Individual-specific (per-row) factor-score definition quantities
fs_indiv.Rdfs_indiv() converts a get_fs() result into a single long data frame
(one row per input row) carrying, for every observation, the
individual-specific standard errors, implied loadings, and error
variances and covariances of the factor scores, reusing get_fs()'s
column naming. In shape this resembles mirt::fscores(full.scores.SE = TRUE) (a per-observation table pairing each score with its standard
errors), but the quantities are read off the fsL/fsT/fsb attributes
of a get_fs() result rather than estimated from a new model.
For lavaan models the per-row values are pattern-resolved: an
observation's fsL/fsT/fsb depend only on its observed-indicator
pattern, not on its response values, so rows within a group differ only
when the data contain missing values (rows across groups may always
differ). For merMod models there is one row per cluster, each carrying
that cluster's own fsL/fsT.
Arguments
- fs
A factor-score object as returned by
get_fs()(orget_fs_lavaan()/get_fs_lmer()): a unified data frame, a named list of per-group data frames (format = "list"), or amerModresult (one row per cluster).- include_intercept
Logical. When
TRUE, also emit the factor score intercepts (thefsbattribute) asint_fs_<f>columns (qof them). Ignored formerModresults, which have nofsb(random effects are mean zero).- ...
Currently unused.
Value
A data frame with nrow() equal to the number of rows of the
input get_fs() result (one row per observation for lavaan
models, one row per cluster for merMod models). Columns, in
order:
* the input's factor-score columns, kept unmodified (fs_<f>; for
merMod models fs_u<k>, or the legacy u<k>_eb name when
get_fs.merMod() was run with legacy_names = TRUE);
* the per-observation standard errors (fs_<f>_se; the square
root of the per-row fsT diagonal, NA where that entry is
negative or non-finite);
* the implied loadings of the latents on the factor scores
(<latent_j>_by_fs_<f>, q^2 of them);
* the error variances and error covariances of the factor scores
(ev_fs_<f>, q of them, and ecov_fs_<a>_fs_<b>, q choose 2 of them), in the same lower-triangular order get_fs() uses;
* optionally (when include_intercept = TRUE) the score
intercepts (int_fs_<f>, q of them);
* a trailing group column for multi-group lavaan results (the
input's own group-column name in "unified" format, or group
in "list" format); and
* a trailing id column holding the cluster/subject id for merMod
results.
See also
vignette("Scoring Matrices: lavaan CFA and lme4", package = "R2spa")for the scoring-matrix internals these per-row quantities come from.
Examples
library(lavaan)
fit <- cfa("visual =~ x1 + x2 + x3", data = HolzingerSwineford1939)
# For a lavaan result the per-row se / loading / ev columns are the same
# ones get_fs() already carries; include_intercept = TRUE adds the score
# intercepts (int_fs_*) from the fsb attribute (non-NULL under mean
# scoring or with prior_mean/prior_cov).
fs_indiv(get_fs(fit, method = "mean"), include_intercept = TRUE)
#> fs_visual fs_visual_se visual_by_fs_visual ev_fs_visual int_fs_visual
#> 1 3.819444 0.5304431 0.9616955 0.2813699 4.424742
#> 2 4.236111 0.5304431 0.9616955 0.2813699 4.424742
#> 3 3.875000 0.5304431 0.9616955 0.2813699 4.424742
#> 4 5.361111 0.5304431 0.9616955 0.2813699 4.424742
#> 5 3.486111 0.5304431 0.9616955 0.2813699 4.424742
#> 6 4.194444 0.5304431 0.9616955 0.2813699 4.424742
#> 7 3.277778 0.5304431 0.9616955 0.2813699 4.424742
#> 8 4.597222 0.5304431 0.9616955 0.2813699 4.424742
#> 9 3.916667 0.5304431 0.9616955 0.2813699 4.424742
#> 10 3.166667 0.5304431 0.9616955 0.2813699 4.424742
#> 11 3.805556 0.5304431 0.9616955 0.2813699 4.424742
#> 12 4.902778 0.5304431 0.9616955 0.2813699 4.424742
#> 13 4.763889 0.5304431 0.9616955 0.2813699 4.424742
#> 14 4.416667 0.5304431 0.9616955 0.2813699 4.424742
#> 15 5.069444 0.5304431 0.9616955 0.2813699 4.424742
#> 16 3.930556 0.5304431 0.9616955 0.2813699 4.424742
#> 17 3.527778 0.5304431 0.9616955 0.2813699 4.424742
#> 18 4.666667 0.5304431 0.9616955 0.2813699 4.424742
#> 19 4.972222 0.5304431 0.9616955 0.2813699 4.424742
#> 20 6.027778 0.5304431 0.9616955 0.2813699 4.424742
#> 21 5.277778 0.5304431 0.9616955 0.2813699 4.424742
#> 22 6.430556 0.5304431 0.9616955 0.2813699 4.424742
#> 23 4.375000 0.5304431 0.9616955 0.2813699 4.424742
#> 24 3.652778 0.5304431 0.9616955 0.2813699 4.424742
#> 25 4.138889 0.5304431 0.9616955 0.2813699 4.424742
#> 26 4.236111 0.5304431 0.9616955 0.2813699 4.424742
#> 27 5.083333 0.5304431 0.9616955 0.2813699 4.424742
#> 28 4.375000 0.5304431 0.9616955 0.2813699 4.424742
#> 29 4.972222 0.5304431 0.9616955 0.2813699 4.424742
#> 30 3.416667 0.5304431 0.9616955 0.2813699 4.424742
#> 31 3.541667 0.5304431 0.9616955 0.2813699 4.424742
#> 32 3.083333 0.5304431 0.9616955 0.2813699 4.424742
#> 33 4.000000 0.5304431 0.9616955 0.2813699 4.424742
#> 34 4.180556 0.5304431 0.9616955 0.2813699 4.424742
#> 35 2.861111 0.5304431 0.9616955 0.2813699 4.424742
#> 36 3.527778 0.5304431 0.9616955 0.2813699 4.424742
#> 37 5.333333 0.5304431 0.9616955 0.2813699 4.424742
#> 38 3.527778 0.5304431 0.9616955 0.2813699 4.424742
#> 39 4.736111 0.5304431 0.9616955 0.2813699 4.424742
#> 40 5.361111 0.5304431 0.9616955 0.2813699 4.424742
#> 41 3.736111 0.5304431 0.9616955 0.2813699 4.424742
#> 42 3.138889 0.5304431 0.9616955 0.2813699 4.424742
#> 43 2.694444 0.5304431 0.9616955 0.2813699 4.424742
#> 44 3.763889 0.5304431 0.9616955 0.2813699 4.424742
#> 45 4.861111 0.5304431 0.9616955 0.2813699 4.424742
#> 46 5.333333 0.5304431 0.9616955 0.2813699 4.424742
#> 47 6.555556 0.5304431 0.9616955 0.2813699 4.424742
#> 48 3.097222 0.5304431 0.9616955 0.2813699 4.424742
#> 49 4.194444 0.5304431 0.9616955 0.2813699 4.424742
#> 50 3.958333 0.5304431 0.9616955 0.2813699 4.424742
#> 51 5.000000 0.5304431 0.9616955 0.2813699 4.424742
#> 52 5.041667 0.5304431 0.9616955 0.2813699 4.424742
#> 53 4.861111 0.5304431 0.9616955 0.2813699 4.424742
#> 54 3.888889 0.5304431 0.9616955 0.2813699 4.424742
#> 55 5.041667 0.5304431 0.9616955 0.2813699 4.424742
#> 56 4.347222 0.5304431 0.9616955 0.2813699 4.424742
#> 57 4.000000 0.5304431 0.9616955 0.2813699 4.424742
#> 58 4.986111 0.5304431 0.9616955 0.2813699 4.424742
#> 59 4.111111 0.5304431 0.9616955 0.2813699 4.424742
#> 60 3.888889 0.5304431 0.9616955 0.2813699 4.424742
#> 61 4.819444 0.5304431 0.9616955 0.2813699 4.424742
#> 62 4.541667 0.5304431 0.9616955 0.2813699 4.424742
#> 63 4.486111 0.5304431 0.9616955 0.2813699 4.424742
#> 64 3.861111 0.5304431 0.9616955 0.2813699 4.424742
#> 65 4.347222 0.5304431 0.9616955 0.2813699 4.424742
#> 66 5.000000 0.5304431 0.9616955 0.2813699 4.424742
#> 67 5.750000 0.5304431 0.9616955 0.2813699 4.424742
#> 68 4.680556 0.5304431 0.9616955 0.2813699 4.424742
#> 69 2.902778 0.5304431 0.9616955 0.2813699 4.424742
#> 70 3.472222 0.5304431 0.9616955 0.2813699 4.424742
#> 71 1.972222 0.5304431 0.9616955 0.2813699 4.424742
#> 72 5.652778 0.5304431 0.9616955 0.2813699 4.424742
#> 73 4.041667 0.5304431 0.9616955 0.2813699 4.424742
#> 74 4.750000 0.5304431 0.9616955 0.2813699 4.424742
#> 75 3.847222 0.5304431 0.9616955 0.2813699 4.424742
#> 76 3.819444 0.5304431 0.9616955 0.2813699 4.424742
#> 77 4.930556 0.5304431 0.9616955 0.2813699 4.424742
#> 78 2.930556 0.5304431 0.9616955 0.2813699 4.424742
#> 79 5.319444 0.5304431 0.9616955 0.2813699 4.424742
#> 80 3.916667 0.5304431 0.9616955 0.2813699 4.424742
#> 81 4.083333 0.5304431 0.9616955 0.2813699 4.424742
#> 82 5.638889 0.5304431 0.9616955 0.2813699 4.424742
#> 83 5.111111 0.5304431 0.9616955 0.2813699 4.424742
#> 84 4.361111 0.5304431 0.9616955 0.2813699 4.424742
#> 85 2.958333 0.5304431 0.9616955 0.2813699 4.424742
#> 86 5.027778 0.5304431 0.9616955 0.2813699 4.424742
#> 87 5.125000 0.5304431 0.9616955 0.2813699 4.424742
#> 88 4.694444 0.5304431 0.9616955 0.2813699 4.424742
#> 89 4.611111 0.5304431 0.9616955 0.2813699 4.424742
#> 90 4.250000 0.5304431 0.9616955 0.2813699 4.424742
#> 91 4.375000 0.5304431 0.9616955 0.2813699 4.424742
#> 92 4.486111 0.5304431 0.9616955 0.2813699 4.424742
#> 93 4.013889 0.5304431 0.9616955 0.2813699 4.424742
#> 94 2.902778 0.5304431 0.9616955 0.2813699 4.424742
#> 95 3.513889 0.5304431 0.9616955 0.2813699 4.424742
#> 96 5.000000 0.5304431 0.9616955 0.2813699 4.424742
#> 97 3.194444 0.5304431 0.9616955 0.2813699 4.424742
#> 98 5.708333 0.5304431 0.9616955 0.2813699 4.424742
#> 99 3.375000 0.5304431 0.9616955 0.2813699 4.424742
#> 100 4.833333 0.5304431 0.9616955 0.2813699 4.424742
#> 101 4.763889 0.5304431 0.9616955 0.2813699 4.424742
#> 102 4.958333 0.5304431 0.9616955 0.2813699 4.424742
#> 103 4.652778 0.5304431 0.9616955 0.2813699 4.424742
#> 104 3.847222 0.5304431 0.9616955 0.2813699 4.424742
#> 105 6.736111 0.5304431 0.9616955 0.2813699 4.424742
#> 106 4.708333 0.5304431 0.9616955 0.2813699 4.424742
#> 107 4.694444 0.5304431 0.9616955 0.2813699 4.424742
#> 108 5.333333 0.5304431 0.9616955 0.2813699 4.424742
#> 109 4.486111 0.5304431 0.9616955 0.2813699 4.424742
#> 110 3.555556 0.5304431 0.9616955 0.2813699 4.424742
#> 111 6.430556 0.5304431 0.9616955 0.2813699 4.424742
#> 112 4.736111 0.5304431 0.9616955 0.2813699 4.424742
#> 113 5.555556 0.5304431 0.9616955 0.2813699 4.424742
#> 114 4.472222 0.5304431 0.9616955 0.2813699 4.424742
#> 115 5.180556 0.5304431 0.9616955 0.2813699 4.424742
#> 116 3.638889 0.5304431 0.9616955 0.2813699 4.424742
#> 117 4.472222 0.5304431 0.9616955 0.2813699 4.424742
#> 118 4.527778 0.5304431 0.9616955 0.2813699 4.424742
#> 119 4.708333 0.5304431 0.9616955 0.2813699 4.424742
#> 120 3.680556 0.5304431 0.9616955 0.2813699 4.424742
#> 121 4.111111 0.5304431 0.9616955 0.2813699 4.424742
#> 122 4.291667 0.5304431 0.9616955 0.2813699 4.424742
#> 123 6.250000 0.5304431 0.9616955 0.2813699 4.424742
#> 124 4.902778 0.5304431 0.9616955 0.2813699 4.424742
#> 125 5.680556 0.5304431 0.9616955 0.2813699 4.424742
#> 126 5.736111 0.5304431 0.9616955 0.2813699 4.424742
#> 127 4.111111 0.5304431 0.9616955 0.2813699 4.424742
#> 128 4.319444 0.5304431 0.9616955 0.2813699 4.424742
#> 129 5.583333 0.5304431 0.9616955 0.2813699 4.424742
#> 130 3.708333 0.5304431 0.9616955 0.2813699 4.424742
#> 131 5.513889 0.5304431 0.9616955 0.2813699 4.424742
#> 132 4.888889 0.5304431 0.9616955 0.2813699 4.424742
#> 133 4.333333 0.5304431 0.9616955 0.2813699 4.424742
#> 134 3.666667 0.5304431 0.9616955 0.2813699 4.424742
#> 135 5.986111 0.5304431 0.9616955 0.2813699 4.424742
#> 136 5.763889 0.5304431 0.9616955 0.2813699 4.424742
#> 137 4.861111 0.5304431 0.9616955 0.2813699 4.424742
#> 138 4.888889 0.5304431 0.9616955 0.2813699 4.424742
#> 139 5.583333 0.5304431 0.9616955 0.2813699 4.424742
#> 140 4.222222 0.5304431 0.9616955 0.2813699 4.424742
#> 141 2.805556 0.5304431 0.9616955 0.2813699 4.424742
#> 142 5.666667 0.5304431 0.9616955 0.2813699 4.424742
#> 143 6.680556 0.5304431 0.9616955 0.2813699 4.424742
#> 144 6.027778 0.5304431 0.9616955 0.2813699 4.424742
#> 145 2.708333 0.5304431 0.9616955 0.2813699 4.424742
#> 146 3.736111 0.5304431 0.9616955 0.2813699 4.424742
#> 147 3.805556 0.5304431 0.9616955 0.2813699 4.424742
#> 148 4.013889 0.5304431 0.9616955 0.2813699 4.424742
#> 149 4.402778 0.5304431 0.9616955 0.2813699 4.424742
#> 150 5.513889 0.5304431 0.9616955 0.2813699 4.424742
#> 151 6.111111 0.5304431 0.9616955 0.2813699 4.424742
#> 152 4.236111 0.5304431 0.9616955 0.2813699 4.424742
#> 153 4.069444 0.5304431 0.9616955 0.2813699 4.424742
#> 154 4.513889 0.5304431 0.9616955 0.2813699 4.424742
#> 155 4.736111 0.5304431 0.9616955 0.2813699 4.424742
#> 156 3.944444 0.5304431 0.9616955 0.2813699 4.424742
#> 157 3.027778 0.5304431 0.9616955 0.2813699 4.424742
#> 158 4.375000 0.5304431 0.9616955 0.2813699 4.424742
#> 159 4.805556 0.5304431 0.9616955 0.2813699 4.424742
#> 160 3.902778 0.5304431 0.9616955 0.2813699 4.424742
#> 161 3.388889 0.5304431 0.9616955 0.2813699 4.424742
#> 162 4.583333 0.5304431 0.9616955 0.2813699 4.424742
#> 163 6.250000 0.5304431 0.9616955 0.2813699 4.424742
#> 164 4.013889 0.5304431 0.9616955 0.2813699 4.424742
#> 165 4.208333 0.5304431 0.9616955 0.2813699 4.424742
#> 166 3.444444 0.5304431 0.9616955 0.2813699 4.424742
#> 167 3.916667 0.5304431 0.9616955 0.2813699 4.424742
#> 168 3.861111 0.5304431 0.9616955 0.2813699 4.424742
#> 169 5.194444 0.5304431 0.9616955 0.2813699 4.424742
#> 170 2.944444 0.5304431 0.9616955 0.2813699 4.424742
#> 171 5.222222 0.5304431 0.9616955 0.2813699 4.424742
#> 172 4.722222 0.5304431 0.9616955 0.2813699 4.424742
#> 173 5.666667 0.5304431 0.9616955 0.2813699 4.424742
#> 174 4.972222 0.5304431 0.9616955 0.2813699 4.424742
#> 175 4.333333 0.5304431 0.9616955 0.2813699 4.424742
#> 176 5.250000 0.5304431 0.9616955 0.2813699 4.424742
#> 177 3.250000 0.5304431 0.9616955 0.2813699 4.424742
#> 178 5.472222 0.5304431 0.9616955 0.2813699 4.424742
#> 179 4.458333 0.5304431 0.9616955 0.2813699 4.424742
#> 180 5.708333 0.5304431 0.9616955 0.2813699 4.424742
#> 181 4.750000 0.5304431 0.9616955 0.2813699 4.424742
#> 182 4.236111 0.5304431 0.9616955 0.2813699 4.424742
#> 183 3.680556 0.5304431 0.9616955 0.2813699 4.424742
#> 184 3.583333 0.5304431 0.9616955 0.2813699 4.424742
#> 185 4.097222 0.5304431 0.9616955 0.2813699 4.424742
#> 186 4.680556 0.5304431 0.9616955 0.2813699 4.424742
#> 187 3.986111 0.5304431 0.9616955 0.2813699 4.424742
#> 188 3.875000 0.5304431 0.9616955 0.2813699 4.424742
#> 189 3.472222 0.5304431 0.9616955 0.2813699 4.424742
#> 190 3.222222 0.5304431 0.9616955 0.2813699 4.424742
#> 191 3.805556 0.5304431 0.9616955 0.2813699 4.424742
#> 192 4.736111 0.5304431 0.9616955 0.2813699 4.424742
#> 193 4.638889 0.5304431 0.9616955 0.2813699 4.424742
#> 194 3.041667 0.5304431 0.9616955 0.2813699 4.424742
#> 195 2.888889 0.5304431 0.9616955 0.2813699 4.424742
#> 196 4.208333 0.5304431 0.9616955 0.2813699 4.424742
#> 197 3.902778 0.5304431 0.9616955 0.2813699 4.424742
#> 198 4.013889 0.5304431 0.9616955 0.2813699 4.424742
#> 199 3.333333 0.5304431 0.9616955 0.2813699 4.424742
#> 200 3.805556 0.5304431 0.9616955 0.2813699 4.424742
#> 201 5.208333 0.5304431 0.9616955 0.2813699 4.424742
#> 202 4.361111 0.5304431 0.9616955 0.2813699 4.424742
#> 203 3.888889 0.5304431 0.9616955 0.2813699 4.424742
#> 204 3.916667 0.5304431 0.9616955 0.2813699 4.424742
#> 205 3.847222 0.5304431 0.9616955 0.2813699 4.424742
#> 206 4.819444 0.5304431 0.9616955 0.2813699 4.424742
#> 207 4.736111 0.5304431 0.9616955 0.2813699 4.424742
#> 208 3.013889 0.5304431 0.9616955 0.2813699 4.424742
#> 209 4.555556 0.5304431 0.9616955 0.2813699 4.424742
#> 210 4.263889 0.5304431 0.9616955 0.2813699 4.424742
#> 211 5.305556 0.5304431 0.9616955 0.2813699 4.424742
#> 212 4.625000 0.5304431 0.9616955 0.2813699 4.424742
#> 213 4.027778 0.5304431 0.9616955 0.2813699 4.424742
#> 214 5.597222 0.5304431 0.9616955 0.2813699 4.424742
#> 215 3.388889 0.5304431 0.9616955 0.2813699 4.424742
#> 216 4.847222 0.5304431 0.9616955 0.2813699 4.424742
#> 217 4.611111 0.5304431 0.9616955 0.2813699 4.424742
#> 218 4.111111 0.5304431 0.9616955 0.2813699 4.424742
#> 219 4.722222 0.5304431 0.9616955 0.2813699 4.424742
#> 220 4.347222 0.5304431 0.9616955 0.2813699 4.424742
#> 221 3.694444 0.5304431 0.9616955 0.2813699 4.424742
#> 222 2.652778 0.5304431 0.9616955 0.2813699 4.424742
#> 223 6.791667 0.5304431 0.9616955 0.2813699 4.424742
#> 224 2.597222 0.5304431 0.9616955 0.2813699 4.424742
#> 225 4.486111 0.5304431 0.9616955 0.2813699 4.424742
#> 226 5.819444 0.5304431 0.9616955 0.2813699 4.424742
#> 227 4.555556 0.5304431 0.9616955 0.2813699 4.424742
#> 228 3.388889 0.5304431 0.9616955 0.2813699 4.424742
#> 229 3.763889 0.5304431 0.9616955 0.2813699 4.424742
#> 230 5.708333 0.5304431 0.9616955 0.2813699 4.424742
#> 231 3.222222 0.5304431 0.9616955 0.2813699 4.424742
#> 232 3.541667 0.5304431 0.9616955 0.2813699 4.424742
#> 233 2.500000 0.5304431 0.9616955 0.2813699 4.424742
#> 234 4.666667 0.5304431 0.9616955 0.2813699 4.424742
#> 235 3.611111 0.5304431 0.9616955 0.2813699 4.424742
#> 236 3.986111 0.5304431 0.9616955 0.2813699 4.424742
#> 237 4.250000 0.5304431 0.9616955 0.2813699 4.424742
#> 238 4.569444 0.5304431 0.9616955 0.2813699 4.424742
#> 239 5.333333 0.5304431 0.9616955 0.2813699 4.424742
#> 240 5.083333 0.5304431 0.9616955 0.2813699 4.424742
#> 241 5.986111 0.5304431 0.9616955 0.2813699 4.424742
#> 242 4.972222 0.5304431 0.9616955 0.2813699 4.424742
#> 243 4.875000 0.5304431 0.9616955 0.2813699 4.424742
#> 244 5.097222 0.5304431 0.9616955 0.2813699 4.424742
#> 245 3.805556 0.5304431 0.9616955 0.2813699 4.424742
#> 246 6.361111 0.5304431 0.9616955 0.2813699 4.424742
#> 247 5.361111 0.5304431 0.9616955 0.2813699 4.424742
#> 248 4.569444 0.5304431 0.9616955 0.2813699 4.424742
#> 249 5.069444 0.5304431 0.9616955 0.2813699 4.424742
#> 250 3.152778 0.5304431 0.9616955 0.2813699 4.424742
#> 251 5.097222 0.5304431 0.9616955 0.2813699 4.424742
#> 252 6.416667 0.5304431 0.9616955 0.2813699 4.424742
#> 253 4.916667 0.5304431 0.9616955 0.2813699 4.424742
#> 254 4.041667 0.5304431 0.9616955 0.2813699 4.424742
#> 255 5.180556 0.5304431 0.9616955 0.2813699 4.424742
#> 256 4.597222 0.5304431 0.9616955 0.2813699 4.424742
#> 257 5.430556 0.5304431 0.9616955 0.2813699 4.424742
#> 258 5.333333 0.5304431 0.9616955 0.2813699 4.424742
#> 259 6.375000 0.5304431 0.9616955 0.2813699 4.424742
#> 260 2.500000 0.5304431 0.9616955 0.2813699 4.424742
#> 261 4.638889 0.5304431 0.9616955 0.2813699 4.424742
#> 262 3.902778 0.5304431 0.9616955 0.2813699 4.424742
#> 263 3.513889 0.5304431 0.9616955 0.2813699 4.424742
#> 264 4.819444 0.5304431 0.9616955 0.2813699 4.424742
#> 265 4.152778 0.5304431 0.9616955 0.2813699 4.424742
#> 266 4.291667 0.5304431 0.9616955 0.2813699 4.424742
#> 267 5.069444 0.5304431 0.9616955 0.2813699 4.424742
#> 268 5.055556 0.5304431 0.9616955 0.2813699 4.424742
#> 269 5.986111 0.5304431 0.9616955 0.2813699 4.424742
#> 270 3.819444 0.5304431 0.9616955 0.2813699 4.424742
#> 271 4.666667 0.5304431 0.9616955 0.2813699 4.424742
#> 272 5.138889 0.5304431 0.9616955 0.2813699 4.424742
#> 273 4.000000 0.5304431 0.9616955 0.2813699 4.424742
#> 274 4.277778 0.5304431 0.9616955 0.2813699 4.424742
#> 275 5.416667 0.5304431 0.9616955 0.2813699 4.424742
#> 276 3.472222 0.5304431 0.9616955 0.2813699 4.424742
#> 277 4.111111 0.5304431 0.9616955 0.2813699 4.424742
#> 278 4.180556 0.5304431 0.9616955 0.2813699 4.424742
#> 279 4.569444 0.5304431 0.9616955 0.2813699 4.424742
#> 280 4.597222 0.5304431 0.9616955 0.2813699 4.424742
#> 281 3.291667 0.5304431 0.9616955 0.2813699 4.424742
#> 282 4.888889 0.5304431 0.9616955 0.2813699 4.424742
#> 283 4.000000 0.5304431 0.9616955 0.2813699 4.424742
#> 284 4.986111 0.5304431 0.9616955 0.2813699 4.424742
#> 285 4.000000 0.5304431 0.9616955 0.2813699 4.424742
#> 286 4.277778 0.5304431 0.9616955 0.2813699 4.424742
#> 287 4.847222 0.5304431 0.9616955 0.2813699 4.424742
#> 288 3.763889 0.5304431 0.9616955 0.2813699 4.424742
#> 289 3.666667 0.5304431 0.9616955 0.2813699 4.424742
#> 290 4.875000 0.5304431 0.9616955 0.2813699 4.424742
#> 291 2.986111 0.5304431 0.9616955 0.2813699 4.424742
#> 292 3.930556 0.5304431 0.9616955 0.2813699 4.424742
#> 293 4.263889 0.5304431 0.9616955 0.2813699 4.424742
#> 294 4.430556 0.5304431 0.9616955 0.2813699 4.424742
#> 295 4.694444 0.5304431 0.9616955 0.2813699 4.424742
#> 296 5.222222 0.5304431 0.9616955 0.2813699 4.424742
#> 297 4.125000 0.5304431 0.9616955 0.2813699 4.424742
#> 298 3.541667 0.5304431 0.9616955 0.2813699 4.424742
#> 299 4.013889 0.5304431 0.9616955 0.2813699 4.424742
#> 300 3.861111 0.5304431 0.9616955 0.2813699 4.424742
#> 301 4.569444 0.5304431 0.9616955 0.2813699 4.424742
# merMod: one row per cluster, with a trailing id column holding the
# cluster (Subject) level
library(lme4)
#> Loading required package: Matrix
lmod <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
fs_indiv(get_fs(lmod))
#> fs_u0 fs_u1 fs_u0_se fs_u1_se u0_by_fs_u0 u0_by_fs_u1
#> 1 2.2585509 9.1989758 9.741415 1.909779 0.7512951 0.03757244
#> 2 -40.3987381 -8.6196806 9.741415 1.909779 0.7512951 0.03757244
#> 3 -38.9604090 -5.4488565 9.741415 1.909779 0.7512951 0.03757244
#> 4 23.6906196 -4.8143503 9.741415 1.909779 0.7512951 0.03757244
#> 5 22.2603126 -3.0699116 9.741415 1.909779 0.7512951 0.03757244
#> 6 9.0395679 -0.2721770 9.741415 1.909779 0.7512951 0.03757244
#> 7 16.8405086 -0.2236361 9.741415 1.909779 0.7512951 0.03757244
#> 8 -7.2326151 1.0745816 9.741415 1.909779 0.7512951 0.03757244
#> 9 -0.3336684 -10.7521652 9.741415 1.909779 0.7512951 0.03757244
#> 10 34.8904868 8.6282652 9.741415 1.909779 0.7512951 0.03757244
#> 11 -25.2102286 1.1734322 9.741415 1.909779 0.7512951 0.03757244
#> 12 -13.0700342 6.6142178 9.741415 1.909779 0.7512951 0.03757244
#> 13 4.5778642 -3.0152621 9.741415 1.909779 0.7512951 0.03757244
#> 14 20.8636782 3.5360011 9.741415 1.909779 0.7512951 0.03757244
#> 15 3.2754656 0.8722149 9.741415 1.909779 0.7512951 0.03757244
#> 16 -25.6129993 4.8224850 9.741415 1.909779 0.7512951 0.03757244
#> 17 0.8070461 -0.9881562 9.741415 1.909779 0.7512951 0.03757244
#> 18 12.3145921 1.2840221 9.741415 1.909779 0.7512951 0.03757244
#> u1_by_fs_u0 u1_by_fs_u1 ev_fs_u0 ecov_fs_u1_fs_u0 ev_fs_u1 id
#> 1 0.6795552 0.8382416 94.89517 -12.50116 3.647254 308
#> 2 0.6795552 0.8382416 94.89517 -12.50116 3.647254 309
#> 3 0.6795552 0.8382416 94.89517 -12.50116 3.647254 310
#> 4 0.6795552 0.8382416 94.89517 -12.50116 3.647254 330
#> 5 0.6795552 0.8382416 94.89517 -12.50116 3.647254 331
#> 6 0.6795552 0.8382416 94.89517 -12.50116 3.647254 332
#> 7 0.6795552 0.8382416 94.89517 -12.50116 3.647254 333
#> 8 0.6795552 0.8382416 94.89517 -12.50116 3.647254 334
#> 9 0.6795552 0.8382416 94.89517 -12.50116 3.647254 335
#> 10 0.6795552 0.8382416 94.89517 -12.50116 3.647254 337
#> 11 0.6795552 0.8382416 94.89517 -12.50116 3.647254 349
#> 12 0.6795552 0.8382416 94.89517 -12.50116 3.647254 350
#> 13 0.6795552 0.8382416 94.89517 -12.50116 3.647254 351
#> 14 0.6795552 0.8382416 94.89517 -12.50116 3.647254 352
#> 15 0.6795552 0.8382416 94.89517 -12.50116 3.647254 369
#> 16 0.6795552 0.8382416 94.89517 -12.50116 3.647254 370
#> 17 0.6795552 0.8382416 94.89517 -12.50116 3.647254 371
#> 18 0.6795552 0.8382416 94.89517 -12.50116 3.647254 372