Predicted values and intervals based on a fitted model object.
# S3 method for ssn_lm
predict(
object,
newdata,
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
level = 0.95,
block = FALSE,
...
)
# S3 method for ssn_glm
predict(
object,
newdata,
type = c("link", "response"),
se.fit = FALSE,
interval = c("none", "confidence", "prediction"),
newdata_size,
level = 0.95,
var_correct = TRUE,
...
)A character vector that indicates the name of the prediction data set
for which predictions are desired (accessible via object$ssn.object$preds).
Note that the prediction data must be in the original SSN object used to fit the model.
If newdata is omitted, predictions
for all prediction data sets are returned. Note that the name ".missing"
indicates the prediction data set that contains the missing observations in the data used
to fit the model.
A logical indicating if standard errors are returned.
The default is FALSE.
Type of interval calculation. The default is "none".
Other options are "confidence" (for confidence intervals) and
"prediction" (for prediction intervals).
Tolerance/confidence level. The default is 0.95.
A logical indicating whether a block prediction over the entire
region in newdata should be returned. The default is FALSE, which returns point
predictions for each location in newdata. Currently only available for
model fit using ssn_lm() or models fit using ssn_glm() where
family is "gaussian".
Other arguments. Not used (needed for generic consistency).
The scale (response or link) of predictions obtained
using ssn_glm objects.
The size value for each observation in newdata
used when predicting for the binomial family.
A logical indicating whether to return the corrected prediction
variances when predicting via models fit using ssn_glm. The default is
TRUE.
If se.fit is FALSE, predict.ssn() returns
a vector of predictions or a matrix of predictions with column names
fit, lwr, and upr if interval is "confidence"
or "prediction". If se.fit is TRUE, a list with the following components is returned:
fit: vector or matrix as above
se.fit: standard error of each fit
The (empirical) best linear unbiased predictions (i.e., Kriging
predictions) at each site are returned when interval is "none"
or "prediction" alongside standard errors. Prediction intervals
are also returned if interval is "prediction". When
interval is "confidence", the estimated mean is returned
alongside standard errors and confidence intervals for the mean.
# 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)
ssn_mod <- ssn_lm(
formula = Summer_mn ~ ELEV_DEM,
ssn.object = mf04p,
tailup_type = "exponential",
additive = "afvArea"
)
predict(ssn_mod, "pred1km")
#> 1 2 3 4 5 6 7
#> 14.6563815 14.6963559 14.8045294 14.0808258 14.1858577 14.5667842 15.0145065
#> 8 9 10 11 12 13 14
#> 14.6297345 14.9009338 15.2402931 15.1355108 15.1875988 15.1627073 14.9902752
#> 15 16 17 18 19 20 21
#> 14.7615680 14.6655120 14.2122319 14.0809766 10.5777507 13.0996798 13.7106117
#> 22 23 24 25 26 27 28
#> 9.1798694 3.8625882 6.8631036 13.1910592 13.8704178 11.4421093 12.1914869
#> 29 30 31 32 33 34 35
#> 11.9059944 10.2075486 10.4876498 10.7563715 9.7136328 9.2602313 10.2477095
#> 36 37 38 39 40 41 42
#> 11.1327979 12.4559124 12.4129502 13.7980889 13.5323743 12.9587195 13.2053219
#> 43 44 45 46 47 48 49
#> 12.2647206 12.5451214 10.5334812 10.8185903 11.1379170 7.7356815 8.5300384
#> 50 51 52 53 54 55 56
#> 9.6657132 12.1276515 11.7448118 11.0619591 11.5867648 11.8614135 12.1651404
#> 57 58 59 60 61 62 63
#> 11.1660976 9.7461962 7.6051681 8.4940758 9.5695913 7.4720775 10.3052974
#> 64 65 66 67 68 69 70
#> 10.8934126 10.2716308 11.4632367 11.6943370 9.5595283 10.2041277 10.7122002
#> 71 72 73 74 75 76 77
#> 14.4973491 14.0748939 14.2079722 8.9680246 11.6946555 13.2951523 8.5007739
#> 78 79 80 81 82 83 84
#> 13.6894328 14.6807917 14.8873005 12.2498400 13.2731761 13.9552643 14.2619799
#> 85 86 87 88 89 90 91
#> 8.7029556 7.8742831 8.7757780 8.4003990 11.0899999 9.1269008 10.0760823
#> 92 93 94 95 96 97 98
#> -0.3864082 4.2490631 6.3251551 7.9575862 2.5376779 0.9631321 6.9393960
#> 99 100 101 102 103 104 105
#> 8.5366536 6.4191929 -3.3142339 -0.2880692 2.8745096 5.6957589 7.3225656
#> 106 107 108 109 110 111 112
#> 5.7764329 8.5758955 1.5118142 3.5878501 8.6968251 14.4646670 13.3400039
#> 113 114 115 116 117 118 119
#> 8.0509565 10.7070108 14.8419069 12.8760912 14.2394533 4.4802227 7.2513351
#> 120 121 122 123 124 125 126
#> 9.5447586 5.8262213 8.9258430 10.8651879 10.5039605 13.2731116 14.9161932
#> 127 128 129 130 131 132 133
#> 12.8312424 6.3336288 9.5137927 4.3386584 8.0150939 11.1795926 8.1475599
#> 134 135 136 137 138 139 140
#> 10.5660098 14.1260092 14.3295653 8.9033099 10.0699457 8.1529409 -4.3851596
#> 141 142 143 144 145 146 147
#> -0.1195973 4.3848365 7.4560414 10.8684912 13.2572061 5.5645821 9.1107947
#> 148 149 150 151 152 153 154
#> 11.5308779 3.9613491 10.4769224 -2.9452086 12.1802353 7.5331144 10.8708259
#> 155 156 157 158 159 160 161
#> 10.4096884 5.1723903 8.8549716 10.0791094 10.6195587 10.8187203 11.0860880
#> 162 163 164 165 166 167 168
#> 3.2295281 10.2763951 10.7490625 11.3581890 11.7284046 7.7797240 7.9294044
#> 169 170 171 172 173 174 175
#> 3.9415182 11.1746028 0.6856936 5.8826486 6.5147241 -0.1986592 5.0538075