326
4 0 4 1322 326
5 0 5 1658 326
6 0 6 1994 326
```

A model:

```
mod.diamonds = glm(CumPrice ~ log(lag(CumPrice)) +log(rn) + Ideal , family = "poisson", data = mydiamonds)
```

Test the model:

```
# new data, pretend we don't know CumPrice but want to use predictions to predict subsequent predictions
mydiamonds.testdata <- mydiamonds %>% select(-CumPrice)
# manual prediction based on lag(prediction), for the first row in each group use InitialPrice
## add coefficients as fields
coeffs <- mod.diamonds$coefficients
mydiamonds.testdata <- mydiamonds.testdata %>%
mutate(CoefIntercept = coeffs['(Intercept)'],
CoefLogLagCumPrice = coeffs['log(lag(CumPrice))'],
CoefLogRn = coeffs['log(rn)'],
CoefIdeal = coeffs['Ideal']
)
```

Here's how my test data look:

```
mydiamonds.testdata %>% head
# A tibble: 6 x 7
# Groups: Ideal [1]
Ideal rn InitialPrice CoefIntercept CoefLogLagCumPrice CoefLogRn CoefIdeal
<dbl> <int> <int> <dbl> <dbl> <dbl> <dbl>
1 0 1 326 0.0931 0.987 0.0154 -0.000715
2 0 2 326 0.0931 0.987 0.0154 -0.000715
3 0 3 326 0.0931 0.987 0.0154 -0.000715
4 0 4 326 0.0931 0.987 0.0154 -0.000715
5 0 5 326 0.0931 0.987 0.0154 -0.000715
6 0 6 326 0.0931 0.987 0.0154 -0.000715
```

Cannot use predict(), since I need to recursively predict where predictions for the previous day/row are input to the current day. Instead try manual prediction using the coefficents:

```
# prediction
mydiamonds.testdata <- mydiamonds.testdata %>%
mutate(
Prediction = CoefIntercept +
# here's the hard bit. If it's the first row in the group, use InitialPrice, else use the value of the previous prediction
(CoefLogLagCumPrice * ifelse(rn == 1, InitialPrice, lag(Prediction))) +
(CoefLogRn * log(rn)) +
(CoefIdeal * Ideal)
)
```

Error: Problem with

`mutate()`

input`Prediction`

. x object

'Prediction' not found Input`Prediction`

is`+...`

. The error

occurred in group 1: Ideal = 0.

How can I mutate in this way, where I'd like to refer to the previous rows mutation? (Unless it's the very first row, in which case use InitialPrice)