试图预测与lmer模型
问题描述:
我试图预测从我的混合效应模型(逻辑回归)的固定效应。我的模型是:试图预测与lmer模型
MOD.MIX.1 <- glmer(PATCH_TYPE~PC1+PC2+PC3+JUL.DAY+(1|Study_area)+
(1|ID),family=binomial,data=FOR.MODEL)
我试图预测模型是这样的:
newdata <- with(MOD.MIX.1, expand.grid(PC1=unique(PC1),
PC2=mean(FOR.MODEL$PC2),
PC3=mean(FOR.MODEL$PC3),
JUL.DAY=mean(FOR.MODEL$JUL.DAY)))
PREDICTPC1<-predict(MOD.MIX.1, newdata)
这是错误我得到:
Error: couldn't evaluate grouping factor Study_area within model frame:
try adding grouping factor to data frame explicitly if possible
这是什么意思哪有我继续?
数据:
structure(list(Study_area = structure(c(1L, 1L, 1L, 1L), .Label = c("GLQ",
"MEN", "STB", "STN", "STO"), class = "factor"), PATCH_CODE = structure(c(2L,
2L, 2L, 91L), .Label = c("A", "A1", "A2", "A3", "A4", "A5", "A6",
"A7", "A8", "A9", "AA1", "AA2", "AA3", "AB1", "AB2", "AB3", "AC1",
"AC2", "AC3", "AD1", "AD2", "AD3", "AE1", "AE2", "AF1", "AF2",
"AG1", "AG2", "AG3", "AH1", "AH2", "AH3", "AI1", "AI2", "AI3",
"AJ1", "AJ2", "AK1", "AK2", "AK3", "AL1", "AL2", "AL3", "AM1",
"AM2", "AM3", "AN1", "AN2", "AO1", "AO2", "AO3", "AP1", "AP2",
"AP3", "AP4", "AQ1", "AQ2", "AQ3", "AR1", "AR2", "AR3", "AS1",
"AS2", "AS3", "AS4", "AT1", "AT2", "AT3", "AT4", "AU1", "AU2",
"AU3", "AU4", "AV1", "AV2", "AV3", "AV4", "AW1", "AW2", "AW3",
"AX1", "AX2", "AX3", "AY1", "AY2", "AY3", "AZ1", "AZ2", "AZ3",
"B", "B1", "B2", "B3", "B4", "BA1", "BA2", "BA3", "BB", "BB1",
"BB2", "BB3", "BC1", "BC2", "BC3", "BD1", "BD2", "BD3", "BE1",
"BE2", "BE3", "BF1", "BF2", "BF3", "BG1", "BG2", "BG3", "BH1",
"BH2", "BH3", "BI1", "BI2", "BI3", "BJ1", "BJ2", "BJ3", "BK1",
"BK2", "BK3", "BL1", "BL2", "BL3", "BM1", "BM2", "BN1", "BN2",
"BN3", "BO1", "BO2", "BO3", "BO4", "BP1", "BP2", "BP3", "BQ1",
"BQ2", "BQ3", "BR1", "BR2", "BR3", "BS1", "BS2", "BT1", "BT2",
"BT3", "BU1", "BU2", "BU3", "BV1", "BV2", "BV3", "BW1", "BX1",
"BX2", "BY1", "BY2", "BY3", "BZ1", "BZ2", "BZ3", "BZ4", "C",
"C1", "C2", "C3", "C4", "C5", "C6", "CA1", "CA2", "CA3", "CB1",
"CB2", "CC", "CC1", "CC2", "CD1", "CE1", "CE2", "CF1", "CF2",
"CG1", "CG2", "CH1", "CH2", "CI1", "CI2", "CJ1", "CJ2", "CK1",
"CK2", "CL1", "CL2", "CM1", "CM2", "CN1", "CN2", "CO1", "CO2",
"CO3", "D", "D1", "D2", "D3", "D4", "D5", "D6", "E", "E1", "E2",
"E3", "E4", "F1", "F2", "F3", "F4", "F5", "G1", "G2", "G3", "G4",
"G5", "G6", "G7", "G8", "H1", "H2", "H3", "H4", "HH", "I1", "I2",
"I3", "I4", "J1", "J2", "J3", "J4", "J5", "J6", "J7", "J8", "J9",
"K1", "K2", "K3", "K4", "K5", "L1", "L2", "L3", "M1", "M2", "M3",
"M4", "M5", "M6", "N1", "N2", "N3", "O1", "O2", "O3", "O4", "O5",
"O6", "P1", "P2", "P3", "P4", "Q1", "Q2", "Q3", "Q4", "Q5", "R1",
"R2", "R3", "S1", "S2", "S3", "S4", "S5", "S6", "T1", "T2", "T3",
"T4", "U1", "U2", "U3", "U4", "U5", "U6", "V1", "V2", "V3", "W1",
"W2", "W3", "X1", "X2", "X3", "Y1", "Y2", "Y3", "Y4", "Z1", "Z2",
"Z3"), class = "factor"), PATCH_NAME = structure(c(1L, 1L, 1L,
35L), .Label = c("A", "AA", "AA ", "AB", "AB ", "AC", "AC ",
"AD", "AD ", "AE", "AE ", "AF", "AF ", "AG", "AG ", "AH", "AI",
"AJ", "AK", "AL", "AM", "AN", "AO", "AP", "AQ", "AR", "AS", "AT",
"AU", "AV", "AW", "AX", "AY", "AZ", "B", "BA", "BB", "BC", "BD",
"BE", "BF", "BG", "BH", "BI", "BJ", "BK", "BL", "BM", "BN", "BO",
"BP", "BQ", "BR", "BS", "BT", "BU", "BV", "BW", "BX", "BY", "BZ",
"C", "CA", "CB", "CC", "CD", "CE", "CF", "CG", "CH", "CI", "CJ",
"CK", "CL", "CM", "CN", "CO", "D", "E", "F", "F ", "G", "G ",
"H", "H ", "I", "I ", "J", "J ", "K", "K ", "L", "L ", "M", "M ",
"N", "N ", "O", "O ", "P", "P ", "Q", "Q ", "R", "R ", "S", "S ",
"T", "T ", "U ", "V", "V ", "W", "W ", "X", "X ", "Y", "Y ",
"Z", "Z "), class = "factor"), REPLICATE = structure(c(1L, 1L,
1L, 1L), .Label = c("1", "2", "3", "4", "5", "6", "7", "8", "9",
"B", "C", "H"), class = "factor"), REP_MES = c(19L, 19L, 19L,
133L), Observer = structure(c(4L, 4L, 4L, 4L), .Label = c("CM",
"JA", "JB", "JC", "SH", "SP", "TP"), class = "factor"), HAB_TYPE = structure(c(2L,
2L, 2L, 2L), .Label = c("Grazed", "Ungrazed"), class = "factor"),
PATCH_TYPE = c(1, 0, 0, 1), Male_visits__all_ = c(3L, 0L,
0L, 1L), Male_visits__successful_ = c(3L, 0L, 0L, 1L), Male_visits__for_young_ = c(0L,
0L, 0L, 0L), Female_visits__all_ = c(1L, 0L, 0L, 0L), Female_visits__successful_ = c(1L,
0L, 0L, 0L), Female_visits__for_young_ = c(0L, 0L, 0L, 0L
), Juv__Visits__all_ = c(0L, 0L, 0L, 0L), Juv__Visits__succ__ = c(0L,
0L, 0L, 0L), HERB_0 = c(0L, 0L, 40L, 10L), HERB_20 = c(0L,
0L, 10L, 0L), HERB_50 = c(0L, 0L, 0L, 0L), GRASS_0 = c(10L,
100L, 60L, 30L), GRASS_20 = c(0L, 20L, 0L, 0L), GRASS_50 = c(0L,
0L, 0L, 0L), RUSH_0 = c(0L, 0L, 0L, 0L), RUSH_20 = c(0L,
0L, 0L, 0L), RUSH_50 = c(0L, 0L, 0L, 0L), ERIC_0 = c(0L,
0L, 0L, 0L), ERIC_20 = c(0L, 0L, 0L, 0L), ERIC_50 = c(0L,
0L, 0L, 0L), BRACK_0 = c(0L, 0L, 0L, 0L), BRACK_20 = c(0L,
0L, 0L, 0L), BRACK_50 = c(0L, 0L, 0L, 0L), MOSS = c(0L, 0L,
0L, 0L), BARE = c(90L, 0L, 0L, 0L), WATER = c(0L, 0L, 0L,
0L), O_HUNG = structure(c(3L, 3L, 3L, 3L), .Label = c("BRA",
"GOR", "N", "RUS", "S"), class = "factor"), DISCREET = structure(c(5L,
17L, 17L, 5L), .Label = c("1", "10", "15", "1.5", "2", "20",
"25", "3", "4", "40", "5", "50", "6", "7", "8", "9", "NO"
), class = "factor"), Notes = structure(c(21L, NA, NA, 21L
), .Label = c("By burn", "Clear-felled conifer", "Concrete reservoir overflow",
"Female feeding 4 rf juvs, male sing", "Foraging figure includes flycatchin",
"Gorse", "Grassy area surrounded by juniper", "Male, female and 4 juvs, male singi",
"Male singing most of time, female f", "Patch of rushes",
"pr around nest (female removing fae", "Pr foraging, didn't appear to be pr",
"Pr provisioning at least 2 fledged", "Pr with 4 rf juvs",
"Pr with at least 1 rf young, male s", "Road", "Road edge",
"rows added as James Bray said the reference patches were the same in extensive bracken",
"Shorter grass under tree", "Shorter veg. surrounded by taller",
"Track", "Willow"), class = "factor"), Site = structure(c(NA_integer_,
NA_integer_, NA_integer_, NA_integer_), .Label = c("A", "B",
"C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N",
"O", "P", "Q", "R", "S", "T"), class = "factor"), Site_visit = c(NA_integer_,
NA_integer_, NA_integer_, NA_integer_), DAY = c(17L, 17L,
17L, 17L), DEAD = c(0L, 0L, 0L, 0L), StartHour = c(6L, NA,
NA, 6L), StartMinute = c(0L, NA, NA, 0L), EndHour = c(6L,
NA, NA, 6L), EndMinute = c(30L, NA, NA, 30L), DURATION = c(30L,
NA, NA, 30L), EASTING = c(297736L, NA, NA, 297991L), NORTHING = c(703033L,
NA, NA, 702934L), ELEV = c(NA_integer_, NA_integer_, NA_integer_,
NA_integer_), MONTH = c(6L, 6L, 6L, 6L), ORIENTATION = structure(c(NA_integer_,
NA_integer_, NA_integer_, NA_integer_), .Label = c("C", "N",
"S"), class = "factor"), PERCH = structure(c(NA_integer_,
NA_integer_, NA_integer_, NA_integer_), .Label = c("B", "F",
"G", "R", "T"), class = "factor"), TERR = structure(c(NA_integer_,
NA_integer_, NA_integer_, NA_integer_), .Label = "M", class = "factor"),
VISIT_NO = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_
), JUL.DAY = c(17, 17, 17, 17), ID = c("GLQ_JC_19", "GLQ_JC_19",
"GLQ_JC_19", "GLQ_JC_133"), PC1 = c(0.0435645668204425, 0.72948034145072,
0.803061560901585, 0.259840578885553), PC2 = c(-0.593400745369881,
0.848541838597916, -1.19902967772894, -0.58625628136995),
PC3 = c(-0.729043630624223, -0.534297045616433, 0.655933470491286,
-0.518820312795394)), .Names = c("Study_area", "PATCH_CODE",
"PATCH_NAME", "REPLICATE", "REP_MES", "Observer", "HAB_TYPE",
"PATCH_TYPE", "Male_visits__all_", "Male_visits__successful_",
"Male_visits__for_young_", "Female_visits__all_", "Female_visits__successful_",
"Female_visits__for_young_", "Juv__Visits__all_", "Juv__Visits__succ__",
"HERB_0", "HERB_20", "HERB_50", "GRASS_0", "GRASS_20", "GRASS_50",
"RUSH_0", "RUSH_20", "RUSH_50", "ERIC_0", "ERIC_20", "ERIC_50",
"BRACK_0", "BRACK_20", "BRACK_50", "MOSS", "BARE", "WATER", "O_HUNG",
"DISCREET", "Notes", "Site", "Site_visit", "DAY", "DEAD", "StartHour",
"StartMinute", "EndHour", "EndMinute", "DURATION", "EASTING",
"NORTHING", "ELEV", "MONTH", "ORIENTATION", "PERCH", "TERR",
"VISIT_NO", "JUL.DAY", "ID", "PC1", "PC2", "PC3"), row.names = c(NA,
4L), class = "data.frame")
模型结构
str(MOD.MIX.1)
Formal class 'glmerMod' [package "lme4"] with 13 slots
[email protected] resp :Reference class 'glmResp' [package "lme4"] with 11 fields
.. ..$ Ptr :<externalptr>
.. ..$ mu : num [1:1208] 0.316 0.341 0.31 0.325 0.222 ...
.. ..$ offset : num [1:1208] -0.42 -0.308 -0.45 -0.38 -0.903 ...
.. ..$ sqrtXwt: num [1:1208] 0.465 0.474 0.462 0.468 0.416 ...
.. ..$ sqrtrwt: num [1:1208] 2.15 2.11 2.16 2.14 2.41 ...
.. ..$ weights: num [1:1208] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ wtres : num [1:1208] 1.47 -0.719 -0.67 1.441 -0.534 ...
.. ..$ y : num [1:1208] 1 0 0 1 0 0 1 0 0 1 ...
.. ..$ eta : num [1:1208] -0.771 -0.659 -0.801 -0.731 -1.254 ...
.. ..$ family :List of 11
.. .. ..$ family : chr "binomial"
.. .. ..$ link : chr "logit"
.. .. ..$ linkfun :function (mu)
.. .. ..$ linkinv :function (eta)
.. .. ..$ variance :function (mu)
.. .. ..$ dev.resids:function (y, mu, wt)
.. .. ..$ aic :function (y, n, mu, wt, dev)
.. .. ..$ mu.eta :function (eta)
.. .. ..$ validmu :function (mu)
.. .. ..$ valideta :function (eta)
.. .. ..$ simulate :function (object, nsim)
.. .. ..- attr(*, "class")= chr "family"
.. ..$ n : num [1:1208] 1 1 1 1 1 1 1 1 1 1 ...
.. ..and 41 methods, of which 29 are possibly relevant:
.. .. aic, allInfo, allInfo#lmResp, copy#envRefClass, devResid, fam,
.. .. initialize, initialize#lmResp, initializePtr, Laplace, link, muEta,
.. .. ptr, ptr#lmResp, resDev, setOffset, setResp, setTheta, setWeights,
.. .. sqrtWrkWt, theta, updateMu, updateMu#lmResp, updateWts, variance,
.. .. wrkResids, wrkResp, wrss, wtWrkResp
[email protected] Gp : int [1:3] 0 220 222
[email protected] call : language glmer(formula = PATCH_TYPE ~ PC1 + PC2 + PC3 + JUL.DAY + (1 | Study_area) + (1 | ID), data = FOR.MODEL, family = binomial)
[email protected] frame :'data.frame': 1208 obs. of 7 variables:
.. ..$ PATCH_TYPE: num [1:1208] 1 0 0 1 0 0 1 0 0 1 ...
.. ..$ PC1 : num [1:1208] 0.0436 0.7295 0.8031 0.2598 1.1722 ...
.. ..$ PC2 : num [1:1208] -0.593 0.849 -1.199 -0.586 -1.66 ...
.. ..$ PC3 : num [1:1208] -0.729 -0.534 0.656 -0.519 2.483 ...
.. ..$ JUL.DAY : num [1:1208] 17 17 17 17 17 17 17 17 17 20 ...
.. ..$ Study_area: Factor w/ 2 levels "GLQ","MEN": 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ ID : chr [1:1208] "GLQ_JC_19" "GLQ_JC_19" "GLQ_JC_19" "GLQ_JC_133" ...
.. ..- attr(*, "terms")=Classes 'terms', 'formula' length 3 PATCH_TYPE ~ PC1 + PC2 + PC3 + JUL.DAY + (1 + Study_area) + (1 + ID)
.. .. .. ..- attr(*, "variables")= language list(PATCH_TYPE, PC1, PC2, PC3, JUL.DAY, Study_area, ID)
.. .. .. ..- attr(*, "factors")= int [1:7, 1:6] 0 1 0 0 0 0 0 0 0 1 ...
.. .. .. .. ..- attr(*, "dimnames")=List of 2
.. .. .. .. .. ..$ : chr [1:7] "PATCH_TYPE" "PC1" "PC2" "PC3" ...
.. .. .. .. .. ..$ : chr [1:6] "PC1" "PC2" "PC3" "JUL.DAY" ...
.. .. .. ..- attr(*, "term.labels")= chr [1:6] "PC1" "PC2" "PC3" "JUL.DAY" ...
.. .. .. ..- attr(*, "order")= int [1:6] 1 1 1 1 1 1
.. .. .. ..- attr(*, "intercept")= int 1
.. .. .. ..- attr(*, "response")= int 1
.. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
.. .. .. ..- attr(*, "predvars")= language list(PATCH_TYPE, PC1, PC2, PC3, JUL.DAY, Study_area, ID)
.. .. .. ..- attr(*, "dataClasses")= Named chr [1:7] "numeric" "numeric" "numeric" "numeric" ...
.. .. .. .. ..- attr(*, "names")= chr [1:7] "PATCH_TYPE" "PC1" "PC2" "PC3" ...
.. .. .. ..- attr(*, "predvars.fixed")= language list(PATCH_TYPE, PC1, PC2, PC3, JUL.DAY)
.. ..- attr(*, "formula")=Class 'formula' length 3 PATCH_TYPE ~ PC1 + PC2 + PC3 + JUL.DAY + (1 | Study_area) + (1 | ID)
.. .. .. ..- attr(*, ".Environment")=<environment: R_GlobalEnv>
[email protected] flist :List of 2
.. ..$ ID : Factor w/ 220 levels "GLQ_JB_58","GLQ_JB_59",..: 19 19 19 4 4 4 5 5 5 6 ...
.. ..$ Study_area: Factor w/ 2 levels "GLQ","MEN": 1 1 1 1 1 1 1 1 1 1 ...
.. ..- attr(*, "assign")= int [1:2] 1 2
[email protected] cnms :List of 2
.. ..$ ID : chr "(Intercept)"
.. ..$ Study_area: chr "(Intercept)"
[email protected] lower : num [1:2] 0 0
[email protected] theta : num [1:2] 0 0.365
[email protected] beta : num [1:5] -0.88409 0.57692 -0.14263 -0.40055 0.00369
[email protected] u : num [1:222] 0 0 0 0 0 0 0 0 0 0 ...
[email protected] devcomp:List of 2
.. ..$ cmp : Named num [1:11] 5.53 29.32 1241.23 1.86 1243.09 ...
.. .. ..- attr(*, "names")= chr [1:11] "ldL2" "ldRX2" "wrss" "ussq" ...
.. ..$ dims: Named int [1:14] 1208 1208 5 1203 2 222 1 1 0 2 ...
.. .. ..- attr(*, "names")= chr [1:14] "N" "n" "p" "nmp" ...
[email protected] pp :Reference class 'merPredD' [package "lme4"] with 18 fields
.. ..$ Lambdat:Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. [email protected] i : int [1:222] 0 1 2 3 4 5 6 7 8 9 ...
.. .. .. [email protected] p : int [1:223] 0 1 2 3 4 5 6 7 8 9 ...
.. .. .. [email protected] Dim : int [1:2] 222 222
.. .. .. [email protected] Dimnames:List of 2
.. .. .. .. ..$ : NULL
.. .. .. .. ..$ : NULL
.. .. .. [email protected] x : num [1:222] 0 0 0 0 0 0 0 0 0 0 ...
.. .. .. [email protected] factors : list()
.. ..$ LamtUt :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. [email protected] i : int [1:2416] 18 220 18 220 18 220 3 220 3 220 ...
.. .. .. [email protected] p : int [1:1209] 0 2 4 6 8 10 12 14 16 18 ...
.. .. .. [email protected] Dim : int [1:2] 222 1208
.. .. .. [email protected] Dimnames:List of 2
.. .. .. .. ..$ : NULL
.. .. .. .. ..$ : NULL
.. .. .. [email protected] x : num [1:2416] 0 0.17 0 0.173 0 ...
.. .. .. [email protected] factors : list()
.. ..$ Lind : int [1:222] 1 1 1 1 1 1 1 1 1 1 ...
.. ..$ Ptr :<externalptr>
.. ..$ RZX : num [1:222, 1:5] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ Ut :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. [email protected] i : int [1:2416] 18 220 18 220 18 220 3 220 3 220 ...
.. .. .. [email protected] p : int [1:1209] 0 2 4 6 8 10 12 14 16 18 ...
.. .. .. [email protected] Dim : int [1:2] 222 1208
.. .. .. [email protected] Dimnames:List of 2
.. .. .. .. ..$ : chr [1:222] "GLQ_JB_58" "GLQ_JB_59" "GLQ_JB_60" "GLQ_JC_133" ...
.. .. .. .. ..$ : NULL
.. .. .. [email protected] x : num [1:2416] 0.465 0.465 0.474 0.474 0.462 ...
.. .. .. [email protected] factors : list()
.. ..$ Utr : num [1:222] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ V : num [1:1208, 1:5] 0.465 0.474 0.462 0.468 0.416 ...
.. ..$ VtV : num [1:5, 1:5] 241 0 0 0 0 ...
.. ..$ Vtr : num [1:5] 33.49 -15.03 34.71 8.54 512.19
.. ..$ X : num [1:1208, 1:5] 1 1 1 1 1 1 1 1 1 1 ...
.. .. ..- attr(*, "dimnames")=List of 2
.. .. .. ..$ : chr [1:1208] "1" "2" "3" "4" ...
.. .. .. ..$ : chr [1:5] "(Intercept)" "PC1" "PC2" "PC3" ...
.. .. ..- attr(*, "assign")= int [1:5] 0 1 2 3 4
.. ..$ Xwts : num [1:1208] 0.465 0.474 0.462 0.468 0.416 ...
.. ..$ Zt :Formal class 'dgCMatrix' [package "Matrix"] with 6 slots
.. .. .. [email protected] i : int [1:2416] 18 220 18 220 18 220 3 220 3 220 ...
.. .. .. [email protected] p : int [1:1209] 0 2 4 6 8 10 12 14 16 18 ...
.. .. .. [email protected] Dim : int [1:2] 222 1208
.. .. .. [email protected] Dimnames:List of 2
.. .. .. .. ..$ : chr [1:222] "GLQ_JB_58" "GLQ_JB_59" "GLQ_JB_60" "GLQ_JC_133" ...
.. .. .. .. ..$ : NULL
.. .. .. [email protected] x : num [1:2416] 1 1 1 1 1 1 1 1 1 1 ...
.. .. .. [email protected] factors : list()
.. ..$ beta0 : num [1:5] 0 0 0 0 0
.. ..$ delb : num [1:5] 0 0 0 0 0
.. ..$ delu : num [1:222] 0 0 0 0 0 0 0 0 0 0 ...
.. ..$ theta : num [1:2] 0 0.365
.. ..$ u0 : num [1:222] 0 0 0 0 0 0 0 0 0 0 ...
.. ..and 42 methods, of which 30 are possibly relevant:
.. .. b, beta, CcNumer, copy#envRefClass, initialize, initializePtr,
.. .. installPars, L, ldL2, ldRX2, linPred, P, ptr, RX, RXdiag, RXi,
.. .. setBeta0, setDelb, setDelu, setTheta, solve, solveU, sqrL, u, unsc,
.. .. updateDecomp, updateL, updateLamtUt, updateRes, updateXwts
[email protected] optinfo:List of 7
.. ..$ optimizer: chr "Nelder_Mead"
.. ..$ control :List of 3
.. .. ..$ xst : num [1:7] 0.02 0.02 0.0641 0.0122 0.0101 ...
.. .. ..$ xt : num [1:7] 1.00e-05 1.00e-05 3.21e-05 6.10e-06 5.05e-06 ...
.. .. ..$ verbose: int 0
.. ..$ derivs :List of 2
.. .. ..$ gradient: num [1:7] 1.35e-03 -1.64e-04 -5.18e-05 -6.99e-04 8.31e-04 ...
.. .. ..$ Hessian : num [1:7, 1:7] 2.70e+02 5.34e-05 3.05e-05 -1.95e-03 -2.29e-05 ...
.. ..$ conv :List of 2
.. .. ..$ opt : num 0
.. .. ..$ lme4: list()
.. ..$ feval : num 321
.. ..$ warnings : list()
.. ..$ val : num [1:7] 0 0.365 -0.884 0.577 -0.143 ...
>
答
如果你想只使用固定效应(即不包括随机效应)预测,那么你需要包括“re.form = NA “在代码的预测行:
PREDICTPC1 < -predict(MOD.MIX.1,newdata,re.form = NA)
默认值在预测中包含随机效果,在这种情况下,您需要一个指定“newdata”数据框中随机效果的列,正如Robinson先生上文指出的那样。
查看文档http://www.inside-r.org/packages/cran/lme4/docs/predict.merMod
MOD.MIX.1'的结构是什么? – 2014-11-03 19:22:01
edit added str(MOD.MIX.1) – user1658170 2014-11-03 19:29:35
您的'newdata'数据框中没有'Study_area'列 – 2014-11-03 19:55:28