The ssn_get_data
function extracts an
sf data.frame for the observation or prediction data from
an SSN
, ssn_lm
, or ssn_glm
object.
Details
The internal name
for observed data in objects of
class SSN
is "obs" and it is the
default. If another name
is specified, it must represent a
prediction data set in the SSN
,
ssn_lm
, or ssn_glm
object. For SSN
objects,
these names are obtained using the call names(x$preds)
. For
all other object classes, the names are obtained using the call
names(x$ssn.object$preds)
.
Examples
## Extract observed data from an SSN object
# Copy the mf04p .ssn data to a local directory and read it into R
# When modeling with your .ssn object, you will load it using the relevant
# path to the .ssn data on your machine
copy_lsn_to_temp()
temp_path <- paste0(tempdir(), "/MiddleFork04.ssn")
mf04p <- ssn_import(temp_path, predpts = "pred1km", overwrite = TRUE)
obs.df <- ssn_get_data(mf04p)
dim(obs.df)
#> [1] 45 26
## Extract prediction data from an SSN object
names(mf04p$preds)
#> [1] "pred1km"
pred1km.df <- ssn_get_data(mf04p, name = "pred1km")
names(pred1km.df)
#> [1] "rid" "pid" "COMID" "AREAWTMAP" "SLOPE"
#> [6] "ELEV_DEM" "FlowCMS" "AirMEANc" "AirMWMTc" "rcaAreaKm2"
#> [11] "h2oAreaKm2" "ratio" "snapdist" "upDist" "afvArea"
#> [16] "locID" "netID" "netgeom" "geom"
## extract observed data from an ssn_lm object
ssn_mod <- ssn_lm(
formula = Summer_mn ~ ELEV_DEM,
ssn.object = mf04p,
tailup_type = "exponential",
additive = "afvArea"
)
obs.mod.df <- ssn_get_data(ssn_mod)
summary(obs.mod.df)
#> rid pid STREAMNAME COMID
#> Min. : 1.00 Min. : 1 Length:45 Min. :23519297
#> 1st Qu.: 21.00 1st Qu.:12 Class :character 1st Qu.:23519365
#> Median : 42.00 Median :23 Mode :character Median :23519479
#> Mean : 42.27 Mean :23 Mean :23519557
#> 3rd Qu.: 61.00 3rd Qu.:34 3rd Qu.:23519529
#> Max. :110.00 Max. :45 Max. :23522805
#> AREAWTMAP SLOPE ELEV_DEM Source
#> Min. : 786.4 Min. :0.000000 Min. :1923 Length:45
#> 1st Qu.: 968.2 1st Qu.:0.002740 1st Qu.:1952 Class :character
#> Median : 995.2 Median :0.005680 Median :2006 Mode :character
#> Mean : 998.6 Mean :0.006743 Mean :1999
#> 3rd Qu.:1032.7 3rd Qu.:0.008430 3rd Qu.:2026
#> Max. :1130.4 Max. :0.044260 Max. :2085
#> Summer_mn MaxOver20 C16 C20 C24
#> Min. : 8.75 Min. :0.0000 Min. : 0.0 Min. : 0.000 Min. :0
#> 1st Qu.:11.02 1st Qu.:0.0000 1st Qu.:17.0 1st Qu.: 0.000 1st Qu.:0
#> Median :12.06 Median :0.0000 Median :32.0 Median : 0.000 Median :0
#> Mean :12.35 Mean :0.3111 Mean :26.4 Mean : 2.867 Mean :0
#> 3rd Qu.:14.58 3rd Qu.:1.0000 3rd Qu.:39.0 3rd Qu.: 3.000 3rd Qu.:0
#> Max. :15.29 Max. :1.0000 Max. :41.0 Max. :19.000 Max. :0
#> FlowCMS AirMEANc AirMWMTc rcaAreaKm2
#> Min. :28.67 Min. :21.12 Min. :35.1 Min. :0.0234
#> 1st Qu.:28.67 1st Qu.:21.12 1st Qu.:35.1 1st Qu.:0.8613
#> Median :28.67 Median :21.12 Median :35.1 Median :2.1780
#> Mean :28.67 Mean :21.12 Mean :35.1 Mean :2.2958
#> 3rd Qu.:28.67 3rd Qu.:21.12 3rd Qu.:35.1 3rd Qu.:3.3993
#> Max. :28.67 Max. :21.12 Max. :35.1 Max. :5.0175
#> h2oAreaKm2 ratio snapdist upDist
#> Min. : 3.399 Min. :0.0143 Min. :0.000e+00 Min. : 909.9
#> 1st Qu.: 16.790 1st Qu.:0.1967 1st Qu.:0.000e+00 1st Qu.: 6281.2
#> Median : 29.092 Median :0.4720 Median :0.000e+00 Median :10020.0
#> Mean : 36.063 Mean :0.4565 Mean :5.174e-12 Mean :10176.9
#> 3rd Qu.: 46.260 3rd Qu.:0.6936 3rd Qu.:0.000e+00 3rd Qu.:14295.2
#> Max. :124.250 Max. :0.9739 Max. :1.164e-10 Max. :19566.2
#> afvArea locID netID netgeom
#> Min. :0.05144 Min. : 1 Min. :1.000 Length:45
#> 1st Qu.:0.15212 1st Qu.:12 1st Qu.:1.000 Class :character
#> Median :0.17089 Median :23 Median :2.000 Mode :character
#> Mean :0.24583 Mean :23 Mean :1.711
#> 3rd Qu.:0.29944 3rd Qu.:34 3rd Qu.:2.000
#> Max. :1.00000 Max. :45 Max. :2.000
#> geometry
#> POINT :45
#> epsg:NA : 0
#> +proj=aea ...: 0
#>
#>
#>