Description Usage Arguments Details Value Author(s) References See Also Examples

Function for processing forest inventory data using systematic sampling.

1 2 3 4 5 6 7 8 9 10 11 12 |

`df` |
a data frame. |

`Yi` |
Quoted name of the volume variable, or other variable one desires to evaluate, in quotes. |

`plot_area` |
Quoted name of the plot area variable, or a numeric vector with the plot area value. The plot area value must be in square meters. |

`total_area` |
Quoted name of the total area variable, or a numeric vector with the total area value.The total area value must be in hectares. |

`age` |
Optional parameter. Quoted name of the age variable. Calculates the average age supplied. |

`.groups` |
Optional argument. Quoted name(s) of additional grouping variable(s) that, if supplied, will be used to run multiple surveys, one for each level.
If this argument is |

`alpha` |
Numeric value for the significance value used in the t-student estimation. Default: |

`error` |
Numeric value for the minimum admitted error value in the survey, in percentage. Default: |

`dec_places` |
Numeric value for the number of decimal places to be used in the output tables. Default: |

`tidy` |
Boolean value that defines if the output tables should be tidied up or not. Default: |

This function allows the user to processes inventory data using simple random sampling for finite or infinite populations.
It's possible to run multiple sampling analysis using a factor variable indicated in the `.groups`

() parameter.

A data frame with the sampling results.

Sollano Rabelo Braga sollanorb@gmail.com

Campos, J. C. C. and Leite, H. G. (2017) Mensuracao Florestal: Perguntas e Respostas. 5a. Vicosa: UFV.

Soares, C. P. B., Paula Neto, F. and Souza, A. L. (2012) Dendrometria e Inventario Florestal. 2nd ed. Vicosa: UFV.

other sampling functions:
`sprs`

for Simple Random Sampling, and
`strs`

for stratified random sampling, and

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 | ```
library(forestmangr)
data("exfm2")
data("exfm5")
# We're trying to run a inventory for an area This data was collected systematically,
# but we'll try to run the data using simple random sampling,
# to show the difference between the two methods:
head(exfm5)
sprs(exfm5, "VWB", "PLOT_AREA", "TOTAL_AREA")
# We get a 22% error value. Now, we run this same data
# considering the data as a systematic inventory, using the
# successive differences method:
exfm5
ss_diffs(exfm5, "VWB", "PLOT_AREA", "TOTAL_AREA")
# The error was significantly lowered.
# Area Values can be numeric;
ss_diffs(exfm5, "VWB", 200, 18)
# Here we run a systematic sampling inventory for each forest subdivision,
# using the STRATA variable as a group variable:
exfm2
ss_diffs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA",.groups = "STRATA")
``` |

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