README.md
In DeivisonSouza/forester: Traditional Methods for Analyzing Forest Data
Hello, welcome! :smiley: :grin:
forester (under development)
The forester package constitutes a set of functions with mathematical
and statistical methods traditionally used by forestry engineers to
analyze forest inventory data.
Installation
You can install the released version of forester from
CRAN with:
install.packages("forester")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("DeivisonSouza/forester")
Simple Random Sampling
A basic example of applying the RS function to obtain simple random
sampling estimates (Sanquetta et al., 2014, page: 110-113):
library(forester)
data("pinus")
RS(x = pinus$Volume, A = 40, a = 0.06, LE = 0.1, FP = F)
#> $Descriptive
#> # A tibble: 13 x 2
#> Parameters Value
#> <chr> <dbl>
#> 1 count 16
#> 2 Mean 23.8
#> 3 sd 4.22
#> 4 var 17.8
#> 5 cv 17.7
#> 6 min 17.8
#> 7 Q1 22.0
#> 8 median 23.7
#> 9 Q3 24.8
#> 10 IQR 2.86
#> 11 max 36.2
#> 12 kurt 5.77
#> 13 skew 1.47
#>
#> $Estimated
#> # A tibble: 14 x 2
#> Parameters Value
#> <chr> <dbl>
#> 1 Count 16
#> 2 Number of potential sample units 667.
#> 3 Sample mean 23.8
#> 4 Sample sufficiency 14
#> 5 Sample sufficiency recalculation (df = 13) 15
#> 6 Variance of the mean 1.09
#> 7 Standard error of the mean 1.04
#> 8 Absolute sampling error 2.22
#> 9 Relative sampling error 9.33
#> 10 Lower confidence interval for the mean 21.6
#> 11 Upper confidence interval for the mean 26.0
#> 12 Total population 15884.
#> 13 Lower confidence interval for the population 14403.
#> 14 Upper confidence interval for the population 17366.
#>
#> $BaseInfo
#> # A tibble: 1 x 7
#> E t A a N f fc
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2.38 2.13 40 0.06 667. 0.0240 0.976
#>
#> attr(,"class")
#> [1] "forester" "RS"
An example using factor:
data("species2")
RS(x = species2$Volume, by = species2$Specie, A = c(40, 50, 60, 70), a = c(0.06, 0.07, 0.05, 0.08), FP = F, DT = F)
#> $Descriptive
#> # A tibble: 13 x 5
#> Parameters Pinus Eucaliptus Teca Acacia
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 count 16 14 11 13
#> 2 Mean 23.8 23.8 24.2 24.1
#> 3 sd 4.22 4.53 4.92 4.53
#> 4 var 17.8 20.5 24.2 20.5
#> 5 cv 17.7 19.1 20.3 18.8
#> 6 min 17.8 17.8 17.8 17.8
#> 7 Q1 22.0 21.2 21.7 22.4
#> 8 median 23.7 23.1 23.7 23.7
#> 9 Q3 24.8 25.0 24.6 24.8
#> 10 IQR 2.86 3.75 2.84 2.41
#> 11 max 36.2 36.2 36.2 36.2
#> 12 kurt 5.77 5.14 4.33 5.04
#> 13 skew 1.47 1.42 1.23 1.34
#>
#> $Estimated
#> # A tibble: 14 x 5
#> Parameters Pinus Eucaliptus Teca Acacia
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Count 16 14 11 1.30e1
#> 2 Number of potential sample units 667. 714. 1200 8.75e2
#> 3 Sample mean 23.8 23.8 24.2 2.41e1
#> 4 Sample sufficiency 14 17 21 1.70e1
#> 5 Sample sufficiency recalculation 15 17 18 1.60e1
#> 6 Variance of the mean 1.09 1.47 2.20 1.58e0
#> 7 Standard error of the mean 1.04 1.21 1.48 1.26e0
#> 8 Absolute sampling error 2.22 2.62 3.31 2.74e0
#> 9 Relative sampling error 9.33 11.0 13.7 1.13e1
#> 10 Lower confidence interval for the mean 21.6 21.2 20.9 2.14e1
#> 11 Upper confidence interval for the mean 26.0 26.4 27.5 2.69e1
#> 12 Total population 15884. 16977. 29060. 2.11e4
#> 13 Lower confidence interval for the popul… 14403. 15108. 25092. 1.87e4
#> 14 Upper confidence interval for the popul… 17366. 18846. 33027. 2.35e4
#>
#> $BaseInfo
#> # A tibble: 4 x 8
#> by E t A a N f fc
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Pinus 2.38 2.13 40 0.06 667. 0.0240 0.976
#> 2 Eucaliptus 2.38 2.16 50 0.07 714. 0.0196 0.980
#> 3 Teca 2.42 2.23 60 0.05 1200 0.00917 0.991
#> 4 Acacia 2.41 2.18 70 0.08 875 0.0149 0.985
#>
#> attr(,"class")
#> [1] "forester" "RS"
Stratified Random Sampling
This is a basic example which shows you how to solve a common problem
(Sanquetta et al., 2014, page: 122-129):
data("native")
SRS(x=native$Volume1, strata=native$Strata, A = c(650, 350), a = 1, LE = 0.1, SA = "PA", FP = FALSE, DT = FALSE, digits = 3)
#> $Descriptive
#> # A tibble: 13 x 3
#> Parameters S1 S2
#> <chr> <dbl> <dbl>
#> 1 count 12 12
#> 2 Mean 89.1 125.
#> 3 sd 8.46 16.2
#> 4 var 71.5 261.
#> 5 cv 9.49 12.9
#> 6 min 74 99
#> 7 Q1 82.8 116.
#> 8 median 90.5 124.
#> 9 Q3 95.2 133.
#> 10 IQR 12.5 16.8
#> 11 max 101 153
#> 12 kurt 2.02 2.22
#> 13 skew -0.233 0.201
#>
#> $Anova
#> Df Sum Sq Mean Sq F value Pr(>F)
#> strata 1 7921 7921 47.61 6.27e-07 ***
#> Residuals 22 3660 166
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> $Estimated
#> $Estimated$parameters
#> # A tibble: 15 x 2
#> Parameters Value
#> <chr> <dbl>
#> 1 Number of potential sample units 1000
#> 2 Count 24
#> 3 Sample sufficiency (df = 23) (Proportional allocation) 5.66
#> 4 Sample sufficiency recalculation (df = 5) (Proportional allocation) 8.72
#> 5 Stratified sample mean 102.
#> 6 Stratified sample variance 138.
#> 7 Variance of the mean stratified 5.05
#> 8 Standard error of the mean stratified 2.25
#> 9 Absolute sampling error 4.66
#> 10 Relative sampling error 4.58
#> 11 Lower confidence interval for the mean 97.1
#> 12 Upper confidence interval for the mean 106.
#> 13 Total population 101800
#> 14 Lower confidence interval for the population 97141.
#> 15 Upper confidence interval for the population 106459.
#>
#> $Estimated$nst
#> strata nh (Proportional allocation)
#> S1 6
#> S2 3
#> Total 9
#>
#>
#> $BaseInfo
#> $BaseInfo$`1`
#> strata Ah Nh Wh
#> S1 650 650 0.65
#> S2 350 350 0.35
#> Total 1000 1000 1.00
#>
#> $BaseInfo$`2`
#> # A tibble: 5 x 2
#> Parameters Value
#> <chr> <dbl>
#> 1 E 10.2
#> 2 t 2.07
#> 3 f 0.024
#> 4 fc 0.976
#> 5 ne 22.0
#>
#>
#> attr(,"class")
#> [1] "forester" "SRS"
Reference
SANQUETTA, C. R.; CORTE, A. P. D.; RODRIGUES, A. L.; WATZLAWICK, L. F.
Inventário Florestal: Planejamento e execução. 3. ed. Curitiba:
Multi-Graphic Gráfica e Editora, 2014. 406p.
DeivisonSouza/forester documentation built on March 9, 2021, 9:52 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Hello, welcome! :smiley: :grin:
forester (under development)
The forester package constitutes a set of functions with mathematical
and statistical methods traditionally used by forestry engineers to
analyze forest inventory data.
Installation
You can install the released version of forester from
CRAN with:
install.packages("forester")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("DeivisonSouza/forester")
Simple Random Sampling
A basic example of applying the RS function to obtain simple random sampling estimates (Sanquetta et al., 2014, page: 110-113):
library(forester)
data("pinus")
RS(x = pinus$Volume, A = 40, a = 0.06, LE = 0.1, FP = F)
#> $Descriptive
#> # A tibble: 13 x 2
#> Parameters Value
#> <chr> <dbl>
#> 1 count 16
#> 2 Mean 23.8
#> 3 sd 4.22
#> 4 var 17.8
#> 5 cv 17.7
#> 6 min 17.8
#> 7 Q1 22.0
#> 8 median 23.7
#> 9 Q3 24.8
#> 10 IQR 2.86
#> 11 max 36.2
#> 12 kurt 5.77
#> 13 skew 1.47
#>
#> $Estimated
#> # A tibble: 14 x 2
#> Parameters Value
#> <chr> <dbl>
#> 1 Count 16
#> 2 Number of potential sample units 667.
#> 3 Sample mean 23.8
#> 4 Sample sufficiency 14
#> 5 Sample sufficiency recalculation (df = 13) 15
#> 6 Variance of the mean 1.09
#> 7 Standard error of the mean 1.04
#> 8 Absolute sampling error 2.22
#> 9 Relative sampling error 9.33
#> 10 Lower confidence interval for the mean 21.6
#> 11 Upper confidence interval for the mean 26.0
#> 12 Total population 15884.
#> 13 Lower confidence interval for the population 14403.
#> 14 Upper confidence interval for the population 17366.
#>
#> $BaseInfo
#> # A tibble: 1 x 7
#> E t A a N f fc
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2.38 2.13 40 0.06 667. 0.0240 0.976
#>
#> attr(,"class")
#> [1] "forester" "RS"
An example using factor:
data("species2")
RS(x = species2$Volume, by = species2$Specie, A = c(40, 50, 60, 70), a = c(0.06, 0.07, 0.05, 0.08), FP = F, DT = F)
#> $Descriptive
#> # A tibble: 13 x 5
#> Parameters Pinus Eucaliptus Teca Acacia
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 count 16 14 11 13
#> 2 Mean 23.8 23.8 24.2 24.1
#> 3 sd 4.22 4.53 4.92 4.53
#> 4 var 17.8 20.5 24.2 20.5
#> 5 cv 17.7 19.1 20.3 18.8
#> 6 min 17.8 17.8 17.8 17.8
#> 7 Q1 22.0 21.2 21.7 22.4
#> 8 median 23.7 23.1 23.7 23.7
#> 9 Q3 24.8 25.0 24.6 24.8
#> 10 IQR 2.86 3.75 2.84 2.41
#> 11 max 36.2 36.2 36.2 36.2
#> 12 kurt 5.77 5.14 4.33 5.04
#> 13 skew 1.47 1.42 1.23 1.34
#>
#> $Estimated
#> # A tibble: 14 x 5
#> Parameters Pinus Eucaliptus Teca Acacia
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Count 16 14 11 1.30e1
#> 2 Number of potential sample units 667. 714. 1200 8.75e2
#> 3 Sample mean 23.8 23.8 24.2 2.41e1
#> 4 Sample sufficiency 14 17 21 1.70e1
#> 5 Sample sufficiency recalculation 15 17 18 1.60e1
#> 6 Variance of the mean 1.09 1.47 2.20 1.58e0
#> 7 Standard error of the mean 1.04 1.21 1.48 1.26e0
#> 8 Absolute sampling error 2.22 2.62 3.31 2.74e0
#> 9 Relative sampling error 9.33 11.0 13.7 1.13e1
#> 10 Lower confidence interval for the mean 21.6 21.2 20.9 2.14e1
#> 11 Upper confidence interval for the mean 26.0 26.4 27.5 2.69e1
#> 12 Total population 15884. 16977. 29060. 2.11e4
#> 13 Lower confidence interval for the popul… 14403. 15108. 25092. 1.87e4
#> 14 Upper confidence interval for the popul… 17366. 18846. 33027. 2.35e4
#>
#> $BaseInfo
#> # A tibble: 4 x 8
#> by E t A a N f fc
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Pinus 2.38 2.13 40 0.06 667. 0.0240 0.976
#> 2 Eucaliptus 2.38 2.16 50 0.07 714. 0.0196 0.980
#> 3 Teca 2.42 2.23 60 0.05 1200 0.00917 0.991
#> 4 Acacia 2.41 2.18 70 0.08 875 0.0149 0.985
#>
#> attr(,"class")
#> [1] "forester" "RS"
Stratified Random Sampling
This is a basic example which shows you how to solve a common problem (Sanquetta et al., 2014, page: 122-129):
data("native")
SRS(x=native$Volume1, strata=native$Strata, A = c(650, 350), a = 1, LE = 0.1, SA = "PA", FP = FALSE, DT = FALSE, digits = 3)
#> $Descriptive
#> # A tibble: 13 x 3
#> Parameters S1 S2
#> <chr> <dbl> <dbl>
#> 1 count 12 12
#> 2 Mean 89.1 125.
#> 3 sd 8.46 16.2
#> 4 var 71.5 261.
#> 5 cv 9.49 12.9
#> 6 min 74 99
#> 7 Q1 82.8 116.
#> 8 median 90.5 124.
#> 9 Q3 95.2 133.
#> 10 IQR 12.5 16.8
#> 11 max 101 153
#> 12 kurt 2.02 2.22
#> 13 skew -0.233 0.201
#>
#> $Anova
#> Df Sum Sq Mean Sq F value Pr(>F)
#> strata 1 7921 7921 47.61 6.27e-07 ***
#> Residuals 22 3660 166
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> $Estimated
#> $Estimated$parameters
#> # A tibble: 15 x 2
#> Parameters Value
#> <chr> <dbl>
#> 1 Number of potential sample units 1000
#> 2 Count 24
#> 3 Sample sufficiency (df = 23) (Proportional allocation) 5.66
#> 4 Sample sufficiency recalculation (df = 5) (Proportional allocation) 8.72
#> 5 Stratified sample mean 102.
#> 6 Stratified sample variance 138.
#> 7 Variance of the mean stratified 5.05
#> 8 Standard error of the mean stratified 2.25
#> 9 Absolute sampling error 4.66
#> 10 Relative sampling error 4.58
#> 11 Lower confidence interval for the mean 97.1
#> 12 Upper confidence interval for the mean 106.
#> 13 Total population 101800
#> 14 Lower confidence interval for the population 97141.
#> 15 Upper confidence interval for the population 106459.
#>
#> $Estimated$nst
#> strata nh (Proportional allocation)
#> S1 6
#> S2 3
#> Total 9
#>
#>
#> $BaseInfo
#> $BaseInfo$`1`
#> strata Ah Nh Wh
#> S1 650 650 0.65
#> S2 350 350 0.35
#> Total 1000 1000 1.00
#>
#> $BaseInfo$`2`
#> # A tibble: 5 x 2
#> Parameters Value
#> <chr> <dbl>
#> 1 E 10.2
#> 2 t 2.07
#> 3 f 0.024
#> 4 fc 0.976
#> 5 ne 22.0
#>
#>
#> attr(,"class")
#> [1] "forester" "SRS"
Reference
SANQUETTA, C. R.; CORTE, A. P. D.; RODRIGUES, A. L.; WATZLAWICK, L. F. Inventário Florestal: Planejamento e execução. 3. ed. Curitiba: Multi-Graphic Gráfica e Editora, 2014. 406p.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.