-
Notifications
You must be signed in to change notification settings - Fork 7
/
Copy pathREADME.Rmd
141 lines (104 loc) · 5.46 KB
/
README.Rmd
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
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
library(forestmangr)
library(dplyr)
```
[](https://travis-ci.org/sollano/forestmangr)
[](https://cran.r-project.org/package=forestmangr)
[](https://codecov.io/gh/sollano/forestmangr?branch=master)
[](https://cran.r-project.org/package=forestmangr)
[](https://cran.r-project.org/package=forestmangr)
[](https://www.repostatus.org/#active)
<a href="https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=ZU4666LFSJSBY" title="Help me out by donating to this project"><img src="https://img.shields.io/badge/paypal-donate-yellow.svg" alt="PayPal donate button" /></a>
# forestmangr
Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and non linear grouped data fitting, and statistical tests.
If you need any help, I’m available for consulting. If you find forestmangr useful, please consider supporting my efforts in developing this open-source R package for the forestry community!
<div style="text-align:center">
<form action="https://www.paypal.com/cgi-bin/webscr" method="post" target="_top">
<input type="hidden" name="cmd" value="_s-xclick" />
<input type="hidden" name="hosted_button_id" value="ZU4666LFSJSBY" />
<input type="image" src="https://www.paypalobjects.com/en_US/i/btn/btn_donateCC_LG.gif" border="0" name="submit" title="PayPal - The safer, easier way to pay online!" alt="Donate with PayPal button" />
<img alt="" border="0" src="https://www.paypal.com/en_BR/i/scr/pixel.gif" width="1" height="1" />
</form>
</div>
## Installation
To install the stable CRAN version, use:
```{r eval = FALSE}
install.packages("forestmangr")
```
Or you can install forestmangr from github, for the latest dev version with:
```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("sollano/forestmangr")
```
## Example
```{r}
library(forestmangr)
library(dplyr)
data("exfm16")
head(exfm16)
```
Now, we can fit a model for Site estimatation. With `nls_table`, we can fit a non-linear model, extract it's coefficients, and merge it with
the original data in one line. Here we'll use Chapman & Richards model:
```{r}
age_i <- 64
exfm16_fit <- exfm16 %>%
nls_table(DH ~ b0 * (1-exp(-b1* age))^b2, mod_start = c( b0=23, b1=0.03, b2 = 1.3), output="merge") %>%
mutate(site = DH *( ( (1- exp( -b1/age ))^b2 ) / (( 1 - exp(-b1/age_i))^b2 ))) %>%
select(-b0,-b1,-b2)
head(exfm16_fit)
```
Now, to fit Clutter's model, we can use the `fit_clutter` function, indicating the DH, B, V, site and Plot variable names:
```{r}
coefs_clutter <- fit_clutter(exfm16_fit, "age", "DH", "B", "V", "site", "plot")
coefs_clutter
```
Now, say we wanted to do a Simple Random Sampling Forest Inventory, with 20% as an accepted error.
First, let's load the package and some data:
```{r}
library(forestmangr)
data("exfm2")
data("exfm3")
data("exfm4")
head(exfm3,10)
```
First we should try a pilot inventory, to see if the number of plots sampled is enough for reaching the desired error:
```{r}
sprs(exfm3, "VWB", "PLOT_AREA", "TOTAL_AREA", error = 20, pop = "fin")
```
We can see that we have 10 plots, but 15 more are needed if we want a minimum of 20% error.
The exfm4 data has new samples, that we now can use to run a definitive inventory:
```{r}
sprs(exfm4, "VWB", "PLOT_AREA", "TOTAL_AREA", error = 20, pop = "fin")
```
The desired error was met.
The exfm2 data has a strata variable. Say we wanted to run a SRS inventory for every stand. We can do this with the .groups argument:
```{r}
head(exfm2,10)
sprs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA",.groups="STRATA", error = 20, pop = "fin")
```
We can also run a stratified random sampling inventory with this data:
```{r}
strs(exfm2, "VWB", "PLOT_AREA", "STRATA_AREA", "STRATA", error = 20, pop = "fin")
```
## Citation
To cite this package in publications, use:
ABNT:
BRAGA S. R.; OLIVEIRA, M. L. R.; GORGENS, E. B. forestmangr: Forest Mensuration and Management. R package version 0.9.2, 2020. Disponível em: <https://CRAN.R-project.org/package=forestmangr>
APA:
Sollano Rabelo Braga, Marcio Leles Romarco de Oliveira and Eric Bastos Gorgens (2020). forestmangr: Forest Mensuration and Management. R package version 0.9.2. https://CRAN.R-project.org/package=forestmangr
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## Acknowledgments
* This project was developed on the Forest Management Lab, DEF, UFVJM, Diamantina/Minas Gerais - Brazil.
* This project came to be as a mean to make the life of a forestry engeneer a little easier and pratical.
We'd like to thank everyone at UFVJM that has in anyway helped this project grow.
* We'd like to thank UFVJM, FAPEMIG, CNPq e CAPES for the support.