spsurvey

https://usepa.github.io/spsurvey/
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### sp_summary Examples

Source: https://usepa.github.io/spsurvey/reference/sp_summary.html

Illustrative examples of how to use the sp_summary function with different data types and formulas.

```APIDOC
## Examples
```R
if (FALSE) {
# Summarize distributions of variables in NE_Lakes
sp_summary(NE_Lakes, ~ ELEV_CAT * AREA_CAT)

# Summarize ELEV for each category of AREA_CAT
sp_summary(NE_Lakes, ELEV ~ AREA_CAT)

# Summarize design sites
sample <- grts(NE_Lakes, 100)
sp_summary(sample, ~ ELEV_CAT * AREA_CAT)
}
```
```

--------------------------------

### Install and Load spsurvey Development Version from GitHub

Source: https://usepa.github.io/spsurvey/index.html

Installs and loads the most recent development version of the spsurvey package from GitHub. Requires the 'remotes' package to be installed first.

```R
# Installing from GitHub requires you first install the remotes package
install.packages("remotes")

# install the most recent development version from GitHub
remotes::install_github("USEPA/spsurvey", ref = "main")
# load the most recent development version from GitHub
library(spsurvey)
```

--------------------------------

### Post-stratification popsize example (table)

Source: https://usepa.github.io/spsurvey/reference/attrisk_analysis.html

Example of specifying population size data for the `postStratify` function using a table.

```R
popsize <- with(MySurveyFrame,
     table(Ecoregion, Type))
```

--------------------------------

### Generate and Plot CDF Analysis

Source: https://usepa.github.io/spsurvey/reference/cdf_plot.html

This example demonstrates how to create a data frame, perform a continuous analysis, and then plot the resulting CDF using the cdf_plot function. It includes setup for variables, subpopulations, and population sizes.

```R
if (FALSE) {
dframe <- data.frame(
  siteID = paste0("Site", 1:100),
  wgt = runif(100, 10, 100),
  xcoord = runif(100),
  ycoord = runif(100),
  stratum = rep(c("Stratum1", "Stratum2"), 50),
  ContVar = rnorm(100, 10, 1),
  All_Sites = rep("All Sites", 100),
  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
)
myvars <- c("ContVar")
mysubpops <- c("All_Sites", "Resource_Class")
mypopsize <- data.frame(
  Resource_Class = c("Good", "Poor"),
  Total = c(4000, 1500)
)
myanalysis <- cont_analysis(dframe,
  vars = myvars, subpops = mysubpops,
  siteID = "siteID", weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
  stratumID = "stratum", popsize = mypopsize
)
keep <- with(myanalysis$CDF, Type == "Resource_Class" &
  Subpopulation == "Good")
par(mfrow = c(2, 1))
cdf_plot(myanalysis$CDF[keep, ],
  xlab = "ContVar",
  ylab = "Percent of Stream Length", ylab_r = "Stream Length (km)",
  main = "Estimates for Resource Class: Good"
)
cdf_plot(myanalysis$CDF[keep, ],
  xlab = "ContVar",
  ylab = "Percent of Stream Length", ylab_r = "Same",
  main = "Estimates for Resource Class: Good"
)
}
```

--------------------------------

### Calibration popsize example

Source: https://usepa.github.io/spsurvey/reference/attrisk_analysis.html

Example of specifying population size data for the `calibrate` function using a named list.

```R
popsize <- list(
     Ecoregion = c(
       East = 750,
       Central = 500,
       West = 250
      ),
     Type = c(
       Streams = 1150,
       Rivers = 350
      )
   )
```

--------------------------------

### Calculate Spatial Balance Metrics Example

Source: https://usepa.github.io/spsurvey/reference/sp_balance.html

Demonstrates calculating spatial balance metrics using the sp_balance function. Includes examples for both simple and stratified sampling designs.

```R
if (FALSE) {
sample <- grts(NE_Lakes, 30)
sp_balance(sample$sites_base, NE_Lakes)
strata_n <- c(low = 25, high = 30)
sample_strat <- grts(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
sp_balance(sample_strat$sites_base, NE_Lakes, stratum_var = "ELEV_CAT", metric = "rmse")
}
```

--------------------------------

### Install spsurvey Package

Source: https://usepa.github.io/spsurvey/articles/start-here.html

Run this command once to install the spsurvey package from CRAN. Ensure you have a stable internet connection.

```r
install.packages("spsurvey")
```

--------------------------------

### Startup Option: Partial_Begin

Source: https://usepa.github.io/spsurvey/reference/revisit_dsgn.html

Initiates a revisit design at the last time period scheduled for sampling in the first panel. For a [2-3-1-4] design, this starts at time period 6 instead of time period 1.

```R
"Partial_Begin"
```

--------------------------------

### IRS Sample Selection Examples

Source: https://usepa.github.io/spsurvey/reference/irs.html

Demonstrates basic IRS sampling, stratified sampling with specified strata sizes, and sampling with additional oversample sites. These examples are within an if(FALSE) block, indicating they are for illustrative purposes and not intended for direct execution without modification.

```R
if (FALSE) {
samp <- irs(NE_Lakes, n_base = 100)
print(samp)
strata_n <- c(low = 25, high = 30)
samp_strat <- irs(NE_Lakes, n_base = strata_n, stratum_var = "ELEV_CAT")
print(samp_strat)
samp_over <- irs(NE_Lakes, n_base = 30, n_over = 5)
print(samp_over)
}
```

--------------------------------

### Revisit Randomization with Panel Control

Source: https://usepa.github.io/spsurvey/reference/revisit_rand.html

This example demonstrates the revisit_rand function with rand_control set to 'panel'. This suggests randomization is controlled at the panel level.

```R
revisit_rand(
  n_period = 20, n_pnl = 10, rand_control = "panel", n_visit = 5,
  nsamp = 10
)
#>            1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
#> Random_01 10  0 10  0  0  0 10  0 10  0  0 10  0  0  0  0  0  0  0  0
#> Random_02  0  0  0  0  0  0  0 10  0 10  0  0 10 10 10  0  0  0  0  0
#> Random_03  0 10 10  0  0 10  0  0  0  0  0  0  0  0 10  0  0  0  0 10
#> Random_04  0 10  0  0  0 10  0 10 10  0  0 10  0  0  0  0  0  0  0  0
#> Random_05 10 10  0  0  0  0  0  0  0  0  0  0 10 10  0  0  0  0  0 10
#> Random_06  0 10  0 10  0  0  0 10  0  0 10 10  0  0  0  0  0  0  0  0
#> Random_07  0  0 10 10  0 10  0  0  0  0 10 10  0  0  0  0  0  0  0  0
#> Random_08  0  0  0  0 10 10 10 10  0  0  0  0  0  0  0  0  0  0 10  0
#> Random_09  0 10  0  0 10  0 10  0 10  0  0  0  0 10  0  0  0  0  0  0
#> Random_10  0  0  0  0  0  0  0 10  0 10  0  0  0 10  0  0  0  0 10 10
#> attr(,"class")
#> [1] "paneldesign"
```

--------------------------------

### ASH Density Estimation Example

Source: https://usepa.github.io/spsurvey/reference/ash1_wgt.html

Demonstrates how to use the `ash1_wgt` function to estimate a density from weighted data and then plot the results. Ensure `x` and `wgt` are properly defined before calling the function.

```R
x <- rnorm(100, 10, sqrt(10))
wgt <- runif(100, 10, 100)
rslt <- ash1_wgt(x, wgt)
plot(rslt)
```

--------------------------------

### Install and Load spsurvey from CRAN

Source: https://usepa.github.io/spsurvey/index.html

Installs and loads the most recent approved version of the spsurvey package from CRAN.

```R
install.packages("spsurvey")
library(spsurvey)
```

--------------------------------

### Post-stratification popsize example (data frame)

Source: https://usepa.github.io/spsurvey/reference/attrisk_analysis.html

Example of specifying population size data for the `postStratify` function using a data frame.

```R
popsize <- data.frame(
     Ecoregion = rep(c("East", "Central", "West"),
       rep(2, 3)),
     Type = rep(c("Streams", "Rivers"), 3),
     Total = c(575, 175, 400, 100, 175, 75)
   )
```

--------------------------------

### Install spsurvey Development Version with Vignettes from GitHub

Source: https://usepa.github.io/spsurvey/index.html

Installs the most recent development version of the spsurvey package from GitHub, including package vignettes. This requires the 'devtools' package.

```R
install the most recent development version from GitHub with package vignettes
devtools::install_github("USEPA/spsurvey", build_vignettes=TRUE)
```

--------------------------------

### Startup Option: Partial_End (Serially Alternating)

Source: https://usepa.github.io/spsurvey/reference/revisit_dsgn.html

For serially alternating panels, initiates the revisit design at the time period that begins the second serially alternating pattern. For a [2-3-1-4] design, this starts at time period 11 instead of time period 1.

```R
"Partial_End"
```

--------------------------------

### Construct and Plot Power Curves

Source: https://usepa.github.io/spsurvey/reference/ppd_plot.html

Demonstrates constructing rotating and fixed panel designs, calculating power for these designs, and then plotting the power curves. Includes examples of plotting all designs, specific designs, and designs with specific trend values.

```R
if (FALSE) {
# Construct a rotating panel design with sample size of 60
R60N <- revisit_dsgn(20, panels = list(R60N = list(
  n = 60, pnl_dsgn = c(1, NA),
  pnl_n = NA, start_option = "None"
)), begin = 1)

# Construct a fixed panel design with sample size of 60
F60 <- revisit_dsgn(20, panels = list(F60 = list(
  n = 60, pnl_dsgn = c(1, 0),
  pnl_n = NA, start_option = "None"
)), begin = 1)

# Power for rotating panel with sample size 60
Power_tst <- power_dsgn("Variable_Name",
  ind_values = 43, unit_var = 280,
  period_var = 4, unitperiod_var = 40, index_var = 90,
  unit_rho = 1, period_rho = 0, paneldsgn = list(
    R60N = R60N, F60 = F60
  ), nrepeats = NULL,
  trend_type = "mean", trend = c(1.0, 2.0), alpha = 0.05
)
ppd_plot(Power_tst)
ppd_plot(Power_tst, dsgns = c("F60", "R60N"))
ppd_plot(Power_tst, dsgns = c("F60", "R60N"), trend = 1.0)
ppd_plot(Power_tst,
  plot_type = "relative", comp_type = "design",
  trend_type = "mean", trend = c(1, 2), dsgns = c("R60N", "F60"),
  indicator = "Variable_Name"
)
}
```

--------------------------------

### Calculate Revisit Design Summary

Source: https://usepa.github.io/spsurvey/reference/pd_summary.html

This example demonstrates how to create a serially alternating panel revisit design using `revisit_dsgn` and then summarize it with `pd_summary`. The output includes various metrics about the design's structure and sampling effort.

```R
# Serially alternating panel revisit design summary
sa_dsgn <- revisit_dsgn(20, panels = list(SA60N = list(
  n = 60, pnl_dsgn = c(1, 4),
  pnl_n = NA, start_option = "None"
)), begin = 1)
pd_summary(sa_dsgn)
#> $n_panel
#> [1] 5
#> 
#> $n_period
#> [1] 20
#> 
#> $n_total
#> [1] 1200
#> 
#> $n_periodunit
#> SA60N_1 SA60N_2 SA60N_3 SA60N_4 SA60N_5 
#>       4       4       4       4       4 
#> 
#> $n_unitpnl
#> SA60N_1 SA60N_2 SA60N_3 SA60N_4 SA60N_5 
#>     240     240     240     240     240 
#> 
#> $n_unitperiod
#>  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 
#> 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 60 
#> 
#> $ncum_unit
#>   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20 
#>  60 120 180 240 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 300 
#> 

```

--------------------------------

### Population Size (popsize) for Post-Stratification (Table)

Source: https://usepa.github.io/spsurvey/reference/cat_analysis.html

Example of defining the popsize argument as a table for the `postStratify` function. The table must have named dimnames corresponding to factor variables.

```R
popsize <- with(MySurveyFrame,     table(Ecoregion, Type))
```

--------------------------------

### Prepare Data for Change Analysis

Source: https://usepa.github.io/spsurvey/reference/change_analysis.html

Create a sample data frame with survey data, including site information, weights, coordinates, strata, and categorical variables. This setup is necessary before performing change analysis.

```R
dframe <- data.frame(
  surveyID = rep(c("Survey 1", "Survey 2"), c(100, 100)),
  siteID = paste0("Site", 1:200),
  wgt = runif(200, 10, 100),
  xcoord = runif(200),
  ycoord = runif(200),
  stratum = rep(rep(c("Stratum 1", "Stratum 2"), c(2, 2)), 50),
  CatVar = rep(c("North", "South"), 100),
  All_Sites = rep("All Sites", 200),
  Resource_Class = sample(c("Good", "Fair", "Poor"), 200, replace = TRUE)
)
```

--------------------------------

### Revisit Randomization with Period Control

Source: https://usepa.github.io/spsurvey/reference/revisit_rand.html

This example shows the revisit_rand function with rand_control set to 'period'. This indicates that randomization is managed at the period level.

```R
revisit_rand(
  n_period = 20, n_pnl = 10, rand_control = "period",
  n_visit = 5, nsamp = 10
)
#>            1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
#> Random_01  0 10  0 10  0  0 10 10 10  0  0 10 10 10 10 10 10 10  0 10
#> Random_02  0 10  0  0  0 10  0  0  0 10 10  0 10 10 10  0  0  0 10  0
#> Random_03 10 10  0  0  0 10 10 10  0  0 10 10 10  0  0 10  0 10  0 10
#> Random_04 10  0 10 10 10 10 10  0 10 10  0 10  0 10  0  0 10  0 10 10
#> Random_05 10  0  0 10 10 10 10 10  0  0  0  0 10  0 10 10  0  0  0 10
#> Random_06 10  0 10 10 10  0 10 10 10  0 10  0  0  0 10  0 10 10  0  0
#> Random_07  0  0  0  0 10  0  0  0  0 10 10  0  0 10  0  0  0 10 10  0
#> Random_08  0  0 10  0 10 10  0  0  0 10  0  0  0  0  0 10 10  0 10 10
#> Random_09 10 10 10 10  0  0  0 10 10  0  0 10 10  0  0  0  0  0 10  0
#> Random_10  0 10 10  0  0  0  0  0 10 10 10 10  0 10 10 10 10 10  0  0
#> attr(,"class")
#> [1] "paneldesign"
```

--------------------------------

### Create rotating panels with units sampled once

Source: https://usepa.github.io/spsurvey/reference/revisit_dsgn.html

This example demonstrates creating rotating panels where each sample unit is visited only once. The `pnl_dsgn = c(1, NA)` indicates sampling once and then skipping. The number of panels equals the number of time periods.

```R
# Rotating panels of 60 units sampled once and never again: [1-n].  Number
# of panels equal n_period.
revisit_dsgn(20,
  panels = list(
    R60N = list(n = 60, pnl_dsgn = c(1, NA), pnl_n = NA, start_option = "None")
  ),
  begin = 1
)
#>          1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
#> R60N_01 60  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
#> R60N_02  0 60  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
#> R60N_03  0  0 60  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
#> R60N_04  0  0  0 60  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
#> R60N_05  0  0  0  0 60  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
#> R60N_06  0  0  0  0  0 60  0  0  0  0  0  0  0  0  0  0  0  0  0  0
#> R60N_07  0  0  0  0  0  0 60  0  0  0  0  0  0  0  0  0  0  0  0  0
#> R60N_08  0  0  0  0  0  0  0 60  0  0  0  0  0  0  0  0  0  0  0  0
#> R60N_09  0  0  0  0  0  0  0  0 60  0  0  0  0  0  0  0  0  0  0  0
#> R60N_10  0  0  0  0  0  0  0  0  0 60  0  0  0  0  0  0  0  0  0  0
#> R60N_11  0  0  0  0  0  0  0  0  0  0 60  0  0  0  0  0  0  0  0  0
#> R60N_12  0  0  0  0  0  0  0  0  0  0  0 60  0  0  0  0  0  0  0  0
#> R60N_13  0  0  0  0  0  0  0  0  0  0  0  0 60  0  0  0  0  0  0  0
#> R60N_14  0  0  0  0  0  0  0  0  0  0  0  0  0 60  0  0  0  0  0  0
#> R60N_15  0  0  0  0  0  0  0  0  0  0  0  0  0  0 60  0  0  0  0  0
#> R60N_16  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 60  0  0  0  0
#> R60N_17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 60  0  0  0
#> R60N_18  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 60  0  0
#> R60N_19  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 60  0
#> R60N_20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 60
#> attr(,"class")
#> [1] "paneldesign"
```

--------------------------------

### Example of revisit_bibd usage

Source: https://usepa.github.io/spsurvey/reference/revisit_bibd.html

Creates a balanced incomplete block design with specific parameters for sample occasions, panels, visits, and units per panel. The output is a matrix representing the sampling plan.

```R
# Balanced incomplete block design with 20 sample occasions, 20 panels,
# 3 visits to each unit, and 20 units in each panel.
revisit_bibd(n_period = 20, n_pnl = 20, n_visit = 3, nsamp = 20)
#>         1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20
#> BIB_01  0  0  0  0  0 20  0  0 20  0  0 20  0  0  0  0  0  0  0  0
#> BIB_02 20  0  0  0  0  0 20 20  0  0  0  0  0  0  0  0  0  0  0  0
#> BIB_03  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0 20  0  0  0 20
#> BIB_04  0  0  0  0  0  0  0 20  0  0  0  0 20  0  0 20  0  0  0  0
#> BIB_05  0  0 20  0  0  0 20  0 20  0  0  0  0  0  0  0  0  0  0  0
#> BIB_06  0  0  0 20  0 20  0  0  0  0  0  0  0  0  0  0  0 20  0  0
#> BIB_07  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0  0 20  0  0 20
#> BIB_08  0  0  0  0  0  0  0  0  0  0 20  0 20  0  0  0 20  0  0  0
#> BIB_09  0 20 20  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0 20  0
#> BIB_10  0  0  0  0  0  0  0  0  0  0 20  0  0 20 20  0  0  0  0  0
#> BIB_11 20  0  0 20  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0  0
#> BIB_12 20  0  0  0  0  0  0  0  0 20  0 20  0  0  0  0  0  0  0  0
#> BIB_13  0 20  0  0  0  0  0 20  0  0  0  0  0  0  0  0  0 20  0  0
#> BIB_14  0  0  0  0 20  0 20  0  0  0  0  0  0  0  0  0 20  0  0  0
#> BIB_15  0  0  0  0 20  0  0  0  0 20  0  0  0  0  0  0  0  0 20  0
#> BIB_16  0  0 20  0  0  0  0  0  0  0  0  0  0  0 20  0  0  0  0 20
#> BIB_17  0  0  0  0  0  0  0  0 20  0  0  0  0 20  0 20  0  0  0  0
#> BIB_18  0 20  0  0  0  0  0  0  0  0 20 20  0  0  0  0  0  0  0  0
#> BIB_19  0  0  0  0 20  0  0  0  0  0  0  0  0 20  0  0  0 20  0  0
#> BIB_20  0  0  0 20  0  0  0  0  0  0  0  0 20  0  0  0  0  0 20  0
#> attr(,"class")
#> [1] "paneldesign"
```

--------------------------------

### Population Size (popsize) for Calibration

Source: https://usepa.github.io/spsurvey/reference/cat_analysis.html

Example of defining the popsize argument as a named list for the `calibrate` function. This specifies population totals for factor variables used in calibration.

```R
popsize <- list(     Ecoregion = c(       East = 750,       Central = 500,       West = 250),     Type = c(       Streams = 1150,       Rivers = 350))
```

--------------------------------

### Revisit Randomization with No Control

Source: https://usepa.github.io/spsurvey/reference/revisit_rand.html

This example shows the revisit_rand function used with rand_control set to 'none'. This likely means no specific randomization strategy is applied beyond basic panel design.

```R
revisit_rand(
  n_period = 20, n_pnl = 10, rand_control = "none", n_visit = 50,
  nsamp = 20
)
#>            1  2  3  4  5  6 7  8  9 10 11 12 13 14 15 16 17 18 19 20
#> Random_01  0  0  0 20 20  0 0  0  0  0  0  0  0 20  0 20  0 20  0  0
#> Random_02  0  0  0  0  0 20 0  0  0  0  0 20  0  0  0  0 20  0  0  0
#> Random_03  0 20  0 20 20  0 0  0  0 20 20  0  0  0  0 20 20  0  0  0
#> Random_04  0 20  0  0 20  0 0  0 20  0 20  0  0  0  0  0  0 20  0  0
#> Random_05 20 20  0  0  0 20 0  0  0 20  0 20  0  0  0 20 20 20  0  0
#> Random_06  0  0 20  0  0  0 0 20  0  0  0  0  0 20  0  0  0 20  0  0
#> Random_07 20  0 20  0  0  0 0  0  0  0  0 20  0  0  0  0  0 20  0  0
#> Random_08 20  0  0 20  0 20 0  0  0  0  0  0  0  0  0 20  0 20  0 20
#> Random_09  0  0  0 20  0  0 0  0  0  0  0  0  0 20  0  0  0  0  0  0
#> Random_10  0  0 20  0  0  0 0  0  0 20  0 20  0 20  0 20  0 20  0  0
#> attr(,"class")
#> [1] "paneldesign"
```

--------------------------------

### Plotting CDF Estimates for a Subpopulation

Source: https://usepa.github.io/spsurvey/reference/plot.sp_CDF.html

This example demonstrates how to create a data frame, perform a continuous analysis, and then plot the CDF estimates for a specific subpopulation. It shows how to filter the CDF results and customize plot labels and titles.

```R
if (FALSE) {
dframe <- data.frame(
  siteID = paste0("Site", 1:100),
  wgt = runif(100, 10, 100),
  xcoord = runif(100),
  ycoord = runif(100),
  stratum = rep(c("Stratum1", "Stratum2"), 50),
  ContVar = rnorm(100, 10, 1),
  All_Sites = rep("All Sites", 100),
  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
)
myvars <- c("ContVar")
mysubpops <- c("All_Sites", "Resource_Class")
popsize <- data.frame(
  Resource_Class = c("Good", "Poor"),
  Total = c(4000, 1500)
)
myanalysis <- cont_analysis(dframe,
  vars = myvars, subpops = mysubpops,
  siteID = "siteID", weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
  stratumID = "stratum", popsize = mypopsize
)
keep <- with(myanalysis$CDF, Type == "Resource_Class" &
  Subpopulation == "Good")
par(mfrow = c(2, 1))
plot(myanalysis$CDF[keep, ],
  xlab = "ContVar",
  ylab = "Percent of Stream Length", ylab_r = "Stream Length (km)",
  main = "Estimates for Resource Class: Good"
)
plot(myanalysis$CDF[keep, ],
  xlab = "ContVar",
  ylab = "Percent of Stream Length", ylab_r = "Same",
  main = "Estimates for Resource Class: Good"
)
}
```

--------------------------------

### Summarizing Design Sites

Source: https://usepa.github.io/spsurvey/articles/EDA.html

Use the summary() function with the default formula (~siteuse) to get a summary of the design sites, including total counts and counts by site use type (e.g., Base, Over).

```R
summary(eqprob_rho)
```

--------------------------------

### Interactive Program Startup Notice (GNU GPL)

Source: https://usepa.github.io/spsurvey/LICENSE.html

This notice should be displayed by interactive programs upon startup to inform users about the program's copyright, lack of warranty, and free software status under the GNU GPL. Commands like 'show w' and 'show c' should provide details on warranty and conditions.

```text
<program>  Copyright (C) <year>  <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type 'show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type 'show c' for details.
```

--------------------------------

### Single-stage unstratified FPC example

Source: https://usepa.github.io/spsurvey/reference/attrisk_analysis.html

Example of defining the finite population correction (FPC) for a single-stage unstratified survey design.

```R
fpc <- 15000
```

--------------------------------

### GNU GPL License Notice for Source Files

Source: https://usepa.github.io/spsurvey/LICENSE.html

Include this notice at the beginning of each source file to state the copyright and licensing terms. It ensures the program is free software under the GNU GPL version 3 or later, distributed without warranty.

```text
<one line to give the program's name and a brief idea of what it does.>
Copyright (C) <year>  <name of author>

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.
```

--------------------------------

### Two-stage unstratified FPC example

Source: https://usepa.github.io/spsurvey/reference/attrisk_analysis.html

Example of defining the finite population correction (FPC) for a two-stage unstratified survey design using a named vector.

```R
fpc <- c(
     Ncluster = 150,
     Cluster_1 = 150,
     Cluster_2 = 75,
     Cluster_3 = 75,
     Cluster_4 = 125,
     Cluster_5 = 75
   )
```

--------------------------------

### Run Continuous Analysis with Mean Statistics

Source: https://usepa.github.io/spsurvey/reference/cont_analysis.html

This example demonstrates how to use the cont_analysis function to calculate mean estimates for a continuous variable. It sets up a sample data frame with site information, weights, coordinates, strata, and the continuous variable. It also defines subpopulations and provides population sizes for specific strata. The function is called with the 'Mean' statistic specified.

```R
dframe <- data.frame(
  siteID = paste0("Site", 1:100),
  wgt = runif(100, 10, 100),
  xcoord = runif(100),
  ycoord = runif(100),
  stratum = rep(c("Stratum1", "Stratum2"), 50),
  ContVar = rnorm(100, 10, 1),
  All_Sites = rep("All Sites", 100),
  Resource_Class = rep(c("Good", "Poor"), c(55, 45))
)
myvars <- c("ContVar")
mysubpops <- c("All_Sites", "Resource_Class")
mypopsize <- data.frame(
  Resource_Class = c("Good", "Poor"),
  Total = c(4000, 1500)
)
cont_analysis(dframe,
  vars = myvars, subpops = mysubpops, siteID = "siteID",
  weight = "wgt", xcoord = "xcoord", ycoord = "ycoord",
  stratumID = "stratum", popsize = mypopsize, statistics = "Mean"
)
#> $CDF
#> NULL
#> 
#> $Pct
#> NULL
#> 
#> $Mean
#>             Type Subpopulation Indicator nResp Estimate   StdError
#> 1      All_Sites     All Sites   ContVar   100 10.09394 0.09919356
#> 2 Resource_Class          Good   ContVar    55 10.15428 0.12675491
#> 3 Resource_Class          Poor   ContVar    45  9.93302 0.14953279
#>   MarginofError LCB95Pct UCB95Pct
#> 1     0.1944158 9.899522 10.28835
#> 2     0.2484351 9.905846 10.40272
#> 3     0.2930789 9.639941 10.22610
#> 
#> $Total
#> NULL
#> 

```

--------------------------------

### Single-stage stratified FPC example

Source: https://usepa.github.io/spsurvey/reference/attrisk_analysis.html

Example of defining the finite population correction (FPC) for a single-stage stratified survey design using a named list.

```R
fpc <- list(
     Stratum_1 = 9000,
     Stratum_2 = 6000
    )
```

--------------------------------

### Two-stage stratified FPC example

Source: https://usepa.github.io/spsurvey/reference/attrisk_analysis.html

Example of defining the finite population correction (FPC) for a two-stage stratified survey design using a named list of vectors.

```R
fpc <- list(
     Stratum_1 = c(
       Ncluster_1 = 100,
       Cluster_1 = 125,
       Cluster_2 = 100,
       Cluster_3 = 100,
       Cluster_4 = 125,
       Cluster_5 = 50
      ),
     Stratum_2 = c(
       Ncluster_2 = 50,
       Cluster_1 = 75,
       Cluster_2 = 150,
       Cluster_3 = 75,
       Cluster_4 = 75,
       Cluster_5 = 125
      )
   )
```

--------------------------------

### Get spsurvey Citation

Source: https://usepa.github.io/spsurvey/index.html

Displays the citation information for the spsurvey package, which should be used when citing the package in publications.

```R
citation(package = "spsurvey")
```

--------------------------------

### View Vignette Documentation

Source: https://usepa.github.io/spsurvey/articles/EDA.html

Opens the 'start-here' vignette for the spsurvey package. It is recommended to read this vignette first to understand the package's basic usage.

```r
vignette("start-here", "spsurvey")
```
Tokens:
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